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title: Machine Learning Techniques for Antimicrobial Resistance Prediction of Pseudomonas
Aeruginosa from Whole Genome Sequence Data
authors:
- Sohail M. Noman
- Muhammad Zeeshan
- Jehangir Arshad
- Melkamu Deressa Amentie
- Muhammad Shafiq
- Yumeng Yuan
- Mi Zeng
- Xin Li
- Qingdong Xie
- Xiaoyang Jiao
journal: Computational Intelligence and Neuroscience
year: 2023
pmcid: PMC9995192
doi: 10.1155/2023/5236168
license: CC BY 4.0
---
# Machine Learning Techniques for Antimicrobial Resistance Prediction of Pseudomonas Aeruginosa from Whole Genome Sequence Data
## Abstract
### Aim
Due to the growing availability of genomic datasets, machine learning models have shown impressive diagnostic potential in identifying emerging and reemerging pathogens. This study aims to use machine learning techniques to develop and compare a model for predicting bacterial resistance to a panel of 12 classes of antibiotics using whole genome sequence (WGS) data of Pseudomonas aeruginosa.
### Method
A machine learning technique called Random Forest (RF) and BioWeka was used for classification accuracy assessment and logistic regression (LR) for statistical analysis.
### Results
Our results show $44.66\%$ of isolates were resistant to twelve antimicrobial agents and $55.33\%$ were sensitive. The mean classification accuracy was obtained ≥$98\%$ for BioWeka and ≥96 for RF on these families of antimicrobials. Where ampicillin was $99.31\%$ and $94.00\%$, amoxicillin was $99.02\%$ and $95.21\%$, meropenem was $98.27\%$ and $96.63\%$, cefepime was $99.73\%$ and $98.34\%$, fosfomycin was $96.44\%$ and $99.23\%$, ceftazidime was $98.63\%$ and $94.31\%$, chloramphenicol was $98.71\%$ and $96.00\%$, erythromycin was $95.76\%$ and $97.63\%$, tetracycline was $99.27\%$ and $98.25\%$, gentamycin was $98.00\%$ and $97.30\%$, butirosin was $99.57\%$ and $98.03\%$, and ciprofloxacin was $96.17\%$ and $98.97\%$ with 10-fold-cross validation. In addition, out of twelve, eight drugs have found no false-positive and false-negative bacterial strains.
### Conclusion
The ability to accurately detect antibiotic resistance could help clinicians make educated decisions about empiric therapy based on the local antibiotic resistance pattern. Moreover, infection prevention may have major consequences if such prescribing practices become widespread for human health.
## 1. Introduction
Antimicrobial resistance (AMR) is one of the leading public health concerns of the 21st century, which hinders the ability to effectively treat and prevent a wide variety of bacterial, viral, and fungal infections [1]. AMR occurs when microorganisms (bacteria, viruses, fungi, and parasites) evolve and lose their sensitivity to existing treatments, making infections more challenging to treat and raising the risk of disease transmission, severe illness, and death [2]. The rapid global spread of multi- and pan-resistant bacteria, also known as “superbugs,” is particularly concerning because these bacteria cause infections that cannot be treated with current antimicrobial medicines like antibiotics [3]. At least 1.27 million people died from AMR-related cases in 2019, according to the CDC (https://www.cdc.gov/drugresistance/biggest-threats.html). Over 2.8 million people in the United States year contract AMR, and over 35,000 people die directly [4]. The most common multidrug-resistant bacteria globally are Escherichia coli, Enterococcus faecium, Streptococcus, Klebsiella, and Pseudomonas aeruginosa, and they are responsible for an estimated 250,000 annual infections and deaths [5]. For instance, the WHO priority pathogen list calls for new antibacterials to treat infections caused by *Pseudomonas aeruginosa* and carbapenem-resistant bacteria (CRE) [6]. There are currently 32 antibiotics in clinical development that target WHO priority pathogens, but only six of them can be considered truly innovative [7].
Various researchers have talked about the resistance prediction of antimicrobials [8]. This lack of treatment options often requires broad-spectrum antibiotics, which may be less effective or safe. Resistance also affects empirical treatment, in which a clinician chooses an antibiotic for an infection without obtaining microbiological results. This can lead to an underestimation of the risk associated with specific infections and the use of inappropriate antibiotics. A meta-analysis found that patients with Enterobacteriaceae resistance are five times more likely to delay receiving an effective therapy than patients infected by a susceptible strain [9, 10]. This may reduce the long-term effectiveness of antibiotics, delay access to effective treatments, increase treatment failure with complications, and increase fatality rates. Infections caused by resistant Gram-positive and Gram-negative bacteria increase hospital stays, surgery needs, and mortality [11].
Another study by Yamani et al., calculated the health burden of antibiotic-resistant bacteria (ARB) in European Union/European Economic Area (EU/EEA) countries in disability-adjusted life-years [12]. Their models were populated with estimated incidence from the European Antimicrobial Resistance Surveillance Network (EARS-Net) and the European Centre for Disease Prevention and Control (ECDC) point prevalence surveys of healthcare-associated infections and antimicrobial use in European acute care hospitals [13, 14]. Systematic reviews of published literature showed attributable case fatality and length of stay for antibiotic-resistant infections [15, 16]. In 2014, 671689 infections occurred in EU/EEA countries [13]. This ratio increased globally between 2015 and 2022 [5, 10, 12]. Different ARB contribute variably to the global burden, so prevention and control strategies should be tailored to each country's needs. All countries must implement effective AMR strategies to combat antibiotic overuse and misuse [17]. All systemic antibiotics globally require a doctor's prescription. Most prescriptions are written in primary care, not secondary or tertiary [6].
In 2018, $74\%$ of all antibiotics prescribed by the National Health Service (NHS) in England were for general practitioners (GPs) patients [18]. GPs are the most frequent antibiotic prescribers, so they focus on primary care literature. Nurse practitioners and community pharmacists play a key role. In the last 10 years, nurses' roles have expanded to include prescribing in many countries and are on the policy agenda in many more [19]. Nurse prescribing was introduced to better utilize the skills and knowledge of health professionals, improve medication access, and reduce the workload of doctors. In China, the number of nurses qualified to prescribe has steadily risen over the last 5 years, and 31,000 nurses now have the same prescribing ability as doctors [20]. Pharmacists in China can register as independent prescribers, often specializing in diabetes prescriptions. More pharmacists work in secondary care than primary. Lastly, dentists are considered antibiotic prescribers because they write fewer prescriptions than general practitioners. Further, most antibiotic prescriptions are for respiratory, urinary, skin, or tooth infections [21]. In addition, most antibiotics are given for acute respiratory tract infections (RTIs) [13]. Some RTIs, such as community-acquired bacterial pneumonia, are treatable with antibiotics, but most acute RTIs are viral and self-limiting.
P. aeruginosa has high baseline antibiotic resistance and can acquire new resistance mechanisms through chromosomal mutations or horizontal gene transfer (HGT), increasing the risk of ineffective antibiotic treatment [22]. Mutations can cause a failed therapeutic outcome during treatment, while resistance increases mortality, hospital stays, and costs. When microorganisms become resistant to antimicrobials, standard treatments are often ineffective. Disc diffusion and minimum inhibitory concentration (MIC) are the most common antimicrobial susceptibility tests [23]. Identification of resistance-specific markers by PCR or microarray hybridization is useful for epidemiological purposes and the validation of phenotypic results. As DNA sequencing throughput and costs increase, whole-genome sequencing (WGS) becomes a viable option for routine resistance profile surveillance and identifying emerging resistances [24]. Pathogenic P. aeruginosa alters genome sequences and protein expression to resist. Resistance disrupts biochemical pathways and protein channels [25]. Antibiotic resistance and susceptibility must be linked to specific resistance genes; all genes in an isolate are added to predict susceptibility [26]. ResFinder, CARD, and Resfams predict genotypes from phenotypes [27]. More and more often, computational tools like machine-learning algorithms are used to build models correlating genomic variations with phenotypes [28]. Both a stimulus and an outcome are present in every supervised learning example. The algorithm will succeed only if it learns a model that faithfully transforms any input into the desired output.
Considering the above, the fundamental objective of this study was to develop an accurate phenotype prediction model against antimicrobials. For this purpose, machine learning approaches called bio-Weka [29], and random forest (RF), and logistic regression (LR) [30–32] were used on the data mining platform called Weka (v3.9.2) (an open source java-based software) [33–35] for acquiring classification accuracy assumptions to accurately predict the phenotypes against a panel of twelve antimicrobial agents, including ampicillin, amoxicillin, meropenem, cefepime, fosfomycin, ceftazidime, chloramphenicol, erythromycin, tetracycline, gentamycin, butirosin, and ciprofloxacin from whole genome sequence data of P. aeruginosa. Significantly, this study can further enhance the antimicrobial predictions of various bacterial agents in clinical trials.
## 2.1. Data Collection
The WGS reads of *Pseudomonas aeruginosa* and binary resistance phenotypes of antimicrobial agents utilized in this study were obtained by accession numbers provided in various studies, consisting of different countries, including China and 65 others (developed and under development), and downloaded from the open access repository called GenBank at NCBI (https://www.ncbi.nlm.nih.gov/genbank/), which is the NIH genetic DNA sequences database. All the descriptive information about the raw data is present in the Supplementary file. The metadata consists of various attributes, including genome name, NCBI taxon id, genome status, associated strains, GenBank accession numbers, country name, number of contigs, genome lengths, isolation sources, resistance genes, twelve antibiotics, and many more.
## 2.2. Model Framework and Parameters
In this study, antimicrobial resistance of P. aeruginosa was predicted using a data mining assessment framework by machine learning algorithms, as shown in Figure 1. There were a total of six stages involved in reaching these conclusions, including the following: objective; data collection and preparation; machine learning techniques on a data mining platform; model building; evaluation and assessment; and implications. Initially, we collected the data and did some preliminary preprocessing to pick the right attributes. Afterward, this data was used for analysis and assessment. Secondly, Weka (v3.9.2), “a java-based machine learning and data mining platform,” was used to measure and evaluate classifications with the most recent bio-Weka and RF plugins. In addition, the results of machine learning classifiers were used in logistic regression (LR) to evaluate the resistance phenotype assessment to twelve different antibiotic drugs, namely, ampicillin, amoxicillin, meropenem, cefepime, fosfomycin, ceftazidime, chloramphenicol, erythromycin, tetracycline, gentamycin, butirosin, and ciprofloxacin.
Furthermore, the data was divided into two sets (training set and testing set) by a ratio of 60: 40. Overfitting was prevented by using 10-fold cross-validation, and training data were used further as efficiently as possible to determine the optimal hyperparameter settings. The training model's evaluation results were based on an average of the hyperparameter values that fared best in the 10-fold scross-validation procedure. Sensitivity, specificity, accuracy, and precision were used to assess the model performance of bio-Weka and RF by equations [1]–[4]. The number of strains that turned out to be resistant was the true positive (TP), the number of strains that turned out to be sensitive was the true negative (TN), and the number of strains that turned out to be resistant when they should have been sensitive was the false positive (FP), and the number of strains that should have been sensitive when they should have been resistant was the false negative (FN) [36].[1]Sensitivity=TPTP+FN,[2]Specificity=TNTN+FP,[3]Accuracy=TP+TNTP+FN+TN+FP,[4]Precision=TPTP+FP.
## 2.3. BioWeka and Random Forest Prediction of Phenotypes Resistance
Weka's datasets are used and stored in a unique file format known as attribute relation file format (ARFF). Due to the wide variety of file types used for biological data, it implements a format-conversion input layer that can transform common file types into the ARFF format. Weka filters any classes that can be applied to a dataset to alter it, and bio-Weka has filters for working with biological sequences. It enabled us to compare and match sequences with BLAST and other sequence alignment tools. In addition, alignment-based classification was performed using auto alignment score evaluation schemes.
A java-based machine learning algorithm called bio-Weka and RF was used to perform the predictive modeling. The DSK (k-mer counting software) [37, 38] was used to generate K-mer profiles (abundance profiles of all unique words of length k in each genome) from the assembled contigs, with $k = 31.$ *This is* a common length for analyzing bacterial genomes [39]. In order to create the dataset, the 31-mer profiles of all strains were combined using the combine kmers tool in SEER [40]. The combined 31-mer counts were converted into presence/absence matrices to be used for model training and prediction. 10-fold cross-validation was used to select the best conjunctive and/or disjunctive model with a maximum of ten rules for binary classification analysis (using S/NS phenotypes based on the two different breakpoints for each drug) [41, 42], which involved testing the suggested broad range of values for the trade-off hyperparameter to determine the optimal rule scoring function (https://aldro61.github.io/kover/doclearning.html). In addition, classification (BW-mC) and regression (BW-R) models were constructed from log2 (MIC) data in bio-Weka and RF for the purpose of comparing the performance of binary classifiers to MIC prediction [29, 43].
Furthermore, the RF method uses a majority voting strategy (MVS) to classify samples based on the results of an ensemble of decision tree (DT) [44]. In other words, the RF method relies on the class indicated by the vast majority of the DT. Having a diverse ensemble of trees is essential for boosting RF performance with respect to a single DT. One way to achieve it is by using bootstrapping with replacement to generate the training set for developing each DT's unique feature set. However, features considered for splitting each node are not chosen from the full feature set but rather from a subset of features [45]. In addition, be aware that RF is more akin to an unintelligible black box model. In RF, as in individual DT, the CART algorithm is taken into account.
Multiple metrics were used to evaluate the model's efficacy, including sensitivity, specificity, accuracy, precision, and the overall bACC (the average of the sensitivity and specificity) [46]. Since the bACC represents false positive and false negative rates equally, regardless of the imbalance in the dataset, it was chosen as the overall measure of model performance. Two measures of MIC prediction accuracy were evaluated: firstly, the proportion of isolates for which the predicted MIC was identical to the phenotypic MIC (rounded to the nearest doubling dilution in the case of regression), and secondly, the proportion of isolates for which the predicted MIC was within one doubling dilution of the phenotypic MIC (1-tier accuracy). The MIC testing criteria for exact match rates and 1-tier accuracies have been removed to include predictions within 0.5 doubling dilutions or 1.5 doubling dilutions of the phenotypic MIC, respectively, to account for MIC variation [47]. Each analysis had 10 replicates, and the mean and $95\%$ confidence intervals were calculated for all metrics. Mean bACC was compared between replicate sets using two-tailed unpaired t-tests with logistic regression (LR) correction for unequal variance (α = 0.05) to assess differential model performance across datasets or methods. In addition, P values were calculated using the results of these unpaired t-tests.
## 2.4. Regression Statistics
Kappa statistics are reliable because they can be tested repeatedly [48, 49], ensuring that researchers have access to accurate, comprehensive data regarding research samples. It evaluates the predicted classification accuracy against a random classification [50]. We used a kappa statistic that relies on binary values, where 0 is considered as a null value and 1 represents the predicted outcome of the evaluation as in equation [5]–[7] [51]. It also serves as an indicator of the reliability of the evaluation. Not only that, but the LR variables help resolve the two-way binary classifications. When applied to the field of binary numbers, it makes predictions in the form of continuous values that allow for the preservation of sensitivity [36]. If the value is greater than the threshold (value > threshold), then the value assigned is 1; otherwise, the value measured is 0 as determined by the equations [8]–[11] [52].[5]K=PA−PE1−PE,[6]PA=TP+TNN,[7]PE=TP+FN∗TP+FP∗TN+FNN2,[8]P=α+β1X1+β2X2+⋯+βmXm,[9]σx11+e−x∈0,1,[10]Pr Y=+1X∼β. X,[11]Pr Y=−1X
## 3. Results
A total of 1200 isolates of P. aeruginosa were included in this study, out of which $44.66\%$ were resistant to 12 antimicrobial agents and $55.33\%$ were sensitive, as shown in Figure 2. Of which $44.66\%$ resistant isolates, 44 were resistant to ampicillin, 37 to amoxicillin, 58 to meropenem, 60 to cefepime, 45 to fosfomycin, 30 to ceftazidime, 52 to chloramphenicol, 58 to erythromycin, 39 to tetracycline, 30 to gentamycin, 20 to butirosin, and 63 to ciprofloxacin. In addition, of $55.33\%$ of sensitive isolates, 56 were sensitive to ampicillin, 63 to amoxicillin, 42 to meropenem, 40 to cefepime, 55 to fosfomycin, 70 to ceftazidime, 48 to chloramphenicol, 42 to erythromycin, 61 to tetracycline, 70 to gentamycin, 80 to butirosin, and 37 to ciprofloxacin, respectively. The most resistant genes to these twelve antimicrobial drugs were included blaOXA-396, blaPAO, aph(3′)-IIb, catB5, qacE, blaOXA-488, aac(6′)-Ib-cr, aph(3′)-Iia, aph[6]-Ic, aac(6′)-Ib3, fosA, sul1, catB7, blaPAO, aac[3]-Ia, aac(6′)-Il, aph(3′)-Iib, sul1catB7, blaPAO, blaOXA-396, blaOXA494, qacE, crpP, catB7, blaPAO, and blaOXA-488. Furthermore, from the analysis total of 19,371,434, k-mers were obtained of length 31. Which were compared from the ResFinder k-mer genes database, and a range of [1,302,507] k-mers of fosA, catB7, crpP, aac(6′)-Ib-cr, fosA, tet(G), aadA6, aph(3′)-Iib, sul1, aph(3′)-XV, aac(6′)-Ib3, blaOXA-488, blaGES-13, blaGES-7, blaGES-5, blaGES-6, blaPAO, qacE, crpT, aph(3′)-Iib, aadA13, blaOXA-50, and qacE genes were detected in genome of 360 stains.
The accuracy percentage obtained from the results of BioWeka was more than $98\%$ (as a mean percentage) including the training set and testing set, as shown in Figure 3 for all twelve antimicrobial drugs, namely, ampicillin, amoxicillin, meropenem, cefepime, fosfomycin, ceftazidime, chloramphenicol, erythromycin, tetracycline, gentamycin, butirosin, and ciprofloxacin with the confidence factor of $0.25\%$ by 10-fold-cross validation. After the loop tests, the final mean accuracy for ampicillin was ($99.31\%$), amoxicillin was ($99.02\%$), meropenem was ($98.27\%$), cefepime was ($99.73\%$), fosfomycin was ($96.44\%$), ceftazidime was ($98.63\%$), chloramphenicol was ($98.71\%$), erythromycin was ($95.76\%$), tetracycline was ($99.27\%$), gentamycin was ($98.00\%$), butirosin was ($99.57\%$), and ciprofloxacin was ($96.17\%$).
In addition, Figure 4 shows the resulted classification accuracy percentage of RF algorithm in contrast to twelve antimicrobial drugs. The mean classification percentage was calculated more than $96\%$ including the training set and testing set, as shown in Figure 5. After the loop testing, the final accuracy by RF for ampicillin was ($94.00\%$), amoxicillin was ($95.21\%$), meropenem was ($96.63\%$), cefepime was ($98.34\%$), fosfomycin was ($99.23\%$), ceftazidime was ($94.31\%$), chloramphenicol was ($96.00\%$), erythromycin was ($97.63\%$), tetracycline was ($98.25\%$), gentamycin was ($97.30\%$), butirosin was ($98.03\%$), and ciprofloxacin was ($98.97\%$). Furthermore, the standard deviation and average percentages of sensitivity, accuracy, precision, and specificity measured on the testing dataset are shown in Table 1. Our results of the testing dataset show that the antimicrobial drugs, namely ampicillin, amoxicillin, meropenem, cefepime, ceftazidime, tetracycline, butirosin, and ciprofloxacin, have no false-positive and false-negative bacterial strains.
## 4. Discussion
A number of studies have highlighted the increasing global prevalence of antimicrobial resistance [12–16, 21, 24, 27, 53–57]. This is related to the challenges of treating bacterial infections, the consequences of which can be severe. P. aeruginosa is one of the most common bacterial species, and its families are responsible for some of the most dangerous infections ever seen in humans. There is a correlation between the resistance of these bacteria to multiple antibiotic classes and the severity of the infection, which complicates treatment. Antibiotic resistance among these microorganisms has been rising steadily over the years, and it is now common to find clinical samples resistant to multiple drugs. The development of antibiotic resistance causes doctors to delay administering the most effective treatment methods and prescribe a larger dosage of antibiotics than is necessary. This is particularly important in the intensive care unit, where patients' health conditions necessitate longer courses of antibiotics. The extensive use of expensive medical interventions, increased mortality rates, and lengthened hospital stays are all consequences of antimicrobial resistance [58]. Another topic of great interest is the need to prevent the spread of bacteria resistant to antibiotics and to identify them in advance so that patients can be isolated as soon as possible. Since this is the case, novel approaches must be proposed for detecting antimicrobial resistance and taking appropriate action without delay. In addition, gaining insight into the factors that contribute to the spread of nosocomial infections is possible by identifying relevant features.
In this paper, we propose a data mining strategy based on two machine learning techniques, namely, bio-Weka and RF with a statistical approach for detecting the antimicrobial resistance of P. aeruginosa with different families of drugs. BioWeka and RF has shown that machine learning-based feature selection works with highly resulted accuracy as in Table 2. Consideration of antimicrobial drug resistance and susceptibility within data mining models and methods has been demonstrated to be useful in accelerating the workflow of clinical centers. Benefits for the individual, the healthcare system, and society may result from the early identification of patients at high risk of being resistant to one or more families of antibiotics. In addition, benefits include potential use in selecting the best antimicrobial treatment immediately.
Furthermore, the best performance achieved when testing this model strategy for resistance identification of antimicrobial drugs was a ROC area of 0.91 with a mean accuracy of more than $97\%$ with all twelve drugs, indicating that our model can distinguish between the different classes of antibiotic susceptibility based solely on the type of the examined sample, the Gram stain classification of the pathogen, and prior antibiotic susceptibility testing results. We can foresee the sensitivity results from the various researchers using the model presented in this study. The ability to accurately detect antibiotic resistance could help clinicians make educated decisions about empiric therapy based on the local antibiotic resistance pattern. There may be major consequences for infection prevention if such prescribing practices become widespread.
The model proposed in this study has only the limitation with the process of filtering by 60: 40 ratio with 10- fold cross-validation. If the ratios change then the accuracy and sensitivity of model might get affected. In addition, once the patient's clinical characteristics are added to the antimicrobial susceptibility dataset, the prediction performance of our model will significantly increase in terms of resistance prediction accuracy to different drugs. However, still, any such inclusion must incur the cost of retrieving the relevant data, which may be an exercise that involves a number of healthcare units, thereby increasing communication costs and complicating the need to align protocols that may operate across departments. After incurring such information, it is important to evaluate how well the additional knowledge acquired in terms of the improved accuracy metrics of the model can be incorporated into the practice of the hospital physicians, who may need to reevaluate their decision-making processes in the context of supporting or contradicting recommendations from a decision support system. To sum up, we think of this study as a node on a spectrum of cost-effectiveness studies that data mining approaches and machine learning techniques will spark in the healthcare industry.
## Data Availability
All data used in this study can be found in the Supplementary file associated with this article, or it can also be made available upon request to the first author or corresponding author.
## Consent
Not applicable.
## Conflicts of Interest
The authors declare that they have no conflicts of interest.
## Authors' Contributions
Sohail M. Noman was responsible for conceptualization, methodology, empirical estimations, writing, and drafting of the original draft by. Supervision was performed by Xiaoyang Jiao. Sohail M. Noman, Muhammad Shafiq, Yumeng Yuan, Mi Zeng, Qingdong Xie, and Xin Li performed data collection. Sohail M. Noman, Muhammad Zeeshan, Jehangir Arshad, and Melkamu Deressa Amentie performed review and editing. All authors have read and approved the final manuscript.
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|
---
title: 'Increased Risk of Non-Alcoholic Steatohepatitis in Patients With Inflammatory
Bowel Disease: A Population-Based Study'
journal: Cureus
year: 2023
pmcid: PMC9995222
doi: 10.7759/cureus.35854
license: CC BY 3.0
---
# Increased Risk of Non-Alcoholic Steatohepatitis in Patients With Inflammatory Bowel Disease: A Population-Based Study
## Abstract
Background and objective The global health burden of inflammatory bowel disease (IBD) stems from its increasing incidence over the years. Comprehensive studies on the topic hypothesize that IBD plays a more dominant in the development of non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH). In light of this, we conducted this study with the aim of assessing the prevalence and risk factors of developing NASH in patients who have had a diagnosis of ulcerative colitis (UC) and Crohn’s disease (CD).
Methodology A validated multicenter and research platform database of more than 360 hospitals from 26 different healthcare systems across the United States from 1999 to September 2022 was utilized for conducting this study. Patients aged 18-65 years were included. Pregnant patients and individuals diagnosed with alcohol use disorder were excluded. The risk of developing NASH was calculated using a multivariate regression analysis to account for potential confounding variables including male gender, hyperlipidemia, hypertension, type 2 diabetes mellitus (T2DM), and obesity. A two-sided p-value <0.05 was considered statistically significant, and all statistical analyses were performed using R version 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria, 2008).
Results A total of 79,346,259 individuals were screened in the database and 46,667,720 were selected for the final analysis based on the inclusion and exclusion criteria. Using multivariate regression analysis, the risk of developing NASH among patients with UC and CD was calculated. The odds of having NASH among patients with UC was 2.37 ($95\%$ CI: 2.17-2.60, $p \leq 0.001$). Similarly, the odds of having NASH were high in patients with CD as well, at 2.79 ($95\%$ CI: 2.58-3.02, $p \leq 0.001$).
Conclusion Based on our findings, patients with IBD have an increased prevalence and higher odds of developing NASH after controlling for common risk factors. We believe that a complex pathophysiological relationship exists between both disease processes. Further research is required to establish appropriate screening times to enable earlier disease identification and thereby improve patient outcomes.
## Introduction
Non-alcoholic steatohepatitis (NASH) and benign steatosis are on the histologic spectrum of non-alcoholic fatty liver disease (NAFLD), a clinicopathological condition. NASH is defined as hepatic steatosis and inflammation with hepatocyte injury, Mallory hyaline inclusions, and mixed lymphocytic and neutrophilic inflammatory infiltrate in perivenular areas with or without fibrosis [1]. On the other hand, inflammatory bowel disease (IBD) comprises Crohn's disease (CD) and ulcerative Colitis (UC). While CD is characterized by colonic transmural inflammation with skip lesions, UC is a chronic inflammatory condition characterized by relapsing and remitting episodes of inflammation limited to the colon's mucosal layer.
Multiple studies have shown strong associations between IBD and NAFLD. In a study by Bessissow et al., the prevalence of NAFLD was $33.6\%$ in patients with IBD [2]. Also, Sourianarayanane et al. found that NAFLD had an incidence of $8.2\%$ in patients with IBD when compared to patients without NAFLD [3]. In another study by Elchert et al., the overall prevalence of NASH in CD patients was $0.34\%$ compared to $0.08\%$ in the general population [4]. Many possible pathophysiological hypotheses have been proposed to explain this association, including disease-specific risk factors, such as chronic inflammation, steroid exposure, drug-induced hepatotoxicity, malnutrition, and alteration of gut microbiota [5].
NASH is the second leading cause of liver transplantation in the US [6]. Hence, it is critical to identify the risk factors for NASH so that the overall disease burden can be reduced. Our study focuses on understanding the prevalence and risk of developing NASH in patients with IBD by taking into account the confounding factors, including male gender, hyperlipidemia, hypertension, type 2 diabetes mellitus (T2DM), obesity, UC, and CD.
## Materials and methods
Study design Our cohort’s data were obtained using a validated, multicentered, and daily-updated database called Explorys (Explorys Inc, Cleveland, OH) developed by IBM Watson Health (now known as Merative; Ann Arbor, MI). Explorys consists of electronic health records from 26 different healthcare systems with a total of about 360 hospitals and more than 70 million patients across the United States. Explorys utilizes Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) for the definition of the diseases and pools large outpatient as well as inpatient deidentified data that can be formulated into numerous cohorts according to the clinical element being studied. Explorys does not record individual patient data such as laboratory or imaging results. The approval of the Institutional Review Board was not required since *Explorys is* a Health Insurance Portability and Accountability Act (HIPAA)-compliant platform. The use of this database has been validated in multiple fields including cardiology, hematology, and gastroenterology.
Patient selection A cohort of patients with a SNOMED-CT diagnosis of NASH between 1999 and May 2022 was identified. Patients aged 18-65 years were included. Pregnant patients and individuals diagnosed with alcohol use disorder were excluded.
Covariates Confounding factors associated with NASH were identified and collected if SNOMED-CT diagnoses were available. These were male gender, hyperlipidemia, hypertension, T2DM, and obesity.
Statistical analysis The prevalence of NASH, UC, and CD was calculated by dividing the respective number of subjects by the total number of subjects in the Explorys database. To account for confounding from the covariates listed above, we conducted 256 searches to explore every probability, with UC and CD representing one of the variables. A univariate analysis was conducted initially for all the variables, followed by a multivariate analysis. Statistical analysis was performed using R version 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria, 2008), and a two-sided p-value <0.05 was considered statistically significant for all analyses. Multivariate analysis was performed to adjust for multiple factors, including male gender, hyperlipidemia, hypertension, T2DM, obesity, UC, and CD.
## Results
A total of 79,346,259 individuals were screened in the database and 46,667,720 were included in the final analysis as per the inclusion and exclusion criteria. There were 34,200 people with NASH with a prevalence rate of 73 per 100,000 (Table 1). The prevalence of NASH was highest among patients with hypertension, hyperlipidemia, and obesity ($66\%$, $64\%$, and $62\%$ respectively).
**Table 1**
| Variables | NASH, n (%) | No NASH, n (%) |
| --- | --- | --- |
| Smokers | 5,640 (16.49) | 2,642,640 (5.66) |
| Male | 15,490 (45.29) | 21,489,460 (48.08) |
| Hyperlipidemia | 21,880 (63.97) | 4,514,960 (9.68) |
| Hypertension | 22,420 (65.55) | 5,512,560 (11.82) |
| T2DM | 16,680 (48.77) | 2,134,800 (4.57) |
| Obesity | 21,140 (61.81) | 3,224,130 (6.91) |
| Ulcerative colitis | 500 (1.46) | 118,920 (0.25) |
| Crohn’s disease | 710 (2.07) | 161,910 (0.34) |
| Total | 34200 | 46633520 |
Based on multivariate regression analysis, the risk of developing NASH was found to be higher in obese individuals (OR: 6.10; $95\%$ CI: 5.93-6.27), and patients with hyperlipidemia (OR: 3.03; $95\%$ CI: 2.94-3.12), hypertension [odss ratio (OR): 2.24; $95\%$ CI: 2.17-2.60], T2DM (OR: 3.08; $95\%$ CI: 3.00-3.17), UC (OR: 2.37; $95\%$ CI: 2.17-2.60), and CD (OR: 2.79; $95\%$ CI: 2.58-3.02) (Figure 1).
**Figure 1:** *Forrest plot depicting the risk of developing NASH as per stepwise multivariate regression analysisNASH: non-alcoholic steatohepatitis; T2DM: type II diabetes mellitus*
## Discussion
The development of NASH, both in patients with or without a diagnosis of IBD, has been strongly associated with T2DM, hypertension, and metabolic syndrome by multiple lines of evidence [7]. A significant proportion, around $30\%$, of patients with IBD have been observed to have altered liver inflammatory markers, suggesting the presence of extra-intestinal manifestations in the liver and biliary tract [5]. In fact, primary sclerosing cholangitis (PSC), an autoimmune condition, is observed in $70\%$ of patients with IBD [8]. These findings have led to an increased interest in investigating the association between IBD and NASH. Our study aimed to examine this relationship based on a database of 360 hospitals from 26 healthcare systems in the United States. The results showed that patients with IBD had an increased prevalence of developing NASH compared to those without IBD. The multivariate analysis revealed that individuals with UC (OR: 2.37; $95\%$ CI: 2.17-2.60) and CD (OR: 2.79; $95\%$ CI: 2.58-3.02) were approximately twice as likely to develop NASH compared to those without IBD. This relationship is not as pronounced compared to other risk factors such as obesity (OR: 6.10; $95\%$ CI: 5.93-6.27); however, it remains substantial and comparable to OR associated with T2DM (OR: 3.08; $95\%$ CI: 3.00-3.17), hypertension (OR: 2.24; $95\%$ CI: 2.17-2.60), and hyperlipidemia (OR: 3.03; $95\%$ CI: 2.94-3.12).
The study by Abenavoli et al. [ 2022] aimed to shed light on the complex interplay between gut microbiota and two increasingly prevalent diseases, NAFLD and IBD [9]. It is worth mentioning that NAFLD encompasses a range of conditions, ranging from simple fatty liver to NASH, which can eventually lead to liver cirrhosis and hepatocellular carcinoma [10]. Several previous studies have demonstrated gut dysbiosis in NAFLD and IBD, each with distinct underlying mechanisms [11-14]. Despite numerous studies supporting a positive association between IBD and NAFLD, a clear and well-established common pathogenesis has yet to be determined, although several emerging hypotheses exist [15]. In effect, they point to a potential interplay between factors such as increased intestinal permeability, changes in gut microbiota, endotoxemia, oxidative stress-induced inflammation, and genetic susceptibility [9,16]. Herein, the overproduction of conjugated bile acids due to gut dysbiosis may contribute to the development of NAFLD when they are returned to the liver [17]. The excessive production of reactive oxygen species (ROS) by Kupffer cells and alteration in mitochondrial DNA demonstrate the slowed progression of NAFLD to NASH, hepatocellular necroinflammation and fibrosis, and lastly carcinoma. Jarmakiewicz-Czaja et al. have also discussed the role of glucocorticoids, a widely used treatment for IBD, as well as other drugs, in developing NAFLD in IBD patients [18]. While investigations into the possible gut-liver immune axis are ongoing, further clinical studies are required to solidify the evidence base for this relationship.
The results of our study endorse previous findings in the literature, notably the most recent ones. Ritaccio et al. conducted a retrospective study of 1,672 IBD patients to examine the prevalence of NAFLD and the progression of NAFLD-related fibrosis [19]. The study found a prevalence of $12.4\%$ for NAFLD, with most patients displaying stable liver fibrosis index at the five-year follow-up. The results were compared to earlier studies from 1992 and 1998, which were limited in terms of their ability to measure the outcomes with the parameters employed adequately. Despite these limitations, the results were consistent with a previous estimate of approximately $20\%$ [20,21]. In a study by Elchert et al., the prevalence of NASH in patients with and without CD was assessed, and the overall prevalence of NASH in CD patients was $0.34\%$ compared to $0.08\%$ in the general population [4]. Magri et al. conducted an observational study involving 178 patients with IBD and found that $40.4\%$ of patients had NASH with varying degrees of steatosis [22]. Another observational study by Principi et al. found a prevalence of $28\%$ of NAFLD in a sample of 465 IBD patients [23]. Lastly, a monocentric cross-sectional study by Hoffmann et al. involving 694 IBD patients found that $48\%$ of patients with CD and $44\%$ of patients with UC had NAFLD, defined by increased echogenicity on liver ultrasound [24]. Despite these concurring results, the influence of the study design employed, sample size, and data analysis on the strength of evidence of these studies still needs to be determined.
A recent study by Yen et al. evaluated the prevalence and risk factors of NAFLD in a retrospective cohort of 81 patients with IBD [25]. Controlled attenuation parameter (CAP) technology with a Fibroscan® was employed for liver stiffness measurement and abdominal ultrasound for the screening program. The study showed that $29.6\%$ of IBD patients were diagnosed with NAFLD based on a CAP value of ≥248 dB/m. However, the results were not statistically significant ($$p \leq 0.761$$). The univariate analysis of CD as a risk factor also showed statistically insignificant results ($$p \leq 0.870$$). Another study by Mancina et al. employed the same advanced diagnostic liver evaluation using CAP in a cohort of 95 IBD patients and 53 healthy volunteers as a control group [26]. The study's results revealed higher fat content and liver stiffness in IBD patients compared to the control group (CD: 238 ±48 dB/m; UC: 246 ±44 dB/m; controls: 214 ±49 dB/m). The multivariate analysis showed that UC was associated with a higher risk of developing mild steatosis (OR: 4.80; $95\%$ CI: 1.64-14.04) and moderate to severe steatosis (OR: 7.49; $95\%$ CI: 2.36-23.76). These results correlate favorably with the findings of our study that showed a two- to three-fold increased risk of developing NASH.
Several meta-analyses have examined the association between NAFLD and IBD [27,28,29]. The most recent meta-analysis, conducted by Zamani et al., employed a rigorous study design and methodology [29]. The study was based on a systematic search of multiple databases, with the final analysis including 44 studies with 14,947 patients: 19 studies were from Europe, 18 were from the Americas, four were from the Western Pacific, two from South-East Asia, and one from the Middle East. The meta-analysis found that the pooled prevalence of NAFLD in IBD patients was $30.7\%$ ($95\%$ CI: 26.5-34.9). A comparison of this result with that of the meta-analysis conducted by Younossi et al. on the global epidemiology of NAFLD in the general population revealed a prevalence of $25.24\%$ ($95\%$ CI: 22.10-28.65) [30]. The study further reported that the European region had the highest prevalence of NAFLD among IBD patients ($36.9\%$, $95\%$ CI: 31.2-42.6), while the Eastern Mediterranean region had the lowest ($11.8\%$, $95\%$ CI: 9.7-13.9). The Americas had a prevalence of $28.2\%$ ($95\%$ CI: 22.2-34.3), which was consistent with the findings of previous studies. The difference in prevalence observed in our study may be attributed to several factors, including the examination of each IBD disease individually, the exclusion criteria employed, the larger sample size, the diagnostic methods used for the diagnosis of IBD and NAFLD, and the potential impact of different levels of steatosis on the results. Furthermore, the meta-analysis found that IBD patients were 1.96 times more likely to develop NAFLD compared to controls (OR: 1.96; $95\%$ CI: 1.13-3.4), based on a comparison of four studies with a total of 3,884 subjects. The study also found that CD was associated with a 1.16-fold increased risk of NAFLD compared to UC (OR: 1.16; $95\%$ CI: 0.93-1.44).
Individuals diagnosed with IBD are at an elevated risk of readmission and death due to the co-occurrence of NAFLD. They are more vulnerable to metabolic syndrome, given the recent advancements in the treatment of IBD. With the growing global incidence of both NAFLD and IBD, driven by the Westernization of global culture, it is crucial to implement screening protocols to identify IBD patients at risk of NAFLD and use noninvasive methods for early detection of fatty liver. This is particularly important given the findings of our study, which highlight the increased risk of NAFLD in IBD patients in a large cohort.
Our study has several limitations that must be considered. Primarily, other potential confounding factors, such as medication exposure and other chronic liver or biliary diseases, were not considered. In addition, the study did not take into account the different grades of liver steatosis. Furthermore, the study utilized a database from the United States, which limits the generalizability of its results to other populations given the differences in phenotypes of IBD. Finally, the retrospective, population-based design of the study limits our ability to establish a causal relationship between IBD and NASH.
## Conclusions
Multiple studies have indicated an association between IBD and the development of NAFLD. Our study, which examined a database of 360 hospitals in the US, found that individuals with IBD were twice as likely to develop NASH compared to those without IBD. Chronic systemic inflammation seen in patients with IBD, eventually leading to excessive production of ROS and alteration in mitochondrial DNA, is believed to be the underlying link. The underlying mechanisms of this relationship remain unknown, and further research is needed to gain a more comprehensive understanding of the molecular basis of this relationship. This study represents a significant contribution to the field, as it is the first study of its kind with a large sample size and provides a strong foundation for future studies to further explore the causality of this relationship through experimental designs such as randomized controlled trials and large-scale cohort studies.
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---
title: 'Primary hyperparathyroidism in young patients is associated with metabolic
disorders: a prospective comparative study'
authors:
- Ekaterina E. Bibik
- Ekaterina A. Dobreva
- Alina R. Elfimova
- Anastasiia P. Miliutina
- Anna K. Eremkina
- Anna M. Gorbacheva
- Julia A. Krupinova
- Ekaterina O. Koksharova
- Igor A. Sklyanik
- Alexander Y. Mayorov
- Natalia G. Mokrysheva
journal: BMC Endocrine Disorders
year: 2023
pmcid: PMC9995253
doi: 10.1186/s12902-023-01302-9
license: CC BY 4.0
---
# Primary hyperparathyroidism in young patients is associated with metabolic disorders: a prospective comparative study
## Abstract
### Background
Components of metabolic syndrome can be observed in patients with primary hyperparathyroidism (PHPT). The link between these disorders remains unclear due to the lack of relevant experimental models and the heterogeneity of examined groups. The effect of surgery on metabolic abnormalities is also controversial. We conducted a comprehensive assessment of metabolic parameters in young patients with PHPT.
### Methods
One-center prospective comparative study was carried out. The participants underwent a complex biochemical and hormonal examination, a hyperinsulinemic euglycemic and hyperglycemic clamps, a bioelectrical impedance analysis of the body composition before and 13 months after parathyroidectomy compared to sex-, age- and body mass index matched healthy volunteers.
### Results
$45.8\%$ of patients ($$n = 24$$) had excessive visceral fat. Insulin resistance was detected in $54.2\%$ of cases. PHPT patients had higher serum triglycerides, lower M-value and higher C-peptide and insulin levels in both phases of insulin secretion compared to the control group ($p \leq 0.05$ for all parameters). There were tendencies to decreased fasting glucose ($$p \leq 0.031$$), uric acid ($$p \leq 0.044$$) and insulin levels of the second secretion phase ($$p \leq 0.039$$) after surgery, but no statistically significant changes of lipid profile and M-value as well as body composition were revealed. We obtained negative correlations between percent body fat and osteocalcin and magnesium levels in patients before surgery.
### Conclusion
PHPT is associated with insulin resistance that is the main risk factor of serious metabolic disorders. Surgery may potentially improve carbohydrate and purine metabolism.
## Background
The prevalence of metabolic syndrome has a tendency to increase and depends on many factors that needs the complex approach to treatment and prevention of this pathology. In the Russian Federation metabolic syndrome occurs in 40–$50\%$ of population [1]. Some epidemiological and experimental studies indicate non-classical effects of both parathyroid hormone (PTH) and calcium on adipose tissue, pancreas, vascular wall, cardiac myocytes and others, thus they may be involved in the regulation of energy processes, blood pressure and metabolism [2]. In the general population the level of PTH is positively associated with the development of metabolic syndrome and an increased risk of cardiovascular diseases. Besides, diabetes mellitus, obesity, insulin resistance (IR) and hypertension are significantly more frequent in patients with primary hyperparathyroidism (PHPT) [3, 4]. But most studies covering the problem of IR in PHPT population were based on results of indirect diagnostic methods [3, 5, 6]. The positive effect of parathyroidectomy (PTE) on metabolic parameters in patients with PHPT is inconsistent [4, 7]. This is probably due to the methodological diversity in study design. Therefore the cardiovascular and metabolic benefits of surgical treatment in PHPT population remain a subject of debate [8, 9].
Based on the above, we conducted a comprehensive assessment of metabolic parameters in young patients with PHPT using gold-standard diagnostic methods. The young age of the studied group was chosen to minimize as much as possible the impact of age-associated and other risk factors on metabolic abnormalities.
## Methods
The prospective comparative study was conducted in the Endocrinology Research Centre (Moscow, Russia) from September 2018 to October 2020. We included 24 patients with PHPT and 20 healthy sex-, age- and body mass index (BMI) matched volunteers. Inclusion criteria for patients were age ≥ 18 years, confirmed diagnosis of PHPT according to the Russian guidelines (a combination of hypercalcemia and an elevated or inappropriately normal PTH level measured twice and excluded familial hypocalciuric hypercalcemia). Exclusion criteria were following: age ≥ 50 years, severe chronic diseases (cerebrovascular disease, coronary heart disease, heart, respiratory or liver failure); decreased estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m2; BMI ≥ 35 kg/m2; diabetes mellitus; hormone-secreting tumors of the pituitary gland, pancreas, gastrointestinal tract, adrenal glands; anamnesis of surgical operations on the pancreas; hypo-/hyperthyroidism; menstrual dysfunction for women; taking drugs affecting mineral metabolism (calcium, vitamin D metabolites, thiazide diuretics, denosumab, bisphosphonates) at the time of inclusion in the study; taking hypoglycemic therapy, somatostatin analogues, dopamine receptor agonists; mental illness; anamnesis of oncology; acute respiratory infection or exacerbation of a chronic disease during the last month; pregnancy; lactation. The control group had the same exclusion criteria and an additional one such as any parathyroid pathology.
Physical examination, laboratory evaluation, clamp-tests and an assessment of body composition were conducted both at baseline and 13 [10; 16] months after PTE. The study design is presented in Fig. 1.Fig. 1The structure of research design *Fasting serum* biochemical parameters (total calcium (reference range (RR) 2.15–2.55 mmol/l), albumin (RR 34–48 g/l for women, 35–50 g/l for men), phosphorus (RR 0.74–1.52 mmol/l), magnesium (RR 0.7–1.05 mmol/l), creatinine (RR 50–98 µmol/l for women, 63–110 µmol/l for men), glucose (RR 3.1–6.1 mmol/l), total cholesterol (RR 3.3–5.2 mmol/l), low-density lipoprotein cholesterol (LDL, RR 1.1–3.0 mmol/l), high-density lipoprotein cholesterol (HDL, RR 1.15–2.6 mmol/l for women, RR 0.9–2.6 µmol/l for men), triglycerides (RR 0.1–1.7 mmol/l), uric acid (RR 142–339 µmol/l for women, RR 202–416 µmol/l for men), alkaline phosphatase (ALP, RR 40–150 U/l) were assessed using ARCHITECH c8000 system (Abbott, USA). Intact PTH (RR 15–65 pg/ml), C-peptide (RR 1.1–4.4 ng/ml), immunoreactive insulin (IRI, RR 2.6–24.9 µU/ml) and osteocalcin (RR 15–46 ng/ml) were measured by the electrochemiluminescence immunoassay with Cobas 6000 (Roche, Germany), serum 25 hydroxyvitamin D (25(OH)D, RR 30–100 ng/ml)—by enzyme-linked immunoassay with Liaison XL analyzer (DiaSorin, Italy). Serum leptin (µg/l) and adiponectin (µg/ml) levels were determined by enzyme-linked immunoassay using Leptin Sensitiv ELISA and Adiponectin ELISA kits (Mediagnost, Germany). Glycated hemoglobin (HbA1c, RR 4 − $6\%$) was measured by high performance liquid chromatography with D10 analyzer (BioRad, USA). The formula for albumin-adjusted calcium (Caadj) is as follows: albumin-adjusted calcium (mmol/l) = serum calcium level (mmol/l) + 0.02 × (40 − serum albumin level (g/l)). eGFR was calculated using the CKD-EPI 2009 equation. BMI (kg/m2) was calculated as the ratio of weight (kg) divided by the height squared (m2).
All participants underwent 75 g oral glucose tolerance test (OGTT) and clamp-tests. Both clamps were performed at the morning after a 12-h overnight fast with minimum 48-h interval between them. IR was measured by the classic DeFronzo hyperinsulinemic euglycemic clamp-test. It included continuous IV infusion of regular insulin at a constant rate of 1 mIU/kg/min using a syringe pump (Perfusor compact, B. Braun, Germany). Simultaneously, $20\%$ glucose solution was infused using Infusomat fmS (B. Braun, Germany) to maintain blood glucose level in the range 5.1–5.6 mmol/l, that was controlled every 5 min with a glucose analyzer (OneTouch “VerioPro + ”, Switzerland). Glucose uptake by tissues (M-value) was calculated as an average amount of glucose (mg/kg/min) required to maintain glycemic targets during 30 min of dynamic equilibrium of the glucose infusion rate. M-values were classified into 4 groups: 0–2 (severe IR), 2–4 (moderate IR), 4–6 (mild IR), and > 6 (no IR). Two-phase insulin secretion was measured using a modification of DeFronzo hyperglycemic clamp test. $20\%$ glucose IV infusion consisted of two stage: 1) a 15-min “priming dose” to raise blood glucose level to hyperglycemic plateau; 2) a “maintenance dose”. The “priming dose” was calculated per body surface area (m2) and equal to 9,622 mg/m2. Then glucose solution was infused to maintain blood glucose level in the range 9.3–10.7 mmol/l during 2 h under the control every 5 min with a glucose analyzer. Every 2 min of the first stage and every 10 min of the second stage blood samples were obtained to evaluate serum C-peptide and IRI levels. The first phase of insulin secretion was assessed by areas under curves (AUC) of C-peptide and IRI concentrations during the priming stage and the second one by respective AUC during the last test period. Multi-frequency bioelectrical impedance analysis of body composition was performed using InBody-770 analyzer (Inbody Co., LTD, North Korea).
Statistical analysis was performed using Statistica 13.0 (StatSoft, USA) and SPSS (IBM, USA) software packages. Descriptive statistics of quantitative characteristics are presented by medians and interquartile ranges (Median, IQR [25;75]%), descriptive statistics of qualitative characteristics—in absolute and relative frequencies. The Mann–Whitney test (U-test) and Chi-square test were applied to compare two independent groups in terms of quantitative and qualitative characteristics respectively. The Wilcoxon signed-rank test (W-test) was used to compare related groups. Spearman’s rank correlation coefficient was applied for the examination of variable correlations. The critical level of significance when testing statistical hypotheses was equal to 0.05.
## Results
The ratio of men and women in the PHPT group before surgery was 1:4 with the median age 37 [33; 41] years and median illness duration (since the first symptoms or laboratory test abnormalities) 3 [1.5; 7] years. The baseline laboratory parameters of mineral metabolism of both groups are presented in Table 1.Table 1The laboratory parameters of mineral metabolism of both groups at baselineParametersPHPT group ($$n = 24$$)Control group ($$n = 20$$)p, U-testPTH, pg/ml141 [111; 228]39.9 [33.8; 47.5] < 0.001Caadj, mmol/l2.73 [2.61; 2.94]2.23 [2.15; 2.28] < 0.001Phosphorus, mmol/l0.76 [0.73; 0.84]1.14 [1.09; 1.25] < 0.001Magnesium, mmol/l0.84 [0.79; 0.86]0.81 [0.78; 0.82]0.014eGFR in CKD-EPI, ml/min/1.73 m2104 [94; 111]102 [95; 106]0.579ALP, U/l71 [60; 85]54 [41; 61.5] < 0.001Osteocalcin, ng/ml46.0 [38.2; 67.7]19.3 [16.6; 21.5] < 0.00125(OH)D, ng/ml19.0 [13.3; 21.9]20.8 [17.1; 28.2]0.154Data are presented by medians and interquartile ranges (Median, IQR [25;75]%). U-test: the Mann–Whitney test;PHPT Primary hyperparathyroidism, PTH Parathyroid hormone, Caadj Albumin-adjusted calcium, eGFR Estimated glomerular filtration rate, ALP Alkaline phosphatase, 25(OH)D 25 Hydroxyvitamin Dp < 0.05 was considered statistically significant According to the OGTT results, only 1 patient had an impaired glucose tolerance with normal HbA1c, others did not show any carbohydrate metabolism abnormalities. Various types of dyslipidemia and hyperuricemia were detected in $54.2\%$ ($$n = 13$$) and in $29.2\%$ ($$n = 7$$) of PHPT patients respectively. 11 patients were overweight ($45.8\%$) and the one was obese (BMI 31.8 kg/m2). $45.8\%$ of patients had excessive visceral fat (the area of visceral adipose tissue > 100 cm2), including those with normal BMI. IR was diagnosed in $54.2\%$ of patients ($$n = 13$$), four of them presented with moderate IR, none had severe IR. The baseline metabolic parameters of both groups are presented in Table 2.Table 2The baseline metabolic parameters of both groupsParametersPHPT group ($$n = 24$$)Control group ($$n = 20$$)p, U-testBMI, kg/m224.6 [22.5; 26.5]23.9 [22.7; 25.9]0.612Fasting glucose, mmol/l5.04 [4.63; 5.23]4.83 [4.50; 5.20]0.5562-h glucose OGTT, mmol/l5.51 [4.78; 6.34]4.89 [4.40; 5.74]0.260HbA1c, %5.30 [5.10; 5.50]5.20 [5.10; 5.50]0.815Total cholesterol, mmol/l4.94 [4.49; 5.41]5.18 [4.36; 5.46]0.732LDL cholesterol, mmol/l3.04 [2.58; 3.69]3.39 [2.36; 3.62]0.962HDL cholesterol, mmol/l1.35 [1.14; 1.75]1.53 [1.26; 1.76]0.494Triglycerides, mmol/l1.13 [0.94; 1.39]0.79 [0.63; 1.01]0.001Uric acid, µmol/l298 [246; 366]253 [231; 311]0.134Adiponectin, µg/ml7.22 [4.56; 8.79]7.23 [4.81; 10.8]0.849Leptin, µg/l12.5 [4.74; 18.8]7.09 [6.28; 11.7]0.247M-value, mg/kg/min5.60 [4.25; 7.45]7.90 [7.00; 10.6]0.001AUC C-peptide phase 161.9 [44.4; 73.9]37.6 [36.1; 42.6] < 0.001AUC C-peptide phase 2160 [145; 198]132 [115; 175]0.019AUC IRI phase 1648 [438; 834]294 [259; 384] < 0.001AUC IRI phase 21150 [961; 1448]760 [657; 1012]0.001Percent body fat, %31.5 [20.9; 36.2]31.2 [25.6; 33.7]0.916Visceral fat area, cm289.9 [61.3; 120]88.1 [75.9; 106]0.770Data are presented by medians and interquartile ranges (Median, IQR [25;75]%). U-test: the Mann–Whitney testPHPT Primary hyperparathyroidism, BMI Body mass index, OGTT Oral glucose tolerance test, HbA1c Glycated hemoglobin, LDL Low-density lipoprotein, HDL High-density lipoprotein, AUC Area under curve, IRI Immunoreactive insulinp < 0.05 was considered statistically significant Patients with PHPT had higher serum triglycerides levels without any differences in other metabolic parameters compared to the control group. They also showed lower M-value, higher serum C-peptide and IRI concentrations in both phases of insulin secretion. Changes of the parameters during the hyperglycemic clamp at baseline are presented in Fig. 2A, B.Fig. 2Changes of parameters during the hyperglycemic clamp at baseline in examined groups We found a negative correlation between percent body fat (PBF) and osteocalcin and magnesium levels (Fig. 3A, B) in the PHPT group. 2-h OGTT glucose level had direct links with PBF and visceral fat area (VFA) (r1 = 0.42, p1 = 0.040 and r2 = 0.44, p2 = 0.031, respectively), M-value had the opposite links with these body composition parameters (r1 = -0.49, p1 = 0.014 and r2 = -0.42, p2 = 0.041, respectively). Concentrations of C-peptide and IRI in both secretion phases demonstrated apparent correlations with BMI. Serum leptin level had a positive correlation with 2-h OGTT glycaemia and a negative with M-value (Fig. 4A, B).Fig. 3Correlations of percent body fat with parameters of mineral metabolism in patients with PHPT at baselineFig. 4Correlations of serum leptin level with parameters of carbohydrate metabolism in patients with PHPT at baseline
Only 17 patients continued the study after radical PTE. The re-examination was completed within 13 [10; 16] months, min 7 months, max 19 months. The follow-up data of mineral and metabolic parameters are presented in Tables 3 and 4. In the post-surgery period we detected decreased fasting glucose, uric acid levels and IRI concentration of the second secretion phase but no significant changes of lipid profile and M-value. BMI and HbA1c had been increased but body composition of patients did not change significantly. Table 3The follow-up laboratory parameters of mineral metabolism in the PHPT groupParametersPHPT group ($$n = 17$$)p, W-testBaselineAfter surgeryPTH, pg/ml138 [106; 210]38.8 [32.7; 49.2] < 0.001Caadj, mmol/l2.71 [2.61; 2.91]2.18 [2.16; 2.24] < 0.001Phosphorus, mmol/l0.76 [0.72; 0.83]0.94 [0.87; 1.07]0.001Magnesium, mmol/l0.84 [0.82; 0.86]0.80 [0.77; 0.84]0.031eGFR in CKD-EPI, ml/min/1.73 m2102 [95; 109]100 [91; 108]0.070ALP, U/l76.0 [65.0; 108]42.0 [36.0; 56.0]0.001Osteocalcin, ng/ml46.7 [41.0; 54.6]17.6 [11.4; 23.2] < 0.00125(OH)D, ng/ml20.5 [16.6; 22.2]30.4 [22.2; 36.5]0.004Data are presented by medians and interquartile ranges (Median, IQR [25;75]%). W-test: the Wilcoxon signed-rankPHPT Primary hyperparathyroidism, PTH Parathyroid hormone, Caadj Albumin-adjusted calcium, eGFR Estimated glomerular filtration rate, ALP Alkaline phosphatase, 25(OH)D 25 Hydroxyvitamin Dp < 0.05 was considered statistically significantTable 4The follow-up metabolic parameters of the PHPT groupParametersPHPT group ($$n = 17$$)p, W-testBaselineAfter surgeryBMI, kg/m225.0 [23.3; 26.8]25.3 [23.8; 27.5]0.004Fasting glucose, mmol/l5.10 [4.81; 5.24]4.69 [4.48; 5.00]0.0312-h glucose OGTT, mmol/l5.51 [4.56; 6.48]5.48 [4.74; 7.22]0.379HbA1c, %5.30 [5.10; 5.50]5.60 [5.30; 5.80]0.001Total cholesterol, mmol/l4.89 [4.19; 5.59]5.08 [4.5; 5.61]0.246LDL cholesterol, mmol/l2.90 [2.50; 3.64]3.23 [2.4; 3.8]0.381HDL cholesterol, mmol/l1.34 [1.14; 1.76]1.33 [1.15; 1.67]0.795Triglycerides, mmol/l1.16 [1.02; 1.35]1.22 [0.93; 1.63]0.278Uric acid, µmol/l307 [254; 357]260 [238; 352]0.044Adiponectin, µg/ml8.08 [6.27; 9.71]7.10 [3.98; 10.3]0.059Leptin, µg/l10.8 [4.36; 17.6]12.1 [4.09; 24.8]0.123M-value, mg/kg/min5.48 [4.30; 7.43]6.17 [4.56; 6.90]0.959AUC C-peptide phase 163.2 [47.5; 73.5]53.9 [44.2; 71.0]0.679AUC C-peptide phase 2161 [149; 193]169 [139; 196]0.737AUC IRI phase 1657 [426; 862]501 [339; 768]0.163AUC IRI phase 21121 [917; 1320]982 [806; 1375]0.044Percent body fat, %37.6 [25.8; 41.7]31.7 [22.5; 37.1]0.266Visceral fat area, cm288.1 [56.6; 140]87.8 [59.7; 141]0.149Data are presented by medians and interquartile ranges (Median, IQR [25;75]%). W-test: the Wilcoxon signed-rankPHPT Primary hyperparathyroidism, BMI Body mass index, OGTT Oral glucose tolerance test, HbA1c Glycated hemoglobin, LDL Low-density lipoprotein, HDL High-density lipoprotein, AUC Area under curve, IRI Immunoreactive insulinp < 0.05 was considered statistically significant The PHPT group after PTE still had higher serum triglycerides ($$p \leq 0.002$$) and HbA1c ($$p \leq 0.011$$) compared to the control group. Also lower M-value ($$p \leq 0.001$$) and higher serum C-peptide and IRI concentrations in both phases of pancreas secretion persisted in patients with previous PHPT compared to the healthy volunteers (Fig. 5A-D).Fig. 5The comparisons of serum C-peptide and IRI concentrations in patients with PHPT after PTE and the control group. U-test: the Mann-Whitney test; $p \leq 0.05$ was considered statistically significant. AUC Area under curve, PHPT Primary hyperparathyroidism, IRI Immunoreactive insulin
## Discussion
The main aim of our study was to estimate the main metabolic parameters in the young PHPT population using the most specific diagnostic methods. We revealed IR in more than half of young patients with PHPT as well as lower M-value and higher serum IRI concentrations compared to people without parathyroid pathology. This fact may be explained by the differences in mineral metabolism between groups because body composition was comparable. Majority of colleagues confirmed a reduction in insulin sensitivity accompanied by increased insulin secretion in PHPT using indirect estimated indices (HOMA-IR, HOMA-B, QUICKI, ISI). It is assumed that serum calcium level contributes to impaired insulin sensitivity [3, 5, 6]. Some researchers demonstrated the suppressed effect of PTH on glucose uptake by tissues [10, 11]. Cvijovic G. et al. used an euglycemic clamp in patients with PHPT too but did not detect difference in M-value and parameters of insulin secretion compared to healthy controls [12]. Using a hyperglycemic clamp technique Prager R. et al. found that insulin secretion was significantly elevated in patients with PHPT compared to controls [13]. Unfortunately, we did not find any significant correlations of PTH or other mineral markers and M-value, IRI or C-peptide levels but it could be because of the small sample size.
We also revealed dyslipidemia in more than half of PHPT patients and especially higher serum triglycerides compared to the control group. Hyperparathyroidism may impair the lipid elimination by decrease in lipoprotein lipase activity [14].
There were no correlations between carbohydrate metabolism parameters and serum calcium or PTH. Probably there is nonobvious link between them because follow-up in the post-surgical period showed a decrease in fasting glucose.
There was a negative correlation between osteocalcin level and PBF in our patients before surgery (accompanied with VFA). This is similar to Chinese study that showed an inverse relationship between serum osteocalcin levels and VFA [15]. Gianotti L. et al. demonstrated osteocalcin was negatively associated with fasting glucose and positively associated with HOMA2-S% before PTE. But after PTE osteocalcin levels had significantly decreased, while HOMA2-S% did not change [16]. Mendonça M. L. et al. did not observe associations between osteocalcin and insulin, glucose levels, HOMA-IR in PHPT group [17]. Therefore, the role of bone remodeling in metabolic processes requires further investigation.
Despite the previously described changes of leptin and adiponectin concentrations in PHPT [18–20], we did not confirm them as well as a statistically significant increase in uric acid compared to the healthy volunteers [21]. Probably this is because of restricted sample size.
The studies demonstrated controversial effects of PTE on metabolic parameters in PHPT [22–27]. Our results are more consistent with data of the recent systematic review. PTE could improve glycemic parameters (including insulin concentration) despite increased BMI but not lipid exchange [28]. We also confirmed the results of Prager R. et al., who did not observe the difference in M-value but noted a decrease in insulin secretion in patients with PHPT before and after surgery [13] whereas Cvijovic G. et al. got opposite results [12]. The differences are more likely determined by various observation periods. We revealed the decrease of uric acid level after surgery similar to results of the meta-analysis [21].
Limitations of the study.
The limitations of our study are the small sample sizes. Besides, we included 2 patients with MEN1 mutation that itself may affect carbohydrate balance. Wijk J.P.H. et al. supposed decreased IR and higher prevalence of impaired fasting glucose were unrelated to MEN-1 manifestations [29]. Another limitation is conducting research during COVID-19 pandemic. According to some studies, COVID-19 is associated with increased blood glucose including people without pre-existing diabetes [30, 31]. The tendency to hyperglycemia because of transient damage of pancreatic islets can persist after recovery from the disease [31, 32]. In addition, COVID-19 pandemic affected the time of examination of patients after surgery, which could also impact the results of the study.
## Conclusion
Changes of bone and mineral parameters can lead to metabolic disorders in young patients with PHPT. PHPT is associated with IR, which is the main risk factor for the severe carbohydrate and fat disorders. The tendency to hypertriglyceridemia in patients with parathyroid tumors compared to the healthy volunteers suggests the contribution of disease to dyslipidemia. Remission of PHPT could improve the carbohydrate and purine balance but further studies are required to clarify this fact.
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---
title: The Mediating Role of Body Acceptance in Explaining the Relation of Mindfulness,
Self-Compassion and Mindful Eating to Body Image in Gay Men and Bisexual Men
authors:
- Harvey Regan
- Rebecca Keyte
- Michael Mantzios
- Helen Egan
journal: Mindfulness
year: 2023
pmcid: PMC9995256
doi: 10.1007/s12671-023-02095-7
license: CC BY 4.0
---
# The Mediating Role of Body Acceptance in Explaining the Relation of Mindfulness, Self-Compassion and Mindful Eating to Body Image in Gay Men and Bisexual Men
## Body
Mindfulness and mindfulness-based concepts, such as self-compassion and mindful eating, have been utilised in research throughout health psychology, especially in assisting populations engaging with problematic eating behaviours and experiencing body image concerns (Jordan et al., 2014; Mantzios, & Wilson, 2015; Tihanyi et al., 2016; Wasylkiw et al., 2012). Mindfulness has been described as paying attention to the present moment, with a non-judgemental attitude (Kabat-Zinn, 2015). Exploration of mindfulness has promoted the development and efficacy of mindfulness (Balciuniene et al., 2021) and compassion-based interventions (Albertson et al., 2015) aiming to attenuate body image and eating related issues in the general population, but also in more specific populations where these issues are prevalent (e.g., adolescent females; individuals with a diagnosis of Cystic Fibrosis) (Egan et al., 2021; Hussein et al., 2017). Exploration of mindfulness and related concepts have not been investigated extensively in gay and bisexual men although evidence shows that this is a population who are also at higher risk of developing eating and body image related issues (e.g., Fussner & Smith, 2015; McClain & Peebles, 2016; Tran et al., 2020). The development of interventions which consider the specific experiences of different populations increases the efficacy of such interventions, hence the need for increased knowledge of experiences of body issues in gay and bisexual men.
Within Western culture, presentation of “thin” or “slim” female body types have contributed to poor body image perception among younger female populations (Bombak et al., 2019). Those who do not meet this ideal expectation often display lower body image and dissatisfaction (Bombak et al., 2019) and perceive a lack of acceptance of their body shape from peers, family, and friends (Tylka & Homan, 2015). Body acceptance has been defined as an acknowledgement of feeling unsatisfied with some aspects of the body but accepting these non-judgementally (Tylka & Wood-Barcalow, 2015); it is often aligned with “fat acceptance” though there are many facets to body acceptance amongst different populations (Ruggiero et al., 2000). The perceived “body acceptance by others” (Swami et al., 2020) in relation to those who are close to the individual (family and friends) has also been linked to body image and mental health outcomes (Layman et al., 2021). Evidence shows that higher rates of body acceptance relate to positive body image (Swami et al., 2021). A higher prevalence for body related issues has been found in female populations including a lack of body acceptance, leading to poor body image and body dissatisfaction (Santonastaso, 1995). A large collection of research has attributed this to women internalizing the “thin” body ideals portrayed by the media (Pidgeon & Appleby, 2014). While findings generally indicate a thin “ideal body” proposition, further explorations of body acceptance and how this relates to body image and mindfulness constructs is needed.
Research that explores body image in men does not often depict the sexuality of participants and therefore does not consider the specific experiences of gay and bisexual men (Fussner & Smith, 2015). This omission is important, as recent literature has highlighted gay and bisexual men as experiencing a higher prevalence of eating and body related issues when compared with heterosexual men (Blashill, 2010). Brewster et al. [ 2017] suggest that the pressure upon gay men to conform to the high standards of bodily appearance in the gay community could result in disordered eating and body dissatisfaction. Yelland and Tiggemann [2003] compared measures assessing disordered eating and desire for muscularity in gay men, using heterosexual men and women as control groups. Gay men scored higher in the disordered eating and desire for muscularity measures than both the control groups (Yelland & Tiggemann, 2003). This suggests that the aspirational male gay body type is both lean and muscular. This body ideal can be difficult to achieve and maintain and may help to explain the higher rates of disordered eating. A perceived failure to achieve such body ideals may also negatively affect wellbeing in gay men (Brewster et al., 2017; Regan et al., 2021; Yelland & Tiggemann, 2003).
Qualitative explorations of body ideals in relation to social perceptions and the impact on the individual have reflected similar findings (Morgan, & Arcelus, 2009). Regan et al. [ 2021] highlighted the appearance-based judgement experienced by gay men who visited “gay spaces”, and how this related to a lack of body acceptance within gay men. Further findings showed that participants were judgemental of themselves when they had eaten unhealthy food, particularly when consumption was unplanned or eaten without attention (i.e., mindless eating). This research highlights the lack of body acceptance experienced and the perceived importance of attaining or maintaining a slim or muscular body type to be “accepted” within this community (Regan et al., 2021). The potential of mindfulness and self-compassion to improve body acceptance and body image is proposed (Brewster et al., 2017; Regan et al., 2021; Yelland & Tiggemann, 2003).
Literature presents several explorations of mindfulness-based interventions and their effectiveness at attenuating body related issues within the general and more specific populations. Balciuniene et al. [ 2021] tested an 8-week intervention programme utilising mindfulness based physical exercise and educational sessions with a sample of female college students who showed an increase in body image scores post intervention. Similarly, Zamzami et al. [ 2015] demonstrated that mindfulness-based exercise attenuated lower body image in female students. This provides evidence of the effectiveness of mindfulness-based interventions when addressing body related issues in specific populations (Balciuniene et al., 2021; Zamzami et al., 2015).
Self-compassion is a mindfulness-based construct which describes compassion directed towards oneself, comprising of three main elements, kindness, a sense of common humanity and mindfulness (Germer & Neff, 2013). This concept has been explored within body image and eating literature, where higher levels of self-compassion relating to higher levels of body satisfaction and lower reports of disordered or maladaptive eating (Egan et al., 2018; Mantzios & Egan, 2017; Rahimi-Ardabili et al., 2018; Regan et al., 2021). Wasylkiw et al. [ 2012] explored self-compassion and self-kindness in relation to body image in a sample of female university students. Higher levels of self-compassion and self-kindness were found to be predictors of higher levels of body image. Self-compassion-based interventions have also shown efficacy in reducing body dissatisfaction in female populations, Albertson et al. [ 2015] tested a daily audio guided self-compassion based-meditation intervention over 3 weeks, results showed higher levels of self-compassion which were associated with higher levels of body image.
Emerging research has looked at the potential influence mindful eating may have on reducing not only maladaptive eating behaviours, but also body dissatisfaction (Olvera-Ruvalcaba & Gómez-Peresmitré, 2021). Mindful eating encompasses non-judgment, eating with awareness and engaging with the physical and emotional sensations associated with eating (Mantzios, 2021). Ponde Nejadan et al. [ 2018] explored mindful eating, body image and quality of life in a sample of married Iranian women, showing increases of mindful eating related to increases in body image and quality of life. This research suggests that mindful eating may also play a role in promoting positive body image. Further links between mindful eating and body image are presented by Webb et al. [ 2018] who investigated the impact of family talk around mealtimes. Results showed that self-denigrating talk was inversely linked with mindful eating while increases in mindful eating behaviours increased positive body image and appreciation. Research proposes the importance of mindful eating (Mantzios et al., 2018), and by extension, of mindful-eating based interventions (Hussain et al., 2017; Mantzios et al., 2020a, b), when considering its potential impact on positive body image.
The links between body image and mindfulness-based concepts have been discussed (Jordan et al., 2014; Tihanyi et al., 2016; Wasylkiw et al., 2012); however, the potential link of body acceptance to mindfulness has not been extensively explored. Exploration of these concepts within gay and bisexual men would highlight elements that may be important when considering a mindful or compassion-based intervention to attenuate body and eating related issues in gay and bisexual men. The aim of this study is to explore body image and the potential relationships to mindfulness, self-compassion, and mindful eating in gay and bisexual men to inform future mindful and/or compassion-based intervention to attenuate body related issues. Importantly, the present research assumes the close association of body acceptance to body-image, and the congruent nature of acceptance to mindfulness, self-compassion, and mindful eating to be a significant indicator of promoting healthier changes towards body perceptions.
## Abstract
### Objectives
Mindfulness and mindfulness-based constructs, such as self-compassion and mindful eating, have been positively associated with healthier eating and body related perceptions. Exploration of mindfulness and related concepts have not been investigated extensively in gay and bisexual men, a population where eating and body related concerns have been found to be widespread.
### Method
Participants completed an online questionnaire, assessing mindfulness, self-compassion, mindful eating, body image and body acceptance. Correlation analysis and further mediation analysis was conducted to explore the relations between these constructs within the present sample ($$n = 163$$).
### Results
A community sample showed a positive association of body image to mindfulness-based concepts, and negative to body non-acceptance, within the target population. Mediation analysis showed the role of body acceptance in explaining the relation between mindfulness, self-compassion and mindful eating to body image.
### Conclusions
Findings highlight the importance of body acceptance when considering the development of a mindfulness or compassion-based intervention to attenuate body related issues among gay and bisexual men.
### Preregistration
This manuscript has not been preregistered.
## Participants
All participants ($$n = 163$$, Mage = 37.29, SD = 12.07; MBMI = 26.37, SD = 4.94) were English-speaking, from the UK and self-identified as either gay ($89\%$, $$n = 145$$), bi-sexual ($7.4\%$, $$n = 12$$), or heteroflexible ($1.2\%$, $$n = 2$$) with 2.4 % ($$n = 4$$) not disclosing any information regarding sexuality. Eligibility criteria included individuals who were over the age of 18 years old and those who has not received a diagnosis of an eating or body-related disorder within the past 2 years, this was screened for within the Participant information sheet and the Informed consent form. According to Fritz and MacKinnon [2007], a sample size of 163 participants would enable observations of an indirect effect of a medium-sized alpha pathway coefficient (i.e., predictor to mediator) and a medium-sized beta pathway coefficient (i.e., mediator to criterion) at $80\%$ power using bias-corrected bootstrapping estimating procedures (Table 1).Table 1Participant demographic informationVariableParticipants ($$n = 163$$)Sexuality Gay145 Bisexual12 Heteroflexible2 Non-disclosure4Gender Trans-male1 Non-binary1 Cis male130 Gender fluid4 Gender non-conforming5 Non-disclosure22Ethnicity White British115 White Irish5 White and black Caribbean4 African3 Caribbean4 White and Black African2 South Asian6 Non-disclosure24
## Procedure
Participants were recruited through volunteer sampling; an advert for the study outlining its nature, target population and link to the questionnaire was used for recruitment. This poster was disseminated by the research team through social media platforms, highlighting the study information and linking to the questionnaire platform to potential participants. The online survey platform Qualtrics was used to contain the questionnaire. Upon clicking the link, participants were presented with an online version of the Information sheet and Consent form which had to be viewed and agreed to before the questionnaire could be accessed. Once all measures were completed, participants were presented with the Debrief form. This included information regarding the contact details of the researcher, further support, and details of their right to withdraw their data from the study should they wish to do so at a later date. Data were collected from March until August 2021. Ethical approval was received from The Business Law and Social Sciences Ethics Committee at Birmingham City University (Regan /#7972 /sub3 /R(B) /2021 /Jan /BLSS FAEC).
## Measures
Participant information sheet. Participants were asked to report their age, gender, height, weight, ethnicity, smoking and exercise engagement.
The Sussex-Oxford Compassion for the Self (SOCS-S; Gu et al., 2020) is a 20-item scale containing 5 sub-scales (Recognising suffering; Understanding the universality of suffering; Feeling for the person suffering; Tolerating uncomfortable feelings; Acting or being motivated to act to alleviate suffering). Total scores were calculated and used within the analysis; with the higher the score meaning higher levels of self-compassion. Responses were recorded using a 5-point Likert scale (1 = Not at all true, 2 = Rarely true, 3 = Sometimes true, 4 = Often true, 5 = Always true), sample items include: “I notice when I’m feeling distressed” and “I connect with my own suffering without judging myself”. Cronbach’s alpha and McDonald’s omega were used to assess the scale reliability for the SOCS-S in the present research (α = 0.95, ω = 0.95).
The Body Image Acceptance and Action Questionnaire -5 (BI-AAQ-5; Basarkod, Sahdra & Ciarrochi, 2018) is a short form of the Body image – Acceptance and Action Questionnaire (BI-AAQ-5) which aims to assess body image acceptance. Total scores were calculated and used within the analysis; with a higher score meaning lower levels of body-acceptance (or higher levels of body non-acceptance). The BI-AAQ-5 is a 5-item scale where responses are recorded using a 7-point Likert scale (1 = Always true and 7 = Never true). Sample items include: “Worrying about my weight makes it difficult for me to live a life that I value” and “I shut down when I feel bad about my body shape or weight”. Cronbach’s alpha and McDonald’s omega were used to assess the scale reliability for the BI-AAQ in the present research (α = 0.92, ω = 0.92).
The Dresden Body Image Questionnaire (DBIQ; Scheffers et al., 2017) is a 35-item questionnaire with positively and negatively worded statements comprising of five subscales (Body Acceptance, Vitality, Physical Contact, Sexual Fulfilment and Self-aggrandizement). The DBIQ aims to assess body image, with higher scores meaning higher levels of a more positive perception of body image; total scores were calculated and used within the analysis. Responses were recorded using a 5-point Likert scale (1 = Not at all true, 2 = Rarely true, 3 = Sometimes true, 4 = Often true, 5 = Always true), sample items include: “I wish I had a different body” and “I use my body to attract attention”. Cronbach’s alpha and McDonald’s omega were used to assess the scale reliability for the BDIQ in the present research (α = 0.91, ω = 0.91).
The Mindful Eating Behaviour Scale (MEBS; Winkens et al., 2018) is a 20-item scale, and has 5 subscales (Focused Eating, Eating with Awareness, Eating without Distraction, Hunger and Satiety Cues). Total scores were calculated and used within the analysis; with a higher score meaning higher levels of mindful eating. Responses were recorded using a 4-point Likert scale (1 = Never to 4 = Usually), sample items include: “I wish I could control my eating more easily” and “I trust my body to tell me when to eat”. Cronbach’s alpha and McDonald’s omega were used to assess the scale reliability for the MEBS in the present research (α = 0.80, ω = 1.08).
The Five Facet Mindfulness Questionnaire (FFMQ-15; Gu et al., 2016) is a 15-item scale, and comprises of 5 subscales (Observing items, Describe items, Acting with awareness items, Non-judging items, Non-reactivity items). Total scores were calculated and used within the analysis; with the higher the score meaning higher levels of mindfulness. Responses were recorded using a 5-point Likert scale (1 = Never or very rarely true to 5 = Very often or always true), sample items include: “I’m good at finding words to describe my feelings” and “I find myself doing things without paying attention”. Cronbach’s alpha for the FFMQ in the present research was α = 0.67. McDonald’s omega was used to assess the scale reliability for the FFMQ in the present research, but the low association of the items and the proposed poor model fit did not allow for a score until Observe items (i.e., 1, 6, and 11) and Item 5 (non-reactivity) were removed (ω = 0.62).
## Data Analyses
All statistical analyses were conducted using IBM SPSS 25. A total of 44 participants were excluded from the study due to incomplete or missing data, which took place within the initial screening process, leaving a total of 163 participants completing all measures described within this study. A significance value of <0.05 was used to determine significant relationships between variables. Bivariate correlation analysis was used to determine the relationship between variables explored within the questionnaire. Mediation analyses were conducted using Hayes’ [2017] PROCESS macro (Model 4) with a bootstrap sample of 5000. Confidence intervals (CI) do not cross zero and are considered significant when upper and lower boundaries are corrected to $95\%$. Body acceptance was used as a mediator to explore the effect on the relationship between Mindfulness, Self-compassion, and Mindful Eating on Body Image.
## Correlation Analyses
Pearson’s Bivariate correlation coefficient was employed using significant values between variables (Body acceptance, Body image, Mindfulness, Self-compassion, and Mindful eating), as well as means and standard deviations as presented in Table 2. Significant negative associations were observed between body non-acceptance and body image (r = -0.63, $p \leq 0.001$), suggesting that with higher body image there is a decrease of non-body acceptance (essentially meaning the higher body image, the higher the scores on measures assessing body acceptance). Significant negative associations were observed between body non-acceptance, mindfulness (r = -0.42, $p \leq 0.001$), self-compassion (r = -0.50, $p \leq 0.001$) and mindful eating (r = -0.43, $p \leq 0.001$). The higher the body non-acceptance, the lower the scores in mindfulness, self-compassion and mindful eating (essentially meaning the higher body acceptance the higher scores in mindfulness, self-compassion and mindful eating). Significant positive associations were observed between body image, mindfulness ($r = 0.32$, $p \leq 0.001$), self-compassion ($r = 0.50$, $p \leq 0.001$) and mindful eating ($r = 0.48$, $p \leq 0.001$). As body image increased, scores on measures assessing mindfulness, self-compassion, and mindful eating also increased. Table 2Means and standard deviations of variables, and bivariate correlations between body image, body acceptance, mindfulness, self-compassion and mindful eatingScales12345MSD[1] BIAAQ18.828.21[2] DBIQ-0.63**106.7520.25[3] FFMQ-0.41**0.32**46.267.29[4] SOCS-S-0.50**0.50**0.65**66.6914.71[5] MEBS-0.43**0.48**0.41**0.52**67.8510.01BI-AAQ-5 – Body image Acceptance and Action scale (higher scores represent higher body non-acceptance); DBIQ – Dresden Body Image Questionnaire; FFMQ – Five Facet Mindfulness Questionnaire; SOCS-O - The Sussex-Oxford Compassion for Others; MEBS – Mindful Eating Behaviour Questionnaire.** *Correlation is* significant at the 0.01 level (two-tailed).
## Mediation Analyses
Further analysis explored the mediating effect of body acceptance on the relationships of mindfulness, self-compassion and mindful eating to body image. First, mindfulness was entered as the predictor variable and body image was entered as the outcome variable. Body acceptance was entered as the potential mediating variable. Findings indicated that mindfulness indirectly relate to body image, through its relationship with body acceptance. Mindfulness significantly predicted body acceptance (b = -0.49, $t = 5.47$, $p \leq 0.001$), as scores on mindfulness increased, scores on body acceptance decreased which related to body acceptance significantly predicting body image ($b = 1.43$, $t = 7.77$, $p \leq 0.001$). A $95\%$ bias-corrected confidence interval based on 5000 bootstrap samples indicated that there was an indirect effect ($b = 0.70$) which was above zero (CI = 0.41, 1.00) Fig. 1.Fig. 1The mediating effect of body acceptance in the relationship between mindfulness and body image. Note: All presented effects are unstandardised; a is the effect of Mindfulness on body acceptance; b is the effect of body acceptance on body image; c’ is the direct effect of mindfulness on body image; c is the total effect of mindfulness on body image. * $p \leq 0.05$, ** $p \leq 0.01$, *** $p \leq 0.001.$ Further note: B-IAAQ-5 – Body image Acceptance and Action scale (higher scores represent higher body non-acceptance) Secondly, self-compassion was entered as the predictor variable, body image as the outcome variable and body acceptance as the potential mediator. Findings indicated that self-compassion indirectly related to body image, through its relationship with body acceptance. Self-compassion significantly predicted body acceptance (b = -0.29, $t = 5.41$, $p \leq 0.001$), as scores of self-compassion increased, scores on body acceptance decreased which related to body acceptance significantly predicting body image ($b = 1.15$, $t = 6.60$, $p \leq 0.001$). A $95\%$ bias-corrected confidence interval based on 5000 bootstrap samples indicated that the indirect effect ($b = 0.33$) was above zero (CI = 0.18, 0.54) Fig. 2.Fig. 2The mediating effect of body acceptance in the relationship between self-compassion and body image. Note: All presented effects are unstandardised; a is the effect of self-compassion on body acceptance; b is the effect of body acceptacnce on body image; c’ is the direct effect of self-compassion on body image; c is the total effect of self-comapssion on body image. * $p \leq 0.05$, ** $p \leq 0.01$, *** $p \leq 0.001.$ Further note: BI-AAQ-5 – Body image Acceptance and Action scale (higher scores represent higher body non-acceptance) Lastly, mindful eating was entered as the predictor variable, body image as the outcome variable and body acceptance as the potential mediator. Findings indicated that mindful eating indirectly related to body image, through its relationship with body acceptance. Mindful eating significantly predicted body acceptance (b = -0.36, $t = 5.30$, $p \leq 0.001$), as scores of mindful eating increased, scores on body acceptance decreased which related to body acceptance significantly predicting body image ($b = 1.14$, $t = 5.70$, $p \leq 0.001$). A $95\%$ bias-corrected confidence interval based on 5000 bootstrap samples indicated that the indirect effect (b = -0.42) was above zero (CI = 0.23, 0.62) Fig. 3.Fig. 3The mediating effect of body acceptance in the relationship between mindful eating and body image. Note: All presented effects are unstandardised; a is the effect of mindful eating on body acceptance; b is the effect of body acceptacnce on body image; c’ is the direct effect of mindful eating on body image; c is the total effect of mindful eating on body image. * $p \leq 0.05$, ** $p \leq 0.01$, *** $p \leq 0.001.$ Further note: BI-AAQ-5 – Body image Acceptance and Action scale (higher scores represent higher body non-acceptance)
## Discussion
The aim of this research was to explore body image, body acceptance and their relationship to mindfulness, self-compassion, and mindful eating among gay and bisexual men. Exploring these concepts among the current population provides novel insight into body image and their relationships to mindfulness and related concepts (i.e., self-compassion and mindful eating) among a previously underrepresented community. Furthermore, the potential relationship between mindfulness, mindful eating and self-compassion to body-image, and the potential of explaining such relationships through body acceptance was also explored. Findings indicated that body acceptance mediated the relationship between mindfulness, self-compassion and body image, and mindful eating and body image. This corresponds with previous literature that outlines the link between mindfulness (Zamzami et al., 2015), self-compassion (Mantzios & Egan, 2017, 2018) and mindful eating (Ponde Nejadan et al., 2018) to body image. These findings presented within a gay population reflect the outcomes of research within general and more specific populations (Albertson et al., 2015). This research also shows the potential association of body acceptance to mindfulness and body image within this population. The indication of the relationships between mindfulness, self-compassion and mindful eating to body image can be explained through body acceptance, highlighting its importance within this community.
Correlation analysis also provided some novel and interesting findings. Within this population, body image related positively to self-compassion, mindfulness and mindful eating. Research exploring mindfulness-based concepts and body image in female populations are similar to the current findings presented (Balciuniene et al., 2021; Zamzami et al., 2015); suggesting that an increase in body image relates to a more mindful and compassion view of the self. Higher body acceptance also related positively to mindfulness, self-compassion and mindful eating, suggesting that this construct relates similarly to body image in its relationship to mindfulness-based constructs.
It is important to consider the demographic of participants included within this sample. The average BMI of participants (26.37) falls into the category deemed as “overweight”; meaning the conclusions drawn from this sample can only be attributed to an overweight population. The majority of participants also identified themselves as “White British” ($$n = 115$$). Future research should endeavour to capture the experiences of queer people of colour within their research, helping to provide insight into body- and mindfulness- related constructs among diverse samples.
The findings from the present study should inform future research and practice in aiming to attenuate body related issues in this population. The role of body acceptance here also provides a clear link that this construct is related to mindfulness, self-compassion, mindful-eating, and body image. Future research exploring these concepts or investigating the efficacy of interventions should consider this construct in relation to body image.
## Limitations and Future Research Directions
This research concedes the following limitations which are significant to consider for future research. First, the cross-sectional nature of this study does provide limited insight, and qualitative explorations should gather more in-depth data from gay men. Second, conclusions can only be drawn from the period that data was collected. Consideration should be given to data being collected during the COVID-19 pandemic (March to August 2021) where variations of restrictions were in place across the United Kingdom. This could have played some role in altering participants’ perceptions of body image and body acceptance, specifically in relation to mindfulness-based constructs. This could be specifically prominent within this community; whereby the social influence of perceived body ideals is compounded by perceptions of other gay men, particularly when in ‘gay spaces’ which negatively impacts on body self-acceptance (Regan et al., 2021). Future research should explore these concepts within gay and bisexual men in more standard social parameters to gain a more a comprehensive understanding.
All scales and corresponding items included in this study were completed at one time by participants, the anticipated completion time was around 15-20 minutes. It is also important to consider the potential risk of survey fatigue experienced by participants in completing a questionnaire with many items and the implications this may have had on the results. Common methods bias (or variance) is a well-documented phenomenon observed in research based on self-reported measures. Multiple constructs are measured on multiple-item scales presented within the same questionnaire which can lead to spurious effects due to the measurement instruments rather than to the constructs being measured. For example, participants are asked to report their own perceptions on two or more constructs in the same questionnaire; this is likely to produce spurious correlations among the items measuring these constructs owing to response styles, social desirability, priming effects which are independent from the true relationships presented among the constructs being measured (Podsakoff et al., 2012). It is also important to consider the impact of the reliability score (Cronbach Alpha) for the FFMQ, as this was below the widely accepted 0.70 (0.67).
The disproportionate number of gay men who took part in this study when compared to bisexual men, highlights a lack of balance when considering the conclusions drawn from this research. The office for National Statistics stated that in 2019, the percentage of gay and bisexual men within the UK was $1.9\%$ (gay men) and $0.6\%$ (bisexual men) (Sanders, 2020); meaning the data from this research does not reflect the representation of bisexual men within the wider population. Future research should endeavour to include a more diverse sample, to produce a balanced approach to drawing conclusions around the body image and related concepts of gay and bisexual men. The authors also consider the complexity of defining “gay men” or “bisexual men”. Non-binary, non-conforming and gender fluid individuals were included within this sample, the authors fully recognise that these individuals may or may not be comfortable with the label “men”. The inclusion of gender minorities within this sample are to strive to provide a greater inclusion of queer experiences within research, and not to label or make assumptions about participants’ gender.
Further research is needed to develop the understanding of mindfulness and related concepts within this community to aid in the development of an appropriate intervention. The necessity for a suitable intervention to attenuate eating and body related issues experienced by gay and bisexual men is clear. Mindful and compassion-based interventions have been effective in reducing body related issues within other populations (Balciuniene et al., 2021; Zamzami et al., 2015), therefore, evaluating the efficacy of such interventions within the gay population may provide novel research. This research shows the unique role of body acceptance to mindfulness, self-compassion and mindful eating when relating these concepts to body image. This provides insight into the potential addition of body acceptance and mindfulness-based concepts when considering potential avenues in overcoming body-related issues experienced by gay and bisexual men.
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|
---
title: 'Experience of living with multimorbidity and health workers perspectives on
the organization of health services for people living with multiple chronic conditions
in Bahir Dar, northwest Ethiopia: a qualitative study'
authors:
- Fantu Abebe Eyowas
- Marguerite Schneider
- Shitaye Alemu
- Fentie Ambaw Getahun
journal: BMC Health Services Research
year: 2023
pmcid: PMC9995260
doi: 10.1186/s12913-023-09250-9
license: CC BY 4.0
---
# Experience of living with multimorbidity and health workers perspectives on the organization of health services for people living with multiple chronic conditions in Bahir Dar, northwest Ethiopia: a qualitative study
## Abstract
### Background
Multimorbidity-the simultaneous occurrence of two or more chronic Non-Communicable Diseases) in an individual is increasing globally and challenging health systems. Although individuals living with multimorbidity face a range of adverse consequences and difficulty in getting optimal health care, the evidence base in understanding the burden and capacity of the health system in managing multimorbidity is sparse in low-and middle-income countries (LMICs). This study aimed at understanding the lived experiences of patients with multimorbidity and perspective of service providers on multimorbidity and its care provision, and perceived capacity of the health system for managing multimorbidity in Bahir Dar City, northwest Ethiopia.
### Methods
A facility-based phenomenological study design was conducted in three public and three private health facilities rendering chronic outpatient Non-Communicable Diseases (NCDs) care in Bahir Dar City, Ethiopia. Nineteen patient participants with two or more chronic NCDs and nine health care providers (six medical doctors and three nurses) were purposively selected and interviewed using semi-structured in-depth interview guides. Data were collected by trained researchers. Interviews were audio-recorded using digital recorders, stored and transferred to computers, transcribed verbatim by the data collectors, translated into English and then imported into NVivo V.12 software for data analysis. We employed a six-step inductive thematic framework analysis approach to construct meaning and interpret experiences and perceptions of individual patients and service providers. Codes were identified and categorized into sub-themes, organizing themes and main themes iteratively to identify similarities and differences across themes, and to interpret them accordingly.
### Results
A total of 19 patient participants (5 Females) and nine health workers (2 females) responded to the interviews. Participants’ age ranged from 39 to 79 years for patients and 30 to 50 years for health professionals. About half ($$n = 9$$) of the participants had three or more chronic conditions.
The key themes produced were feeling dependency, social rejection, psychological distress, poor medication adherence and poor quality of care.
Living with multimorbidity poses a huge burden on the physical, psychological, social and sexual health of patients. In addition, patients with multimorbidity are facing financial hardship to access optimal multimorbidity care. On the other hand, the health system is not appropriately prepared to provide integrated, person-centered and coordinated care for people living with multiple chronic conditions.
### Conclusion and recommendations
Living with multimorbidity poses huge impact on physical, psychological, social and sexual health of patients. Patients seeking multimorbidity care are facing challenges to access care attributable to either financial constraints or the lack of integrated, respectful and compassionate health care. It is recommended that the health system must understand and respond to the complex care needs of the patients with multimorbidity.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12913-023-09250-9.
## Introduction
Multimorbidity, the simultaneous occurrence of two or more chronic non-communicable diseases (NCDs) in an individual, is an emerging global public health problem [1].
Although studies are diverse in methodology and context, the prevalence of multimorbidity is increasing [2]. Recent reviews reported a pooled prevalence of $42.4\%$ in high-income countries (HICs) [3], $43\%$ in Latin America and Caribbean [4] and $36.4\%$ in low-and middle-income countries (LMICs) [5]. A scoping review of multimorbidity studies in LMICs reported wide prevalence estimates ranging between 3.2 to $90.5\%$ [6], with a recent facility based study among adults attending chronic outpatient NCDs care in northwest Ethiopia showing a prevalence of $54.8\%$ [7].
The prevalence of multimorbidity increases with age [5, 6], economic deprivation [5, 8], female gender [5, 9], obesity [5, 10, 11] and among individuals with limited social network and support systems [7, 12]. Although the prevalence of multimorbidity is highest among adults aged 65 or older, younger persons also represent a large proportion of those with multimorbidity [13]. In addition, multimorbidity appears 10–15 years earlier in people living in the most deprived areas than for those living in the most affluent areas [8].
Individuals living with multiple chronic conditions face a range of adverse consequences, including premature mortality [14], poor quality of life [15, 16], impaired functioning [5, 17], treatment burden [18], reduced productivity [19] and high cost of care [20, 21], among others. In the era of high vulnerability to life-threatening infections such as COVID-19, the probability of dying prematurely is also greater among patients with multimorbidity [22, 23].
Management of people with multimorbidity is challenging in many ways [24]. On the one hand, there is no conclusive evidence on the best model of care [24–26]. On the other hand, the current models of care tend to focus on diseases in isolation rather than the needs and circumstances of the person with complex care needs as a whole [27–29].
In addition, the incurable nature of the diseases requires patients to have regular investigations, take different medications and attend multiple medical care follow ups and adhere to lifestyle recommendations [30–32], all of which pose psychological and financial burden [33]. Moreover, caring for people with multiple chronic health conditions is challenging because there are several potentially competing treatments and health outcomes [24].
*In* general, although patients with multimorbidity require a holistic approach, clinicians may not have the key skills needed to balance the priorities given to single-disease and management of multiple long-term conditions [32]. The lack of physicians’ time further limits the provision of optimal care and efforts to meaningfully engage patients in collaborative decision-making about their care [24].
The lack of patient-centered care, fragmented approach and poor coordination lead patients to see multiple health professionals in primary and secondary care facilities [34], resulting in these patients being dissatisfied and sometimes confused with the care they receive [21]. Even if treatment is appropriate, inadequate use of medication and polypharmacy may increase the risk of complications [35]. This is particularly common in low-income countries such as Ethiopia, where access to the diagnosis and management of chronic conditions is inadequate [36, 37].
Health services in Ethiopia are organized around single conditions, although patients could have more than one diagnosed condition. Further, clinicians tend to use one-size-fits-all chronic care guidelines in a fragmented and siloed fashion [38]. Visits to different specialists often in different facilities, suggests that people living with multiple conditions are receiving potentially uncoordinated care that may lead to negative health outcomes, including mortality.
Moreover, multimorbidity is relatively new in the health system and health professional education context in the country, and there are substantial gaps in our knowledge about people’s lived experiences of multimorbidity. A better understanding of the journey and challenges patients are facing while seeking care and exploring the views and perspectives of care providers on the organization and availability of essential resources to care for and improve the outcomes of people with multimorbidity is a priority research agenda [30, 39].
This study aimed at understanding the lived experiences of patients with multimorbidity and perspective of service providers on multimorbidity and its care provision, and capacity of the health system for managing multimorbidity in Bahir Dar City, northwest Ethiopia.
## Methodology
This study is part of an ongoing research project whose protocol has been published elsewhere [40].
## Design
A phenomenological study design was employed to explore participants’ experience of living with multimorbidity and perceptions of service providers on the concept of multimorbidity, their experience in managing multimorbidity and their views on the health system’s capacity and its effectiveness in responding to the needs of people seeking chronic multimorbidity care. The phenomenological study design is suitable when a researcher is interested to deeply understand about the views, perceptions, perspectives and experiences of study subjects on the phenomenon under study [41].
## Study setting
This is a facility-based qualitative study conducted in public and private health facilities rendering health services in Bahir Dar city, Ethiopia. The city is the capital of the Amhara regional state, the second largest and populous region in the country with a population of ~ 30 million people.
## Study population, sample and recruitment of participants
A broad sample of health facilities, patients and health workers was purposively recruited to capture maximum variation of the phenomenon under study [42]. Participating health facilities involved three public hospitals (one primary, one tertiary and one specialized teaching hospitals), one private general hospital and two private specialized clinics that provide long-term NCDs care.
Nineteen patient participants with two or more chronic NCDs were purposively selected with the aim to satisfy maximum variation sampling method based on the nature of chronic conditions the patients are living with, sex, age and residence (Table 1). For the service providers, six medical doctors (2 GPs, 1 internist, 1 cardiologist, 1 endocrinologist and 1 internal medicine resident) and three nurses working in chronic outpatient care departments were recruited. The two sub-specialists (cardiologist and endocrinologist) and one nurse were working in both public and private health facilities, while the rest were working in public hospitals only. Table 1Characteristics of patient participants Bahir Dar, EthiopiaSexAgeFacilityNumber and types of conditionsDuration of living with the disease/s (years)Female50Public specialized teaching hospital3 (HPN, KD, Gastritis)13Female75Public specialized teaching hospital3(HPN, heart problem and RA)16Male50Public specialized hospital2(DM, HPN)6Female55Public specialized hospital3(HPN, DM and hypercholesteremia)7Male79Public specialized hospital3(HPN,DM &KD)30Male74Public specialized hospital3(HPN, DM and hypercholesteremia)17Female39Public specialized teaching hospital3(HPN, HD, TB)2.5Female50Public specialized teaching hospital4(HPN, HF, RA, Asthma2Male66Public specialized teaching hospital3(DM,HPN, HD)15Male52Public primary level hospital2(HPN, BPH)8Male48Private general hospital3(HPN, DM, RF)24Female50Public primary level hospital2(HPN, DM)1Female66Public primary level hospital2(HPN, DM)4Male45Private general hospital2(HPN,DM)5Female54Private specialized medical center3(DM, HPN and hypercholestremia)21Female60Private specialized medical center2(HPN, DM)18Male75Private specialized clinic2(HPN, HD)7Male45Public specialized hospital2(HPN, DM)8Male58Public specialized hospital2(HPN, DM)3 Patient participants were recruited on the day of their appointment following the completion of their follow-up care. Medical doctors and nurses who work in the chronic care units of the selected facilities supported the identification and recruitment of study participants. Patients who attended chronic care follow-up for at least six months and care providers who have had at least one-year experience of managing patients with chronic conditions were eligible for the study. All the in-depth interviews were conducted in the vicinity of the facilities where patients attend chronic care follow-up. The first author in collaboration with facility leaders and study facilitators arranged convenient rooms for the interviews in each facility.
## Patients
Patient participants were interviewed in-person by four PhD fellows and the first author. In-depth interviews were conducted using semi-structured interview guides outlining a broad set of questions crafted and shared with the co-authors for feedback and revision. The topic guide for patients (S1) asked about their views on living with multiple chronic conditions, the impact of multimorbidity on daily living, family management, social functioning and self-management, as well as their experience in accessing health services and their views on care coordination and continuity of care.
The first author organized training and discussion sessions for the data collectors to ensure clarity and shared understanding on the semi-structured tools and the techniques for conducting the interviews. The tool was pilot-tested with two patients in Felegehiwot specialized hospital by the first author and the interviewers together. The authors and data collectors reviewed the process followed in these pilot interviews, listened to the audio recordings, reviewed the field notes, clarified concerns and revised the tool for subsequent interviews.
Data collectors were grouped into two teams, each with two interviewers (one note taker and the other interviewer and audio recorder). Interviews, transcription and coding of the data continued until the first nine patients were interviewed. The first author listened to the recordings and reviewed the transcriptions of the first nine interviews carefully. Gaps noted were perspectives of female patients, Muslims and rural residents and given the importance of such perspectives, the data collectors were advised to recruit patients from these backgrounds as well. The two authors (FAE and FAG) and interviewers held regular meetings to discuss responses to the topic guide and evolving perspectives to improve richness of the remaining data.
Interviews were audio-recorded using digital recorders, stored and transferred to a computer, transcribed verbatim by the data collectors, translated into English and then imported into NVivo V12 software for data analysis. Field notes were taken to complement the general feeling and specific observations made during the interviews.
## Service providers
The first author conducted in-depth interviews with medical doctors and nurses using a semi-structured tool (S2) to explore the perspectives and experiences of service providers on multimorbidity. Providers were asked about the concept of multimorbidity, the way healthcare is organized and capacity of the health system to screen, diagnose and manage patients with chronic multimorbidity. In addition, the providers were asked about their experiences in managing multimorbidity, challenges in screening and managing multimorbidity and to reflect their opinion about patients’ self-management skills and recommendations to improve access and management of people with multimorbidity. Interviews were audio-recorded using digital recorders, stored and transferred to a computer, transcribed verbatim, translated into English and analyzed using NVivo V12.
## Quality assurance
The process we followed and experiences of patients and service providers was described to enhance credibility [43]. We have employed maximum variation sampling and clearly described study participants, the study setting and research process to ensure transferability of our findings to a similar context [42–44]. Dependability, the consistency and quality of data collection process, was substantiated with use of field notes and an audit trail of the decisions made during the study [44]. We tried to show confirmability of the interpretations through incorporating participants directs quotes in the findings [43, 44].
## Data analysis
We employed a six-step inductive thematic analysis approach [45, 46] to construct meaning and interpret experiences and perceptions of individual patients. Thematic analysis is an appropriate and powerful method to analyze a set of experiences, thoughts, or behaviors across a data set [45].
In step 1, the first author listened to all interview recordings while simultaneously reading the transcripts and field notes to understand the overall meaning of responses provided by the participants.
In step 2, each transcript was read line by line to make sense of the data and drive initial coding. The initial codes were organized in MS word to assign coding schemes inductively. Then, focused coding was applied to reduce the volume of the raw information and to identify significant patterns for categorizing and assigning with themes and sub-themes. Codes were identified and categorized into sub-themes and themes iteratively (constant comparison) to compare and identify similarities and differences across themes [47].
Twenty sub-themes (basic themes), five organizing themes and two global themes were constructed (Fig. 1). Sub themes are the most basic premises of characteristics derived from the textual data. The themes that organize the basic themes into a cluster of similar issues are organizing themes. Whereas, global themes encompasses the principal metaphor in the data as a whole. The global themes summarize and make sense of the cluster of lower-order themes abstracted from and supported by the data [46].Fig. 1 Network of the sub-themes, organizing themes and main themes In step 5 and 6, we interpreted the themes and developed a written report of the themes generated [43, 45, 46]. The themes constructed are organized around the effect of multimorbidity on physical, psychosocial and sexual health, and functioning, self-management, access to care and organization of health services. Perspectives of health workers in each of the domains studied are described along with the themes related to self-management, access to care, service organization and institutional capacities.
## Results
A total of 19 patient participants responded to the interview. The interview length for patients varied from 17 to 48 min and data saturation was reached after interviewing 16 patients; three more patients were interviewed with no new themes emerging. Percipient ages ranged from 39 to 79 years. About half (9 of the 19 patients) have three or more chronic conditions. All of the patient participants had hypertension as one of the comorbid conditions and diabetes was the second most frequently reported condition (Table 1).
Nine health professionals (6 medical doctors and 3 nurses) were recruited from different facilities to explore their views on multimorbidity and its challenges and to describe how the health system is organized and capacitated to manage multimorbidity in their context. The interview length for health professionals ranged from 26 to 49 min and data saturation was achieved after interviewing five medical doctors and two nurses; one internal medicine resident and one nurse were interviewed with no new themes emerging. Their age ranged between 30 and 50 years with a minimum three years working experience in NCDs management (Table 2).Table 2Characteristics of service providers enrolled Bahir Dar, EthiopiaSexAgeProfessionFacilityService year in chronic NCDs careMale31General practitionerPublic specialized hospital3Female37Nurse professional (BSc.)Public specialized hospital4Male39Medical internistPublic specialized hospital5.5Male45Medical internist + cardiologistPrivate specialized clinic + Public specialized teaching hospital20Male50Medical internist + endocrinologistPrivate specialized medical center + Public specialized teaching hospital25Male30General practitionerPublic specialized hospital3Female38Post graduate nurse (adult medical nursing) Public specialized hospital5Male36Resident physicianPublic specialized teaching hospital7Male30Nurse practitionerPrivate specialized clinic6
## Overview of the thematic analysis findings
About 256 codes were generated (S3). The codes were organized into 21 sub-themes and five organizing themes, including dependency, poor adherence, feeling rejected, psychological distress and poor quality of life (Fig. 1).
The lived experiences of individuals with multimorbidity, service provision and the perspectives of health care providers are presented in accordance with the five organizing themes abstracted above.
## Feeling dependent
Patients living with multimorbidity face a huge challenge with their physical health. Many patients are in chronic pain and suffer from reduced physical mobility, both of which affect activities of daily living and quality of life (QoL) as reflected in the following narratives:“I was a soldier and hard worker, but after I had those diseases, I am held back. I stopped working, feeling sad, suffer pain, feel fainting at exertion [shirgata] and unable to go to church. I could have done much at this age, but….you see I am disabled.” [ Male, 66, 3 NCDs]“The physical limitation is so disabling that I could not take care of myself and go to church. I spend days perhaps weeks at home, gazing around with despair and feeling my diseases progressively worsening.” [ Male, 58, 2 NCDs] Patient participants reported impaired physical functioning and difficulty in maintaining jobs and accomplishing activities of daily living. “I stopped working. I feel pain and discomfort while trying to do the job I used to do. It becomes the responsibility of my husband to earn money to the house. I could not assist him as I did before.” [ Female, 39, 3 NCDs].“When it comes to my work it is really hard, I am limited physically and cannot do the work I was doing before, including home based activities.” [ Female, 50, 4 NCDs].
Another patient participant described that he stopped working his fields and restricted to only working in the morning at office. “ I am unable to do the routines. Colleagues help me doing my jobs, including field works. I feel weak and often spend hours sitting. I became less productive, particularly in the last two years.” [ Male, 50, 2 NCDs]
## Psychological distress
Living with multimorbidity affects patients’ psychological health and social lives because they feel vulnerable and worry about disease management and restrictions while participating in social events. “*It is* difficult to be free from stress and anger. I often feel emotionally unstable, easily annoyed and intolerant to people around me. People do not understand your problem, perhaps they blame it on you and you feel stigmatized and rejected. That is why I prefer to avoid social gatherings.” [ Male, 52, 2 NCDs] Most patients mentioned that they are suffering from sleep deprivation, and the lack of sleep is posing distress “I cannot sleep after midnight, although I go to bed late in the evening.” [ Male, 48, 3 NCDs] Some participants said that multimorbidity affects their sexual life. “ I find it difficult to be sexually active. I have erectile problem and worrying that this may affect my wife.” [ Male, 58, 2 NCDs].
The psychological burden of multimorbidity on the family is well understood by service providers. “Living with multiple long-term conditions affects the whole family. Family members also get stressed due to the financial burden they share to cover for laboratories, drugs and follow-up services.” [ Male 45, internist cardiologist]“People living with multimorbidity have disrupted quality of life, poor family management and compromised financial security owing to high expense of care, reduced productivity and due to their demand for a full time care taker from their family. This will eventually pose a burden to the family, health system and the country at large.” [ Female, 38 adult medical nursing specialist]
## Social isolation and rejection
Most patients spoke about being socially isolated when thinking about their conditions and social lives. “I tend to separate myself, except in case of funerals. I am now weak, they say please remain at home, ‘simply pray’.” [ Male, 74, 3 NCDs] Some patients feel that religious leaders do not understand their problems. “I often go to holy places to get holy water bath. People there force you to choose one: ‘either the holy water or drugs.’ They do not seem to help you. It is a lip service.” [ Male, 48, 3 NCDs] Another patient mentioned that he is worried about failing to comply with religious rules. “ I stopped going to church. Because I stopped fasting as I am required to take medicines morning and evening. I live in conflict with my values.” [ Male, 45, 2 NCDs] However, some patients described feeling supported and trying to establish and maintain strong and supportive relationship with the family and the community they are living with. “ People around me, including my families support me financially and morally to cope with my illnesses.” [ Female, 75, 3 NCDs]
## Poor adherence to treatment
Adherence to medications and self-management is challenging for patients. Patients spoke of the confusion and stress of taking multiple medications. “ Taking many drugs several times a day for several years is burdensome. I defaulted my regimen several times hoping a herbal medicine [Shiferaw] would help. I also tried sport, but it doesn’t work.” [ Male, 45, 2 NCDs] Some patients failed to comply with doctors’ advice and pretend they are taking medicines as prescribed. “I kept fasting. I lied to doctors that I am taking medications twice a day. Actually, I only take it once per day. I never stopped alcohol and salt intake although doctors recommended that.” [ Female, 55, 3 NCDs] A doctor who manages patients with chronic illness described the problems of taking multiple medications. “ Individuals living with multimorbidity take several medicines. That will have a biological and psychological effect. There may be drug-to-drug interaction. People may default treatments or take them selectively.” [ Male 31, GP, 31] Poor adherence is also a commonly held opinion by the nurses too. “ Patients living with multiple chronic conditions face difficulty in adhering to multiple prescriptions and to tolerate medication side effects. They often skip doses and take medications selectively and come with complications later.” [ Female 37, Nurse].“Patients take 2–8 tablets per day. Owing to the lack of knowledge, particularly rural residents do not take their treatments according to the prescribed manner. Sometimes, they visit traditional healers to avoid taking drugs. Some of the patients may gradually become fed up [bored with pill burden] taking several medicines for incurable diseases. Some of them default treatments due to financial burden and lack of social support.” [ Male 30, Nurse]
The challenges faced are not only related to medication burden, but also due to prescriptions being frequently changed because first line treatments are being unavailable. “I have to take and adapt new therapies every time; because I couldn’t obtain the medicines I am already familiarized and comfortable with.” [ Male, 58, 2NCDs] A female patient with three morbidities mentioned that the medicines she takes do not work. “I have to live until I die. It is Devil’s disease. I am not improving and I told to doctors that the treatment does not work.” [ Female, 54, 4 NCDs]“Some patients have doubts about the quality of drugs. I think it is because of the lack of knowledge and we need to work together to clarify that misconception.” [ Male 50, internist endocrinologist] Conversely, however, some patients learnt to accept the reality and adapted to living with multimorbidity. “ I know that the diseases are dangerous; they could damage me at any point in time. I am taking care of myself, take medicines accordingly, and comply with doctors’ advice. Except for the fear of sudden death and the financial burden, I am okay now.” [ Male, 75, 2 NCDs].
Another patient described his adjustment to living with multimorbidity, as “I am caring for myself. I optimized my diet and attend medical follow ups; because I know individuals died of [high blood] pressure and sustained a half-body weakness [paralysis] because of a lack of treatment.” [ Male, 52, 2 NCDs]“*It is* my duty to follow doctors’ advice; else I will die. I never skip medicines and appointments. I feel confident that I am able to control the diseases I am living with.” [ Male, 66, 2 NCDs] Facing high cost of drugs was the other challenge that might have contributed to poor adherence. In this study, almost all of the participants (both patents and service providers) mentioned the cost of medicines and that patients are facing severe financial burden buying drugs from private drug vendors. “We spend much of our income to purchase medications; I have to live, no option.” [ Female, 39, 3 NCDs].“*It is* recently that I came to this hospital, because the private hospital I used to attend care was expensive. Unfortunately, medicines are not available here [public hospital]. I am only given the prescriptions to buy drugs outside. That is not helpful, because I cannot afford.” [ Female, 50, 2 NCDs] The majority of the participants described that they are facing huge financial burden due buying medicines at private pharmacies rather than picking them up at the public facility. “Buying medicine in private pharmacies is expensive. Medicines are often unavailable in the [public] hospital at which I am currently attending my care; sadly, they [health professionals] directed me to find medicines in private pharmacies” [Male, 58, 2 NCDs] Some patients mentioned that the huge expense of medications affects their relationship with family. “ The problem with my wife is related to expenses, she doesn’t understand the financial burden I am facing to purchase medicines.” [ Male, 45, 2 NCDs]
Health care providers have also described the economic burden of multimorbidity on patients, their family and the government. “Patients with multimorbidity are facing a huge financial burden. The expenditure would also extend to affect family and the health system. Health systems suffer depletion of resources because patients with multimorbidity demand more resources.” [ Male 30, Nurse] The financial burden for some of the patients was related to the ineffective community-based health insurance (CBHI) scheme. Membership to the CBHI does not allow individuals to fully and sustainably access diagnostic and therapeutic services in public hospitals. Some patients come with the anticipation that their CBHI membership helps in getting medicines and laboratory services without difficulty and additional fees. However, neither of these services available consistently in the public hospitals. Hence, patients do not receive the services they need and are eventually forced to either visit private facilities (out of pocket) or return home without receiving the needed services. “We pay the premium annually without any interruption, but we are not getting all the services we need. Often times, we do not get laboratory and the choice of medicines here [in the public hospital]. You may get some of the drugs here. If you have money, you may buy the rest outside. If not, you will go with only a few of them and you can imagine what the result will be.” [ Female, 55, 3 NCDs] Another woman explains about her challenges related to accessing medications. “ We don’t obtain medicines in public facilities. In private pharmacies on the other hand, we are asked to pay 700 to 800 Birr for every regimen; because I don’t have that much money, I keep the prescriptions at home and come for the next appointment hoping to get the drugs for a lower cost in public hospitals.” [ Female, 50, 4 NCDs] Despite the challenges, senior specialist physicians working both at public and private facilities mentioned that membership to the CBHI scheme is still an important alternative to access most of the essential services. “The CBHI is a highly useful mechanism to equip public health facilities and optimize their services; and the cost of health services at public hospital is cheap compared to those in private facilities. I would always recommend people to have a membership to CBHI.”” [ Male 50, internist, endocrinologist].“Despite some problems with the collection and management of insurance fees, the CBHI is an important avenue for the poor to access chronic NCDs care. Although membership alone may not help getting all of the prescribed medications, patients may get two or three of the medications they need in the facilities integrated with CBHI schemes. It is a useful approach and should rather be strengthened.” [ Male 45, internist, cardiologist]
## Poor quality of care
Most participants face difficulties in navigating the pathway to chronic outpatient care. The most common challenge in public hospitals is the lack of an easy access to routine medical consultations by physicians. Participants reported that the staff (clerks) working in medical registration rooms are disrespectful, unfair and insulting. “The biggest problem I always face is in the ‘card’ [registration] room, the staff working there insult us, and they embarrass and push everyone, even weak patients. The queue is never maintained, they shuffle patient charts and there is a much nepotism.” [ Male, 79, 3 NCDs]“….The staff in the ‘card’ room do not listen to you; sometimes they tell you that your chart is lost. Without my children attending with me, I cannot get registered at all. It is by force you keep your turn; otherwise, you will spend the whole day screaming.” [ Female, 50, 3 NCDs]“Care provision begins at the gates [with the guards]. The staff working in card room are rude and they do not keep chart orders accordingly. There is a long waiting time; we push each other, no order. I am weak and I could fall down. It is annoying.” [ Male, 74, 3 NCDs]Participants described that doctors and nurses do not have the time to properly assess and discuss with every patient. “Doctors do not offer the opportunity to share concerns and to ask questions. They simply give us a refill prescriptions and rush to do the same for the next patient awaiting. I want my voice to be heard. I want to be checked and reassured. Otherwise, I could get the refills anywhere, may be in pharmacies.” [ Male, 58, 2 NCDs] In addition, providers do not initiate communication, they do not invite patients to ask questions or share concerns. Some participants feel reluctant to ask questions, because they think providers are busy. “I don’t think I have the right to ask the doctors. Doctors appear busy and rushing. I have to accept what they [doctors] say and leave.” [ Female, 50, 2 NCDs]“If you appear knowledgeable about to your conditions, they [nurses] embarrass you by saying ‘if you know, don’t come’. Doctors are good.” [ Male, 45, 2 NCDs] Doctors and nurses have also agreed with the concerns raised by patients. Interviewed providers reported the presence of a large demand (workload) to manage patients with chronic conditions every day. “About 30 patients are waiting for me outside. It is unthinkable to give more than five minutes to a given patient, let alone to discuss about their circumstances, needs, priorities and treatment related issues. This is due to the high number of patients we are expected to manage daily.” [ Male 39, Internist].“The capacity to pay for and receive multimorbidity care in private facilities often declines gradually. Patients may need to be referred to public facilities where access to a free/subsidized care is somehow available. But there is high workload and problems of integration and optimization of care in public hospitals.” [ Male 45, internist cardiologist] Patient participants and providers reported that the healthcare system is not designed to foster the most effective support that people with multimorbidity need. People with multimorbidity are often in contact with multiple doctors that are working in different facilities indicating problems of service integration and continuity of care, and the doctors managing patients in public hospitals change often posing additional challenges to the continuity of care. “How many times should I tell my personal concerns? Every time I come to this facility, I meet a different doctor. I have to tell him the whole history again. I wish I had one doctor who knows my life and capacities in detail.” [ Male, 50, 2 NCDs]“*It is* true that doctors change every month. Shifting is a norm and we try to record patients’ information on their charts as detail as possible. Patients may have, however, confidential issues that may not be shared with every doctor.” [ Female 37, Nurse]“Specialized services in public hospitals are given in scheduled days. For instance *Tuesday is* for patients with lung diseases; Wednesday for hypertension and heart disease, Thursday for endocrine problems and Friday for renal problems. Therefore, a patient having multiple system diseases should come multiple times in a week, which obviously poses a huge burden for patients and their family.” [ Male 45, internist cardiologist]
Although most patients with chronic NCDs could have comorbid mental illnesses such as depression, physicians generally forget to assess these and refer to appropriate care providers. “Associated mental conditions, including depression are often missed. I think doctors lack the awareness that any patient with NCD could have psychiatric problems. Private clinics have no mental health care corners and the referral to other facilities is limited too.” [ Male 45, internist cardiologist] However, patients attending care in private facilities have the chance to see their physicians regularly and the navigation through the care pathway and support is less challenging compared to public hospitals. It is almost 21 years since I began attending diabetic care with Dr…..[Endocrinologist]. They [providers in a private facility] are polite and supportive, they know my diseases, and they teach me about the treatment, diet and follow ups.” [ Female, 60, 2 NCDs] Lack of patient-centered care was a common problem. Involvement of patients, both in terms of identifying needs and prioritizing interventions to individual context was limited in public facilities. “Patient involvement in decision-making is almost none. Patients are too many. Seniors may be consulted to check some patients requiring specialty care. However, specialists have limited time to adequately see every patient we refer. They usually give a refill prescription and there is no opportunity to talk to every patients and identify individual needs. Quality is not a concern for this hospital. The norm is rather to see all patients registered for receiving care on each day.” [ Male 31, GP]“Honestly speaking, we have no time to listen to every patient. We have too many patients to manage daily. Doctors have also limited time to address every patient’s concern. Because of the limited time, it is also difficult to teach patients and support them to comply with recommendations.” [ Female 37, Nurse].“In private facilities, we try to provide individualized care. Patients are usually involved in decision- making. We empower them. Patient care can be adjusted based on financial capacity, cognition and educational level. However, we do not have guidelines to standardize care for everyone.” [ Male 50, internist endocrinologist]
## Overall capacity and availability of guidelines to manage multimorbidity
Multimorbidity poses a heavy burden on the health system. The volume of work causes difficulties in organizing a formalized system for managing multimorbidity in the practice setting. However, institutions lack the readiness to provide the resources to diagnose and manage multimorbidity. “About 90 percent of the patients I manage in public hospital have chronic multimorbidity. They are placing a higher expenditure, consume huge amount of resources and supplies. As a result, public facilities face a lingering stock-out of diagnostic reagents and medicines. The time we take to treat patients with multimorbidity is so long that we often fail to provide individualized and holistic care.” [ Male 45, Internist cardiologist] Service providers invariably reported that public health facilities are less equipped with diagnostic facilities and medications to manage multimorbidity. “Laboratory resources are lacking to identify NCDs and monitor the progress. We are often dependent on physical findings alone.” [ Male 31, GP public hospital]“Laboratory service are incomplete, medicines are scarce, even anti-pain [medication]. You feel sad. Patients always complain about these challenges and we have no solutions unfortunately, and the so-called community-based health insurance does not help either. Because most of the diagnostic services covered by CBHI are often unavailable upon request and patients are directed to find them outside [private clinics].” [ Female 37, Nurse public hospital]“Most public facilities have limited capacity to manage patient with multiple chronic conditions. Facilities are loaded with high numbers of patients with limited number of resources and staff to effectively manage multimorbidity.” [ Male 50, internist, endocrinologist]“Neither diagnostic technologies and reagents nor medicines are sustainably available. There is a weak leadership to plan and procure essential commodities. Those readily available in the market have sub-standard quality. Market inflation and lack of currency [US Dollar] place another challenge to suppliers to provide commodities regularly.” [ Male 45, internist cardiologist] Providers describe how people with multimorbidity compete for and deplete the scarce resource available to manage other conditions. “ Patients with co-morbidity demand more supplies, more health workers, more infrastructure and consume a disproportionate amount of health care resources and supplies. This will have implication on budget and overall service delivery.” [ Male 30, Nurse] Lastly, multimorbidity is not addressed in the national treatment guidelines and there is no formalized or standardized system for managing multimorbidity in general practice. “I don’t think there is a nationally customized guideline to manage patients with multimorbidity. Doctors do not have standard protocols and the quality of care is sub-optimal in my judgement.” [ Male 30, Nurse]“We rely on the science written in text-books and usually, we refer to international treatment guidelines written for an American context. Locally adapted guidelines specify NCDs management in silos. They do not account of the notion of multiple diseases in a given patient.” [ Male 33, internal medicine resident]
In addition, multimorbidity is a hidden problem. Thus, it is not integrated into the health management information system (HMIS).“Chronic NCDs are registered and reported individually and the notion of multimorbidity is new for most of us.” [ Female 37, Nurse]
## Perceived satisfaction and quality of care
Most patients attending care in public hospitals were not satisfied with the care they received on the date of interview. The reasons were related to the behavior of staff working in the registration room, long waiting hours, the lack of opportunity to discuss with their doctors, the challenges to obtain drugs and laboratory services, and the lack of education and information, among others. “I am not satisfied, because the people working at registration room are rude and the doctors do not offer the chance to ask. They simply write a prescription to find them [drugs] in private. I do not have money. It is disappointing.” [ Male, 58, 2 NCDs]“Doctors came late, I am diabetic I want to get the service timely. Poor time management, no medicine.” [ Male, 45, 2 NCDs]
## Suggestions to improve
The majority of the patients suggested improving the problems in the registration (card) room, to sustainably provide medicines and laboratory services in public hospitals, and ensure doctors and other care providers have more time to discuss, educate and provide necessary information. Strengthening CBHI systems is another important area that both patients and care providers underlined.
## Discussion
This study provides a broad description of experiences of individuals living with multimorbidity and perspectives of service providers on multimorbidity and capacity of the health system to screening for and manage multimorbidity.
*In* general, there was consistency across the individual patient’s stories and the perspectives of service providers on the impact of multimorbidity on patients and the health system.
Findings show that patients with multiple chronic conditions face a wide range of challenges, including difficulty in physical mobility, impaired physical functioning, psychological distress and poor social and community support, and reflect findings from other studies [21, 48].
Patient participants reported suffering from pain and severe physical limitations in doing activities of daily living and performing their organizational and household duties. Such limitations led patients to face both physical and economic dependency. The lack of capacity to earn money leads to difficulties in accessing quality care thus contributing to psychological distress, poor QoL and reduced survival. Our findings are congruent with previous studies [49, 50].
The participants with multimorbidity also reported psychological distress, negative emotional reactions, including sadness due to living with multiple incurable conditions. Some of the psychological disturbances are aspired to the fear of neglect and disrespect by members of the community in their surroundings. The disabling nature of the diseases and the restrictions imposed on some food items and drinks often served in social gatherings made patients to refrain from social engagements. The impact of multimorbidity on psychological and social health has also been reported by other studies [51, 52].
The cost of medicines causes high out of pocket expenses for most patients. The resulting poverty and failure to support their family affected the relationship with their family and the quality of support they receive from them. The lack of social and family support will further compromise patients’ self-management capacity and overwhelm patient resources. These results are similar to those of previous studies [48, 53].
There are complex and interrelated challenges in self-management of multimorbidity as shown in this study. Patients with multimorbidity experience treatment burden related to taking several medications for different diseases. In addition, the possible side effects and drug-to-drug or drug-to-diseases interaction would further impact individuals’ capacity to self-manage and comply with treatment regimens [47, 54]. Some patients in this study reported difficulty experiencing pill burden and having to take multiple medications was the major aspect of treatment burden. Health workers were aware of the inconvenience suffered by patients related to taking several medications. The lack of adherence to treatments would add complexities to the total burden of multimorbidity, QoL and survival [55, 56].
Consistent with previous studies [36, 47], navigating through the chronic NCDs care pathways was difficult for most of the participants, particularly in public facilities. Patients were not adequately supported to move (transit) between care units. There were long waiting times, inequity and mistreatment of patients at entry point to care (registration rooms). Most patients reported experiencing difficulty obtaining their charts on time and they were not treated respectfully and in order of their place in the queue. The lack of a well-organized registration process and communication among service points in public hospitals have contributed to the complexities to receiving timely care and support. Such challenges will eventually compromise the wellbeing and prognosis of individuals with multiple chronic conditions [57].
Participants also reported that the public healthcare system is creating barriers to access the health care they deserve, because of overcrowding, long waiting hours, short consultation time, and lack of counseling, education and information. The lack of access to most diagnostic services and medicines required in public hospitals posed a further major challenge for most patients. Patients reported recurrent stock-out of laboratory reagents and medicines in public facilities, and being forced to find these services in private facilities at high costs. Only patients having the ability to pay for these services in private facilities would have received optimal care, thus denying an equitable health services for the poor, as also reflected in other studies [58].
Service providers described the challenges of managing patients with multimorbidity and inadequacy of health facilities to diagnose and organize care appropriate for this group of patients in a holistic manner, and reflects findings from other studies [59, 60].
The organization of the care for patients with multimorbidity is fragmented, particularly in public facilities. For instance, appointments for different NCDs were arranged on different days, instead of a more convenient arrangement of the different appointments on specific days in consecutive timeslots. Further, given the culture of limited communication on integrated care among doctors, would be consulting different specialists usually in different facilities and receive a variety of, perhaps conflicting, medication or advice [61]. This will further complicate self-management and overall provision of multimorbidity care. Such challenges have also been reported in previous studies [52, 56].
On the other hand, management of patients with co-morbid mental illnesses in private facilities was also fragmented owing to the lack of capacity for in-housing experts in each field of specializations. The presence of untreated co-morbid depression can negatively affect adherence to medications and the lifestyle that are needed to control other medical conditions [24, 62].
Person-centered coordinated care is believed to improve outcomes and experiences of people with multiple-long term conditions [63, 64]. Common factors in this model are regarding the patient as whole person, sharing power and responsibility in decision and establishing personal doctor–patient relationship [65, 66]. However, in public hospitals, the practice of making patients at the center of care provision and decision-making is limited. Most patients described that doctors do not offer the time to ask questions and discuss concerns. Patients’ perception of a lack of compassion from and communication with health care workers, and limited counseling and information provided could lead to poor treatment adherence resulting in complications, including mortality. Some studies have also reported the challenges of providing person-centered care in poor resource settings [67].
How people get access and navigate the care pathway to receive the services they need, the way diagnostic services and medications are made available, the way health services are organized and delivered, and the way in which health care workers communicate with and treat patients are the major quality metrics for people seeking multimorbidity care [21, 32, 47, 68]. However, our finding shows that these quality of care dimensions are not well understood and implemented in the study area.
Consistent with other studies [69], study participants raised several priorities that the health system must address to meeting their needs. The priorities for the participants in this study were: (i) better access to their doctors, medicines and laboratory services, (ii) strengthening CBHI, and (iii) getting enough time to receive counseling, education and information from health care providers.
However, the health system in the study area is not prepared to deal with multimorbidity. It has a constrained capacity to ensure access to diagnostic services and care for treating patients with multiple long-term conditions. Health care providers affirmed that multimorbidity is not well understood and integrated in the health care system in the country, as confirmed by a recent review [6].
## Implications for practice, policy and research
The prevalence of NCDs is rapidly increasing with associated multimorbidity [1, 5, 7]. Multimorbidity affects both patients and the health system through the need for multiple medications, multiple consultations with doctors, and multiple impacts on daily life. The health system must respond to these evolving needs through providing resources and integrated care across service points. Further, living with multimorbidity requires the health system to make available a person-centered approach that improves quality of life and clinical outcomes [65]. Service provision needs to be guided by treatment protocols that also address the possible interaction between physical and mental health proactively from diagnosis to management [70].
Further, it is imperative to understand and address the complex interaction between multimorbidity and socioeconomic deprivation [71]. This includes addressing social determinants of health, financial capacity and optimization of CBHI system to ensure access to essential laboratory services and medicines particularly in public health facilities.
The science of understanding multimorbidity should drive a shift in the way health policies are developed and guide the health care system in tackling this challenge. Policymakers need to better understand how medical education and service configuration should change to meet the needs of people with multimorbidity [70]. Hence, it is clear that priority should be directed to reorient and strengthen the health care services.
It is also imperative to define the best possible model of care for people with multimorbidity. In this sense, the development of treatment guidelines should fuel a reform in the academic curriculum and continuing training programs to accommodate the new scenario in health professions’ education and practice.
In the face of a struggle against communicable and non-communicable diseases, the emergence of multimorbidity is Ethiopia portends a rise in a triple burden of diseases [7]. However, there is a lack of focus on studying the magnitude of multimorbidity, understanding the risk factors associated longitudinally and defining the best model of care. It is imperative to explore the burden of multimorbidity at population level and understand the pattern of disease clustering, its impact on individuals, the society and the healthcare system, and to design the best health care model which is responsive to the current and growing needs of the people with multiple long term conditions, especially those living in poorer socio-economic conditions.
## Strength and limitation of the study
Our findings made a new contribution to our understanding of the burden of multimorbidity on patients and the health system. In addition, we explored the way the health system is organized and its capacity to respond to the complex needs of people with multimorbidity. We got perspectives of both patients and service providers, which maximized the variation in sampling of the study participants However, conducting interview at patients’ homes and inclusion of patient families and staff working at registration rooms, medical laboratory and pharmacy units might have given a broader understanding of the phenomenon under study.
## Conclusion and recommendations
Living with multimorbidity is posing a huge burden on physical, psychological, social and sexual health of patients. Patients seeking multimorbidity care are facing challenges to access care attributable to either financial constrains or the lack of respectful and compassionate health care providers. On the other hand, the health system in the study area is not designed to provide integrated, person-centered and coordinated care for patient living with multimorbidity. Without profound changes to the current views and organization of care, it is highly likely that patients in this category will continue to suffer compromised quality of life, increasing expenditure and premature mortality.
Hence, it is recommended that the health system must understand and respond to the complex care needs of the patients with multimorbidity. There should an enhanced support for patients to get an easy access to the care pathway and the opportunity to talk to their doctors, to play active role in decision making around their care and receive high quality integrated health services. Further, health facilities should devise mechanisms to avail essential resources needed in a sustainable way with reasonable prices. The way in which CBHI is organized could be strengthened to support individual patients and their family in getting equitable access to multimorbidity care. Future research endeavors may need to focus on designing and testing interventions to improve QoL and health service delivery of patients living with multiple long-term conditions in the country.
## Supplementary Information
Additional file 1.
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|
---
title: 'A cost-effectiveness analysis of patiromer in the UK: evaluation of hyperkalaemia
treatment and lifelong RAASi maintenance in chronic kidney disease patients with
and without heart failure'
authors:
- Thomas Ward
- Ruth D. Lewis
- Tray Brown
- Garth Baxter
- Antonio Ramirez de Arellano
journal: BMC Nephrology
year: 2023
pmcid: PMC9995261
doi: 10.1186/s12882-023-03088-3
license: CC BY 4.0
---
# A cost-effectiveness analysis of patiromer in the UK: evaluation of hyperkalaemia treatment and lifelong RAASi maintenance in chronic kidney disease patients with and without heart failure
## Abstract
### Background
Chronic kidney disease (CKD) patients with and without heart failure (HF) often present with hyperkalaemia (HK) leading to increased risk of hospitalisations, cardiovascular related events and cardiovascular-related mortality. Renin–angiotensin–aldosterone system inhibitor (RAASi) therapy, the mainstay treatment in CKD management, provides significant cardiovascular and renal protection. Nevertheless, its use in the clinic is often suboptimal and treatment is frequently discontinued due to its association with HK. We evaluated the cost-effectiveness of patiromer, a treatment known to reduce potassium levels and increase cardiorenal protection in patients receiving RAASi, in the UK healthcare setting.
### Methods
A Markov cohort model was generated to assess the pharmacoeconomic impact of patiromer treatment in regulating HK in patients with advanced CKD with and without HF. The model was generated to predict the natural history of both CKD and HF and quantify the costs and clinical benefits associated with the use of patiromer for HK management from a healthcare payer’s perspective in the UK.
### Results
Economic evaluation of patiromer use compared to standard of care (SoC) resulted in increased discounted life years (8.93 versus 8.67) and increased discounted quality-adjusted life years (QALYs) (6.36 versus 6.16). Furthermore, patiromer use resulted in incremental discounted cost of £2,973 per patient and an incremental cost-effectiveness ratio (ICER) of £14,816 per QALY gained. On average, patients remained on patiromer therapy for 7.7 months, and treatment associated with a decrease in overall clinical event incidence and delayed CKD progression. Compared to SoC, patiromer use resulted in 218 fewer HK events per 1,000 patients, when evaluating potassium levels at the 5.5–6 mmol/l; 165 fewer RAASi discontinuation episodes; and 64 fewer RAASi down-titration episodes. In the UK, patiromer treatment was predicted to have a $94.5\%$ and $100\%$ chance of cost-effectiveness at willingness-to-pay thresholds (WTP) of £20,000/QALY and £30,000/QALY, respectively.
### Conclusion
This study highlights the value of both HK normalisation and RAASi maintenance in CKD patients with and without HF. Results support the guidelines which recommend HK treatment, e.g., patiromer, as a strategy to enable the continuation of RAASi therapy and improve clinical outcomes in CKD patients with and without HF.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12882-023-03088-3.
## Background
Hyperkalaemia (HK) is a potentially life-threatening electrolyte abnormality, clinically defined as serum potassium levels above 5.0 mmol/L. Patients with HK are more likely to suffer sudden cardiac arrhythmias, muscle weakness or paralysis [1–4], and are at an increased risk of hospitalisations and mortality [5]. In the clinic, HK is often present in patients with chronic kidney disease (CKD) as a result of renal dysfunction, and is associated with worsening clinical outcomes. Subsequently, CKD patients with HK versus without HK are at increased risk of hospitalisations, cardiovascular-related events and cardiovascular-related mortality [6–14]. Furthermore, HK risk is heightened in patients who are receiving renin–angiotensin–aldosterone system inhibitor (RAASi) treatment, a standard therapy for CKD.
The clinical benefits of using RAASi therapy are well known, with increased cardiovascular and renal protection in cardiorenal patients. In CKD, RAASi use has been shown to decrease blood pressure and proteinuria [15], reduce the risk of kidney failure, cardiovascular morbidity and cardiovascular-related and all-cause mortality [16], and slow CKD progression [17]. Despite RAASi having a significant impact on slowing CKD progression and reducing cardiovascular events, its use in the clinic is often suboptimal and treatment is frequently discontinued due to its association with HK [18, 19], resulting in worsening clinical outcomes in both CKD and heart failure (HF) populations [8, 11, 20–26]. In the UK, major adverse cardiac events (MACE) and mortality were consistently higher in patients receiving sub-optimal RAASi dose (< $50\%$ of the recommended RAASi dose) [19]. Subsequently, these patients are at significant risk of hospitalisation, significantly impacting resource use and overall health care costs [27, 28].
Patiromer, a non-absorbed cation exchange polymer, has demonstrated effectiveness in cardiorenal patients receiving RAASi therapy, both in terms of reducing potassium levels and enabling the initiation and up-titration of RAASi in patients at risk of HK [29–32]. The objective of this study is to evaluate the cost-effectiveness of patiromer in the UK healthcare setting. A further objective is to evaluate the relationship between HK incidence and optimal RAASi management, and lifetime economic outcomes.
## Patiromer OPAL-HK trial
The modelling approach has previously been published [33] and was developed in order to extrapolate results from the OPAL-HK trial. This trial was used to assess the efficacy and safety of patiromer and was an international, multicentre, single blind, phase III clinical trial investigating the acute treatment of HK, and the ongoing maintenance of normokalaemia. The study was carried out in two sequential parts over 12 weeks.
The treatment phase (Part A) was a single blind, single arm trial of patiromer for four weeks. Patients were eligible for inclusion if they had stage 3 or 4 CKD, a serum potassium level of 5.1 to < 6.5 mmol/L and were receiving a stable RAASi dose. At the time of screening, patients were assigned to receive a starting dose of 4.2 g twice daily or 8.4 g twice daily depending on the severity of HK. In this phase, RAASi doses were not adjusted; they were only discontinued if the potassium level was ≥ 6.5 mmol/L (≥ 5.1 mmol/L if on the maximum permitted patiromer dose).
The withdrawal phase (Part B) was a placebo controlled, single blind, randomised withdrawal trial of patiromer for eight weeks. The objective of the withdrawal phase was to evaluate the effect of withdrawing patiromer on serum potassium control and to assess whether chronic treatment with patiromer prevents the recurrence of HK.
## Cost-effectiveness model
A Markov cohort model was developed to assess the health economic impact of patiromer therapy in comparison to standard of care (SoC) in controlling HK in advanced CKD patients with and without HF. The model was designed to predict the natural history of CKD and HF and quantify the costs and benefits associated with the use of patiromer for serum potassium management from a payer perspective in the UK. CKD and HF are chronic and progressive diseases associated with increased risk of mortality. As such, a lifetime horizon was modelled in line with technology assessment guidelines [34, 35]. A monthly cycle length was adopted and disease progression followed over a lifetime.
## Model structure and disease progression
Patients enter the model (Fig. 1) with either CKD alone or CKD with HF. The progression of CKD patients was modelled via transitions to more progressed CKD stages and eventually end-stage renal disease (ESRD), comprising of separate dialysis and transplant states. Similarly, the progression of HF in CKD + HF patients was modelled via transitions between New York Heart Association (NYHA classifications (I to IV) [36–39]. Both CKD and HF are modelled independently, with progression through health states in one not impacting progression through health states in the other, except for those exiting the model in the death health state. As a simplifying assumption, patients without HF at model initiation do not develop HF during the modelled time horizon. The starting distribution of patients is presented in Table 1, alongside baseline age and sex, whilst baseline rates of CKD and HF disease progression are described further in Supplemental Appendix A.Fig. 1Model flow diagram. States highlighted in grey represent starting health statesTable 1Starting health state distribution and baseline patient characteristicsMeanSESourceStarting health state distribution Proportion with HF$41.98\%$-OPAL-HK CSR [43] Proportion CKD stage $355.14\%$$3.19\%$OPAL-HK CSR; CKD stage 2 patients included [43] Proportion CKD stage 4a$44.86\%$$3.19\%$OPAL-HK CSR [43] Proportion CKD stage 5 a$0.00\%$$0.00\%$ Proportion NYHA I$18.63\%$$3.85\%$ Proportion NYHA II$64.71\%$$4.73\%$ Proportion NYHA III$16.67\%$$3.69\%$ Proportion NYHA IV$0.00\%$$0.00\%$ *Proportion normokalaemia* (K + ≤ 5)$0.00\%$$0.00\%$Assumed Proportion HK (K + > 5 to ≤ 5.5)$0.00\%$$0.00\%$ Proportion HK (K + > 5.5 to ≤ 6)$81.35\%$$3.17\%$OPAL-HK CSR; distributed across upper threshold categories in line with published data [43] Proportion HK (K + > 6)$18.65\%$$3.17\%$Patient characteristics Age (years)65.300.89OPAL-HK CSR [43] Proportion female0.460.05CKD Chronic kidney disease, HF Heart failure, K + Potassium, NYHA New York Heart AssociationaNote in the OPAL-HK CSR, patients were described only as “stage 4 or worse” [43]. The proportion of patients pre-RRT in stage 5 is thus unknown and here taken as 0 As the simulated cohort progresses through the model, the value of alternative treatments is captured through the occurrence of HK events, changes in RAASi use and treatment discontinuation. The likelihood of other events (MACE, hospitalisation and mortality) is also predicted and is impacted directly by a patient’s health state (i.e., CKD and HF) and by RAASi use and HK incidence (i.e., potassium level); baseline rates may be found in Supplemental Appendix A [23, 40–42]. MACE was defined as events of coronary heart disease, HF, ischemic stroke, and peripheral arterial disease leading to hospitalisation. Hospitalisation was defined as any hospitalisation. The probability of MACE, hospitalisation and mortality, stratified by disease severity, are estimated for a CKD-only and HF-only patient, and the higher of the two probabilities are then applied for the cohort with CKD + HF. In both cohorts, where all-cause mortality estimates from UK-specific life tables exceeded mortality estimates based on comorbidities and RAASi use, the greater mortality rate was assumed. As a simplifying assumption based on results of the OPAL-HK trial, there is assumed to be no significant difference in the likelihood of therapy-attributable adverse events between treatment and comparator arms, and they are therefore not incorporated into the model.
## Hyperkalaemia
The occurrence of HK was categorized as a serum potassium level greater than 5 mmol/l, consistent with the definitions used in the OPAL-HK trial and widely accepted in the broader HK literature [29, 44]. Events were further stratified by severity (i.e., 5–5.5 mmol/l, 5.5–6 mmol/l and > 6 mmol/l). During the first three months of the modelled time horizon, incident HK events are predicted based on data from the OPAL-HK trial [29, 45]. For all subsequent months, annual rates of HK were obtained from Horne et al. [ 2019] and applied to the SoC arm [46]. Hazard ratios relating to reduced (or increased) incidence in those receiving patiromer in subsequent years were obtained from the OPAL-HK trial and applied to the annual rates of HK obtained from Horne et al. [ 2019]. HK event rates are summarised in Table 2. Increased potassium levels negatively impact the incidence of MACE, hospitalisation and death (Fig. 2); the magnitude of these impacts is further described in Supplemental Appendix A.Table 2HK incidenceTime appliedPotassium levelMonthly probabilitySourcePatiromerSoCMeanSEMeanSEMonth 1K + > 5 to ≤ $5.521.13\%$$3.32\%$$21.13\%$$3.32\%$OPAL-HK CSR; distributed across threshold categories in line with published data [43, 46]K + > 5.5 to ≤ $61.66\%$$1.04\%$$1.66\%$$1.04\%$K + > $60.38\%$$0.50\%$$0.38\%$$0.50\%$Month 2 & 3K + > 5 to ≤ $5.514.00\%$$4.68\%$$15.00\%$$4.81\%$OPAL-HK CSR [43]K + > 5.5 to ≤ $66.10\%$$3.23\%$$25.22\%$$5.86\%$K + > $61.40\%$$1.58\%$$5.78\%$$3.15\%$Subsequent monthsaK + > 5 to ≤ $5.50.543\%$$0.054\%$$1.158\%$$0.116\%$Horne et al. [ 2019]; 'OPAL-HK CSR [43, 46]K + > 5.5 to ≤ $60.022\%$$0.002\%$$0.092\%$$0.009\%$K + > $60.005\%$$0.001\%$$0.021\%$$0.002\%$HK Hyperkalaemia, RAASi Renin–angiotensin–aldosterone system inhibitor, SE Standard error, SoC Standard of careaSoC probabilities informed by HK recurrence rates observed in Horne et al. [ 2019] with recurrence events distributed in line with the distribution of initial HK events across potassium categories; patiromer estimates informed by Horne et al. [ 2019] after application of a HR based on OPAL-HK data from months 2 and 3; SE assumed as $10\%$ of meanFig. 2Influence of RAASi use and HK events on disease progression and events. References below each box describe the baseline probabilities/rates; references alongside arrows describe the influence of one disease component on the other, with influences applied to the baseline probabilities rates
## RAASi use
In both treatment arms, all patients are initiated in the model on RAASi and are assumed to be receiving a maximum dose. Down-titration to a sub-maximal dose, or discontinuation of RAASi treatment (from any dose) may occur. RAASi use favourably impacts the progression of CKD and the incidence of MACE, hospitalisation and death (Fig. 2), with an increase in the incidence of HK; the magnitude of these impacts is further described in Supplemental Appendix A [23, 36–42, 46–50].
The proportion of patients still on RAASi at the end of the first month is specified for both arms and based on OPAL-HK trial data. For the patiromer arm, this proportion relates only to those that have achieved response, with the remaining patients assumed to be receiving RAASi therapy in line with the SoC arm. Rates of RAASi discontinuation and down-titration are taken from the OPAL-HK trial for months 2 and 3 [43]. From month 4 onwards, potassium level dependent RAASi discontinuation and down-titration rates were taken from Linde et al. [ 2019] and applied to the SoC arm [23]. Hazard ratios relating to reduced (or increased) rates of discontinuation/down-titration in those receiving patiromer in subsequent months were obtained from the OPAL-HK trial and applied to the rates from Linde et al. [ 2019]. To reflect the impermanent nature of RAASi treatment changes in clinical practice, patients could return to optimal RAASi use independent of their potassium level with a monthly probability of $3.51\%$ [23]. Due to a lack of relevant data, patients who down-titrated RAASi use were assumed to not return to maximum use. RAASi discontinuation and down-titration rates are summarised in Table 3.Table 3RAASi discontinuation, down-titration and up-titration, by potassium categoryMonthly probability of RAASi max discontinuation (%)Monthly probability of RAASi max down-titration (%)Monthly probability of RAASi sub-max discontinuation (%)SourceSoCPatiromerSoCPatiromerSoCPatiromerMonth 2–$334.438\%$ ($6.589\%$)$3.336\%$ ($2.421\%$)$35.549\%$ ($6.589\%$)$0.000\%$ ($0.000\%$)$34.438\%$ ($6.589\%$)$3.336\%$ ($2.421\%$)OPAL-HK [43]Subsequent months K + ≤ $52.600\%$ ($0.009\%$)$0.181\%$$1.800\%$ ($0.026\%$)$1.800\%$$2.600\%$ ($0.009\%$)$0.181\%$Linde et al. [ 2019] [23] K + > 5 to ≤ $5.53.029\%$ ($0.102\%$)$0.211\%$$2.617\%$ ($0.102\%$)$2.617\%$$3.029\%$ ($0.102\%$)$0.211\%$ K + > 5.5 to ≤ $64.547\%$ ($0.230\%$)$0.319\%$$5.306\%$ ($0.230\%$)$5.306\%$$4.547\%$ ($0.230\%$)$0.319\%$ K + > $610.000\%$ ($0.663\%$)$0.721\%$$8.900\%$ ($0.638\%$)$8.900\%$$10.000\%$ ($0.663\%$)$0.721\%$RAASi Renin–angiotensin–aldosterone system inhibitor, K + Potassium, SE Standard error, SoC Standard of careNote: Complete derivation described further in Supplemental Appendix A
## Treatment
The model evaluates patiromer use against current SoC, as previously published. [ 33] It should be noted that modelling SoC is particularly challenging, due to the considerable heterogeneity associated with HK pathogenesis, methods to correct and manage potassium levels (particularly non-pharmacological interventions, and variable levels of adherence to pharmacological methods), and patient responses to such interventions. As such, SoC has been defined consistently with the broad definitions used in the OPAL-HK study, where SoC can be considered acute management for the correction of potassium and lifestyle interventions for the background maintenance of potassium (e.g., dietary intervention and modification of concomitant medications).
All patients initiated in the treatment arm were assumed to receive patiromer for at least one month. At the end of the first month, patients were stratified into those that do ($60.93\%$) and do not ($39.07\%$) respond to treatment. Within the patiromer arm, those that respond to treatment continue to receive patiromer and the associated event risks. Those that do not respond to patiromer cease treatment and incur the risk of events in line with SoC (i.e., assuming no legacy effect of patiromer treatment). For the SoC arm, treatment with SoC could not be discontinued. Beyond month 1, patients receiving patiromer could discontinue at a constant monthly rate of $10.33\%$ based on the OPAL-HK trial, or if they reached ESRD; subsequently incurring event risk in line with the SoC arm. Patients repeated treatment if their potassium levels were equal to or exceeded 5.5–6 mmol/l in subsequent months after discontinuation.
## Costs and utilities
Supplemental appendix B summarises the direct medical costs (2019–20 GBP) applied to modelled health states and events. UK-specific cost data were used, and all costs were inflated to $\frac{2019}{20}$ values [51–68]. Supplemental appendix C summarises the utilities (and disutilities) applied to modelled health states (and events) [54, 55, 69–72]. Utility estimates were broadly informed by a recent National Institute for Health and Care Excellence (NICE) technology appraisal [61]. All cost and utility outcomes were discounted at an annual rate of $3.5\%$ in line with UK health technology assessment guidelines.
## Base cost-effectiveness analysis
The model was used to evaluate the lifetime impact of patiromer use against SoC for the treatment of HK in patients with CKD with and without HF, as previously published. [ 33] Modelled outcomes focused on health care costs, life years and quality-adjusted life years (QALYs), with comparisons between treatments made using the incremental cost-effectiveness ratio (ICER).
Probabilistic sensitivity analysis was undertaken to evaluate uncertainty in clinical and economic outcomes. Patient characteristics and demographics were sampled using a normal distribution, probabilities and utility and disutility values were sampled using a beta distribution, and costs, hazard ratios and odds ratios were sampled using a gamma distribution. Deterministic sensitivity analysis was also undertaken to assess the impact of individual model parameters on model outcomes; the most influential and uncertain input parameters were incorporated in the analysis.
Base case cost-effectiveness results are presented in Table 5. Treatment with patiromer was associated with an increase in discounted life years (8.93 versus 8.67) and an increase in discounted QALYs (6.36 versus 6.16). Incremental discounted costs were predicted at £2,973 per patient, with an incremental cost-effectiveness ratio of £14,816 per QALY gained. Discounted incremental costs were predominantly driven by an initial increase in costs associated with patiromer treatment, increased costs of disease management due to extension of life and reductions in RAASi titration costs over the patient’s lifetime, as a consequence of improved RAASi enablement. Table 5Cost-effectiveness resultsPatiromerSoCIncrementalDiscounted results Total costs (£)£116,675£113,701£2973 Treatment£1283£0£1283 HK£1091£1287-£196 CKD£27,535£26,628£907 RRT£56,877£56,155£721 MACE£9227£9280-£53 Hospitalisation£18,684£18,226£458 RAASi drug usage£153£130£23 RAASi titration£1824£1995-£170 Total life years8.9358.6700.264 Total QALYs6.3566.1560.201 ICER (£/QALY)--£14,816Undiscounted results Total costs£168,834£164,306£4528 Total life years11.68511.3210.364 Total QALYs8.1767.9040.272 ICER (£/QALY)--£16,672CKD Chronic kidney disease, HK Hyperkalaemia, ICER Incremental cost-effectiveness ratio, QALY Quality-adjusted life year, RAASi Renin–angiotensin–aldosterone system inhibitor, RRT Renal replacement therapy, SoC Standard of care Patients remained on patiromer treatment for an average of 7.7 months, with treatment associated with a reduction in the rate of adverse clinical event incidence and a delay in CKD disease progression. However, due to patients in the patiromer arm observing an increased life expectancy, the total incidence of hospitalisation, dialysis and kidney transplantation was greater, despite rates being reduced. Per 1,000 patients, patiromer compared to SoC was associated with 218 and 50 fewer HK events, when evaluating potassium levels at the 5.5–6 mmol/l and > 6 mmol/l levels, respectively. Patiromer when compared with SoC was also associated with 165 fewer RAASi discontinuation episodes and 64 fewer RAASi down-titration episodes. Subsequently, improvements in RAASi management enabled an overall increase in the time it took patients to reach renal replacement therapy (RRT), resulting in a similar number of incident dialysis and transplant episodes, despite improvements in life extension which inherently increase the likelihood of such incidence.
Probabilistic sensitivity analysis is presented in Fig. 3 and supports the conclusions of the base case analysis. Treatment with patiromer was estimated to have a $94.5\%$ and $100\%$ chance of cost-effectiveness compared to SoC when evaluated at willingness-to-pay thresholds of £20,000/QALY and £30,000/QALY in the UK. One-way sensitivity analyses, presented in Supplemental Appendix D demonstrates that cost-effectiveness conclusions are relatively robust to changes in individual parameters, with results most sensitive to rates of discounting, the modelled time horizon, baseline patient age, the magnitude of the impact of RAASi use on CKD progression, and RAASi and treatment discontinuation. Fig. 3Probabilistic sensitivity analysis
## Impact of HK incidence
The incidence of HK can vary significantly across individual patients and so, to evaluate the potential impact of HK on total cost, QALY and life year outcomes, the annual rates of HK were varied over a meaningful range (0–0.5) and outcomes compared over a patient’s lifetime. The model stratifies HK events by severity and so, to incorporate an evaluation of the impact of HK severity, event rates for potassium levels 5–5.5 mmol/l, 5.5–6 mmol/l and > 6 mmol/l were evaluated separately. This scenario is evaluated without the impact of patiromer treatment, assuming input values in line with the SoC arm. All other model parameters remained as in the base cost-effectiveness analyses, and results are presented as incremental results versus an assumed scenario of no HK incidence for the evaluated potassium level.
The impact of HK incidence is presented in Fig. 4. Increasing HK incidence was associated with QALY and life year reductions, with increases in the most severe HK events resulting in the greatest losses. Increasing the annual rate of HK to 0.5 resulted in QALY losses of 0.017, 0.093 and 0.229 per patient, when compared to a similar cohort in which no HK incidence was observed, for potassium levels 5–5.5 mmol/l, 5.5–6 mmol/l and > 6 mmol/l, respectively. Life year and QALY reductions come as a consequence of HK being associated with additional morbidity and mortality. With regards to costs, there are three core components associated with HK incidence that influence total cost accrual: the cost of managing the individual HK event (£0, £223.11 and £2,933.49 for potassium levels 5–5.5 mmol/l, 5.5–6 mmol/l and > 6 mmol/l, respectively), increased morbidity associated with HK (increasing costs) and, increased mortality associated with HK (reducing costs). Increasing the rate of the most severe HK events (i.e., potassium > 6.0 mmol/l) resulted in increased lifetime per-patient costs of up to £8,109 when event rates were increased to 0.5 per year (predominantly due to the increased cost associated with HK management). In contrast, increasing the rate of less severe HK events (i.e., potassium levels ≤ 6.0 mmol/l) resulted in reduced lifetime per-patient costs (albeit marginal cost reductions). Cost reductions were attributed to the much lower cost of managing these HK events (compared to severe HK events) and the reduction in life expectancy, resulting in less time for patients to accrue costs of general disease management associated with CKD and HF.Fig. 4Impact of changes in the annual rate of HK on costs, QALYs and life years (compared to no HK incidence). All other inputs remain as in the base case cost-effectiveness analysis
## Value of optimal RAASi control
Management of HK often involves the discontinuation or down-titration of RAASi therapy. The enablement of RAASi therapy is extremely important for the clinical management of patients with CKD with or without HF. To illustrate the potential lifetime benefits associated with optimal RAASi control, we evaluate two hypothetical patient cohorts, one that maintains optimal RAASi control over their entire lifetime (from the point of model initiation), and one that is not ever managed with RAASi therapy (or at least, not managed with RAASi therapy from the point of model initiation). We evaluate each of these management approaches in patient cohorts aged 40, 50, 60 and 70, utilising different starting CKD health states (CKD stages 3, 4 and 5) and assuming patients do or do not suffer from HF.
Given a strong association between age and ESRD treatment modalities (dialysis and transplant) and their outcomes, the likelihood of transplant and the likelihood of death from ESRD are modified for each age cohort; input parameters are detailed in Table 4. This scenario is evaluated without the impact of patiromer treatment, assuming input values in line with the SoC arm. All other model parameters remained as in the base cost-effectiveness analyses. Table 4Age-dependent ESRD input parametersParameterAgeSource40506070Monthly probability of transplant from CKD stage $52.15\%$$1.68\%$$0.18\%$$0.18\%$NHSBT [38]; Renal Registry [73]Monthly probability of transplant from dialysis$0.70\%$$0.55\%$$0.06\%$$0.06\%$NHSBT [38]; Renal Registry [73, 74]Monthly probability of death from dialysis$0.18\%$$0.37\%$$0.61\%$$1.23\%$Renal Registry [74]Monthly probability of death from transplant$0.07\%$$0.18\%$$0.32\%$$0.55\%$NHSBT [38]; Karim et al. [ 2014] [75]CKD Chronic kidney disease, NHSBT National Health Service Blood and TransplantNote: Examples of the derivation of the above inputs are provided in Supplemental Appendix A The value of maintaining optimal RAASi control is presented in Fig. 5, in terms of total costs and QALYs, with results presented for patients with optimal RAASi use and patients with no RAASi use. Patients with optimal RAASi management were consistently estimated to observe greater quality-adjusted life expectancy, with the largest differences between optimal RAASi management and no RAASi management typically observed in patients without HF, those of younger age and those starting in less severe CKD stages. These groups typically gain the most due to their greater propensity to avoid ESRD and its consequences. As expected, those with both CKD and HF observe much lower QALY gains than those with CKD alone, where HF-related mortality is a dominant factor and there is less time available for RAASi use to influence outcomes. Fig. 5The impact of lifetime optimal RAASi management (compared to no RAASi use) and the association of outcomes with patient’s baseline age, starting CKD stage and HF disease status. A: Total per-patient discounted costs in patients with CKD and HF; B: Total per-patient discounted costs in patients with CKD without HF; C: Total per-patient discounted QALYs in patients with CKD and HF; D: Total per-patient discounted QALYs in patients with CKD without HF; Note: All other inputs remain as in the base case cost-effectiveness analysis In those without HF, optimal RAASi management is typically associated with greater cost due to extension of life and the increased amount of time managing CKD and ESRD. Since, in cases where a technology increases survival in people for whom the NHS is currently providing care that is expensive, NICE may consider, alongside the reference-case analysis, a non-reference-case analysis with the background care costs removed. [ 76] Hence, the greater costs associated with RAASi management may be exempt during a NICE technology appraisal process.
A similar relationship is observed amongst those with HF, although the differences are less pronounced (and in the case of some CKD stage 3 patients, reversed), due to smaller gains in life expectancy, and the increased costs associated with disease management being partially offset by the avoidance of MACE and hospitalisation events.
Across non-HF and HF populations, optimal RAASi management in younger patients is typically associated with greater cost due to increased life expectancy, and subsequently, a greater amount of time spent managing CKD. An exception to this is observed in patients with HF starting in CKD stage 5, where the non-linear relationship between ESRD modalities (e.g., transplant eligibility), ESRD transition rates and death play a more influential role given patients immediate proximity to these health states. Total costs are on average greater in those without HF, than those with HF, for similar reasons. In contrast, total costs increase as the starting CKD stage worsens due to a closer proximity of patients to resource intensive ESRD health states.
Importantly, these results highlight the complexity of the economic relationships observed when modelling HK in a cohort of patients with CKD with or without HF. Inherently, these are complicated conditions with treatment and outcomes from one disease component influencing treatment and outcomes in another, and vice versa, often leading to results which require additional interpretation before appearing intuitive.
## Discussion
This study evaluates the cost-effectiveness of patiromer for the treatment of HK in patients with CKD with or without HF and demonstrates that patiromer is a cost-effective treatment in the UK setting. Further, this study adds to the published literature by undertaking extensive sensitivity analyses exploring the impact of HK and RAASi use on UK patient lifetime outcomes. To our knowledge, this is the first study to estimate the lifetime economic impact of optimal RAASi use in HK patients with CKD with or without HF.
The analysis in this study demonstrates that the avoidance of HK and the maintenance of optimal RAASi therapy is associated with both life year and QALY gains, and in some scenarios cost-savings. These findings are in accordance with other studies evaluating the benefits of HK management; HK avoidance and RAASi enablement, in CKD and HF. [ 77, 78] Evans M et al. modelled the natural history of CKD in order to demonstrate the relationship between potassium levels, RAASi therapy and long-term clinical outcomes in CKD patients. [ 77] Authors demonstrated that normalisation of potassium levels and optimal RAASi use was associated with delayed CKD progression and RRT initiation, better quality of life, increased survival and cost savings. In another study, the health and economic benefits of HK normalisation and continuation of RAASi therapy was evaluated in HF patients. [ 78] Analysis showed that patients who maintained normal potassium levels and RAASi use had increased life expectancy, QALYs, cost savings and associated net monetary benefit over a lifetime horizon. Together, these results highlight the importance of implementing a successful strategy for HK management and maintenance of RAASi therapy and should be actively pursued given that both HK treatment and in particular RAASi use are relatively inexpensive in the UK.
Patiromer, a non-absorbed polymer which binds to potassium in exchange for calcium within the gastrointestinal tract, has been demonstrated to be cost effective in the UK as a treatment option for HK patients. [ 79] Clinical trials have demonstrated the benefits of patiromer as an effective, well tolerated and fast acting strategy to normalise potassium levels, enable RAASi therapy and allow long-term management in patients with HK. [ 29, 30, 80] Moreover, ongoing studies of patiromer are underway to determine patient reported outcomes as a measure of quality of life and mortality in the RELIEHF clinical trial. [ 81] Findings from such trials will further inform cost-effectiveness modelling and our understanding of the effect of patiromer treatment, HK incidence and RAASi therapy on increased survival and its impact on the quality of life of patients living with chronic diseases. Furthermore, patiromer has been recommended in the UK for the treatment of HK in patients with CKD or HF. [ 79] Nevertheless, in the clinic HK is often managed by down-titration or discontinuation of RAASi therapy, resulting in worsening clinical outcomes [22, 47, 82] and increased burden on the healthcare systems, with increased hospitalisations and resource use.
Current economic evaluations often do not take into consideration indirect health care consequences, such as the benefits associated with reduced hospitalisations, and instead, assume that capacity is not an issue. Despite the UK adopting a national “healthcare for all” health service approach, significant increases in need over recent years have resulted in a healthcare service stretched beyond its capacity. As such, the benefits of interventions which keep patients out of hospital are likely underestimated. For instance, reducing hospitalisations would free up resource use which could impact on the cost-effectiveness of other interventions. Furthermore, the additional resource available would allow capacity for other health care to be provided.
This is particularly relevant given the current challenges healthcare systems are facing, during the Covid 19 pandemic. In the UK, NHS hospitals were already operating at $90\%$ capacity pre-pandemic. [ 83] Requirement for in-patient care has significantly increased over the last two years and adjusting to free-up the number of hospital beds to meet demand is challenging. [ 84] In England, one of the approaches taken was cancellations of elective surgery at the detriment of non-Covid-19 patients’ health, resulting in an increased length of waiting lists for patients needing healthcare. [ 85–89] Subsequently, the current challenge for healthcare managers is to obtain sufficient hospital capacity to care for COVID-19 patients whilst also being able to continue treatment for non-COVID 19 patients. Our results suggest an alternative approach to increasing hospital bed capacity, through improved HK management. In our model, normalisation of potassium levels and continuation of RAASi therapy resulted in reductions in the rate of all adverse clinical outcomes and time spent in the healthcare system due to RAASi management issues.
The results of this study also highlight the complexity of the modelled relationships, which attempt to capture outcomes associated with several multi-faceted disease areas. Not only are these complex disease areas, but each has the potential to impact the other through the influence of either treatment or outcomes. Only by further exploring the impact of HK incidence and lifetime RAASi use on model outcomes and providing this additional interpretation, do these relationships and interactions become more apparent and intuitive. Models are inherently designed to explore such uncertainty, however, without confirmation of their ability to model these dynamic relationships, through either validation to large observational studies or validation with clinical experts, there will remain doubt over modelled results. As such, future research may focus on first extending model validation beyond the core model application (for instance cost-effectiveness of a specific treatment in a specific static setting) to further fully validate model relationships and scenarios which might only be realised when undertaking exploratory analyses, and second, to provide a more comprehensive set of guidelines for model validation processes which direct the validation of complicated disease areas beyond the ‘base case’ setting.
Limitations of this study are mainly due to the relative paucity of the literature. In the base case cost-effectiveness analysis, extrapolation of outcomes was based on a 3-month trial, which is inherently uncertain. Furthermore, whilst the influence of RAASi management on CKD and HF outcomes is well accepted in the published literature, the magnitude of such influence is more uncertain. In addition, our exploration of optimal RAASi use scenarios only captures the influence of age on some dialysis and transplant input parameters, due to limitations in available data. It is likely that modification of other clinical parameters, particularly in relation to the influence of age and disease status would more accurately reflect real-world clinical practice. However, this study can be seen as an indicative first step in quantifying the value of optimal RAASi use.
## Conclusions
In summary, findings from this study highlight the value of both HK normalisation and RAASi maintenance in CKD patients with and without HF. HK treatment was associated with a reduction in overall clinical event incidence and a delay in CKD disease progression. In addition, the value of lifetime optimal RAASi control was associated with increased QALY and life year gains, and in some scenarios cost savings. Together, these results support the guidelines which recommend HK treatment, e.g., patiromer, as a strategy to enable the continuation of RAASi therapy and improve clinical outcomes in CKD patients with and without HF.
## Supplementary Information
Additional file 1: This appendix provides details of disease progression data utilised in the model. Additional file 2: This appendix provides details of cost data utilised in the model. Additional file 3: This appendix provides details of utility and disutility input parameters utilised in the model. Additional file 4: This appendix provides details of additional results not presented in the main manuscript body.
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|
---
title: Altered cardiac and vascular stiffness in pregnancy after a hypertensive pregnancy
authors:
- James S. Castleman
- Alena Shantsila
- Richard A. Brown
- Eduard Shantsila
- Gregory Y. H. Lip
journal: Journal of Human Hypertension
year: 2022
pmcid: PMC9995268
doi: 10.1038/s41371-022-00662-4
license: CC BY 4.0
---
# Altered cardiac and vascular stiffness in pregnancy after a hypertensive pregnancy
## Abstract
Hypertensive disorders of pregnancy are an important cause of morbidity and mortality, impacting on both maternal and fetal wellbeing. Affected women are at higher risk of future cardiovascular morbidity and mortality. Our study objective was to assess differences in cardiovascular function in pregnant women previously affected by gestational hypertension or preeclampsia. Pregnant women diagnosed with gestational hypertension or preeclampsia in a previous pregnancy were recruited at the start of a subsequent pregnancy and compared to healthy pregnant and non-pregnant controls. All patients underwent pulse wave analysis and echocardiography. Indexes of echocardiography-derived arterial and left ventricular elastance were calculated. In our study women with prior hypertension ($$n = 25$$) were more likely to have blood pressure in the 120–$\frac{139}{80}$–99 mmHg (prehypertension) range. Women with previous hypertension in pregnancy had increased late diastolic transmitral flow velocities (A wave) and increased augmentation index. Women without prior hypertension ($$n = 50$$) demonstrated more compliance (reduced EaI and Ees) compared to the non-pregnant controls ($$n = 40$$). This adaptation was not seen in pregnancy with prior hypertension, where increased arterial stiffness was observed. In conclusion we have shown increased prevalence of prehypertension and increased arterial stiffness in pregnant women previously affected by gestational hypertensive disease. An increased atrial component to ventricular filling reflects altered diastolic function after hypertensive pregnancy. These women are at increased future cardiovascular risk due to altered cardiac and vascular function and require effective risk mitigation.
## Introduction
Hypertensive disorders of pregnancy (HDP) are an important cause of morbidity and mortality, impacting on both maternal and fetal wellbeing. The definition of hypertension in pregnancy requires either a systolic blood pressure (BP) of at least 140 mmHg or a diastolic BP (DBP) of at least 90 mmHg, with a second confirmatory reading separated in time usually by 4 h [1]. BP should be measured with a device validated in pregnancy [2]. Significant proteinuria (urine protein/creatinine ratio of at least 30 mg/mmol [3]) has traditionally been the second criterion required to distinguish gestational hypertension (GH) from preeclampsia (PE). The International Society for the Study of Hypertension in Pregnancy [4, 5] describe PE as a syndrome comprising hypertension and end organ dysfunction, with renal, hepatic, haematological, neurological or placental manifestations. Various maternal and fetal sequelae of the disease now appear in international guidelines for the diagnosis of PE, with proteinuria no longer mandatory [6–9]. A woman affected by PE is at higher risk of future cardiovascular morbidity and mortality [10, 11]. This may be due to persistent changes in cardiac structure and function, or to irreversible injury to the cardiovascular system [12, 13]. We have described echocardiographic cardiac structure and function in HDP in a systematic review [14].
Arterial stiffness, or elastance, defines the change in pressure (∆P, stress) relative to a change in volume (∆V, strain) of blood flow through an artery [15]. Increased vascular and left ventricular (LV) stiffening may lead to an alteration in ventriculo–arterial coupling, which can be assessed reproducibly by echocardiographic measurement of arterial and cardiac elastance [16–19]. A recent systematic review and meta-analysis has demonstrated a significant increase in arterial stiffness indices in women with PE compared to women with GH and normotensive pregnant women [20]. Despite the growing evidence of abnormal arterial stiffness and diastolic dysfunction in pregnancy, the mechanisms of these changes and mutual relationship between arterial and cardiac abnormalities are not clear [21].
We aimed to investigate how maternal cardiac structure and function is affected by a previous hypertensive pregnancy. Ultrasound is currently used to perform a first trimester risk assessment for the fetus but its role in maternal cardiovascular risk assessment is not yet defined. We hypothesised that a history of gestational hypertensive disease would be associated with abnormal ventricular-arterial interaction in a subsequent pregnancy, with reduced arterial elastance and altered ventricular elastance reflecting maladaptation to the pregnant state.
## Methods
“Evaluating Cardiovascular Changes in Hypertension in Obstetrics” (ECCHO) was a prospective observational study in which women were recruited at the beginning of pregnancy and studied throughout their gestation. Cross-sectional comparison in the first trimester of pregnancy tested the hypothesis that prior hypertension in pregnancy is associated with altered arterial and ventricular function. Pregnant women with prior HDP were compared to pregnant women without prior hypertension and to healthy non-pregnant controls.
## Study groups
Three study groups were recruited. Group 1 comprised pregnant women with previous HDP (based on the National Institute of Health and Care Excellence guidance [3]). Group 2 comprised pregnant women with no history of hypertension. Group 3 included healthy non-pregnant women as controls. Patients were recruited from women attending the Department of Maternity and Perinatal Medicine at Sandwell and West Birmingham Hospitals NHS Trust. Eligible women were identified from referrals for antenatal care and approached during their first hospital visit. Non-pregnant controls were recruited from hospital and university staff. A comprehensive medical history was taken from each woman, to assess them against the inclusion and exclusion criteria and to provide the necessary data for the study. Hospital case notes were also cross-examined to confirm the past medical history in order to reduce recall error and bias. Gestational age was determined by fetal biometry at 11–14 weeks. The pregnant women were followed throughout pregnancy and the pregnancy outcome recorded. There were no changes in the routine antenatal care of patients. Clinical management of pregnancy was in accordance with established local protocols, based on national guidelines. Exclusion criteria were pre-existing cardiac disease (ischaemic heart disease, valvular heart disease, congenital heart defect), chronic hypertension, significant co-morbidities, use of vasoactive medication, multiple pregnancy, inability to consent (language barrier with no translator available, lacks capacity), obstetric emergency (haemorrhage, severe symptomatic (pre)eclampsia, presentation in labour) and age under 16 years.
## Echocardiography
Echocardiography was performed using a Philips iE33 ultrasound machine (Bothell, WA, USA) with a phased array transducer. The images were converted to Digital Images and Communications in Medicine format. Xcelera software (Philips Medical Systems, Netherlands) was used to analyse the stored images. After a period of at least 5 min rest, the women were examined in a comfortable left lateral position on the couch. A left lateral tilt was employed throughout the longitudinal study for standardisation. Appointments were routinely made in the morning. Two investigators (AS and RAB), experienced in cardiac imaging and accredited with the British Society of Echocardiography, performed the transthoracic echocardiograms throughout the study. The examination protocol was in accordance with the latest published guidelines from the international societies [22–24].
The images were anonymised and digitally stored prior to offline analysis by a single observer (JSC). All measurements were performed after completion of the study in a random order, with the investigator blinded to the identity of the patient, their clinical characteristics, including BP, and their pregnancy outcome. Measurements from 2D structural images were recorded once. Measurements based on flow/waveforms were performed on four beats and the mean was taken. Measurements of the parameters of arterial-vascular interactions have been previously validated and were done in accordance with established protocols [17].
## Blood pressure measurement
BP was measured with a digital BP monitor (Omron Corporation, Tokyo, Japan). This automated, electronic, oscillometric device is validated for use in pregnancy [2] and was calibrated throughout the study. The brachial artery of the non-dominant arm was used for the BP recording. Care was taken to ensure that the arm was free of clothing and that appropriate cuff size was used depending on mid-arm circumference. BP was recorded whilst the patient was seated comfortably and silently, with the arm supported at the level of the heart. The woman was asked to sit upright and still, with her back well supported, legs uncrossed and feet flat on the floor. Three readings were taken 1 min apart and the mean calculated.
## Pulse wave analysis
The SphygmoCor device (Atcor Medical, West Ryde, Australia) equipped with a hand-held tonometer like a pencil (Millar Instruments, Houston, Texas, USA) was used to perform pulse wave analysis. There is a micromanometer within the tip of the tonometer to record the pressure within the radial artery. The clinic room was temperature controlled and kept quiet and undisturbed. The women were asked to abstain from caffeine, alcohol and smoking, starting from the night before the appointment. Women were asked not to move or speak during the measurements, which were performed in a semi-recumbent position with a left lateral tilt to ensure consistency with the echocardiogram methodology. The radial artery was palpated, and the point of maximal pulsation identified. The tonometer tip was placed at this point. The radial artery waveforms were recorded over 10 s. The aortic pressure waveform is derived from the radial artery waveform by a mathematical transfer function in the Sphygmocor software (Sphygmocor Cardiovascular Management Suite Version 9). Similarly, the software was used to derive the aortic pressure and augmentation index (a measure of arterial stiffness and wave reflection). Good reliability was achieved after a period of supervised training, prior to acquiring measurements for the study.
## Statistical analysis
Baseline characteristics were tested for normality using the Shapiro-Wilk test. Normally distributed data are expressed as mean and standard deviation (SD). Non-normal continuous data are expressed as median and interquartile range). Categorical data are expressed as number and percentage.
Cross-sectional data were subjected to one-way analysis of variance (ANOVA) or Kruskal–Wallis test. Post hoc testing was performed to account for comparisons between the three groups, using Tukey’s test of pairwise comparisons for normally distributed and the Dunn–Bonferroni method for non-normally distributed data respectively. Categorical data are compared using Fisher’s exact test for two groups and the chi-squared test with appropriate degrees of freedom for three groups. A 2-tailed P-value of <0.05 was considered statistically significant. Analysis was performed using Stata® (StataCorp. 2015. Stata Statistical Software: Release 14. College Station, TX: StataCorp LP).
## Ethical considerations
This research was approved by the Research and Development Department of Sandwell and West Birmingham Hospitals NHS Trust (Reference 13CARD65, $\frac{27}{11}$/13), following review by the institution’s Ethics Committee. Prospective approval for the study was also obtained from the Local Research Ethics Committee for West Midlands (Reference 13/WM/0472, $\frac{07}{01}$/14). All participants gave written informed consent and confirmed ongoing consent at each follow up appointment. The study was conducted in accordance with the Declaration of Helsinki.
## Results
A total of 115 women were enrolled in the study. The study groups were well matched for age ($$P \leq 0.74$$) and ethnicity ($$P \leq 0.33$$) with a slight majority ($50.4\%$) coming from non-white racial groups (Table 1). There was no difference in maternal medical history apart from gestational hypertensive disease, which by design is unique to Group 1. Where asthma is noted, the cases were mild. The difference in parity between the groups is also a feature of the study design, since women in Group 1 are required to have a previous pregnancy. The number of women taking aspirin was significantly higher in Group 1, since this daily medication is recommended to women previously affected by hypertension in pregnancy for secondary prevention. Table 1Demographic and clinical characteristics. CharacteristicGroup 1Previous hypertension($$n = 25$$)Group 2Previous normotensive($$n = 50$$)Group 3Non-pregnant($$n = 40$$)PAge, years30 (27–33)29 (25–33)28 (25–34)0.74Parity, n (%) Nulliparous0 [0]22 [44]28 [70]<0.001 Parous25 [100]*†28 [56]12 [30]Ethnicity, n (%) White13 [52]21 [42]23 [58]0.49 South Asian9 [36]19 [38]9 [22] Black1 [4]7 [14]5 [13] East Asian0 [0]1 [2]2 [5] Other/mixed2 [8]2 [4]1 [2] White13 [52]21 [42]23 [58]0.33 Non-white12 [48]29 [58]17 [42]Medical history, n (%) Asthma3 [12]2 [4]3 [8]0.36 Diabetes1 [4]3 [6]0 [0]0.30Medications, n (%) Aspirin11 [44]*†1 [2]0 [0]<0001 Hormonal contraception0 [0]†0 [0]‡13 [33]<0.001 Antidepressant0 [0]0 [0]1[3]0.39 Azathioprine1 [4]1 [2]0 [0]0.48 Enoxaparin0 [0]1 [2]0 [0]0.52 Metformin0 [0]2 [4]0 [0]0.30 Insulin0 [0]1 [2]0 [0]0.52 Salbutamol3 [12]1 [2]2 [5]0.19 Thyroxine0 [0]3 [6]0 [0]0.14Family history, n (%) Essential hypertension11 [44]13 [26]15 [38]0.25 Pregnancy hypertension3 [12]4 [8]5 [13]0.77Smoking, n (%) Current smoker2 [8]2 [4]1 [2]0.57 Never smoked17 [68]43 [86]38 [95]0.01Continuous data are expressed as median (interquartile range). Categorical data are expressed as n (%). Post hoc testing: *$P \leq 0.01$ Group 1 vs Group 2; †$P \leq 0.01$ Group 1 vs Group 3; ‡$P \leq 0.01$ Group 2 vs Group 3.
There was no difference in family history of hypertension or current smoking status, however previous smoking habits varied, with the pregnant women previously affected by hypertension more likely to have smoked in the past ($$P \leq 0.03$$ for Group 1 vs Group 3). Group 1 comprised 9 women with previous GH, 9 with previous early onset PE and 7 with previous late onset PE. All had normal BP and were free of antihypertensive medication at enrolment.
The pregnant participants were studied at 13 ± 1 completed weeks of gestation. There was no significant difference in weight ($$P \leq 0.55$$) or height ($$P \leq 0.054$$), although the non-pregnant women tended to be taller. The groups were matched for body mass index (BMI) ($$P \leq 0.12$$) and body surface area (BSA) ($$P \leq 0.74$$) (Table 2).Table 2Clinical measurements. CharacteristicGroup 1Previous hypertension($$n = 25$$)Group 2Previous normotensive($$n = 50$$)Group 3Non-pregnant controls($$n = 40$$)PGestation (weeks)13 ± 113 ± 1n/a0.42Height (m)1.61 ± 0.081.64 ± 0.071.66 ± 0.080.054Weight (kg)73 (58–85)72 (59–81)67 (58–83)0.55BMI (kg/m2)27.7 (23.2–31.2)26.5 (22.3–31.6)23.8 (21.0–28.8)0.12BSA (m2)1.78 ± 0.291.80 ± 0.221.76 ± 0.210.74SBP (mmHg)115 (113–121)*†108 (100–114)112 (101–119)0.001DBP (mmHg)71 (65–75)*64 (50–69)66 (60–72)0.003MAP (mmHg)87 (81–91)*77 (72–82)81 (74–86)<0.001Heart rate (bpm)79 ± 10†74 ± 9 ‡65 ± 10<0.001Hb (g/L)120 ± 11†122 ± 11‡132 ± 11<0.001WCC (×109/L)9.4 (7.2–10.9)†8.3 (7.0–9.4)‡6.3 (5.3–6.9)<0.001Platelets (×109/L)253 (210–291)247 (202–292)270 (249–303)0.20Creatinine (µmol/L)51 (49–54)†53 (50–56)‡66 (62–71)<0.001Data expressed as median (interquartile range) or as mean ± standard deviation. BMI body mass index, bpm beats per minute, BSA body surface area, DBP diastolic blood pressure, Hb haemoglobin, MAP mean arterial pressure, n/a not applicable, SBP systolic blood pressure, WCC white cell count. Post hoc testing:*$p \leq 0.01$ Group 1 vs Group 2.†$p \leq 0.01$ Group 1 vs Group 3.‡$p \leq 0.01$ Group 2 vs Group.
The protocol mandated that BP be normal for all women at the start of the study. Accordingly, systolic BP (SBP) was less than 140 mmHg and DBP was less than 90 mmHg for all. Women in Group 1 had significantly higher peripheral SBP at baseline compared to Groups 2 and 3 ($P \leq 0.01$ for both). DBP and mean arterial pressure (MAP) and central SBP were significantly higher in Group 1 compared to Group 2. Group 2 demonstrated a non-significant reduction in BP compared to Group 3. Heart rate was increased in pregnant women compared to non-pregnant controls ($P \leq 0.001$).
## Haemodynamics
Whilst the median stroke volume was the same for both groups of pregnant women, the increase compared to non-pregnant controls reached statistical significance only for Group 2 ($$P \leq 0.003$$), which persisted after adjusting for BSA (see Table 3). Cardiac output and cardiac index were significantly increased in pregnant women ($P \leq 0.001$), with similar values observed regardless of previous hypertension. This higher cardiac output was observed alongside reduced resistance, with total vascular resistance index significantly lower in Group 1 ($$P \leq 0.001$$) and Group 2 ($P \leq 0.001$) compared with Group 3. There was no significant difference in the systolic tissue Doppler average velocity at the septal and lateral mitral valve annuli. Table 3Echocardiographic parameters. ParameterGroup 1Previous hypertension($$n = 25$$)Group 2Previous normotensive($$n = 50$$)Group 3Non-pregnant controls($$n = 40$$)PHaemodynamics Stroke volume (ml)68 (59–72)68 (58–76)‡59 (51–69)0.019 Stroke volume index (ml/m2)37.3 (34.0–42.8)37.6 (33.6–43.6)‡32.8 (30.0–37.3)0.017 Cardiac output (L/min)5.0 (4.6–5.8)†4.8 (4.5–5.6)‡3.8 (3.1–4.5)<0.001 Cardiac index (L/min/m2)3.0 (2.7–3.2)†2.8 (2.4–3.3)‡2.1 (1.8–2.4)<0.001 Total vascular resistance(dyne.s/cm5)1.4 (1.2–1.5)†1.2 (1.1–1.5)‡1.7 (1.5–2.1)<0.001 Total vascular resistance index(dyne.s/cm5/m2)0.80 (0.65–0.89)†0.69 (0.58–0.89)‡0.97 (0.83–1.20)<0.001 Average s’ (cm/s)9.6 (8.5–10.2)9.5 (8.8–10.1)9.5 (8.9–10.4)0.84Structure *Left atrium* volume (ml)41 (31–45)39 (31–46)42 (30–51)0.71 *Left atrium* volume index (ml/m2)22 (18–28)22 (17–26)23 (18–28)0.76 Left ventricle volume (ml)98 (84–127)†110 (91–121)‡91 (80–101)<0.001 Left ventricle volume index (ml/m2)59 (52–64)†60 (50–71)‡51 (44–58)<0.001 Left ventricular mass (g)117 (95–152)119 (97–143)103 (81–129)0.10 Left ventricular mass index (g/m2)70.9 (57.4–82.5)†65.4 (56.8–78.7)61.6 (49.7–69.2)0.03 Left ventricular end diastolic dimension (cm)4.5 (4.3–5.0)4.5 (4.2–4.8)4.4 (4.3–4.8)0.93 Posterior wall thickness at end diastole (cm)0.9 (0.7–1.1)0.9 (0.8–1.0)‡0.8 (0.7–0.9)0.047 *Interventricular septum* thickness at end diastole (cm)0.79 (0.69–0.91)0.73 (0.66–0.87)0.72 (0.57–0.82)0.10 Relative wall thickness0.38 (0.32–0.49)0.40 (0.33–0.46)0.36 (0.29–0.41)0.11Diastolic function (mitral inflow) Early filling (E) (cm/s)85 (75–99)†88 (77–96)‡74 (67–85)<0.001 Atrial filling (A) (cm/s)62 (51–67)*†48 (44–62)49 (40–74)0.005 E/A ratio1.40 (1.15–1.64)*†1.79 (1.55–2.25)2.03 (1.54–2.32)<0.001e’ (cm/s) Septal11.2 (9.7–12.5)11.2 (9.9–12.9)11.6 (10.2–12.7)0.90 Lateral14.0 (12.6–16.2)15.4 (13.5–18.3)15.7 (14.7–17.7)0.17 E/e’ (average from septal and lateral)6.6 (5.7–8.2)†6.5 (5.2–7.5)‡5.4 (4.6–6.4)0.001Derived values including elastance Left ventricular ejection fraction0.63 (0.59–0.67)0.63 (0.60–0.67)0.66 (0.62–0.69)0.066 Left ventricular end diastolic pressure (mmHg)10.1 (9.0–12.1)†9.9 (8.4–11.2)‡8.6 (7.9–9.9)0.008 Arterial elastance (Ea)1.6 (1.4–1.8)1.4 (1.2–1.7)‡1.7 (1.5–1.9)0.005 Arterial elastance index (EaI)0.95 (0.79–1.11)0.79 (0.72–0.95)‡0.95 (0.80–1.10)0.004 End-systolic elastance (Ees)2.0 (1.8–2.3)1.9 (1.6–2.2)‡2.3 (1.8–2.7)0.015 End-diastolic elastance (Eed)0.9 (0.08–0.11)0.08 (0.07–0.10)0.08 (0.07–0.10)0.24 Arterial-ventricular interaction (Ea/Ees)0.85 (0.78–0.88)0.81 (0.74–0.90)0.76 (0.71–0.85)0.084 Arterial-ventricular interaction index (EaI/Ees)0.47 (0.42–0.59)0.44 (0.40–0.53)0.45 (0.41–0.48)0.22Data expressed as median (interquartile range) or as mean ± standard deviation. E mitral valve early filling on Pulsed-Wave Doppler, e’ velocity of early myocardial relaxation measured on tissue Doppler imaging, A mitral valve late (atrial) filling. Post hoc testing:*$p \leq 0.01$ Group 1 vs Group 2.†$p \leq 0.01$ Group 1 vs Group 3.‡$p \leq 0.01$ Group 2 vs Group.
## Cardiac structure
The size of the left atrium was unchanged across groups ($P \leq 0.05$). Pregnant women exhibited a significant increase in LV volume and its index to BSA ($P \leq 0.001$). Women with previous GH had a significantly higher LV mass index compared to non-pregnant women ($$P \leq 0.006$$), whilst the increase compared to pregnant women without hypertension history was not significant after Bonferroni adjustment. There was no significant difference in LV end diastolic dimension, interventricular septum thickness or relative wall thickness ($P \leq 0.05$). The difference in posterior wall thickness was not significantly different after post hoc testing.
## Diastolic function
Significant differences were observed for mitral inflow velocities. The maximum early mitral valve inflow velocity on Pulsed-Wave Doppler (E wave) was significantly greater in the pregnant women irrespective of former hypertension history ($$P \leq 0.003$$ Group 1 vs Group 3; $P \leq 0.001$ Group 2 vs Group 3). The maximum late diastolic mitral inflow velocity was significantly increased in the women with previous hypertension, compared to both unaffected pregnant women and non-pregnant women ($$P \leq 0.001$$ for both comparisons). Similarly, the calculated E/A ratio of early to late ventricular filling in diastole, was significantly lower when pregnancy was complicated by previous gestational hypertensive disease ($P \leq 0.001$). There was no difference in e’, the velocity of early myocardial relaxation measured on tissue Doppler imaging, between the three groups ($P \leq 0.05$). The E/e’ ratio of early mitral inflow to the average tissue Doppler early myocardial relaxation at the septal and lateral mitral valve annuli, was significantly increased in both groups of pregnant women compared to the non-pregnant controls ($$P \leq 0.001$$ for both).
## Elastance parameters and other derived values
Left ventricular ejection fraction (LVEF) was similar in all groups ($P \leq 0.05$). LV end diastolic pressures were higher in each group of pregnant women compared to non-pregnant controls but not to each other ($$P \leq 0.008$$). There was a significant decrease in arterial elastance index (EaI, $P \leq 0.001$) and LV end-systolic elastance (Ees, $$P \leq 0.002$$) in healthy pregnant women in Group 2 compared to non-pregnant controls. Similar values of EaI were seen in Groups 1 and 3 and the reduction in Ees in Group 1 was not statistically significant after post hoc testing. There was no difference in EaI/Ees, the arterial-ventricular interaction index, between the three groups ($P \leq 0.05$).
## Arterial stiffness
Augmentation index, standardised to heart rate 75 beats per minute, was significantly increased in Group 1 with previous hypertension compared to Group 2 ($$P \leq 0.004$$) and to Group 3 ($P \leq 0.001$, Table 4). There was no significant difference in arterial stiffness in pregnancy without hypertension history compared to non-pregnant women ($P \leq 0.05$).Table 4Pulse wave analysis. ParameterGroup 1Previous hypertension($$n = 25$$)Group 2Previous normotensive($$n = 50$$)Group 3Non-pregnant controls($$n = 40$$)PAugmentation index15 (6–19)5 (−2 to 12)7 (2–16)0.06Augmentation index HR7516 (4–21)*†4 (−3 to 14)2 (−6 to 11)0.003Augmentation index HR75 adjusted for MAP0.16 (0.05–0.24)*†0.05 (−0.03 to 0.17)0.03 (−0.08 to 0.13)0.003Data expressed as median (interquartile range) or as mean ± standard deviation. HR75 denotes adjustment to standardise for heart rate 75 beats per minute; MAP mean arterial pressure. Post hoc testing: *$p \leq 0.01$ Group 1 vs Group 2; †$p \leq 0.01$ Group 1 vs Group 3.
## Discussion
The SBP in Group 1 at 13 ± 1 weeks of gestation was significantly higher than both other groups after correcting for multiple comparisons ($P \leq 0.001$ vs Group 2; $$P \leq 0.007$$ vs Group 3). This observation was also reported in a 2-year follow up study of women after preeclamptic pregnancy [12]. The concept of prehypertension was formally introduced into American guidelines over a decade ago [25]. Patients with a SBP of 120–139 mmHg or a DBP of 80–90 mmHg are classified as prehypertensive. European guidelines [26, 27] designate this group ‘high normal’. Some $\frac{5}{50}$ women in Group 2 and $\frac{10}{25}$ women in Group 1 would have been classed as prehypertensive according to the Joint National Committee Guideline [25]. The proportion of women with prehypertension in Group 1 is significantly higher ($$P \leq 0.002$$). People with prehypertension have double the odds of developing hypertension compared to those with lower BP [25]. When observed at postpartum follow up over 4–10 years after PE, prehypertension is associated with asymptomatic heart failure [28]. Over time, prehypertension causes LV hypertrophy and diastolic dysfunction [29]. Previous PE, especially in association with prehypertension, is independently associated with an increased risk of subclinical cardiac failure [28]. The relationship between BP and risk of cardiovascular disease events is continuous and independent of other risk factors. If prehypertension is identified (rather than simply stating that BP is in the “normal” range), primary prevention strategies, starting with lifestyle modification, can be implemented.
The increase in stroke volume index was only significant in the group of pregnant women unaffected by previous hypertension. Previous studies have reported a wide range of values for stroke volume in pregnancy, with considerable disagreement regarding the expected physiological, longitudinal changes [30]. Stroke volume is affected by extracardiac factors (preload and afterload). Arterial BP and vascular tone create the afterload against which the ventricles must eject blood. If afterload is increased then the stroke volume will decrease. The interplay between the pressure and volume components means that the reduction in stroke volume with an increased afterload depends on the end diastolic volume, and whether a secondary increase in preload can result in a greater contractile force according to the Frank-Starling principle.
The physiological increase in LV volume and LV mass corresponded with the increase in BSA and hence did not reach statistical significance after indexation. A large component of the increased body weight throughout pregnancy is the increasing size of the feto-placental unit. Therefore, if the left ventricle increases in size relative to placenta and its demands for blood supply, it makes mathematical sense that indexation would ‘cancel out’ any significance in the longitudinal comparison of LV structure.
The increase in LV mass compared to non-pregnant women was only significant for the women previously affected by hypertension. This increase in LV mass in Group 1 occurred in conjunction with increased cardiac output. In a systematic review of echocardiographic structure and function in HDP [14] and in a review of cardiac function in normal pregnancy by Melchiorre et al. [ 30], cardiac output was a parameter with considerable variation in reported measures and trends. This is likely to be due to different patient characteristics, timing of assessment and methodological variation in relation to measurement of stroke volume. It is also possible that the legacy of a hypertensive disorder in pregnancy is different according to its association with fetal growth restriction. A recent study demonstrated that PE is associated with increased cardiac output, but when fetal growth restriction coexists, cardiac output is reduced and peripheral vascular resistance is increased [31]. It remains to be shown whether the cardiovascular profile is altered due to maladaptation in the index pregnancy, or whether there was pre-existing pathology in the cardiovascular system unmasked by pregnancy.
A recent meta-analysis showed that LVM and RWT both increase in normal pregnancies, which demonstrates concentric rather than eccentric hypertrophy [32]. Eccentric hypertrophy is seen in healthy athletes in response to training. Physiological remodelling is seen in healthy pregnancy, as a woman develops a heart akin to a sportswoman. This type of hypertrophy was previously thought to be a sign of pathology in hypertensive pregnancies [33]. When there is increased volume load, the end diastolic pressure increases. The cardiac myocytes increase so the wall thickness increases. This compensates for the increase in pressure.
A greater increase in LVM and RWT was demonstrated in hypertensive pregnancies. This shows that in some cases the pregnant heart reaches its limit of physiological adaptation (where the remodelling would be expected to be eccentric) and the hypertrophy becomes the kind more associated with pathology. This maladaptation to chronic volume overload even in healthy pregnancy has recently been reported in an echocardiographic study [34]. It has been shown that some $40\%$ of women with PE have persistently abnormal cardiac structure and function up to 1 year postpartum, with diastolic dysfunction amounting to subclinical heart failure [12].
The increased early diastolic mitral inflow (E wave) in pregnancy is typical of healthy, fit individuals. As the ventricular wall recoils there is negative pressure in the ventricle, so blood is sucked down the pressure gradient from the atrium across the mitral valve. This results in a higher E wave. There is relatively little atrial filling as most of the volume has already flowed out of the atrium, leaving a smaller atrial contribution. This is the pattern seen in healthy pregnancy and in the non-pregnant controls. Since there is an increased volume load in pregnancy, the already stretched heart is less compliant. This explains the reduction in E/A seen in normal pregnancy. In the women affected by previous hypertension, late diastolic filling caused by atrial contraction was greater, leading to a significantly lower E/A ratio in this group. The larger A wave usually reflects compensation for reduced early filling, after its being impaired by a stiffer ventricle. In the case of Group 1, the results suggest a trend towards a ‘pseudonormal’ filling pattern which is a marker of diastolic dysfunction. As the left atrial pressure rises as a marker of progressive diastolic dysfunction, an increase in the E wave is caused not by reduced pressure in the ventricle, but rather by increased pressure from the atrium driving blood through the mitral valve in early diastole and in this pattern both E and A are increased as seen in Group 1. The E and A waves are affected by the loading conditions of pregnancy [35, 36]. Pregnancy affects volume haemostasis with an ~1600 ml increase in the intravascular compartment (1300 ml extra plasma volume and 30 ml extra red blood cell volume) [37].
In the healthy pregnant women arterial elastance index was significantly lower, than in women with prior hypertension. Arterial elastance relates to the ability of the aorta to receive blood. In healthy pregnancy there is increased compliance in the arterial system. In our study the women with prior hypertension did not demonstrate this physiological adaptation. Both peripheral and aortic arterial stiffness has been demonstrated in PE [38]. Increased arterial stiffness has been shown to be present postpartum [39, 40]. To our knowledge this is the first demonstration of increased arterial stiffness in a normotensive pregnancy following gestational hypertensive disease. This is consistent with other studies showing increased arterial stiffness after pregnancy hypertension [41, 42]. This adverse effect leads to increased risk of future hypertension, coronary artery disease and heart failure [43].
There were some notable limitations. Firstly, indexing in pregnant women may be misleading because the change in the body contour is not consistent with the standard way in which BSA is calculated [44]. Furthermore it is debatable whether pre-pregnancy or baseline body weight should be used in the calculations or whether the actual weight at the time of assessment should be incorporated [44]. Scaling according to maternal size is a crude correction, since it is not possible to correct for the metabolic demands of pregnancy. There are various approaches to adjusting for maternal height and weight for example using height only [45], BSA [46] or no quantitative adjustment for maternal anthropometry [47]. We have used indexing for specific pre-specified derived measurements, and in each case have displayed the raw data alongside the adjusted data. The confounding effect of body size in cardiovascular medicine and in particular in pregnancy is important, and it would be useful to establish a consensus on how indices should be reported. Using BSA as an indicator for TVR has been challenged in the literature and attention is drawn to the relationship between vascular resistance and aging [48]. No adjustment for age was made in this study given that all subjects belonged to a relatively narrow reproductive age. Furthermore, adjustment of augmentation index to heart rate of 75 beats per minute is not universally accepted. This method is recommended by the manufacturers of SphygmoCor®, and has been used in previous studies in pregnancy [49–51].
Thirdly, the pregnant heart is more spherical compared to the more ellipsoid non-pregnant heart. There is no geometrical assumption in the Simpson method of discs to calculate the LV volume. This method was employed to calculate LVEF. Fourthly, Simpson’s method represents the longitudinal contractile function only and does not account for the radial contractile function. Additionally, speckle tracking technology (which is more independent of the loading conditions) can be used to assess complex torsional heart movement in order to detect preclinical impairment of LV function [52]. Speckle tracking was not available during recruitment for this study.
Finally, this study lacks data from before conception and the earliest weeks of pregnancy when haemodynamic changes begin. Preconceptual data, although methodologically challenging, can determine a woman’s baseline cardiac function, rather than using non-pregnant or postpartum indices as a control. Future study designs should seek to incorporate the preconceptual period.
## Conclusion
We have described cardiovascular system differences in pregnancy, depending on history of GH or PE. We have shown increased prevalence of prehypertension, and increased arterial stiffness in pregnant women previously affected by gestational hypertensive disease. An increased atrial component to ventricular filling reflects altered diastolic function after hypertensive pregnancy.
## What is known about topic
Hypertensive disorders of pregnancy are an important cause of morbidity and mortality, impacting on both maternal and fetal wellbeing, but mechanisms implicated are not well understood. There is a significant increase in arterial stiffness indices in women with preeclampsia compared to women with gestational hypertension and normotensive pregnant women.
## What this study adds
Pregnant women with previous hypertension in pregnancy have features of diastolic dysfunction manifested by increased late diastolic transmitral flow velocities. Pregnant women without prior hypertension demonstrated adoptive changes in arterial compliance, which is not seen in pregnancy with prior hypertension, where increased arterial stiffness was observed. Women with previous gestational hypertension are at increased future cardiovascular risk due to altered cardiac and vascular function and require effective risk mitigation.
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|
---
title: 'Premature T cell aging in major depression: A double hit by the state of disease
and cytomegalovirus infection'
authors:
- Maria S. Simon
- Magdalini Ioannou
- Gara Arteaga-Henríquez
- Annemarie Wijkhuijs
- Raf Berghmans
- Richard Musil
- Norbert Müller
- Hemmo A. Drexhage
journal: Brain, Behavior, & Immunity - Health
year: 2023
pmcid: PMC9995284
doi: 10.1016/j.bbih.2023.100608
license: CC BY 4.0
---
# Premature T cell aging in major depression: A double hit by the state of disease and cytomegalovirus infection
## Abstract
### Introduction
Previous research indicates that premature T cell senescence is a characteristic of major depressive disorder (MDD). However, known senescence inducing factors like cytomegalovirus (CMV) infection or, probably, childhood adversity (CA) have not been taken into consideration so far.
### Objective
Differentiation and senescent characteristics of T cells of MDD patients were investigated in relation to healthy controls (HC), taking the CMV seropositivity and CA into account.
### Methods
127 MDD and 113 HC of the EU-MOODSTRATIFICATION cohort were analyzed. Fluorescence activated cell sorting (FACS) analysis was performed to determine B, NK, and T cell frequencies. In a second FACS analysis, naïve, effector memory (Tem), central memory (Tcm), effector memory cells re-expressing RA (TEMRA), as well as CD28+ and CD27+ memory populations, were determined of the CD4+ and CD8+ T cell populations in a subsample ($$n = 35$$ MDD and $$n = 36$$ HC). CMV-antibody state was measured by IgG ELISA and CA by the Childhood Trauma Questionnaire.
### Results
We detected a CMV-antibody positivity in $40\%$ of MDD patients ($35\%$ HC, n. s.) with seropositive MDD cases showing a higher total childhood trauma score. Second, a higher inflation of memory CD4+ T helper cells in CMV seronegative patients as compared to seronegative HC and reduced numbers of naïve CD4+ T helper cells in CMV seropositive patients (not in CMV seropositive HC) were found. Third, a higher inflation of memory CD8+ T cytotoxic cells in CMV seropositive cases as compared to CMV seropositive HC, particularly of the TEMRA cells, became apparent. Higher percentages of CD4+ TEMRA and late stage CD27−CD28− TEMRA cells were similar in both HC and MDD with CMV seropositivity. Overall, apportioning of T cell subpopulations did not differ between CA positive vs negative cases.
### Conclusions
MDD patients show several signs of a CMV independent “MDD specific” premature T cell aging, such as a CMV independent increase in CD4+ T memory cells and a latent naïve CD4 T-cell reduction and a latent CD8+ T-cell increase. However, these two latent T cell senescence abnormalities only become evident with CMV infection (double hit).
## Highlights
•Increased T helper memory cells irrespective of CMV seropositivity in MDD.•Latent decrease in T helper naïve cells uncovered by CMV seropositivity in MDD.•Latent increase in T cytotoxic memory cells uncovered by CMV seropositivity in MDD.•*Childhood trauma* did not have effects on T cell senescence in MDD.
## Introduction
Major Depressive Disorder (MDD) is a prevalent disorder with a heterogeneous array of symptoms. Evidence is accumulating that immune dysfunction plays an important role in this heterogeneity, and perhaps even in the causation of MDD (Dantzer et al., 2008; Irwin and Miller, 2007; Miller, 2010; Raison and Miller, 2011). Many studies have been published on abnormalities on the level of pro-inflammatory cytokines in the circulation of patients (Köhler et al., 2017) and have been interpreted as a sign of low-grade inflammation, affecting gray and white matter function (Gibney and Drexhage, 2013; Branchi et al., 2021). There is, however, a relative paucity of studies on abnormalities in the apportioning and function of monocytes and subsets of lymphocytes in the circulation of patients, while these cells are also important for brain function (Beumer et al., 2012; Poletti et al., 2017).
We recently published a study showing the expression of inflammation related genes in circulating monocytes of MDD patients of the EU-MOODSTRATIFICATION cohort (www.moodstrtaification.eu) (Simon et al., 2021). Outcomes of this monocyte study pointed toward a premature aging (senescence) of the cells in MDD, while in patients with a history of childhood adversity (CA) the senescent monocytes showed an additionally increased inflammatory state with higher expression of genes for typical pro-inflammatory cytokines such as IL-1 and IL-6. In studies on the apportioning of lymphocyte subsets in the circulation of patients of the EU-MOODSTRATIFICATION cohort we also found similar signs of premature aging, but then related to the CD4+ T helper compartment (Schiweck et al., 2020). Increased CD45RO + T helper memory cells were found in the MDD patients as compared to healthy controls (HC), also during young adulthood (Schiweck et al., 2020). In that study we concluded that future lymphocyte studies on our MDD cohort should take more detailed parameters for premature immune aging into account, such as anti-cytomegalovirus (CMV) titers (as a sign of chronic CMV infection) and percentages of end-stage differentiated senescent CD4+T helper and CD8+T cytotoxic cells. Chronic CMV infection is associated with accelerated immuno-senescence, while having outspoken effects on senescence characteristics of the CD8+ cytotoxic T cell compartment, but with lesser effects on the CD4+ T helper cell population (Nikolich-Žugich and van Lier, 2017; Pawelec et al., 2009; Weltevrede et al., 2016). Moreover, chronic CMV infection has been associated with depression (Gale et al., 2018). With regard to differentiation characteristics relevant for T cell aging, memory T cells appear in different forms (Abbas et al., 2021). We recognize central memory T cells (recirculating in the lymphoid compartment and playing a role in long duration memory), effector memory T cells (capable of migrating to the tissues and involved in immediate combat with the microbes) and effector memory T cells re-expressing RA (TEMRA; a further differentiated form of effector memory cells). Particularly in the TEMRA population T cells may show signs of exhaustion, which is a poor responsiveness to antigen presenting cells and characterized by an absence of CD28 and positivity for CD57. This population is often referred to as end-stage differentiated memory T cells. While the total repertoire of memory T cells provides a broad spectrum of antigen specificities, exhausted end-stage TEMRA cells are often monoclonal expansions specific for only a few immunodominant antigens which are predominantly derived from persistent viral infections, such as CMV (Derhovanessian et al., 2011; Tian et al., 2017). These cells have a poor proliferative capacity, a high sensitivity to apoptosis, are pro-inflammatory, and - in the case of CD8+ T cells - highly cytotoxic (Geginat et al., 2003; Hamann et al., 1997; Sallusto et al., 1999).
In the present study the differentiation and senescent characteristics of the T cells of the MDD patients of the EU-MOODSTRATIFICATION cohort are investigated in more detail, also taking into account the seropositivity of MDD patients and HC for CMV as a sign of chronic infection. For this purpose, we first re-analyzed the previous T cell staining (staining A and staining B of Schiweck et al., 2020) taking the CMV-antibody state of the patients and controls into account. Thereafter, we performed an additional FACS analysis (staining C) in the CD4+ and CD8+ populations of the patients and HC for the determination of naïve (CCR7+CD45RA+), effector memory (Tem, CCR7negCD45RAneg), central memory (Tcm, CCR7+CD45RAneg), and effector memory cells re-expressing RA (TEMRA, CCR7negCD45RA+). In the memory populations we also investigated the expression of CD28 and CD27. We studied whether CMV-antibody state and CA had an impact on the apportioning of the T lymphocyte subpopulations and senescence characteristics of T cells.
## Participants
Using a cross-sectional design, participants were recruited at the three psychiatric university hospital sites Munich, Münster, and Leuven. The MDD group comprised $$n = 127$$ and the HC group $$n = 113$$ participants for whom CMV titer determinations were available. The present investigation is a continuation of the work by Schiweck et al. [ 2020] using the same staining data (staining A and B). For part of the investigation, a subset of the population was drawn according to the availability of lab samples to carry out an addition staining C. Female and male adults aged 18–65 years were included. MDD patients had to be free of the following diseases: clinical inflammation-related symptoms (including fever), current or recent inflammatory or infectious disease, uncontrolled systemic disease, uncontrolled metabolic disease, other uncontrolled somatic disorder affecting mood. Additionally, patients were excluded if they used somatic medication affecting mood or the immune system, e.g., statins, corticosteroids, non-steroidal anti-inflammatory drugs.
HC were excluded if they were not in self-declared health (specifically lacking any form of auto-immune disease and/or atopic disease) and/or used somatic medication that affects mood or the immune system. In both groups, pregnant women or women who had delivered within the previous 6 months were excluded.
Presence of MDD was diagnosed by the Mini-International Neuropsychiatric Interview (MINI; Sheehan et al., 1998). Healthy controls were screened for absence of psychiatric symptoms using the MINI Screening version. The study was conducted in accordance with the declaration of Helsinki and its subsequent revisions and approved by the respective ethical committees of the participating universities (reference numbers: Leuven: S51723; Munich: 291–09, Münster: 2009-019-f-S). Written informed consent was obtained from all participants.
## Clinical assessment
Childhood adverse events were measured by the Childhood Trauma Questionnaire (CTQ; Bernstein et al., 2003). The presence of trauma on at least one subscale of the CTQ, according to the criteria by Walker et al. [ 1999], defined childhood adversity (CA) positive cases. The cut-offs for each scale were as follows: physical neglect ≥8, physical abuse ≥8, emotional neglect ≥15, emotional abuse ≥10, sexual abuse ≥8 (Walker et al., 1999). Body Mass Index (BMI) was calculated after assessing self-reported height and body weight. BMI was dichotomized at 25 kg/m2 to classify overweight/obese individuals. Medication was obtained from clinical records.
## Laboratory assessment
Flow cytometry analysis: Peripheral Blood Mononuclear cells (PBMCs) had been collected from the patients, frozen and stored in liquid nitrogen as described before (Counotte et al., 2018). Fluorescence-activated cell sorting (FACS) was performed on defrosted PBMCs, once washed with complete culture medium (RPMI-1640 culture medium plus $10\%$ fetal calf serum (FCS) plus $1\%$ penicillin/streptomycine). Recovery and viability of cells were determined by Trypan blue staining.
Staining A – Percentages of T cells (CD3+), T helper (CD3+CD4+) lymphocytes, T cytotoxic (CD3+CD8+) lymphocytes, natural killer cells (CD3−CD56+), and B cells (CD19+) were assessed by staining 50.000 PBMCs in a 8-color membrane staining (CD45, CD3, CD4, CD8, CD19, CD14, CD56 and CD15). Cell determinations from staining A are presented as frequency of PBMCs.
Staining B – Percentages of T helper cell subsets (Th1, Th2, Th17, T regulator cells) were determined by performing a 8-color (membrane and intracellular) staining on 1 x 106 of PBMCs after a 4 h stimulation at 37 °C in RPMI-1640 culture medium with 50 ng/ml phorbol 12-myristrate 13-acetate (PMA; Sigma Aldrich, St. Louis, MO, USA) and 1.0 μg/ml ionomycin (Sigma) in the presence of Golgistop (BD Biosciences). T helper cell subsets were identified by their secreting cytokines: Th1 (CD3+CD4+IFNγ+), Th2 (CD3+CD4+IL4+), Th17 (CD3+CD4+IL17A+). T regulatory (Treg) cells were identified by their transcription factor FOXP3 (CD3+CD4+CD25hiFOXP3+). We also measured proportions of memory CD4+ T cells (CD3+CD4+CD45RO+) and naïve CD4+ T cells (CD3+CD4+CD45RO-); the latter was indicated by subtracting the proportion of memory CD4 T cells from the total T helper lymphocytes. T cell subsets of staining B were expressed as percentages of total lymphocytes, which could be reliably detected as a clear population in scatter properties after the 4-h culture. Also, when expressed as a percentage of total lymphocytes as found in staining A (the non-cultured sample), the outcomes did not alter.
Staining C – For more profound analysis of T helper and cytotoxic naïve/memory cell subsets a second vial of PBMCs was defrosted. Average recovery of cells after thawing was $73\%$ and viability $93\%$. 1,5 x 106 of PBMCs were stained with a cocktail of CD45-V500, CD45RA-BB515, CD3-Alexa Fluor700, CD4-PE-Cy7, CD8-BV786, CD197-BV421 (BD Biosciences), CD28-BV711, CD27-APC and CD57-PE (BioLegend) for 15 min at Room temperature, washed twice with PBS, pH 7.8 and subsequently stained with viability dye eFluor780 (Thermo Fisher Scientific). 500.000 events in a live/CD45 stopping gate were collected on a BD LSR Fortessa-4 laser instrument. Analysis was performed using FlowJo software. Quadrant gating on CD45RA and CD197 (CCR7) was used to define subsets of the CD4+ and CD8+ populations: naïve like (CD45RA + CD197+), central memory (CD45RA + CD197+), effector memory (CD45RA + CD197+) and effector memory RA (TEMRA; CD45RA + CD197-). The total CD8 and CD4 Tmemory populations were calculated by adding the respective T memory subpopulations Tcm, Tem, and TEMRA. The expression of CD27 and CD28 was assessed within each indicated T cell subset (Gating strategy is given in Supplementary Fig. 1). Subset determinations from staining C are presented as frequency of CD3+ T cells.
CMV antibody titer determination: Antibodies to CMV were measured using the Cytomegalovirus IgG ELISA kit manufactured by Demeditec Diagnostics GmbH, Germany, according to the instructions for use. Briefly, samples were diluted 1:101 and subsequently incubated together with four calibrators in a CMV antigen (strain AD-169) coated ELISA plate. Unbound material was removed during a washing step whereafter polyclonal rabbit anti-human IgG antibodies labeled with the enzyme HRP were added for incubation. After a final washing step, chromogen was added, and the resulting color development was measured spectrophotometrically. The values of the samples were calculated by interpolation from the calibrator curve ranging from 1 to 90 U/ml, with cut-off value of 10 U/ml. Values exceeding the highest calibrator were reported as such. Intra-assay precision reported by the manufacturer is within 9.6–$12.2\%$, while clinical specificity and clinical sensitivity of the assay is $98\%$ and $100\%$, respectively. CMV titers were dichotomized into qualitative data at 10 U/mL into CMV seropositive and CMV seronegative cases.
## Statistics
For descriptive statistics, number and frequencies or median and interquartile range are reported. Chi-square test or Fisher's exact test were performed for contingency tables. For multiple group comparisons on continuous outcomes Kruskal-Wallis test was used, followed by pairwise Wilcox post-hoc test with Benjamini-Hochberg correction for multiple testing for single group comparisons. Non-parametric tests were chosen with regards to deviations from normal distribution and thus to demonstrate findings with a more conservative test. Adjusted multivariate analyses were performed using multiple linear regression to predict lymphocyte subset frequencies by diagnosis and CMV serostatus. Applying the central limit theorem, this approach was chosen to control for the potential confounders CA, age, sex, BMI. Further, the model was run twice investigating only additive main effects of diagnosis and CMV and the interaction effect of both variables. Due to the small percentage of missing values, mean imputation was used to replace missing values of cell frequencies, separately for MDD patients and HCs. All calculations and figures were done using R studio version 4.0.3.
## Demographics
Table 1 shows the population demographics of the investigated MDD population in staining A and B. Following inspection of the descriptive data, selected comparisons were tested for statistical significance: Women are overrepresented in both the HC and MDD CMV positive groups (χ2 = 13.82; $p \leq 0.001$), while the CMV titers are comparable between HC CMV positive cases and MDD CMV positive cases ($W = 920$; $$p \leq 0.35$$). Interestingly, MDD CMV positive cases had a lower BMI as compared to the MDD CMV negative cases ($W = 1466$; $$p \leq 0.02$$); they were also less overweight/obese (χ2 = 5.24; $$p \leq 0.02$$). Regarding childhood trauma, MDD CMV positive and MDD CMV negative cases had a similar prevalence (χ2 = 0.66; $$p \leq 0.42$$), however the MDD CMV positive cases were more severely traumatized ($W = 2023.5$; $$p \leq 0.04$$). Demographics of the staining C subsample can be found in Supplementary Table 1. Although numbers of patients and controls were less, similar patient and control characteristics were observed. Table 1Demographic and clinical characteristics of patients and healthy controls per CMV status (population staining A and B).Table 1HC CMV-HC CMV+MDD CMV-MDD CMV+Test statisticp-valuen (%)73 (64.6)40 (35.4)75 (59.1)52 (40.9)χ2 = 0.560.45female, n (%)46 (63.0)32 (80.0)a37 (49.3)42 (80.8)aχ2 = 17.94<0.001age (years), Md (IQR)37 (20.88)40.32 (26.55)37 [19]44.2 (15.675)χ2 = 5.630.13CMV titer (U/mL), Md (IQR)4 (1.9)58.9 (43.85)3.7 (2.02)59.59 (40.83)χ2 = 170.08<0.001BMI, Md (IQR)23.55 (4.97)23.13 (4.39)24.54 (5.03)23.39 (4.34)aχ2 = 9.620.02overweight/obese, n (%)21 (28.8)13 (32.5)35 (46.7)13 (25.0)aχ2 = 251.95<0.001CTQ sum score, Md (IQR)31 [9]30 (11.25)33.5 (14.75)42 [23]aχ2 = 22.37<0.001childhood trauma present, n (%)25 (34.2)11 (27.5)38 (50.7)31 (59.6)χ2 = 13.750.003medicated (psychotrop.), n (%)0 (0.0)0 (0.0)53 (94.6)37 (92.5)0.69Notes. MDD Major depressive disorder; HC healthy controls; CMV cytomegalovirus; Md median; IQR interquartile range. Chi-square test or Fisher's exact test were performed for contingency tables, as well as Kruskal-Wallis tests for continuous outcomes.aIndicating significant differences as described in the section 3.1 Demographics.
## Lymphocyte subset frequencies in staining A in patients with and without CMV seropositivity
Table 2 shows the lymphocyte subset cell frequencies for HC and MDD patients with and without positive CMV antibodies. In staining A, differences between the groups were found for CD3+ T cells and CD8+ T cytotoxic cells. Here, in both HC and MDD this was due to an increase in CD8+ T cell frequency in CMV-seropositive individuals as compared to seronegative individuals. Fig. 1 displays the data of the CD8+ T cytotoxic cells. Although T cytotoxic cell frequencies were increased in both CMV-antibody positive HC and MDD patients, we found the increase in MDD patients significantly larger than in HC. Thus, main effects of diagnosis and CMV infection became apparent leading to the highest values in the MDD CMV-seropositive group as an additive effect. The adjusted multivariate analysis confirms the results regarding the diagnosis and CMV main effects with highly significant coefficients (R2 = 0.24; F[7,232] = 10.23; $p \leq 0.001$; see Supplementary Table 3). Adding the interaction term in a second model reduced the explained variance by the main effects (diagnosis: β = 1.18; $$p \leq 0.17$$, CMV: β = 3.12; $$p \leq 0.005$$) and was itself not significant (β = 1.76; $$p \leq 0.22$$), thus again supporting the presence of an additive effect. Table 2Staining A and B cell frequencies of lymphocyte subsets per diagnosis and CMV status. Table 2HC CMV-HC CMV+MDD CMV-MDD CMV+Test statisticp-valuestaining ACD3, Md (IQR)57.1 (10.6)63.7 (7.83)*61 (11.5)63.9 (11.2)*χ2 = 16.89<0.001CD4, Md (IQR)37.8 (9.2)40.1 (8.85)39.1 (9.55)38.51 [11]χ2 = 4.270.23CD8, Md (IQR)16.4 (7.6)18.2 (6.75)*17.6 (5.7)21.25 (7.25)*χ2 = 25.54<0.001NK, Md (IQR)9 (4.97)7.7 (5.21)8.2 (5.22)7.81 (5.65)χ2 = 4.060.26B, Md (IQR)7.07 (3.14)8.03 (3.03)7.28 (2.76)8.02 (3.97)χ2 = 4.330.23staining BTh naïve, Md (IQR)28.9 (9.1)28.55 (13.08)25.8 [11]22.3 (12.03)*χ2 = 20.24<0.001Th memory, Md (IQR)21.9 (8.1)22.45 (8.18)24.7 (9.05)*24.61 (10.35)*χ2 = 10.690.01Th1, Md (IQR)4.37 (2.21)5.9 (3.59)*4.36 (2.88)5.42 (2.94)*χ2 = 23.31<0.001Th2, Md (IQR)0.47 (0.23)0.43 (0.22)0.41 (0.31)0.44 (0.26)χ2 = 6.900.08Th17, Md (IQR)0.30 (0.16)0.26 (0.15)0.32 (0.24)0.29 (0.16)χ2 = 3.970.26Treg, Md (IQR)1.87 (0.95)1.84 (0.92)2.12 (0.10)*1.85 (0.69)χ2 = 9.170.03Notes. MDD Major depressive disorder; HC healthy controls; CMV cytomegalovirus; Md median; IQR interquartile range. Kruskal-Wallis tests were performed. * CD3 cell frequencies in the MDD and HC groups with CMV were significantly higher compared to HC without CMV ($$p \leq 0.002$$ in both cases). CD8 cell frequencies in the HC and MDD groups with CMV were significantly higher than their counterparts without CMV ($$p \leq 0.04$$ and $p \leq 0.001$, respectively), while the latter group also had increased cell frequencies compared to both HC groups ($p \leq 0.001$ and $$p \leq 0.04$$). Th naïve cell frequencies of the MDD group with CMV were significantly lower than all other groups ($p \leq 0.001$, $$p \leq 0.002$$, and $$p \leq 0.005$$, respectively). Th memory cell frequencies of the MDD groups with and without CMV were significantly higher than HC without CMV ($$p \leq 0.02$$ in both cases). Th1 cell frequencies were significantly increased in the CMV positive MDD and HC groups compared to their CMV negative counterparts ($$p \leq 0.004$$ in both cases). Treg cell frequencies of the MDD group without CMV were significantly higher than MDD patients with CMV ($$p \leq 0.03$$).Fig. 1T cytotoxic cell frequency in MDD and HC. Notes. Staining A as frequency of PBMCs. See Table 2 for median and IQR. * $p \leq 0.05$; **$p \leq 0.01$; ***$p \leq 0.001.$Fig. 1 Table 2 also shows the data on NK and B cells. In the previous analysis of Schiweck et al. [ 2020] significantly higher B cells and lower NK cells in the entire group of MDD patients had been found, while our analysis shows that these differences cannot be explained by CMV-seropositivity.
## Lymphocyte subset frequencies in staining B in CMV seropositive and seronegative patients
With regard to the subdivision of the CD4+ T helper populations (staining B) we found significantly higher Th1 cell frequencies in both the CMV-seropositive HC and MDD patients (Table 1). In the previous analysis of Schiweck et al. [ 2020] differences in Th1, Th2, and Th17 were not found between MDD patients and HC, while there was a trend for a difference in T regulatory cells. Taking the CMV-antibody state of patients and controls into account, the present analysis shows that particularly seronegative MDD patients show higher frequencies of T regulatory cells (Table 1).
In staining B we also found that the frequency of CD4+ T helper memory cells (CD45RO + cells) was raised in MDD patients irrespective of the CMV-antibody state (Table 1, Fig. 2). With regard to the CD4+ T helper naïve cells (CD45RO-) a significantly reduced frequency was only found in the CMV-seropositive MDD patients pointing towards an important role of CMV infection (Table 1, Fig. 3). Based on these findings and also based on the previous conclusions of Schiweck et al. [ 2020], we analyzed the memory and naïve populations of the CD8+ and CD4+ T cell populations in more detail using another staining strategy, i.e., staining C.Fig. 2T helper memory cell frequency in MDD and HC. Notes. Staining B as frequency of total lymphocytes. See Table 2 for median and IQR. * $p \leq 0.05.$Fig. 2Fig. 3T helper naïve cell frequency in MDD and HC. Notes. Staining B as frequency of total lymphocytes (left), staining C as frequency of CD3+ (right). See Table 2 and Table 4 for median and IQR. ** $p \leq 0.01$; ***$p \leq 0.001.$Fig. 3
## CD4 and CD8 lymphocyte subpopulation frequencies in CMV seropositive and seronegative patients and HC
CD8+T cells in CMV seropositive and seronegative MDD patients and HC: Staining C data shows that the exaggerated increase of CD8+ cytotoxic T cells in CMV-antibody positive MDD patients (as illustrated in Fig. 1) was mainly due to a statistically significant increase in memory cells (although naïve cells were also increased, be it not significantly; Table 3). This increase in memory cells was driven by the CD8 TEMRA subpopulation (Table 3 and Fig. 4). Late stage CD27−CD28− TEMRA cells were similarly increased in CMV seropositive HC and MDD patients (Supplementary Table 2).Table 3T cytotoxic cell subpopulations in MDD and HC as frequency of CD3+cells (staining C).Table 3HC CMV-HC CMV+MDD CMV-MDD CMV+Test statisticp-valueCD8 T naive, Md (IQR)14.7 (8.8)12.5 (6.8)15.6 (6.3)19 (9.3)χ2 = 2.360.50CD8 T memory, Md (IQR)11.4 (6.2)14.2 (6.5)9.62 (6.8)18.2 (11.4)*χ2 = 10.60.01CD8 Tcm, Md (IQR)0.62 (0.47)0.77 (0.72)0.65 (0.67)0.45 (0.75)χ2 = 2.240.52CD8 Tem, Md (IQR)2.43 (2.35)2.22 (1.72)1.95 (2.34)1.8 (2.61)χ2 = 0.450.93CD8 TEMRA, Md (IQR)7.9 (4.2)9.4 (5.25)6.85 (3.9)12 (7.45)*χ2 = 15.660.001Notes. As frequency of CD3+. * CD8 T memory cell frequencies of the MDD group with CMV were increased compared to their counterparts without CMV as a strong statistical trend ($$p \leq 0.05$$) and CD8 TEMRA cell frequencies of the MDD group with CMV were significantly higher than their counterparts without CMV ($$p \leq 0.01$$).Fig. 4T cytotoxic cell TEMRA subpopulation frequency in MDD and HC. Notes. Staining C as frequency CD3+. See Table 3 for median and IQR. * $p \leq 0.05$; **$p \leq 0.01$; ***$p \leq 0.001.$Fig. 4 CD4+T helper memory cell frequencies in CMV seropositive and seronegative MDD patients and HC: In staining B T helper memory cell frequencies were increased in MDD patients as compared to HC, irrespective of the CMV-antibody state (Fig. 2). In fact, differences between frequencies of CD4+ T helper memory cells were negligible between seropositive and seronegative subjects. Multivariate adjusted analysis confirms these results (R2 = 0.30; F[7,232] = 13.95; $p \leq 0.001$) with a highly significant main effect for diagnosis (Supplementary Table 4). Adding the interaction term of diagnosis and CMV was not significant (ß = −1.50; $$p \leq 0.43$$) and did not alter the explained variance to a relevant degree. From staining C data it becomes apparent that the increase in CD4+ T helper memory cells in MDD patients was due to a slightly (and not significant) different cell apportioning in CMV seronegative and seropositive patients (Table 4): In seronegative MDD patients T central memory cell frequency was the highest, while in seropositive MDD patients T effector memory cell frequency was the highest. The TEMRA cell frequency was significantly increased in both seropositive HC and MDD patients as compared to their seronegative counterparts. The frequencies of terminally differentiated CD27−CD28− TEMRA cells were also equally raised in both seropositive HC and MDD patients as compared to their seronegative counterparts (Supplementary Table 2).Table 4Staining C cell frequencies of the CD4 T naïve and memory cell subsets. Table 4HC CMV-HC CMV+MDD CMV-MDD CMV+Test statisticp-valueCD4 Tnaive, Md (IQR)45.3 (16.1)45.9 (12.05)41.5 [17]36.4 (20.45)χ2 = 6.000.11CD4 Tmemory, Md (IQR)21.35 (9.7)21.5 (3.5)24.7 (10.2)21.3 (10.8)χ2 = 2.610.46CD4 Tcm, Md (IQR)12.2 (6.8)12.9 (4.8)14.9 (6.95)10.8 (6.2)χ2 = 4.750.19CD4 Tem, Md (IQR)6.05 (3.26)5.54 (3.86)5.85 (6.91)8.23 (4.22)χ2 = 2.710.44CD4 TEMRA, Md (IQR)1.23 (1.35)2.4 (1.56)*1.56 (0.78)2.44 (2.11)*χ2 = 15.230.002Notes. As frequency of CD3+. * CD4 TEMRA cell frequencies of the HC and MDD groups with CMV were significantly higher than their counterparts without CMV ($$p \leq 0.04$$ and $$p \leq 0.006$$, respectively).
CD4+T helper naïve cell frequencies in CMV seropositive and seronegative MDD patients and HC: In staining B the CD4+ T helper naïve (CD45RO-) cell frequency was decreased in CMV seropositive MDD patients as compared to the three other groups (Fig. 3 left and Table 2), while in seropositive HC CD4+ T helper naïve (CD45RO-) cell frequency was not decreased. Thus, reduced percentages of T helper naïve cells are an interactive effect of diagnosis and CMV seropositivity. The same pattern emerged from staining C (Fig. 3 right); however, differences did not reach statistical significance, most likely due to lacking statistical power with the low number of samples we were able to test in this analysis. The adjusted multivariate analysis confirms these results (R2 = 0.18; F[8,231] = 6.32; $p \leq 0.001$) after adding the interaction term by showing the significant diagnosis by CMV interaction effect on T helper naïve cell frequency (Supplementary Table 5). In the additive model, a significant diagnosis main effect and a trend for CMV main effect emerged (diagnosis: ß = −3.32; $$p \leq 0.004$$, CMV: ß = −2.47; $$p \leq 0.11$$).
## CMV prevalence in relation to childhood adversity
The highest prevalence of CA is found in CMV-antibody positive MDD patients (Table 1). Based on a previous publication by Ford et al. [ 2020] in which a lower prevalence was found in males, we also took sex into account. Fig. 5 shows the prevalence of CMV seropositivity in healthy controls and MDD patients per sex and with and without CA. Females had higher rates of seropositivity as compared to males in MDD patients and as a trend HC (MDD: χ2 = 11.61; $p \leq 0.0001$, HC: χ2 = 2.74; $$p \leq 0.10$$). An overall significant difference between HC and MDD patients was not found (χ2 = 0.56; $$p \leq 0.45$$). With regard to CA, a higher prevalence of CMV seropositivity was found numerically but not statistically significant in MDD patients with CA in both females and males compared to their HC counterparts with CA (females: χ2 = 1.15; $$p \leq 0.28$$, males: χ2 = 0.05; $$p \leq 0.82$$). Female MDD patients with a history of CA showed the highest percentage of CMV seropositivity, i.e., $56.3\%$.Fig. 5Prevalence of CMV positivity per diagnosis, sex, and presence of CA. Notes. Frequency of CMV positive cases in each subgroup. CMV cytomegalovirus; HC healthy control; MDD major depressive disorder; m male; f female; noCA no childhood adversity; CA childhood adversity. Exact percentages are (from left to right): 41.2; 9.1; 41.5; 40; 22.2; 19; 48.4; 56.3.Fig. 5
## Childhood adversity and T cell apportioning
Supplementary Table 6 gives the results of cell frequencies of the analyzed lymphocyte populations for cases with and without CA. In fact, relevant differences in the tested lymphocyte subpopulations could not be found. Interestingly, in staining B CD4+ T helper memory cell (CD45RO+) frequency was clearly and significantly increased in MDD patients without CA as compared to HC without CA. This supports the idea that the increased memory cell frequency may be an effect of the diagnosis and not of the CA status. CD4+ T helper naïve cell (CD45RO-) frequency was not altered with respect to CA.
## Discussion
We previously described higher percentages of circulating T helper memory cells in the EU-MOODINFLAME cohort of MDD patients and we interpreted the findings as pointing towards a premature aging of the T cell compartment in MDD patients (Schiweck et al., 2020). Here we studied such putative accelerated aging in more detail, focusing on the role of CMV-antibody seropositivity (as a sign of chronic CMV infection) and involving better defined naïve and memory T cell subpopulations, including late stage differentiated memory T cells. Differences in parameters of T cell aging were investigated with respect to disease state and CMV seropositivity. Our main findings are: 1) A higher inflation of memory CD4+ T helper cells in CMV seronegative MDD patients as compared to seronegative healthy controls. 2) Reduced numbers of naïve CD4+ T helper cells in CMV seropositive patients, while such reductions were not found in CMV seropositive HC. 3) A higher inflation of memory CD8+ T cytotoxic cells in CMV seropositive MDD cases as compared to CMV seropositive HC, particularly of the TEMRA cells.
We interpret these findings as showing that the previously found premature senescence of the T cell compartment as described by Schiweck et al. [ 2020] is a type of senescence driven by a „double hit” being the MDD disease state and the state of chronic CMV infection. The state of MDD is mainly associated with the high inflation of the memory CD4+ T helper compartment, while the state of MDD together with a chronic CMV infection (a double hit“) is associated with the high inflation of the memory CD8+ cytotoxic T cell compartment (as an additive effect) and the reduced percentage of naive CD4+ T cells (as an interaction effect). Several possibilities may explain these findings. Enhanced recurrent activations of the T cell system (perhaps due to an enhanced susceptibility to infections other than CMV) might have led to the increased frequencies of the CD4+ and CD8+ memory cells with a reciprocal loss of the naïve CD4+ T cells. The loss of the naive CD4+ T cells might also be due to a premature aging-related atrophy of the thymus with an insufficient output of the CD4+ T cells or to an increased apoptosis of these CD4+ cells after leaving the thymus. Thymic atrophy is a normal mechanism of the T cell senescence process (Ongrádi and Kövesdi, 2010; Miller, 2010), while an enhanced apoptosis of recent thymic emigrants has been described as a genetically determined defect in the autoimmune model of the BB rat (Sommandas et al., 2007). Our study also shows that CMV seropositivity (and not the state of MDD) was associated with another aspect of aging, namely a higher percentage of TEMRA and late stage CD27−CD28− TEMRA cells in both HC and MDD patients. Therefore, this type of senescence characterized by exhausted T cells was entirely due to the state of chronic CMV infection in both HC and MDD patients.
Ford et al. [ 2020] also found a premature aging of the T cell compartment in MDD patients. However, they found it to be entirely explainable by CMV seropositivity, while refuting a role of the MDD state and a “double hit” effect in T cell aging (Ford et al., 2020). Differences in results between this study and ours may lie in the investigated study populations. Comparing their patient and control groups with the ones analyzed in our study some similarities (such as the in general young age) but also some remarkable differences can be detected. While similar overall prevalence of around 40–$50\%$ CMV seropositivity in HC and patients were present in both study populations, Ford et al. [ 2020] detected a very low prevalence of $16\%$ seropositivity in MDD males and a very high prevalence of $71\%$ in MDD females. Our sample did not show such an extreme sex difference in MDD, although MDD males had a lower prevalence of CMV seropositivity. However, this was in line with the well-accepted higher prevalence of seropositivity for females in general and present in our HC (see Fig. 5). Another difference between the two study populations is the BMI of patients and controls. While in the US population subjects had a BMI ranging from 26 kg/m2 to 29 kg/m2 (Ford et al., 2020), the European population had a BMI ranging from 24 to 25 kg/m2. Moreover, their CMV seropositive subjects had increased weight (Ford et al., 2020), while our seropositive individuals had reduced weight. Since obesity has well known effects on immune aging (Shirakawa and Sano, 2021), we used it as a covariate in the adjusted analyses. However, no significant effects for BMI were found. Thus, it is difficult to attribute differences in study results simply to the BMI.
This also indicates that other differences such as cultural, dietary, microbial, and geographical differences between the US and Europe must have played a role in the discrepancies. Last, in the cohort of Ford et al. [ 2019] early life stress was associated with a higher prevalence of CMV-seropositivity. We detected a trend for such association, particularly in male MDD patients with CA who showed more CMV seropositive cases than HC with CA. However, this observation should be interpreted with caution due to the low number of healthy males with childhood trauma in our cohort (only 1 out of 11 males with childhood trauma was positive for CMV). Anyhow, CMV seropositive patients had experienced worse early life stress in general, i.e., had higher CTQ sum scores. Regarding CA and T cell aging, we were unable to find any relevant associations. On the contrary, our analysis showed a clear and significant difference according to the MDD disease state in cases without CA for the CD4+ T helper memory and naïve cells.
Noteworthy, we cannot rule out effects of chronic infections other than CMV for the associations of the MDD disease state with the increase in frequency of the CD4+ and CD8+ T memory cells and the reduction in frequency of CD4+ T naïve cells presented in this study. It is possible that our MDD patients might have higher prevalence of other chronic infections influencing T cell aging, such as toxoplasma, EBV, herpes simplex virus and others, although a recent large scale Finnish study did not find such higher prevalence (Markkula et al., 2020). Future studies should take the presence of such chronic infections into account and/or find genetic polymorphisms related to the here found aspects of T cell aging. Also, the role of current chronic stress should not be neglected, since it is known that such stress plays a role in immune aging (Bauer et al., 2015).
## Limitations
The present study has several methodological limitations. With the cross-sectional study design no causality can be established and it is not possible to measure whether the differences in cell frequencies between the study groups are long-lasting. Further, in this study convenience sampling was used which limits the external validity of study results as the study population may not be representative of the general MDD population. However, the inclusion of multiple international sites increases generalizability of study results. Another drawback is the small sample size in the staining C population. Consequently, negative results may be due to type II error, especially since no sample size calculation was performed for this analysis. This study presents secondary explorative analyses in the project where the sample size was determined by availability of material for additional determinations such as CMV-antibody status. Last, three types of potential confounders were not considered in this work: First, it was not possible to control for duration of the CMV-seropositive state and we based our analysis on the determination of IgG anti-CMV titers and did not test IgM anti-CMV titers. This would have informed on potential recent acute exacerbations of the CMV infection. Second, data was pooled from three sites where potential differences between sites may exist. Third, the vast majority of patients was medicated by a variety of drugs with high variability of multiple medication usage, which was not assessed systematically.
## Conclusion
Despite these limitations we conclude that MDD patients show several signs of a CMV independent MDD state associated premature T cell aging, such as a CMV independent increase in CD4+ T memory cells and a latent naïve CD4 T-cell reduction and a latent CD8+ T-cell increase, these two latent T cell senescence abnormalities only becoming evident after CMV infection (double hit). Whether the here reported CMV independent and MDD state associated T cell senescence abnormalities are due to an intrinsic genetic predisposition or due to environmental influences other than a chronic CMV infection needs further exploration.
## Funding
This work was supported by the European Commission: EU 7th Framework program (grant number EU-FP7-CP-IP-2008-222963) and Horizon 2020 (grant number H2020-SC1-2016-2017/H2020-SC1-2017-Two-Stage-RTD) grants were received by HAD, Erasmus Medical Center Rotterdam. The funding source had no role in the study design, the data collection, the analysis and interpretation of data, the manuscript writing, and in the decision to submit the article for publication.
## Submission declaration
The present work has not been published previously, has not been submitted to another journal while under consideration for Brain, Behavior, and Immunity, and will not be published elsewhere upon acceptance. The manuscript in its current form has been approved by all co-authors.
## Declaration of competing interest
MSS, MI and AW declare no potential conflict of interest. GA received personal fees from Janssenoutside the submitted work. RB is an employee of Advanced Practical Diagnostics BVBA. RM declares personal fees from Otsuka/Lundbeck outside the submitted work. NM was supported by the foundation ‘Immunität und Seele’. HAD is the coordinator of the EU-MOODSTRATIFICATION project and declares no further potential conflict of interest.
## Supplementary data
The following is the *Supplementary data* to this article. Multimedia component 1Multimedia component 1
## Data availability
Data will be made available on request.
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---
title: Association of smoking and osteoarthritis in US (NHANES 1999–2018)
authors:
- Senbo Zhu
- Lichen Ji
- Zeju He
- Wei Zhang
- Yu Tong
- Junchao Luo
- Zheping Hong
- Yin Zhang
- Dongsheng Yu
- Qiong Zhang
- Qing Bi
journal: Scientific Reports
year: 2023
pmcid: PMC9995311
doi: 10.1038/s41598-023-30644-6
license: CC BY 4.0
---
# Association of smoking and osteoarthritis in US (NHANES 1999–2018)
## Abstract
Little is currently known about the effect of smoking on osteoarthritis (OA). This study aimed to investigate the relationship between smoking and OA in the United States (US) general population. Cross-sectional study. Level of evidence, 3. 40,201 eligible participants from the National Health and Nutrition Examination Survey 1999–2018 were included and divided into OA and non-arthritis groups. Participants demographics and characteristics were compared between the two groups. Then the participants were divided into non-smokers, former smokers, and current smokers based on their smoking status, also demographics and characteristics among the three groups were compared. Multivariable logistic regression was used to determine the relationship between smoking and OA. The current and former smoking rate in the OA group ($53.0\%$) was significantly higher than that in the non-arthritis group ($42.5\%$; $p \leq 0.001$). Multivariable regression analysis including body mass index (BMI), age, sex, race, education level, hypertension, diabetes, asthma and cardiovascular disease showed that smoking was an association for OA. This large national study highlights a positive association between smoking and OA prevalence in the general US population. It is necessary to further study the relationship between smoking and OA in order to determine the specific mechanism of smoking on OA.
## Introduction
Osteoarthritis is a common disease in the elderly, characterized by progressive degeneration of articular cartilage, subchondral bone changes, and osteophyte formation1. OA is estimated to affect more than 240 million people worldwide, with more than 32 million estimated in the United States, resulting in a significant public health burden2. Based on data from the National Health Interview Survey, recently in the United States, there are approximately 14 million people who have symptomatic knee osteoarthritis of which approximately 3 million ethnic/racial minorities3. Thus, identifying potentially modifiable protective or risk factors may lead to the development of available strategies to delay the progression of OA and reduce the public health burden.
It is well-established that smoking is linked to chronic diseases such as diabetes, cardiovascular disease, and cancer. It is also a recognized risk factor for many chronic musculoskeletal diseases, including rheumatoid arthritis, degenerative disc disease, and low back pain. However, smoking is reportedly negatively associated with ulcerative colitis and Parkinson's disease. The relationship between smoking and OA has not been established4. Many clinical studies in different regions and populations have shown that smoking is negatively associated with the incidence of OA1,5–7. There is some evidence suggesting that the BMI of non-smokers is usually higher than that of smokers, leading to a high OA prevalence8,9. However, some scholars have raised doubts on the protective effect of smoking on OA observed in some epidemiological studies due to selection bias, especially hospital selection bias10. In a cross-sectional research of nationally representative data from South Korea, no association was found between direct smoking and previous smoking with the prevalence of OA11. A cross-sectional study from Denmark also found no association between current smoking and knee OA prevalence12. Given that the relationship between smoking and OA remains unclear, the results vary widely among different populations and regions. There are still contradictions and inconsistencies in the relationship between smoking and OA risk. In addition, few studies have examined in detail the relationship between current and former smokers and OA.
Therefore, this study aimed to explore the relationship between smoking and previous smoking on OA among US adults using national cross-sectional data. It also offered a valuable opportunity for assessing the potential of pooling OA preventive health information.
## Study population data
Data from ten discrete 2-year cycles (1999–2000 to 2017–2018) of the continuous National Health and Nutrition Examination Survey were used to examine the association of OA with smoking and BMI in US adults. NHANES (http://www.cdc.gov/nchs/nhanes.htm) is a national cross-sectional health survey in the US that collects laboratory, imaging, and radiological data in addition to health interviews and examination data13. Importantly, the Center for Disease Control (CDC) and Prevention uses a complex multi-stage probabilistic sampling design to examine a nationally representative sample across the country every 2 years. The NHANES study protocol has been approved by the Ethics Review Board of the National Center for Health Statistics (NCHS) Research, and all adult participants provided written informed consent. All studies were carried out in accordance with the Declaration of Helsinki. Details on Institutional Review Boards of the CDC and NCHS are available at (http://www.cdc.gov/nchs/nhanes/irba98.htm). Given the thoroughness of its methodology, NHANES data have been widely used to assess risk factors and prevalence of many diseases14. In this study, 116,876 participants older than or equal to 20 were selected from 1999 to 2018 NHANES (As shown in Fig. 1). Participants without OA data and with other types of arthritis were excluded. Participants with missing smoking, BMI, and other covariate data were also excluded. Figure 1Flowchart of study participants.
## OA criteria
The NHANES Medical Conditions *Questionnaire is* usually based on the "Medical condition" questionnaire of the American National Health Interview survey, and the OA status is investigated and collected. Participants are asked, "*Has a* doctor or other health professional ever told (you/s/he) that (you/s/he)...had arthritis?" If the answer to that question is "yes" the next question is "What type of arthritis was it?". Based on the different responses to these two questions, participants were divided into an OA group (solely had OA), other arthritis participants (which were excluded from further analysis) and no-arthritis group. Self-reported doctor-diagnosed OA is the most predominantly used case definition in epidemiological studies14. The agreement between self-reported OA and clinically confirmed OA was $81\%$ in a previous study15.
## Assessment of smoking volume, and other covariates
Variables associated with clinical characteristics were collected through physical examinations and self-reported questionnaires. Participants were divided into three categories based on their smoking status: never smokers (smoked less than 100 cigarettes in life), past smokers (smoked more than 100 cigarettes in life and smoke not at all now), and current smokers (smoked moth than 100 cigarettes in life and smoke some days or every day). The BMI (kg/m2) was recorded for all examinees by a trained examiner in the mobile examination center. According to the National Institutes of Health (NIH) guidelines16, BMI was divided into four grades: underweight (< 20.0 kg/m2), normal weight (20.0–24.9 kg/m2), overweight (25.0–29.9 kg/m2), and obese (≥ 30.0 kg/m2). Cardiovascular disease (CVD) included coronary heart disease, congestive heart failure, heart attack, stroke, and angina. Other covariates included discrete variables (sex, race/ethnicity, education, hypertension, diabetes mellitus (DM), asthma and marital status) and continuous variables (age, poverty status (family income to poverty ratio, PIR).
## Statistical analyses
Data were processed by SPSS version 22.0 and R version 4.1.3. The Chi-square test was used to compare the constituent ratios of each group. Continuous variables were compared using Student's t-test or one-way analysis of variance (ANOVA), followed by multiple post-hoc comparisons using least significant difference (LSD), selected based on Levene statistics in the homogeneity of variance test. Variables with a p-value < 0.25 during univariate analysis were included in multivariable logistic regression analysis17. After adjusting for all covariables, logistic regression was performed to assess the association between smoking and OA. A p-value < 0.05 (two-sided) was statistically significant.
## Characteristics of participants
Participants younger than 20 years of age were excluded based on the epidemiological characteristics of OA. The prevalence of OA was $12.49\%$ ($$n = 5022$$/40,201). The demographics and characteristics of participants in this study are shown in (Table 1).Table 1Demographics and characteristics of study participants from NHANES 1999–2018.CharacteristicOsteoarthritis $$n = 5022$$ ($12.49\%$)Non-arthritis $$n = 35$$,179 ($87.51\%$)p valueSmoke, N (%)< 0.001a Never2361 (47.0 1)20223 (57.49) Ever1847 (36.78)7572 (21.52) Now814 (16.21)7384 (20.99)Age, year, N (%)< 0.001a 20–2962 (1.23)7503 (21.33) 30–39210 (4.18)7318 (20.80) 40–49469 (9.34)6915 (19.66) 50–59901 (17.94)5142 (14.62) 60–691430 (28.47)4431 (12.60) 70–791146 (22.82)2431 (6.91) ≥ 80804 (16.01)1439 (4.09)Mean ± SD63.92 ± 13.3145.45 ± 16.97< 0.001bGender, N (%)< 0.001a Male1821 (36.26)18,481 (52.53) Female3201 (63.74)16,698 (47.47)BMI, kg/m2, N (%)< 0.001a < 20 (underweight)118 (2.35)1761 (5.0) 20–24.9 (normal weight)915 (18.22)9494 (26.99) 25.0–29.9 (overweight)1636 (32.58)12,023 (34.18) > 30.0 (obese)2353 (46.85)11,901 (33.83)Mean ± SD30.86 ± 7.6428.51 ± 6.57< 0.001bRace, N (%)< 0.001a Mexican American407 (8.10)6318 (17.96) Non-hispanic black744 (14.81)7272 (20.67) Non-hispanic white3227 (64.26)14,476 (41.15) Other hispanic285 (5.68)3027 (8.60) Other race including multiracial359 (7.15)4086 (11.61)Education, N (%)< 0.001a Under high school1002 (19.95)8208 (23.33) High school or equivalent1169 (23.28)7964 (22.64) College graduate or above2851 (56.77)19,007 (54.03)PIR, N (%)< 0.001a < 1747 (14.87)6903 (19.62) 1–3.52527 (50.32)16,787 (47.72) ≥ 3.51748 (34.81)11,489 (32.66)Mean ± SD2.74 ± 1.602.60 ± 1.63< 0.001bMarital, N (%)< 0.001a Married/Living with partner2949 (58.72)21,636 (61.50) Never married340 (6.77)7295 (20.74) Separated/Divorced/Widowed1733 (34.51)6248 (17.76)Hypertension, N (%)< 0.001a Yes3025 (60.23)9586 (27.25) No1997 (39.77)25,593 (72.75)Diabetes, N (%)< 0.001a Yes1086 (21.62)3467 (9.86) No3936 (78.38)31,712 (90.14)CVD, N (%)< 0.001a Yes1140 (22.70)2447 (6.96) No3882 (77.30)32,732 (93.04)Asthma, N (%)< 0.001a Yes952 (18.96)4210 (11.97) No4070 (81.04)30,969 (88.03)ameans Chi-square test, bmeans Student's t-test.
We found that OA was more common in older women (age ≥ 50), the average age of the OA group was significantly higher than the non-arthritis group (63.92 ± 13.31 vs. 45.45 ± 16.97, $p \leq 0.001$), and the proportion of females with OA was higher than males ($63.74\%$ vs. $36.26\%$). The proportion of obese patients in the OA group was significantly higher than in the non-arthritis group ($46.85\%$ vs. $33.83\%$), and the proportion of non-Hispanic White also increased significantly ($64.26\%$ vs. $41.15\%$). At the same time, OA was associated with a higher level of education and annual household income. Besides diabetes, the prevalence of other comorbidities in the OA group was significantly higher than in the non-arthritis group ($p \leq 0.001$). Most importantly, the proportions of non-smokers and smokers in the OA group were lower than in the non-arthritis group, but the proportion of former smokers was significantly higher in the OA group ($36.78\%$ vs. $21.52\%$, $p \leq 0.05$).
## Characteristics of participants by smoking status
The 40,201 participants were divided into never-smokers ($$n = 22$$,584), former smokers ($$n = 9419$$) and current smokers ($$n = 8198$$). The characteristics of 40,201 participants are shown in (Table 2).based on their smoking status. The average age of former smokers was the highest, and the average age of current smokers was the youngest (56.08 ± 17.12 vs. 42.96 ± 15.07 vs. 46.03 ± 17.59, $p \leq 0.001$). Never-smokers were predominantly women ($57.59\%$). Compared with never-smokers, former smokers had a higher BMI, and current smokers had a lower BMI (28.89 ± 6.87 vs. 29.38 ± 6.43 vs. 27.90 ± 6.71, $p \leq 0.001$). This finding suggests that smoking may lead to a decrease in BMI, and quitting smoking increases BMI. Current smokers were associated with significantly lower levels of education, family income, and single status (including separated, divorced, widowed, and not being married, $p \leq 0.001$). In addition to asthma (smoking is considered an important risk factor) and CVD, other underlying diseases: hypertension, diabetes, smokers had significantly lower rates than non-smokers and quit smoking people ($p \leq 0.001$). Quit smoking people also had a higher prevalence of comorbidities than current smokers, which may be attributed to the fact that past smokers quit smoking after being diagnosed with comorbidities. The incidence of OA in the smoking group was 2.089 times higher than in the non-smoking group. Table 2Characteristics of participants based on smoking status. CharacteristicSmoking statusp valueNeverEverNowN22,58494198198Age, years, N (%)< 0.001a 20–294887 (21.64)790 (8.39)1888 (23.03) 30–394496 (19.91)1139 (12.09)1893 (23.09) 40–494260 (18.86)1413 (15.00)1711 (20.87) 50–593187 (14.11)1564 (16.60)1292 (15.76) 60–692850 (12.62)2030 (21.55)981 (11.97) 70–791709 (7.57)1516 (16.10)352 (4.29) ≥ 801195 (5.29)967 (10.27)81 (0.99)Mean ± SD46.03 ± 17.59 c*** d***56.08 ± 17.12 e***42.96 ± 15.07< 0.001bSex, N (%)< 0.001a Male9578 (42.41)5841 (62.01)4884 (59.58) Female13,006 (57.59)3578 (37.99)3314 (40.42)BMI, kg/m2, N (%)< 0.001a < 20 (underweight)1010 (4.47)249 (2.64)620 (7.56) 20–24.9 (normal weight)5941 (26.31)2023 (21.48)2445 (29.82) 25.0–29.9 (overweight)7506 (33.24)3548 (37.67)2605 (31.78) > 30.0 (obese)8127 (35.99)3599 (38.21)2528 (30.84)Mean ± SD28.89 ± 6.87 c*** d***29.38 ± 6.43 e***27.90 ± 6.71< 0.001bRace, N (%)< 0.001a Mexican american4095 (18.13)1510 (16.03)1120 (13.66) Non-hispanic black4747 (21.02)1318 (13.99)1951 (23.80) Non-hispanic white8624 (38.19)5140 (54.57)3939 (48.05) Other hispanic2073 (9.18)701 (7.44)538 (6.56) Other race including multiracial3045 (13.48)750 (7.96)650 (7.93)Education, N (%)< 0.001a Under high school4514 (19.99)2217 (23.54)2479 (30.24) High school or equivalent4549 (20.14)2141 (22.73)2443 (29.80) College graduate or above13,521 (59.87)5061 (53.73)3276 (39.96) PIR, N (%)< 0.001a < 13867 (17.12)1347 (14.30)2436 (29.71) 1–3.510,506 (46.52)4654 (49.41)4154 (50.67) ≥ 3.58211 (36.36)3418 (36.29)1608 (19.61)Mean ± SD2.75 ± 1.65 c* d***2.79 ± 1.60 e***2.05 ± 1.63< 0.001bMarital, N (%)< 0.001a Married/Living with partner14,012 (62.04)6295 (66.83)4278 (52.18) Separated/Divorced/Widowed3892 (17.23)2201 (23.37)1888 (23.03) Never married4680 (20.72)923 (9.80)2032 (24.79)Hypertension, N (%)< 0.001a Yes6445 (28.54)3954 (41.98)2212 (26.98) No16,139 (71.46)5465 (58.02)5986 (73.02)Diabetes, N (%)< 0.001a Yes2327 (10.30)1557 (16.53)669 (8.16) No20,257 (89.70)7862 (83.47)7529 (91.84)CVD, N (%)< 0.001a Yes1445 (6.40)1470 (15.61)672 (8.20) No21,139 (93.60)7949 (84.39)7526 (91.80)Asthma, N (%)< 0.001a Yes2729 (12.08)1244 (13.21)1189 (14.50) No19,855 (87.92)8175 (86.79)7009 (85.50)ameans Chi-square test, bmeans one-way ANOVA test, c,d,epost-hoc comparisons using LSD between Never and Ever, Never and Now, and Ever and Now, respectively. * $p \leq 0.05$, **$p \leq 0.01$ ***$p \leq 0.001.$
## Association between OA and smoking
Figure 2 shows the crude and adjusted odds ratios for the association between OA and smoking. In the crude model, the smoking status was statistically significantly associated with OA as a categorical variable. The incidence of OA in the former smoker group was 2.09 times higher than in the non-smoker group (OR = 2.09, $95\%$CI 1.96–2.23, $p \leq 0.001$). However, there was no significant difference between the smoker and non-smoker groups (OR = 0.94, $95\%$CI 0.87–1.03, $$p \leq 1$$). After adjusting for age, gender, BMI, race, education, PIR, marital state, hypertension, diabetes, CVD, asthma, both current smoker and former smoker groups were associated with increased risk of OA compared to the non-smoker group (OR = 1.54, $95\%$CI 1.40–1.70, $p \leq 0.001$ and OR = 1.38, $95\%$CI 1.27–1.49, $p \leq 0.001$, respectively).Figure 2Logistic regression analysis of smoke for OA in participants ≥ 20 years old in NHANES (1999–2018).
## Discussion
Herein, we conducted a comprehensive study of OA using a large multi-year sample from NHANES 1999–2018. Indeed, previous knee OA studies on the NHANES database have been published in the literature, but one study was too old (being published in the 1970s), with a small number of participants and only cross-sectional associations between knee OA and various putative risk factors were explored18, and some studies did not focus on the relationship between smoking and OA19–21. The strength of this research is the numerous OA patients identified from a US population-based cross-sectional cohort. Another advantage is that, since they are acquired in the community, a lack of selection bias in exposure is assumed. Accordingly, we sought to explore the association between smoking and quitting smoking and OA. In the crude model, quitting smoking has a positive correlation with OA, while smoking has no correlation with OA. However, in our multivariable analysis model, which included variables measured in NHANES such as age, sex, BMI, and underlying disease to minimize the possibility of spurious association, we substantiated that current and past smokers had a higher risk of OA. The increased risk of OA attributed to smoking was not decreased significantly after quitting smoking (OR = 1.54, $95\%$CI 1.40–1.70, $p \leq 0.001$ and OR = 1.38, $95\%$CI 1.27–1.49, $p \leq 0.001$, respectively), continuing to smoke was associated with a greater risk of OA than in former smokers. Our study points out that high BMI and advanced age are significant risk factors for OA, and that women are at higher risk than men, which is consistent with previous reports22,23. In addition, our study shows that education level and underlying diseases such as hypertension and diabetes are also related to OA.
Large cohort studies based on the general population of the United Kingdom have shown that a weight gain of 4–5 kg after 12 months of smoking cessation may lead to increased knee degenerative disease8. Consistently, cohort studies in the Korean population have shown that a decrease in BMI caused by smoking may account for the protective effect of smoking against OA1. In accordance with previous studies, in this study, we found that an increase in BMI in former smokers and smoking led to a decrease in BMI. Among smokers, the proportion of overweight and obese people is $63.6\%$, which is the lowest among the three groups; among ex-smokers, the proportion of overweight and obese people is $75.9\%$, which is the highest among the three groups; among non-smokers, the proportion of overweight and obese people is $69.2\%$, the difference was statistically significant. However, after adjusting for BMI, a confounding factor, we still found a positive association between smoking and OA.
Smoking has been documented to exert heterogeneous effects on OA in different countries and regions. For example, smoking was found to be a protective factor against joint replacement with severe knee OA in a Singaporean Chinese cohort5. A cohort study on a Rotterdam population further showed that smoking was a risk factor for OA24. The reliability of many studies that concluded that smoking is a protective factor for OA was questioned due to study design bias. Indeed, hospital-based studies represent a significant source of bias. Smoking is more likely to cause health problems, which led to more non-osteoarthritis patients who smoke being included in the study10. It is widely established that the U.S. population is dominated by non-Hispanic whites. During multivariable regression analysis, the risk of OA in non-Hispanic whites was 2.39 times higher than in Mexican Americans. We suspect that the effects of smoking on different ethnic groups may lead to regional variations in studies. A multicenter trial in 2022 found an increased risk of gastrointestinal metaplasia (GIM) among Hispanics born outside the United States, but not among Hispanics born in the United States25. This finding suggests that members of the same ethnic group, born and living in different areas, may have different susceptibility to the same risk factors. Accordingly, the effect of smoking on OA varies across countries and regions.
At present, the exact mechanism between smoking and OA is poorly understood. Interestingly, studies have shown that nicotine alters 19 proteins, including several proteases and cytokines, in models of joint inflammation, including increased nicotine secretion of matrix metalloproteinase 1 and increased secretion of two proposed markers of OA, chitinase 3-like protein 1 fibronectin. Indeed, it is essential to increase awareness of the risk of OA, prevent its occurrence and improve the quality of life of this patient population. Current evidence suggests that age is the most significant risk factor for OA after age 30. In addition, the risk of OA was also significantly correlated with BMI, gender and race. Our findings suggest that people should reduce their smoking to reduce their risk of OA. In addition smoking causes a variety of harmful effects of carcinogens and chemicals.
The strengths of this study included its large-scale nature, the use of national cohort data, and the absence of selection bias. However, although NHANES is a representative sample of the general population in the United States, this is a cross-sectional study, which requires further validation of the effect of smoking on OA in prospective studies. In addition, a questionnaire was used to determine whether the participants were OA patients. Although the response rate was $81\%$, the gold standard for determining OA was imaging, and no physical examination was performed to determine whether participants had OA. And the absence of other covariates, such as job strain, physical activity, vitamin C, D, and K levels, and subsequent multivariate analysis in NHANES was also a limitation. Besides, we did not quantify smoking to explore the influence of the amount of smoking on the incidence of OA. However, we demonstrated that smoking increases the risk of OA in current smokers and past smokers based on data from a large population.
## Conclusion
This large national cross-sectional study showed that smoking is positively associated with the prevalence of OA in the US population. It is necessary to further study the relationship between smoking and OA to determine the specific mechanism of smoking on OA.
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---
title: Comparison of the association intensity of creatinine and cystatin C with hyperphosphatemia
and hyperparathyroidism in patients with chronic kidney disease
authors:
- Byungju Min
- Sung-Ro Yun
- Se-Hee Yoon
- Jong-Dai Kim
- Wan Jin Hwang
- Won Min Hwang
- Yohan Park
journal: Scientific Reports
year: 2023
pmcid: PMC9995313
doi: 10.1038/s41598-023-31048-2
license: CC BY 4.0
---
# Comparison of the association intensity of creatinine and cystatin C with hyperphosphatemia and hyperparathyroidism in patients with chronic kidney disease
## Abstract
Herein, we compared the association intensity of estimated glomerular filtration rate (eGFR) equations using creatinine (Cr) or cystatin C (CysC) with hyperphosphatemia and secondary hyperparathyroidism occurrence, which reflect the physiological changes occurring during chronic kidney disease (CKD) progression. This study included 639 patients treated between January 2019 and February 2022. The patients were divided into low- and high-difference groups based on the median value of the difference between the Cr-based eGFR (eGFRCr) and CysC-based eGFR (eGFRCysC). Sociodemographic and laboratory factors underlying a high difference between eGFRCr and eGFRCysC were analyzed. The association intensity of eGFRCr, eGFRCysC and both Cr- and CysC-based eGFR (eGFRCr-CysC) was compared using the area under the receiver operating characteristic curve (AuROC) values for hyperphosphatemia and hyperparathyroidism occurrence in the overall cohort and the low- and high-difference groups. Age > 70 years and CKD grade 3 based on eGFRCr were significant factors affecting the high differences. eGFRCysC and eGFRCr-CysC showed higher AuROC values than that of eGFRCr, especially in the high-difference group and in patients with CKD grade 3. Our results show that CysC should be evaluated in patients with significant factors, including age > 70 years and CKD grade 3, to accurately assess kidney function to better determine the physiological changes in CKD progression and predict prognosis accurately.
## Introduction
Creatinine (Cr) is the most widely used indicator to estimate kidney function, and assessing the estimated glomerular filtration rate (eGFR) using the serum *Cr is* recommended for assessing kidney function in the clinical field1,2. However, since serum Cr can be affected by various factors, including age, muscle mass, race, diet, drugs, and renal tubular secretion, its ability to reflect kidney function alone is limited3. Cystatin C (CysC) is constantly produced in all nucleated cells, and is excreted by the kidneys. The measurement of CysC is recommended as an alternative indicator of kidney function as it is less affected by other factors than serum Cr is4–6. However, CysC is affected by smoking, inflammation, adiposity, certain malignancies, and glucocorticoid use; therefore, it also has some limitations as an indicator of kidney function7–9.
The Chronic Kidney Disease-Epidemiology Collaboration (CKD-EPI) presented equations for Cr-based eGFR (eGFRCr), CysC-based eGFR (eGFRCysC), and both Cr- and CysC-based eGFR (eGFRCr-CysC)10. Many previous studies have reported that the eGFRCr-CysC has the highest accuracy as a direct measurement of GFR (mGFR) and that it is also the most accurate index in patients with diabetes, liver cirrhosis, or solid tumors4,11–13. Serum Cr tends to be high in Black people regardless of their kidney function; therefore, race is considered in previous CKD-EPI equations containing serum Cr. However, in 2021, the CKD-EPI proposed new equations regardless of race, and the new eGFRCr-CysC equation showed higher accuracy with mGFR compared to those of previous equations10.
Hyperphosphatemia and secondary hyperparathyroidism are common clinical features of chronic kidney disease (CKD)14. As CKD gradually progresses to an advanced stage, the prevalence of hyperphosphatemia and secondary hyperparathyroidism is increased via various mechanisms, including a decrease in renal phosphorus excretion and increase in bone resorption. In other words, hyperphosphatemia and secondary hyperparathyroidism are considered to be physiological changes following CKD progression, and are associated with cardiovascular complications and mortality in CKD15–17.
Many studies have reported on the accuracy of the three eGFR equations (eGFRCr, eGFRCysC, and eGFRCr-CysC) and mGFR. However, the association of each of the eGFR equations for the occurrence of hyperphosphatemia and secondary hyperparathyroidism has not yet been elucidated. Therefore, the present study compared the association intensity of eGFRCr, eGFRCysC, and eGFRCr-CysC with the occurrence of hyperphosphatemia and hyperparathyroidism with hyperphosphatemia in patients with CKD, especially in patients with a high difference between the eGFRCr and eGFRCysC.
## Comparison of baseline characteristics between the low- and high-difference groups
Patients were divided into low- and high-difference groups based on the median value of the difference between the eGFRCr and eGFRCysC (|eGFRCr-eGFRCysC|: < 6.353 ml/min/1.73 m2 and ≥ 6.353 ml/min/1.73 m2, respectively). Table 1 shows the baseline characteristics of the low- and high-difference groups. The proportion of patients aged > 70 years with CKD grade 3 based on eGFRCr was significantly higher in the high-difference group than in the low-difference group. The proportion of males and the prevalence of hypertension (HTN) were lower in the high-difference group compared with those in the low-difference group, with marginal statistical significance ($$P \leq 0.059$$ and $$P \leq 0.061$$, respectively). The high-difference group had significantly higher eGFRCr and lower eGFRCysC values than those of the low-difference group. The eGFRCr-CysC showed no significant difference between the two groups. Most laboratory test findings, including the urine protein/creatinine ratio (PCR), showed no significant difference between the two groups, except for serum Cr and intact parathyroid hormone (PTH) levels. Table 1Comparison of the baseline characteristics between the low- and high-difference groups. Low-difference ($$n = 320$$)High-difference ($$n = 319$$)P valueAge > 70 years172 ($53.8\%$)216 ($67.7\%$) < 0.001Male (n, %)204 ($63.7\%$)180 ($56.4\%$)0.059CKD grade based on eGFRCr < 0.001 Grade 3 (n, %)156 ($48.8\%$)235 ($73.7\%$) Grade 4 (n, %)164 ($51.2\%$)84 ($26.3\%$)eGFR (ml/min/1.73 m2) eGFRCr31.90 ± 11.8237.22 ± 9.74 < 0.001 eGFRCysC29.73 ± 12.2027.72 ± 10.370.025 eGFRCr-CysC31.14 ± 12.6732.07 ± 10.410.311Comorbidities HTN (n, %)194 ($60.6\%$)170 ($53.3\%$)0.061 DM (n, %)126 ($39.4\%$)109 ($34.2\%$)0.172Laboratory findings Cr (mg/dl)2.23 ± 0.741.81 ± 0.48 < 0.001 Cystatin C (mg/L)2.19 ± 0.662.24 ± 0.650.348 Hemoglobin (g/dl)11.25 ± 1.6211.25 ± 1.830.992 Albumin (g/dl)3.97 ± 0.403.93 ± 0.400.294 Uric acid (mg/dl)6.09 ± 2.176.29 ± 2.270.261 CRP (mg/dl)0.27 ± 0.760.32 ± 0.740.580 Calcium (mg/dl)8.88 ± 0.598.87 ± 0.640.822 *Inorganic phosphorus* (mg/dl)3.70 ± 0.713.72 ± 0.660.632 Intact PTH (pg/dl)102.26 ± 85.1983.81 ± 59.970.002 Urine PCR (g/g Cr)1.56 ± 2.051.38 ± 1.930.271Continuous variables are expressed as the mean ± standard deviation. Categorical variables are expressed as numbers (percentages).Ca Calcium, CKD Chronic kidney disease, Cr Creatinine, CRP C-reactive protein, DM Diabetes mellitus, eGFR Estimated glomerular filtration rate, eGFRCr eGFR based on creatinine, eGFRCysC eGFR based on cystatin C, eGFRCr-CysC eGFR based on both creatinine and cystatin C, HTN Hypertension, PCR Protein/creatinine ratio, PTH Parathyroid hormone.
## Distribution of the differences between eGFRCr and eGFRCysC and scatter plot of eGFRCr and eGFRCysC
Figure 1 shows the distribution of the differences between eGFRCr and eGFRCysC. The median value of the difference between eGFRCr and eGFRCysC was 6.353 ml/min/1.73 m2, and the highest number of patients ($$n = 58$$) had an eGFRCr and eGFRCysC difference of 3–4 ml/min/1.73 m2. Figure 2 shows the scatter plot of eGFRCr and eGFRCysC in the overall cohort and in the low- and high-difference groups. All three groups showed a positive correlation between eGFRCr and eGFRCysC. The low-difference group had the highest correlation coefficient. In the scatter plot of the high-difference group; eGFRCr was greater than eGFRCysC in most patients ($\frac{307}{319}$, $96.2\%$).Figure 1Distributions of differences between the eGFRCr and eGFRCysC. eGFR, estimated glomerular filtration rate; eGFRCr, eGFR based on creatinine; eGFRCysC, eGFR based on cystatin C.Figure 2Scatter plot and correlation analysis of eGFRCr and eGFRCysC. Panels (A), (B), and (C) show the scatter plots of the overall cohort, low-difference group, and high-difference group. The Spearman’s correlation coefficient is noted on each scatter plot. eGFR, estimated glomerular filtration rate; eGFRCr, eGFR based on creatinine; eGFRCysC, eGFR based on cystatin C.
## Factors responsible for the high difference between eGFRCr and eGFRCysC
Table 2 shows the results of a logistic regression analysis conducted to identify factors in baseline characteristics that affect the occurrence of high differences in |eGFRCr-eGFRCysC|. Factors that showed statistically significant differences at baseline between the two groups were analyzed. CKD grade 3 based on the eGFRCr and age > 70 years were significant factors in the univariate analysis, with odds ratios (OR) of 3.179 ($P \leq 0.001$) and 2.011 ($P \leq 0.001$), respectively. CKD grade 3 and age > 70 years were also observed as independent factors for a high difference in |eGFRCr-eGFRCysC|, with multivariate ORs of 3.191 ($P \leq 0001$) and 2.048 ($P \leq 0.001$), respectively. Table 2Logistic regression analysis of the high difference between the eGFRCr and eGFRCysC.Univariate OR ($95\%$ confidence interval)Multivariate OR ($95\%$ confidence interval)CKD grade 3 based on eGFRCr (Ref. CKD grade 4)3.179 (2.113–4.783)3.191 (2.210–4.606)Male (Ref. Female)1.195 (0.826–1.729)–Age > 70 years2.011 (1.383–2.923)2.048 (1.433–2.927)HTN0.846 (0.591–1.212)–Intact PTH0.999 (0.996–1.002)–Urine PCR1.054 (0.961–1.156)–Excluding patients with missing values, a total of 557 ($87.2\%$) patients were included in multivariate logistic regression model. CKD Chronic kidney disease, eGFR Estimated glomerular filtration rate, eGFRCr eGFR based on creatinine, eGFRCysC eGFR based on cystatin C, HTN Hypertension, OR Odds ratio, PCR Protein/creatinine ratio, PTH Parathyroid hormone.
## Comparison of AuROC values of eGFR equations for hyperphosphatemia and hyperparathyroidism with hyperphosphatemia
Figure 3 shows the receiver operating characteristic (ROC) curves and area under the ROC curve (AuROC) values of the eGFRCr, eGFRCysC, and eGFRCr-CysC for hyperphosphatemia occurrence in the overall cohort and in the low- and high-difference groups. In the overall cohort, the AuROC values of eGFRCysC and eGFRCr-CysC were slightly higher than that of eGFRCr (0.682 for eGFRCysC and 0.683 for eGFRCr-CysC vs. 0.663 for eGFRCr). In the low-difference group, the AuROC values of eGFRCysC and eGFRCr-CysC showed no significant difference from eGFRCr. However, in the high-difference group, the AuROC values of eGFRCysC and eGFRCr-CysC were higher than that of eGFRCr, and the increase was more prominent in the high-difference group than in the overall cohort (0.655 for eGFRCysC and 0.644 for eGFRCr-CysC vs. 0.615 for eGFRCr).Figure 3ROC curves and AuROC values of each eGFR equation for hyperphosphatemia. Panels (A), (B), and (C) show the ROC curves for each eGFR equation for the overall cohort, low-difference group, and high-difference group. The AuROC values of the eGFR equations are noted in each panel. AuROC, area under the ROC curve; eGFR, estimated glomerular filtration rate; eGFRCr, eGFR based on creatinine; eGFRCysC, eGFR based on cystatin C; eGFRCr-CysC, eGFR based on both creatinine and cystatin C; ROC, receiver operating characteristic.
Figure 4 shows the ROC curves and AuROC values of eGFRCr, eGFRCysC, and eGFRCr-CysC for hyperparathyroidism with hyperphosphatemia occurrence in the overall cohort and low- and high-difference groups. In the overall cohort and low-difference group, the AuROC values of eGFRCysC and eGFRCr-CysC were not markedly different from that of eGFRCr. However, in the high-difference group, the AuROC values of eGFRCysC and eGFRCr-CysC were slightly higher than that of eGFRCr (0.677 for eGFRCysC and 0.675 for eGFRCr-CysC vs. 0.658 for eGFRCr).Figure 4ROC curves and AuROC values of each eGFR equation for hyperparathyroidism with hyperphosphatemia. Panels (A), (B), and (C) show the ROC curves for each eGFR equation for the overall cohort, low-difference group, and high-difference group. The AuROC values of the eGFR equations are noted in each panel. AuROC, area under the ROC curve; eGFR, estimated glomerular filtration rate; eGFRCr, eGFR based on creatinine; eGFRCysC, eGFR based on cystatin C; eGFRCr-CysC, eGFR based on both creatinine and cystatin C; ROC, receiver operating characteristic.
## Comparison of AuROC values of the eGFR equations for hyperphosphatemia in the subpopulation with CKD grade 3
Table 3 shows the AuROC values of eGFRCr, eGFRCysC, and eGFRCr-CysC in the total, low- and high-difference groups for hyperphosphatemia occurrence in patients with CKD grade 3 based on eGFRCr. Similar to the results of the entire cohort (includes CKD grades 3 and 4), eGFRCysC and eGFRCr-CysC showed higher AuROC values than that of eGFRCr for hyperphosphatemia in the overall and high-difference group. Moreover, the increase in AuROC values of eGFRCysC and eGFRCr-CysC was more pronounced in patients with CKD grade 3 (0.719 for eGFRCr-CysC vs. 0.676 for eGFRCr) compared with those of the entire cohort (0.683 for eGFRCr-CysC vs. 0.663 for eGFRCr).Table 3Comparison of AuROC values of eGFR equations for hyperphosphatemia in the CKD grade 3 subpopulation. AuROC value ($95\%$ confidence interval)eGFRCreGFRCysCeGFRCr-CysCTotal ($$n = 391$$)0.676 (0.574–0.778)0.725 (0.639–0.810)0.719 (0.628–0.810)Low-difference group ($$n = 156$$)0.852 (0.727–0.977)0.865 (0.754–0.976)0.862 (0.743–0.981)High-difference group ($$n = 235$$)0.602 (0.478–0.727)0.651 (0.526–0.775)0.644 (0.519–0.769)AuROC Area under the receiver operating characteristic curve, CKD Chronic kidney disease, eGFR Estimated glomerular filtration rate, eGFRCr eGFR based on creatinine, eGFRCysC eGFR based on cystatin C, eGFRCr-CysC eGFR based on both creatinine and cystatin C.
## Discussion
In this study, eGFRCysC and eGFRCr-CysC were better indicators of hyperphosphatemia than eGFRCr was, both in the overall cohort and in the high-difference group. Similarly, eGFRCysC and eGFRCr-CysC were better indicators of hyperparathyroidism with hyperphosphatemia than eGFRCr was in the high-difference group. These findings were more pronounced in patients with CKD grade 3 based on the eGFRCr.
In the present study, age > 70 years and CKD grade 3 (low CKD grade) based on the eGFRCr were independent factors for a high difference in |eGFRCr-eGFRCysC|. Male sex, history of HTN, and intact PTH levels showed statistically significant differences between the low- and high-difference groups, but these factors were not observed as significant factors in the binary logistic regression analysis. Sarcopenia increases with age, and the serum Cr level, which is affected by muscle mass, is lower in older people; therefore, the difference between eGFRCr and eGFRCysC may be high in these groups18. Previous studies reported a positive correlation between age and eGFR difference19–21, consistent with the results of the present study. Whether urine PCR affects the eGFR difference is unclear; however, the degree of proteinuria tends to be low in the high-difference group22–24. In the present study, the urine PCR was also low in the high difference group, although the difference was not statistically significant. CysC is known to be associated with inflammation25; however, no significant difference was observed in C-reactive protein (CRP) levels between the low- and high-difference groups in the present study. This may be due to a low level of acute inflammation, as the participants in the present study were stable outpatients.
As CKD progresses, urinary phosphate excretion decreases, which causes hyperphosphatemia followed by secondary hyperparathyroidism through interrelated mechanisms26,27. In addition, the skeleton plays a very important role in the phosphorus balance. As CKD progresses, bone resorption increases and outpaces bone formation, which in turn increases the release of phosphorus into the blood. For this reason, we excluded patients taking phosphate-binding agents, calcitriol, and cinacalcet from our analysis, as these could significantly affect phosphorus and intact PTH levels. In any case, it is well-known that hyperphosphatemia and hyperparathyroidism are physiologic changes accompanying CKD progression28. The eGFRCysC and eGFRCr-CysC were identified as more useful indicators associated with hyperphosphatemia and hyperparathyroidism with hyperphosphatemia than eGFRCr was in the overall cohort, especially in the high-difference group. This suggests that measuring and evaluating CysC in addition to *Cr is* necessary to accurately diagnose the patient’s current condition and predict prognosis in the clinical field in patients with risk factors, such as CKD grade 3 or old age.
Interestingly, the average serum Cr level was lower and the proportion of CKD grade 3 based on eGFRCr was higher in the high-difference group. As CKD progresses, tubular Cr secretion increases, which is known to result in underestimation of kidney function29. However, tubular CysC secretion rarely occurs as it is freely filtered in the glomerulus and is mostly absorbed and degraded in the proximal tubule30. Therefore, the risk of underestimating the GFR by CysC may be relatively lower compared with that by Cr. In patients with CKD grade 3 based on the eGFRCr, the AuROC values of eGFRCysC and eGFRCr-CysC improved more markedly compared with that of eGFRCr. This suggests that evaluating CKD grade 3 using only eGFRCr is likely to result in a significant underestimation of kidney function.
Patients with eGFR difference > 15 ml/min/1.73 m2 were excluded from the present study according to a previous report which stated that a > 15 ml/min/1.73 m2 difference between eGFRCr and eGFRCysC is probably linked to a disproportionate effect of non-GFR factors by one of the two biomarkers (Cr and CysC)24,31. Although many previous studies have shown that eGFRCr-CysC has the highest accuracy with mGFR, it is necessary to keep in mind that the eGFRCr-CysC equation was developed in a relatively healthy population with an average age of 47 years and an mGFR of 68 ml/min/1.73 m2. This study is significant in that it reveals for the first time the usefulness of eGFRCr-CysC in relation to hyperphosphatemia and hyperparathyroidism, which are physiological changes following the progression of advanced CKD.
Our study has several limitations. First, because this was a retrospective, cross-sectional study, we could not analyze the patients’ future clinical course. Second, we used inorganic phosphorus and PTH levels as indicators of physiological changes in CKD, however, the change in fibroblast growth factor 23 (FGF-23) level precedes hyperphosphatemia and is observed concurrently with PTH level elevation32,33. Although FGF-23 may better reflect the physiological changes in CKD, it is not yet routinely measured in clinical practice. Nevertheless, it is significant that we analyzed a large population of over 600 patients, and this is the first study to compare Cr and CysC levels in association with hyperphosphatemia and secondary hyperparathyroidism with hyperphosphatemia.
In conclusion, patients aged > 70 years and with a low CKD grade according to the eGFRCr could be at high risk for a high difference in the eGFRCr and eGFRCysC. Compared with that of eGFRCr, the eGFRCysC and eGFRCr-CysC showed a stronger association with the physiological changes of CKD progression (hyperphosphatemia and hyperparathyroidism), and the effect was particularly evident in the high-difference group and low CKD grade subpopulation. Although the usefulness of CysC is well known, in actual clinical practice many physicians still evaluate kidney function based only on Cr. Evaluating kidney function by including CysC is necessary to accurately evaluate patient kidney function and predict prognosis, at least in selected patients (such as the elderly or those with CKD grade 3) based on factors related to the high-difference of |eGFRCr-eGFRCysC|.
## Study design
This was a single-center retrospective cross-sectional study. We used outpatient clinic data from the Clinical Data Warehouse system of Konyang University Hospital between January 2019 and February 2022. We collected the data of adult patients (≥ 19 years old) diagnosed with CKD grade 3 or 4 as defined by the Kidney Disease: Improving Global Outcomes guideline (eGFRCr categories of grade 3: 30–59 ml/min/1.73 m2, grade 4: 15–29 ml/min/1.73 m2)34. Serum Cr, CysC, inorganic phosphorus, calcium, and PTH levels were tested on the same day ($$n = 835$$).
We excluded patients taking phosphate-binding agents (sevelamer, lanthanum, calcium-based phosphate binder) or PTH-lowering agents (calcitriol, cinacalcet) ($$n = 56$$), those with a history of cancer ($$n = 9$$), and those with a difference between eGFRCr and eGFRCysC > 15 ml/min/1.73 m2 ($$n = 131$$). Therefore, 639 patients were enrolled in this study and divided into low- and high-difference groups based on the median value of |eGFRCr-eGFRCysC|: < 6.353 ml/min/1.73 m2 and ≥ 6.353 ml/min/1.73 m2, respectively (Fig. 5).Figure 5Study design. Of the 835 patients with CKD grades 3 and 4 based on the eGFRCr, 196 were excluded according to the exclusion criteria, and 639 were finally included. Patients were divided into two groups based on a median |eGFRCr-eGFRCysC| of 6.353 ml/min/1.73 m2. CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; eGFRCr, eGFR based on creatinine; eGFRCysC, eGFR based on cystatin C; PTH, parathyroid hormone.
This study was performed in accordance with the Declaration of Helsinki, and was approved by the Institutional Review Board of Konyang University Hospital (KYUH 2022-10-003). The need to obtain informed patient consent was waived by the Institutional Review Board of Konyang University Hospital (KYUH 2022-10-003) because the patient data were extracted in an anonymized form.
## Data collection
Baseline sociodemographic characteristics, including sex; age; history of HTN (systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg or use of antihypertensive medications), diabetes mellitus (HbA1c ≥ $6.5\%$, fasting glucose level ≥ 126 mg/dl, or oral hypoglycemic agent or insulin administration), and cancer (patients diagnosed with the 10th Revision code of the International Classification of Diseases: C16, C18, C19, C22, C34, and C50), were obtained. We further collected information on the serum hemoglobin, albumin, CRP, uric acid levels, and urine PCR.
## Calculations of eGFRCr, eGFRCysC, and eGFRCr-CysC
Serum Cr and CysC levels were measured using a Beckman Coulter AU5800. Serum Cr levels were measured with the isotope dilution mass spectroscopy-traceable Jaffe method using picric acid. Serum CysC levels were measured by turbidity analysis. The CKD-EPI eGFR equations, the 2021 CKD-EPI creatinine equation (eGFRCr), 2012 CKD-EPI cystatin C equation (eGFRCysC), and 2021 CKD-EPI creatinine-cystatin C equation (eGFRCr-CysC) were as follows: eGFRCr = 142 × min (SCr/κ, 1)a × max (SCr/κ, 1)−1.200 × 0.9938age × (1.012 if female), eGFRCysC = 133 × min (SCys/0.8, 1)−0.499 × max (SCys/0.8, 1)−1.328 × 0.996age × (0.932 if female), and eGFRCr-CysC = 135 × min (SCr/κ, 1)b × max (SCr/κ, 1)−0.544 × min (SCys/0.8, 1)−0.323 × max(SCys/0.8, 1)−0.778 × 0.9961age × (0.963 if female). SCr is the serum Cr level; SCys is the serum CysC level; κ is 0.7 for females and 0.9 for males; a is − 0.241 for females and − 0.302 for males, and b = − 0.219 for females and − 0.144 for males3,4,10.
## Measurements and definition of hyperphosphatemia and hyperparathyroidism
Serum inorganic phosphorus levels were measured using a photometric ultraviolet test with molybdate using a Beckman Coulter AU5800. Serum PTH levels were measured by two-site immunoenzymatic assay using a Beckman Coulter DxI. Hyperphosphatemia was defined as a serum inorganic phosphorus level > 4.6 mg/dl, while hyperparathyroidism was defined as a serum PTH level > 65 pg/ml35. The AuROC values of the eGFR equations for the occurrence of hyperphosphatemia and hyperparathyroidism with hyperphosphatemia were analyzed.
## Statistical analyses
The baseline sociodemographic characteristics and laboratory findings were compared between the low- and high-difference groups. Continuous variables are expressed as the mean and standard deviation and were compared using the Student’s t-test. Categorical variables are expressed as numbers (percentages) and were compared using the Chi-square or Fisher’s exact test, as appropriate. Since both eGFRCr and eGFRCysC showed a non-normal distribution, Spearman’s correlation analysis was performed in the overall cohort and low- and high-difference groups to analyze the correlation between eGFRCr and eGFRCysC. Multivariate binary logistic regression analysis was performed to delineate the relationship between the factors and the high difference in |eGFRCr-eGFRCysC|. We compared the AuROC values between eGFRCr, eGFRCysC, and eGFRCr-CysC to analyze the association intensity related to the occurrence of hyperphosphatemia and hyperparathyroidism with hyperphosphatemia in the overall cohort and low- and high-difference groups. A subgroup analysis was performed for patients with CKD grade 3 based on the eGFRCr, and the AuROC values of the three eGFR equations were compared. Statistical significance was defined as $p \leq 0.05.$ All statistical analyses were performed using SPSS version 23.0 (IBM Corporation, Armonk, NY, USA).
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|
---
title: 'Aging-related predictive factors for oxygenation improvement and mortality
in COVID-19 and acute respiratory distress syndrome (ARDS) patients exposed to prone
position: A multicenter cohort study'
authors:
- Marieta C.A. Cunha
- Jociane Schardong
- Natiele C. Righi
- Adriana C. Lunardi
- Guadalupe N. Sant'Anna
- Larissa P. Isensee
- Rafaela F. Xavier
- Jose E. Pompeu
- Ranata M. Weigert
- Darlan L. Matte
- Rozana A. Cardoso
- Ana C.V. Abras
- Antonio M.V. Silva
- Camila C. Dorneles
- Roberta W. Werle
- Ana C. Starke
- Juliana C. Ferreira
- Rodrigo D.M. Plentz
- Celso R.F. Carvalho
journal: Clinics
year: 2023
pmcid: PMC9995337
doi: 10.1016/j.clinsp.2023.100180
license: CC BY 4.0
---
# Aging-related predictive factors for oxygenation improvement and mortality in COVID-19 and acute respiratory distress syndrome (ARDS) patients exposed to prone position: A multicenter cohort study
## Highlights
•Prone position in intubated patients with COVID-19 improves gas exchange.•Elderly and severe comorbidities increase mortality risk after prone sessions for ARDS-COVID-19.•ARDS-COVID-19 better respond when prone is applied early in patients with good health status.
## Abstract
### Background
Elderly patients are more susceptible to Coronavirus Disease-2019 (COVID-19) and are more likely to develop it in severe forms, (e.g., Acute Respiratory Distress Syndrome [ARDS]). Prone positioning is a treatment strategy for severe ARDS; however, its response in the elderly population remains poorly understood. The main objective was to evaluate the predictive response and mortality of elderly patients exposed to prone positioning due to ARDS-COVID-19.
### Methods
This retrospective multicenter cohort study involved 223 patients aged ≥ 65 years, who received prone position sessions for severe ARDS due to COVID-19, using invasive mechanical ventilation. The PaO2/FiO2 ratio was used to assess the oxygenation response. The 20-point improvement in PaO2/FiO2 after the first prone session was considered for good response. Data were collected from electronic medical records, including demographic data, laboratory/image exams, complications, comorbidities, SAPS III and SOFA scores, use of anticoagulants and vasopressors, ventilator settings, and respiratory system mechanics. Mortality was defined as deaths that occurred until hospital discharge.
### Results
Most patients were male, with arterial hypertension and diabetes mellitus as the most prevalent comorbidities. The non-responders group had higher SAPS III and SOFA scores, and a higher incidence of complications. There was no difference in mortality rate. A lower SAPS III score was a predictor of oxygenation response, and the male sex was a risk predictor of mortality.
### Conclusion
The present study suggests the oxygenation response to prone positioning in elderly patients with severe COVID-19-ARDS correlates with the SAPS III score. Furthermore, the male sex is a risk predictor of mortality.
## Introduction
Viral pneumonia is the most severe manifestation caused by the novel coronavirus, leading to acute respiratory distress syndrome related to COVID-19 disease (COVID-19-ARDS).1, 2, 3, 4 COVID-19-ARDS is diagnosed by confirming SARS-CoV-2 infection and the presence of ARDS signs classified according to *Berlin criteria* [2012].1, 2, 3, 4 COVID-19-ARDS is caused by an exacerbated increase in proinflammatory cytokines and other inflammatory markers, known as a cytokine storm. The inflammatory reaction causes diffuse alveolar damage and hyaline membrane formation in the alveoli, generating edema and fibroblast proliferation.4 Associated with the inflammatory exacerbated reaction, COVID-19-ARDS presents coagulation dysfunction, detected by high levels of D-dimer. This association may explain the atypical manifestations found in patients with COVID-19, such as dilatation of the pulmonary vessels, which is rarely found in patients with classic ARDS.4 The prone position is considered an adjunct treatment for intubated patients with severe COVID-19-ARDS since the Surviving Sepsis Campaign and the World Health Organization recommendations.5,6 Using the prone position is well known to improve oxygenation and reduce the risk of mortality in classic ARDS with refractory hypoxemia.5, 6, 7, 8, 9 Better outcomes are achieved if the prone position is applied in the first 48h and at least for 12‒16h. Additionally, it may be associated with protective ventilatory strategies, neuromuscular blockade, and permissive hypercapnia.3,5,6,10, 11, 12 Lung protective ventilation plays important role in improving prone outcomes. It is recommended that the patients be ventilated with low tidal volumes (4–8 mL/kg of predicted body weight), low plateau pressures (< 30–32 cm H2O.), and driving pressures below 14‒15 cm H2O.13 The PaO2/FiO2 is used to assess the oxygenation response in patients with ARDS. Although the cutoff value has not been well established, most studies use improvement cutoff values of 10‒20 mmHg PaO2 or PaO2/FiO2 or a $10\%$‒$20\%$ increase in PaO2/FiO2.13,14
Several studies have shown that older adults present the most severe form of the disease and high mortality rate. Evidence suggests that advanced age is the most important predictor of mortality, especially among adults aged > 80 years.15,16 Advanced age causes progressive lung function impairment due to structural changes that impaired gas exchange and immunological changes, predisposing to infections. Molecular and immunological changes may explain why elderly patients have a worse prognosis with COVID-19.15,16 In healthy aging, lung reserve is naturally reduced. The lung in the elderly is characterized by a lower density of bronchioles and an increase in their diameter. There is a loss of alveolar surface area and an increase in the size of the alveoli and airspace. In addition, there is a reduction in lung elasticity, making it more rigid. It is expected that the lungs of the elderly have a greater functional residual capacity and a lower forced expiratory volume in the 1st second/forced vital capacity ratio (FEV1/FVC). Also, both FEV1 and FVC are lower with advanced age.17 Due to the frailty of elderly patients, it is mandatory to understand the response to prone positioning for ARDS due to COVID-19. Understanding this treatment's effectiveness may result in more humanized care and therapeutic proportionality. However, there is a lack of understanding of oxygenation improvement and mortality risk after a prone position in this population. Therefore, the primary objective of this study was to identify predictors of oxygenation response and mortality risk after prone positioning in elderly patients with severe COVID-19-ARDS. The secondary objective was to assess the response to prone positioning in elderly patients who developed the most severe form of the disease.
## Methods
This multicenter retrospective cohort study was conducted in six hospitals and approved by the Clinical Research Ethics Committee of all centers (31881520.3.1001.5335). Due to the retrospective nature of the study, the need for informed consent was waived. The study included patients under invasive mechanical ventilation with suspected or confirmed SARS-CoV-2 infection, who received prone position sessions for severe COVID-19-ARDS treatment. The inclusion criteria were individuals diagnosed with COVID-19, requiring invasive mechanical ventilation and severe ARDS (PaO2/FiO2 < 150 mmHg). The exclusion criterion was age < 65 years.
Confirmed COVID-19 patients were considered for analysis if they presented a positive real-time reverse transcription-polymerase chain reaction (PCR-RT). Additionally, patients with suspected or negative PCR-RT who presented clinical symptoms of COVID-19, including fever, cough, tiredness, anosmia, ageusia, headache, pain, diarrhea, and/or dyspnea, were also included.
The trained researchers collected data from electronic medical records using standardized forms. All contributors had access to the electronic medical records of their affiliated institutions and were committed to ensuring data protection. Patients were followed up from hospital admission to discharge or death, and the study group did not interfere with medical decisions.
The PaO2/FiO2 ratio was used to assess the oxygenation response. Patients who presented a 20-point improvement in PaO2/FiO2 after the first prone session were considered the responders group. Patients who did not present 20-point of improvement in PaO2/FiO2 after the first prone session were included in the non-responders group. Mortality was defined as deaths that occurred between hospitalization and discharge.
## Data collection
The following data were collected: demographic information, comorbidities, complications, D-dimer level, Simplified Acute Physiology Score (SAPS III), Sequential Organ Failure Assessment (SOFA) score, PaO2/FiO2 ratio, Body Mass Index (BMI), comorbidities, and use of anticoagulants and vasopressors. SAPS III and SOFA scores considered for analysis were calculated at the Intensive Care Unit (ICU) admission. D-dimer levels were evaluated using the HemosIL HS-500 automated immunoassay (HemosIL® D-dimer HS 500, Instrumentation Laboratory, 80003610270, Instrumental Laboratory Company, Bedford, MA, USA).
Comorbidities were assessed, including immunosuppression, arterial hypertension, diabetes, obesity, smoking, alcohol consumption, and neurological, hematological, respiratory, and cardiovascular diseases. Furthermore, immunosuppression was defined as a history of organ transplantation, chronic kidney disease, HIV infection, AIDS, and cancer treatment.
Clinical data included arterial blood gas analysis before and after the first prone session. In addition, the time until the first prone positioning, duration of the first prone session (in hours), number of prone sessions, and complications related to prone positioning were also collected. The time between the first intubation and the prone session was considered the first prone position. Unfortunately, due to hospital bed overload, it was impossible to collect data for blood gas analysis from the health staff on time. Therefore, the data considered for the analysis were obtained closest to the beginning and end of the first prone session.
Ventilator settings and respiratory mechanics calculations, such as Driving Pressure (DP), Plateau Pressure (Pplat), and respiratory system static Compliance (Cst), were collected before and after the first prone session. The total duration of the first prone session and a number of prone cycles were recorded. Furthermore, adverse effects, such as decreased oxygenation level, accidental extubation, central venous or arterial line removal, hemodynamic instability, acute arrhythmia, cardiopulmonary arrest, and vomiting, were recorded. Patient outcomes, including duration of invasive mechanical ventilation, length of hospital and ICU stay, reintubation, and survival, were also recorded.
## Statistical analysis
The sample size was calculated considering a $25\%$ mortality and including at least five independent variables. A sample calculation was performed according to the following formula: (10*[k+1]), where k represents the number of explanatory variables of the predictive model.18 A total of 60 patients was calculated. Continuous variables were expressed as medians and $25\%$−$75\%$ interquartile ranges, while categorical variables were expressed as the number of patients and percentages. Between-group comparisons were performed using the Mann-Whitney test. Logistic regression was used to evaluate the factors associated with the response to prone positioning and mortality. Variables with a p-value < 0.2 were used for multivariate regression. Finally, multicollinearity was assessed by examining variance inflation factors. The results are presented as Odds Ratios (OR) with a $95\%$ Confidence Interval (CI). The IBM SPSS Statistics package (Version 26.0) was used for statistical analysis, and a p-value < 0.05 was set as significant.
## Results
The study included 223 patients; the patients were divided into two groups according to an increase in PaO2/FiO2 (responders [$72.6\%$] and non-responders [$27.3\%$]). The average age of the patients was 72 (68−76) years, most patients were male ($60.1\%$), and the most prevalent comorbidities were hypertension, diabetes mellitus, and obesity (Table 1).Table 1Baseline characteristics of patients with COVID-19 and ARDS at ICU admission. Table 1OutcomesAll patients($$n = 223$$)Responders($$n = 162$$)Non-responders($$n = 61$$)p-valueAnthropometric data Age (y/o)72 (68‒76)71 (67‒76)73 (69‒77)0.25 Male sex, n (%)134 (60.1)96 (59.3)38 (62.3)0.68 BMI (kg/m²)27.7 (25‒31)27.6 (25‒31)28.2 (25‒31)0.37Comorbidities AH, n (%)164 (73.5)117 (72.2)47 [77]0.46 DM, n (%)102 (45.9)74 (45.7)28 (46.7)0.89 Obesity, n (%)72 (32.6)52 (32.1)20 (33.9)0.80 Smoker, n (%)52 (23.5)37 (22.8)15 (25.4)0.69 CPD, n (%)32 (14.3)24 (14.8)8 (13.3)0.78 Immunosuppression, n (%)29 [13]20 (12.3)9 (15.3)0.57SAPS III (score)73 (59‒81)72 (56‒79)78 (66‒86)0.011SOFA (score)10 (7‒13)9 (6‒12)11 (9‒14)0.018D-dimer (ng/mL)2.80 (949‒5.79)1.86 (865‒5.34)2.45 (1.35‒7.53)0.47Data are presented as the average and interquartile range ($25\%$‒$75\%$) or the number of subjects and percentage, in parenthesis. BMI, Body Mass Index; AH, Arterial Hypertension; DM, Diabetes Mellitus; CPD, Chronic Pulmonary Disease; SAPS III, Simplified Acute Physiology Score; SOFA, Sequential Organ Failure Assessment. Clinical data were evaluated, and there was no interference by the investigators.
Both groups presented similar anthropometric characteristics, such as age, sex, BMI, comorbidities, and D-dimer levels. However, higher SAPS III and SOFA scores were observed in the non-responder group, indicating an independent mortality risk between the two groups (Table 1).
A protective ventilatory strategy was applied in both groups, following the guideline recommendations for low DP and Pplat. The average DP was 12 cm H2O, and the average respiratory Cst was 31 mL/cm H2O. No differences were observed between DP and respiratory Cst between the two groups (Table 2). Furthermore, the ventilatory settings were similar between the groups, except for a lower FiO2 at baseline found in the non-responders group ($$p \leq 0.05$$).Table 2Ventilator settings, prone position response, and patient outcomes. Table 2OutcomesAll patients($$n = 223$$)Responders($$n = 162$$)Non-responders($$n = 61$$)p-valuePre-prone mechanical ventilation PEEP (cm H2O)10 (9‒12)10 (10‒12)10 (9‒12)0.35 FiO2 (%)80 (65‒100)80 (65‒100)70 (60‒90)0.05 RR (bpm)28 (23‒32)26 (22‒31)30 (25‒33)0.47 Tidal volume (mL)390 (340‒440)389 (329‒442)390 (345‒420)0.62 Driving pressure (cm H2O)12 (10‒15)12 (10‒15)13 (10‒15)0.84 Pplat (cm H2O)24 (21‒26)24 (21‒27)23 (20‒25)0.47 Cst (mL/cm H2O)31 (24‒39)31 (24‒38)31.5 (27‒39)0.46Pre prone blood gases analysis Arterial Ph7.32 (7.25‒7.38)7.30 (7.30‒7.40)7.32 (7.24‒7.38)0.09 PaO2 (mmHg)72 (63.5‒81)72 (63‒82)71 (63.5‒81)0.78 PaCO2 (mmHg)51.7 (45‒61)52 (45‒61)51.5 (45.1‒61.6)0.58 HCO3 (mEq/L)26.3 (22‒30)26.5 (22‒31)25.4 (22.1‒28.4)0.43 Initial PaO2/FiO2 (mm Hg)95 (79‒120)93 (77‒118)106 (82‒120)0.16 Δ PaO2/FiO2 (mmHg)43 (15‒80)62 (39‒101)0.5 (-10.1‒8.2)< 0.001 Δ PCO2 (mmHg)-0.9 (‒10‒5)-0.9 (-10‒6)-0.9 (-10‒3.4)0.75Complications15 (6.7)3 (1.9)12 (19.7)< 0.001Drug ACO, n (%)219 (98.6)158 (98.1)61 [100]0.29 Vasopressors, n (%)205 (91.9)147 (90.7)58 (95.1)0.28Outcomes Time until 1st prone, days2 (1‒6)2 (1‒6)3 (2‒6)0.15 Duration of 1st prone, hours18 (16.2‒20)18 (16.5‒20)17.4 (15.6‒19.7)0.09 Prone sessions, no. sessions2 (1‒3)2 (1‒3)2 (1‒2)0.39 Duration of IMV, days15 (9‒22)15 (9‒22)16 (10‒24)0.59 ICU length of stay, days17 (11‒24)17 (11‒24)18 (10‒25)0.98 Hospital length of stay, days19 (11‒30)20 (12‒31)19 (11‒27)0.39 Reintubation, n (%)23 (10.3)16 (10.1)7 (11.5)0.73 Tracheotomy, n (%)46 (20.6)34 [21]12 (19.7)0.87 In-hospital mortality, n (%)188 (84.3)133 (82.1)55 (90.2)0.14Data are presented as average and interquartile range ($25\%$‒$75\%$) or the number of subjects and percentage, in parenthesis. PEEP, Positive End-Expiratory Pressure; FiO2, The fraction of inspired oxygen; RR, Respiratory Rate; Pplat, Plateau Pressure of the respiratory system; Cst, Static Compliance of the respiratory system; Ph, Potential Hydrogen; PaO2, Partial Pressure of Arterial Oxygen; PaCO2, Partial Pressure of Arterial Carbon Dioxide; HCO3, Bicarbonate; PaO2/FiO2, The difference between the initial and final ratio of arterial oxygen partial pressure (PaO2: in mmHg) to fractional inspired oxygen (FiO2); Δ PCO2, The difference between the initial and final PaCO2; ACO, Anticoagulants; IMV, Invasive Mechanical Ventilation; ICU, Intensive Care Unit.
The non-responders group presented with more complications, leading to an interruption in the prone position. The most common complications include reduced oxygen saturation, unplanned extubation, hemodynamic instability, acute arrhythmia, and cardiac arrest. Furthermore, most patients received anticoagulants and vasopressors during treatment.
Although the non-responders group was placed in a prone position after the first 48h, there was no statistical difference between the two groups. On average, the non-responders group was placed in the prone position for at least 16h in 2 (1−3) sessions. The duration of the prone position and number of cycles did not differ between the two groups.
Similarly, no differences were observed between the duration of mechanical ventilation (in days), length of hospital or ICU stay (in days), and reintubation index. The in-hospital mortality rate was higher in the non-responders than responders group ($90.2\%$ vs. $82.1\%$), but the difference was not significant.
## Oxygenation improvement
A logistic regression analysis was performed to evaluate the factors associated with oxygenation improvement. Independent variables included age, sex, lung impairment observed on chest CT, previous immunosuppression, lung disease, SAPS III score, the total number of prone sessions, time taken to the first prone session, total duration of prone, D-dimer value, respiratory Cst, and occurrence of complications. Only SAPS III and complication rates were associated with improved oxygenation (Table 3). However, logistic regression analysis showed that only the SAPS III score predicted a better response to oxygenation after prone positioning (OR = 0.97 [0.94−0.99; $$p \leq 0.02$$]).Table 3Predictors factors of oxygenation improvement and mortality. Table 3VariablesORCI ($5\%$)CI ($95\%$)p-valueOxygenation improvement SAPS III0.970.940.990.02Mortality rate Male sex0.210.060.700.01Data from the logistic regression model. OR, Odds Ratio; CI, Confidence Interval; SAPS III, Simplified Acute Physiology Score.
## Mortality rate
The mortality rate analysis was performed using stepwise forward logistic regression, which includes age, sex, pulmonary impairment, previous immunosuppression, time until the first prone session, respiratory Cst, occurrence of complications, D-dimer value, and baseline pH. Variables associated with mortality were sex, previous immunosuppression, and respiratory Cst. In a logistic regression analysis, the male sex was a significant variable associated with worse mortality risk (OR = 0.21 [0.06−0.70; $$p \leq 0.01$$]), (Table 3).
## Discussion
This retrospective multicenter cohort study involved many elderly patients who underwent prone positioning due to a diagnosis of severe COVID-19-ARDS. The non-responders group (< 20-point in PaO2/FiO2) had higher SAPS III and SOFA scores than the responders group. In addition, the non-responders group had a higher incidence of complications; however, there was no difference in mortality rate. Finally, the authors found that a lower SAPS III score was a predictor of the response to oxygenation after the first prone session and that the male sex was a predictor of mortality risk.
It is well known that the elderly are disproportionately affected by COVID-19, representing a higher risk of infection and a severe form of the disease.7 Additionally, the elderly with a greater number of comorbidities may be more susceptible to mortality.7 However, the authors identified a gap in the literature that clarifies the response of this elderly group when subjected to prone positioning. Brazier et al. [ 2021] proposed a prone protocol model for elderly patients diagnosed with ARDS and respiratory failure; but their patients were not intubated, and the study did not assess their response to prone positioning.16 Therefore, to the best of our knowledge, this is the first study to analyze the effect of prone positioning in elderly patients intubated due to COVID-19-ARDS.
Reports indicate that COVID-19 is more prevalent among the male sex. Men are likely to be more severely affected, required to stay longer in the Intensive Care Unit (ICU), and present a greater fatality rate.19 Furthermore, older men are predisposed to a higher prevalence of metabolic syndrome, obesity, diabetes mellitus, and other chronic diseases.19 These results support our data suggesting that the female sex had a protective effect on mortality.
The present study identified that the responders group was more prevalent, but this response did not significantly reduce mortality. The mortality rate was $82.1\%$ and $90.2\%$ for the responders and non-responders groups, respectively. This may have occurred because the patients were older, had more comorbidities, and had high SAPS III and SOFA scores at admission to ICU.
The authors did not observe a difference in anthropometric data such as comorbidities or D-dimer levels, between the two groups. *In* general, the patients had high D-dimer levels and low lung compliance. The D-dimer is considered to play an important role in hypoxemia in ARDS. Consistent data show that the involvement of the pulmonary system in patients with COVID-19 has distinct characteristics and may cause endothelial damage. Furthermore, the combination of a high concentration of D-dimer and low lung compliance may dramatically increase the risk of mortality.20 This may also have affected the high mortality observed in the present study.
In contrast, the authors observed that SAPS III, a scale that includes the number of comorbidities and the initial patient's clinical condition, was a predictor of oxygenation response. The elderly in developing countries generally present a high number of comorbidities, mainly in males. These results suggest that better responses to the prone position occur when a patient has fewer comorbidities than a specific one (Table 1).
The present study suggests that each unit of SAPS III presents a probability of increasing by $0.03\%$ in oxygenation. Thus, the authors can say that a difference of 20 points in SAPS III represents a $31\%$ chance of response to prone positioning. Although no significant difference was found between improved oxygenation and mortality, a previous study showed that SAPS III and age predict mortality factors among ICU hospitalized COVID-19 patients.21 In addition, a Brazilian study concluded that patients with SAPS III value greater than 57 had higher mortality rates.22 The authors suggest that the high mortality observed in both groups may be associated with poor socioeconomic status. Evidence suggests that patients with COVID-19 had a higher mortality rate in a socially and economically disadvantaged region of New York, with a mortality rate over $75\%$.23 *The previous* conditions observed in the patients can be explained by the population in the developing country. In Brazil, even a population including younger patients with COVID-19 had poor results. The group showed a mortality rate of $69.3\%$.24 The study was part of the current study, including intubated patients, undergoing a prone positioning due to COVID-19-related ARDS. On the other hand, a similar study conducted by the Rush University Medical Center, presented a mortality rate of $21.4\%$.25 *The* general patient's age appears to be not different in both groups (59 [49‒69] and 58.5 [51.8‒69.3], respectively), but the mean SOFA score was higher in the studied group (9 vs. 6.8), strengthening the authors’ hypothesis.
The present study has several limitations. First, the main limitation of this study was missing data from the electronic medical records. Second, as opposed to a clinical trial, the decision and timing of prone positioning cannot be controlled in an observational study. This can lead to selection bias. Finally, the criteria used to assess the response to prone positioning are not universal; therefore, comparisons with other studies should be performed with caution. Despite these limitations, to our knowledge, this is the first multicenter cohort study to verify the response of patients with COVID-19-ARDS in an elderly group, which raises some hypotheses about the interventions of this group. However, prospective studies are required to better understand the response to the prone position in elderly patients affected by COVID-19-ARDS.
## Conclusion
The present study suggests that the oxygenation response to prone positioning in elderly patients with severe COVID-19-ARDS may correlate with the SAPS III score. Male sex may also be a risk predictor of mortality.
## Authors' contributions
All authors contributed to the study design, data collection, and manuscript revision.
## Funding
The study was supported by grant $\frac{312279}{2018}$-3 from the Ministério da Ciência, Tecnologia e Inovação ‒ Conselho Nacional de Pesquisa (CNPq) and by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001.
## Conflicts of interest
The authors declare no conflicts of interest.
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|
---
title: 'Metabolic syndrome for the prognosis of postoperative complications after
open pancreatic surgery in Chinese adult: a propensity score matching study'
authors:
- Yuanqiang Dai
- Yaping Shi
- Heng Wang
- Tianhua Cheng
- Boyang Xia
- Yu Deng
- Tao Xu
journal: Scientific Reports
year: 2023
pmcid: PMC9995346
doi: 10.1038/s41598-023-31112-x
license: CC BY 4.0
---
# Metabolic syndrome for the prognosis of postoperative complications after open pancreatic surgery in Chinese adult: a propensity score matching study
## Abstract
To investigate the relationship between metabolic syndrome (MS) and postoperative complications in Chinese adults after open pancreatic surgery. Relevant data were retrieved from the Medical system database of Changhai hospital (MDCH). All patients who underwent pancreatectomy from January 2017 to May 2019 were included, and relevant data were collected and analyzed. A propensity score matching (PSM) and a multivariate generalized estimating equation were used to investigate the association between MS and composite compositions during hospitalization. Cox regression model was employed for survival analysis. 1481 patients were finally eligible for this analysis. According to diagnostic criteria of Chinese MS, 235 patients were defined as MS, and the other 1246 patients were controls. After PSM, no association was found between MS and postoperative composite complications (OR: 0.958, $95\%$CI: 0.715–1.282, $$P \leq 0.958$$). But MS was associated with postoperative acute kidney injury (OR: 1.730, $95\%$CI: 1.050–2.849, $$P \leq 0.031$$). Postoperative AKI was associated with mortality in 30 and 90 days after surgery ($P \leq 0.001$). MS is not an independent risk factor correlated with postoperative composite complications after open pancreatic surgery. But MS is an independent risk factor for postoperative AKI of pancreatic surgery in Chinese population, and AKI is associated with survival after surgery.
## Introduction
According to the American Cancer Society, pancreatic cancer is predicted to remain the second leading cause of cancer deaths within the next decade1. Both the incidence and death rates of pancreatic cancer are increasing globally2. Surgery remains the only option which could offer curative potential for pancreatic patients3–5. However, complications after pancreatic surgery frequently reported and considerably associated with mortality or severe clinical outcome6,7. Identifying the potential risk factors for postoperative complications is the first challenge for the prevention.
Metabolic syndrome (MS) is a syndrome involving a variety of metabolic disorders, and a predictor of several diseases such as cardiovascular diseases, osteoarthritis, and certain cancers8. The association between MS and postoperative complications after pancreatic surgery are still largely unclear and currently, only a limited number of studies have analyzed this association, showing a debating result. Some studies suggested that MS9 and its components like obesity10, diabetes11,12 and hypertension13 would increase the risk of medical complications14–16, overall and cancer-specific mortality17. Additionally, only a few of them concentrated on open pancreatic cancer surgery with controversial conclusions10,18–20.
According to previous studies, the prevalence and clinical diagnostic criteria of MS were different in races9,17,21,22. Notably, the diagnostic criteria of BMI adopted in MS for *Chinese is* lower than that for the Whites, which might have impact on postoperative complications after pancreatic surgery. Therefore, studies only adopted universal criteria for all race for small portion of Chinese population were not exactly correct18,19. This study examined a large group of Chinese patients hospitalized with pancreatic cancer who had underwent open pancreatic cancer surgery to determine if MS was associated with postoperative complications.
## Data collection and study design
The study was conducted in accordance with the ethical standard specified by national health commission of China (Act 11, 2016) and approved by the ethics committee of Changhai Hospital (CHEC2020-170). The requirement for written informed consent was waived by the ethics committee. The clinical registration number is ChiCTR2000031167 (available on http://www.chictr.org.cn/). This study retrieved the medical records of elective pancreatectomies in Changhai Hospital from January 2017 to May 2019.
For the eligible patients in this study, relevant information was retrieved: descriptive and surgical information; diagnostic information of metabolic syndrome; information of complications during hospitalization; prognosis during hospitalization. Telephone follow-ups were made after data collections.
## Diagnosis of MS
The diagnosis of metabolic syndrome is defined on recommendations of Chinese Diabetes Society17: [1] Overweight and (or) obesity, BMI ≥ 25 kg/m2; [2] Hyperglycemia, fasting blood glucose (FPG) ≥ 6.1 mmol/l (110 mg/dl) and (or) 2 h PG ≥ 7.8 mmol/l (140 mg/dl), and (or) diabetes mellitus diagnosed and treated; [3] Hypertension, systolic/diastolic blood pressure ≥ $\frac{140}{90}$ mmHg, and (or) hypertension diagnosed and treated; [4] Dyslipidemia, fasting blood triglyceride ≥ 1.7 mmol/L (150 mg/dl), and/or fasting blood HDL-C < 0.9 mmol/L (35 mg/dl) for male and < 1.0 mmol/L (39 mg/dl) for female. Patients qualified 3 or more of the above 4 components will be diagnosed as MS.
## Primary and secondary outcomes
Our primary outcome was the correlation of composite complications (CC) with MS. A composite of postoperative complications defining as the overall occurrence of any symptoms of the following five components during hospitalization: [1] cardiovascular and cerebrovascular events (CCE), [2] non-pulmonary postoperative infection (NPPI), [3] pulmonary complications (PC), [4] complications requiring surgical intervention (CRSI), and [5] postoperative acute kidney injury (AKI). CCE included myocardial infarction, heart failure, cardiac arrest, stroke, and pulmonary embolism. NPPI were differentiated according to the location or system, such as superficial wound infection, pancreatic fistula, surgical incision, abdominal infection, urinary infection, systemic infection. PC10 included pulmonary infection, atelectasis, pneumothorax, hemothorax, pleural effusion and respiratory related hypoxemia. Postoperative AKI was defined as a categorical variable according to the Kidney Disease Improving Global Outcomes work group, as any increase in postoperative serum creatinine of 0.3 mg/dL or more (to convert to micromoles per liter, multiply by 88.4) or a $50\%$ increase from preoperative baseline serum creatinine level. The Cockcroft–Gault equation was adopted for eGFR evaluation, depending on patients’ gender. Secondary outcome were correlations of components of CC with MS and prognosis of complications.
## Statistical analysis
A propensity score matching (PSM) was performed at a 1:4 fixed ratio nearest-neighbor matching to control for bias from covariates including gender, preoperative biliary stented, and the operation method. The caliper value was 0.2. The normality of data distribution was tested by Kolmogorov–Smirnov test. Continuous variables were expressed as means ± standard deviation or medians (IQR), as appropriate for the data distribution. Group differences were assessed by ANOVA test or Mann–Whitney U tests. Categorical variables were expressed as the number of cases or the percentages (%). Chi-squared or Fisher’s exact tests were adopted for assessing the differences between groups. Given that incidences varied considerably across the four components. For the primary hypothesis that MS patients have increased risk for postoperative complications, a multivariate distinct effect generalized estimating equations model with an unstructured working correlation was employed and the odds ratios (ORs) across the five components were assessed. Binary logistic regression was selected to fit the regression model. Results were reported as a covariate-adjusted OR and its $95\%$ confidence interval (CI) that summarizing the relationship between MS and postoperative composite complications at the 0.05 significance level. Cox regression model was used to analyze the survival of patients within 30 and 90 days after operation. A two-tailed P value < 0.05 was considered to be significant. All analyses were performed using IBM SPSS Statistics v21 (IBM Corporation, NY, USA) or RStudio (version 4.1.3).
## Characteristic data and outcomes in hospital
After screening, there were 3265 patients who had pancreatic cancer during the studied period, of whom, 2415 patients met the inclusion criteria and had complete clinical information. After excluding 585 patients who did not receive pancreatic surgery, 256 patients who had tumor metastasis, 15 patients whose were aged under 18, 44 patients underwent total pancreatectomy, and 34 patients were lost of follow-ups, a total of 235 MS patients and 1246 non-MS patients were analyzed in this study (Fig. 1). After the PSM, there were a total of 1163 patients finally left in the study cohort (MS group = 235, control group = 928).Figure 1Flowchart for the process of inclusion.
Patients’ characteristics were summarized in Table 1. After PSM, in addition to the diagnostic inclusion of metabolic syndrome, age and ASA were statistically different between the two groups. Table 1Descriptive data and outcomes of patients. ParametersBefore PSMAfter PSMMS ($$n = 235$$)Non-MS ($$n = 1246$$)PMS ($$n = 235$$)Non-MS ($$n = 928$$)PGender, n (male%)155 (66.0)706 (56.7)0.008*155 (66.0)626 (66.6)0.853Age, y62.8 ± 10.059.9 ± 12.50.001*62.8 ± 10.060.9 ± 11.70.023*ASA < 0.001* < 0.001*I, n (%)0 (0.0)434 (34.8)0 (0.0)304 (32.3)II, n (%)91 (38.7)519 (41.7)91 (38.7)408 (43.4)III, n (%)134 (57.0)293 (23.5)134 (57.0)228 (24.3)IV, n (%)10 (4.3)0 (0.0)10 (4.3)0 (0.0)Operation time, h3.0 ± 0.92.8 ± 1.00.003*3.0 ± 0.92.9 ± 1.00.132Chemotherapy, n (%)6 (2.6)32 (2.6)0.9896 (2.6)20 (2.1)0.692Biliary stented, n (%)21 (8.9)116 (9.3)0.85621 (8.9)84 (8.9)1.000Blood transfusion, n (%)48 (20.4)264 (21.2)0.79348 (20.4)213 (22.7)0.461Operation method0.006*0.854PD, n (%)156 (66.4)707 (56.7)156 (66.4)618 (65.7)PP, n (%)79 (33.6)539 (43.3)79 (33.6)322 (34.3)BMI, kg/m225.6 ± 2.722.4 ± 2.9 < 0.001*25.6 ± 2.722.3 ± 2.9 < 0.001*Diagnosis of MSHypertension, n (%)182 (77.4)237 (19.0) < 0.001*182 (77.4)181 (19.3) < 0.001*Hyperlipidemia, n (%)198 (84.3)394 (31.6) < 0.001*198 (84.3)323 (34.4) < 0.001*Obesity, n (%)157 (66.8)202 (16.2) < 0.001*157 (66.8)146 (15.5) < 0.001*Diabetes, n (%)197 (83.8)426 (34.2) < 0.001*197 (83.8)337 (35.9) < 0.001*Total length of stay, d14 (10.21)13 (10.18)0.001*14 (10.21)13 (10.19)0.083Clavien-Dindo grades0.0520.2740, n (%)171 (72.7)971 (77.9)171 (72.7)692 (74.6)I, n (%)29 (12.3)119 (9.5)29 (12.3)104 (11.2)II, n (%)31 (13.2)122 (9.8)31 (13.2)105 (11.3)III, n (%)2 (0.9)20 (1.6)2 (0.9)15 (1.6)IV, n (%)0 (0.0)12 (1.0)0 (0.0)10 (1.1)V, n (%)2 (0.9)2 (0.2)2 (0.9)2 (0.2)ICU treatment days, d2 (0.4)2 (0.3)0.0562 (0.4)2 (0.3)0.150Outcomes in 30 days1.000Death, n (%)2 (0.9)8 (0.6)2 (0.9)8 (0.9)Survival, n (%)233 (99.1)1238 (99.4)233 (99.1)932 (99.1)Outcomes in 90 days0.2100.341Death, n (%)5 (2.1)14 (1.1)5 (2.1)14 (1.5)Survival, n (%)230 (97.9)1232 (98.9)230 (97.9)926 (98.5)*P value was less than 0.05, with statistical significance. MS Metabolic syndrome, PSM Propensity score matching, Non-MS Non-metabolic syndrome, Chemotherapy Preoperative chemotherapy, Biliary stented Preoperative biliary stented, PD Pancreatoduodenectomy, PP Partial pancreatectomy.
After PSM, intravenous infusion rate, and the volume of perioperative bleeding were significantly different between the two groups ($P \leq 0.001$, Table 2).Table 2Intraoperative fluid management between MS and non-MS.ParametersBefore PSMAfter PSMMS ($$n = 235$$)Non-MS ($$n = 1246$$)PMS ($$n = 235$$)Non-MS ($$n = 940$$)PR colloidal/crystal0.6 (0.3, 0.9)0.5 (0.3, 0.9)0.035*0.6 (0.3, 0.9)0.5 (0.3, 0.9)0.034*Total fluid, L2.6 (2.1, 3.1)2.3 (2.1, 3.0)0.002*2.6 (2.1, 3.1)2.6 (2.1, 3.1)0.068IV rate, ml/kg/h12.4 (9.3, 16.3)14.7 (11.4, 18.9) < 0.001*12.4 (9.3, 16.3)14.2 (10.9, 18.1) < 0.001*Bleeding, L0.4 (0.3, 0.6)0.3 (0.2, 0.5) < 0.001*0.4 (0.3, 0.6)0.3 (0.2, 0.5) < 0.001**P value was less than 0.05, with statistical significance. MS Metabolic syndrome, PSM Propensity score matching, Non-MS Non-metabolic syndrome, R colloidal/crystal Ratio of colloidal fluid/crystal fluid, Total fluid Total volume of fluid, IV rate Intravenous fluid rate, Bleeding The volume of perioperative bleeding.
## Composite complications
The ORs of MS on postoperative complications after pancreatic surgery were illustrated in Table 3. After PSM, the occurrence of postoperative AKI was significantly different between the two groups (OR: 1.730, $95\%$CI: 1.050–2.849, $$P \leq 0.031$$), and difference in the postoperative composite complications between patients with and those without MS were not different (OR: 1.116, $95\%$CI: 0.808–1.542, $$P \leq 0.504$$). No other postoperative component was found to be significantly different in patients with or without MS ($P \leq 0.05$).Table 3Components of Composite complications after open pancreatic surgery. Before PSMAfter PSMMS ($$n = 235$$)Non-MS($$n = 1246$$)POR$95\%$ CIMS ($$n = 235$$)Non-MS ($$n = 940$$)POR$95\%$ CICC64 (27.2)275 (22.1)0.0851.3220.963–1.81464 (27.2)236 (25.1)0.5041.1160.808–1.542AKI24 (10.2)65 (5.2)0.004*2.0671.265–3.37524 (10.2)58 (6.2)0.031*1.7301.050–2.849CCE3 (1.3)17 (1.4)0.9150.9350.272–3.2163 (1.3)15 (1.6)0.7220.7970.229–2.777PC10 (4.3)45 (3.6)0.6331.1860.589–2.38810 (4.3)35 (3.7)0.7041.1490.561–2.356NPPI43 (18.3)206 (16.5)0.5071.1310.787–1.62543 (18.3)182 (19.1)0.7110.9330.645–1.348CRSI3 (1.3)26 (2.1)0.4160.6070.182–2.0213 (1.3)19 (2.0)0.4550.6270.184–2.136*P value was less than 0.05, with statistical significance. MS Metabolic syndrome, PSM Propensity score matching, Non-MS Non-metabolic syndrome, CC Composite complications, AKI Acute kidney injury, CCE Cardiovascular and cerebrovascular event, PC Pulmonary complications, NPPI Non-pulmonary postoperative infection, CRSI Complications requiring surgical intervention.
The association between each covariate and MS with the postoperative composite complications were summarized in Table 4. After PSM, the results showed patients who underwent partial pancreatectomy had a lower risk of postoperative CC compared to those who underwent pancreatoduodenectomy (OR: 0.524, $95\%$CI: 0.366–0.752, $P \leq 0.001$). A younger age (OR: 0.980, $95\%$CI: 0.968–0.993, $$P \leq 0.002$$) and a shorter operation time (OR: 0.792, $95\%$CI: 0.689–0.911, $$P \leq 0.001$$) were also factors that reduce postoperative CC.Table 4Covariates and MS on postoperative composite complications after pancreatic surgery. ParameterBefore PSMAfter PSMPOR$95\%$ CIPOR$95\%$ CIOperation (PP vs. PD) < 0.001*0.5210.380–0.714 < 0.001*0.5240.366–0.752Age (Young vs. Old)0.004*0.9840.973–0.9950.002*0.9800.968–0.993Gender (F vs. M)0.034*0.7700.604–0.9810.2750.8640.665–1.123Low IV rate0.7710.9970.974–1.0190.4000.9900.967–1.014Short operation time < 0.001*0.7750.686–0.8760.001*0.7920.689–0.911MS (Non-MS vs. MS)0.5190.9070.676–1.2190.7710.9580.715–1.282*P value was less than 0.05, with statistical significance. MS: Metabolic syndrome. Operation (PP vs. PD): Operation of partial pancreatectomy versus operation of pancreatoduodenectomy; Gender (F vs. M): *Female versus* male in term of gender; Age (Young vs. Old): Young age versus old age; Low IV rate: low intravenous infusion rate; Short operation time: short duration of operation time.
## Survival analysis
CCE, AKI, and CSRI were significantly associated with both 30-day and 90-day mortality in multivariate cox regression, whereas PC was only significantly associated with 90-day mortality (All $P \leq 0.001$). The detailed data of multivariate COX regression were shown in Table 5. Cox survival curves of CCE, AKI and CSRI were illustrated in Fig. 2.Table 5Multivariate Cox regression analysis of patients within 30 days or 90 days after surgery. Parameter30 days after surgery90 days after surgeryPOR$95\%$ CIPOR$95\%$ CICCE0.000*28.9155.305–157.5980.000*43.98615.079–128.307AKI0.001*20.1963.653–111.6380.000*11.4783.754–35.098CRSI0.001*15.9593.215–79.2170.000*34.37511.694–101.040PC0.2812.2120.522–9.3700.001*5.7541.955–16.940NPPI0.2043.2180.530–19.5550.9201.0580.351–3.191MS0.8041.2290.241–6.2610.7250.7610.166–3.487*P value was less than 0.05, with statistical significance. CCE Cardiovascular and cerebrovascular event, AKI Acute kidney injury, CRSI Complications requiring surgical intervention, PC Pulmonary complications, NPPI Non-pulmonary postoperative infection, MS Metabolic syndrome. Figure 2Cox survival curves of CCE, AKI and CSRI. CCE: cardiovascular and cerebrovascular event; AKI: Acute Kidney Injury; CRSI: complications requiring surgical intervention.
Overall correlations between the outcomes within 30 and 90 days after the surgery and MS, CC and their components were illustrated in Fig. 3.Figure 3Overall correlations between outcomes and MS or CC and their components. *** $P \leq 0.001$, **$P \leq 0.01$,*$P \leq 0.05.$ ( A) 30-day spearman results; (B) 90-day spearman results. MS: Metabolic syndrome; HT: hypertension; DM: diabetes mellitus; DL: dyslipidemia; OO: overweight or obesity; CCE: cardiovascular and cerebrovascular event; PC: pulmonary complications; AKI: Acute Kidney Injury; NPPI: non-pulmonary postoperative infection; CRSI: complications requiring surgical intervention; CC: composite complications.
## Discussion
Although the diagnostic criteria for MS adopted differently, the percentage of MS patients in pancreatic cancer ($15.9\%$) was consistent with the previous studies (11–$24\%$)18,23.
Postoperative complications of pancreatic surgeries were complex9,24. After PSM, GEE was employed to analyze the overall complications without discussing the distribution form of dependent variables, which fits a marginal model in the context of longitudinal studies25,26. There were no significant association between MS and postoperative composite complications after open pancreatic surgery (OR = 0.958, $95\%$CI: 0.715–1.282, $$P \leq 0.771$$). It indicated that MS did not increase the overall risk in postoperative complications after pancreatic surgery independently.
The conclusion of our analysis was consistent with some studies18,27. However, May C Tee et al. reported that MS patients who received selective pancreatic surgery had an increased risk of postoperative morbidity and some complications9. It inferred that MS may increase some certain complications.
In our results, AKI was significantly associated with MS after PSM (OR: 1.730, $95\%$CI: 1.050–2.849, $$P \leq 0.031$$). Several studies on cardiac surgery have indicated that MS patients were associated with increased rates of postoperative morbidity, infections, cardiovascular and renal adverse events14–16. Congruently, our research demonstrated that MS patients was susceptible to postoperative AKI after pancreatic surgery.
MS components such as obesity, hypertension, elevated TG, low HDL-C, impaired fasting glucose, and MS per se were contributed to decreased GFR28. The Chinese Diabetes Society (CDS) diagnostic criteria of MS adopted a relatively smaller BMI standard (BMI ≥ 25) for Chinese population compared to the western population. The smaller BMI did not prevent occurrence of AKI, although there is a possibility that the risk of postoperative AKI after non-cardiac surgery would be increased with severity of obesity29.
Intraoperative fluid therapy may affect postoperative AKI. Restrict fluid therapy was not used in our center for the pancreatic surgery (in Table 2). According to the previous studies that restricted infusion is more likely to cause postoperative AKI, the statistical differences in intraoperative fluid therapy may not be the key point for AKI30. Multivariate regression analysis showed that intraoperative bleeding was an independent risk factor for postoperative AKI compared with intraoperative volume management (in Supplementary table). It was suggested that strategies to reduce intraoperative bleeding may help to reduce AKI after pancreatic surgery.
Although NPPI accounted for $80\%$ of CC and all complications were associated with outcomes in the spearman analysis, only CCE, AKI and CRSI were associated with survival within 30 and 90 days of surgery after multivariate Cox proportional hazards model analysis. It was implicated that the effective prevention of CCE, AKI and CRSI after pancreatic surgery is helpful to reduce postoperative mortality rate. They require more attention from anesthesiologists and surgeons due to their impact on 30-day and 90-day mortality following pancreatic surgery. Because AKI is more susceptible and increases perioperative mortality for MS patients after the pancreatic surgery. It indicated that MS patients received pancreatic surgery should be paid more attention to postoperative AKI for both anesthesiologists and surgeons.
There are several limits in this study. Firstly, this is a single-centered retrospective study, which largely limited the quality of prognosis. But fortunately, our center is a high volume of pancreatic center, and patients received from all over the country. Because the study is based on Chinese diagnostic criteria, patients across the country can provide a certain degree of reference. Secondly, the definition of MS adopted in this study relied on the CDS criteria published in 2004 exclusively characterized for Chinese population31, which might inherently differentiate the characteristics of our patients from those in previous studies. Yet the influence of different diagnostic criteria for MS on postoperative complications is our study.
In conclusion, we observed that MS patients who received open pancreatic surgery had no association with postoperative composite complications during hospitalization. But MS is an independent risk factor for postoperative AKI of pancreatic surgery in Chinese population. And AKI was associated with perioperative survival of MS.
## Supplementary Information
Supplementary Information 1.Supplementary Information 2.Supplementary Information 3. The online version contains supplementary material available at 10.1038/s41598-023-31112-x.
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|
---
title: Inhibitory effect of 405 nm laser light on bacterial biofilm in urethral stent
authors:
- Luluil Maknuna
- Van Nam Tran
- Byeong-Il Lee
- Hyun Wook Kang
journal: Scientific Reports
year: 2023
pmcid: PMC9995349
doi: 10.1038/s41598-023-30280-0
license: CC BY 4.0
---
# Inhibitory effect of 405 nm laser light on bacterial biofilm in urethral stent
## Abstract
The clinical use of urethral stents is usually complicated by various adverse effects, including dysuria, fever, and urinary tract infection (UTI). Biofilms (formed by bacteria, such as Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus) adhering to the stent cause UTIs in stented patients (approximately $11\%$). The undesirable consequences of antibiotics use include bacterial resistance, weight gain, and type 1 diabetes, which occur when antibiotics are used for a long time. We aimed to assess the efficacy of a new optical treatment with a 405 nm laser to inhibit bacterial growth in a urethral stent in vitro. The urethral stent was grown in S. aureus broth media for three days to induce biofilm formation under dynamic conditions. Various irradiation times with the 405 nm laser light were tested (5, 10, and 15 min). The efficacy of the optical treatment on biofilms was evaluated quantitatively and qualitatively. The production of reactive oxygen species helped eliminate the biofilm over the urethral stent after 405 nm irradiation. The inhibition rate corresponded to a 2.2 log reduction of colony-forming units/mL of bacteria after 0.3 W/cm2 of irradiation for 10 min. The treated stent showed a significant reduction in biofilm formation compared with the untreated stent, as demonstrated by SYTO 9 and propidium iodide staining. MTT assays using the CCD-986sk cell line revealed no toxicity after 10 min of irradiation. We conclude that optical treatment with 405 nm laser light inhibits bacterial growth in urethral stents with no or minimal toxicity.
## Introduction
Urethral stents (USs) are used to treat bladder outlet obstruction caused by various diseases. Urethral stricture disease, benign prostatic hyperplasia (BPH), and detrusor sphincter dyssynergia (DSD) are indications for US insertion in eligible patients1. Typically, US is constructed of a metal alloy (nitinol), polymeric materials, or biodegradable material that is robust enough to maintain urethral patency. Culha et al. have reported long-term US efficacy of $63\%$ in patients with recurrent urethral strictures2. Therefore, the unsuccessful result of stent insertion, such as stent migration, encrustation, chronic urinary infection, urethral pain, and restenosis, are the complications of stent placement3. Riedl et al. discovered that all patients with chronic indwelling stents have stent colonization and bacteriuria. Temporary stents are also widely colonized, with a $69\%$ occurrence rate, and $45\%$ of patients are affected by bacteriuria; such microbial colonization causes UTI4. UTIs are caused by Gram-positive and negative bacteria, such as Escherichia coli, Klebsiella pneumoniae, Proteus mirabilis, Enterococcus faecalis, and *Staphylococcus aureus* (S. aureus), and certain fungi5.
Urinary infection can occur when sterile US is implanted into the human body. Biofilm refers to an accumulation of bacteria and their extracellular byproducts that form an organized community on the surface6. Biofilm growth significantly impacts foreign objects or equipment implanted in the human body. A wide range of the foreign bodies, such as US, implantable device, sling, have been developed for urological applications over the past few decades6. Thus, on the surface of an implant, the biofilms are highly structured and comprise actively growing populations of bacteria, proteins, and extracellular polymers. The increased biofilm formation can result in bacterial resistance to antibiotics and chronic infections7.
Currently, antibiotics and stent surface coatings are used to prevent urinary infections8. Although most antibiotics are common, some antimicrobials have potential side effects if used for a long period of time9. The risks of using antibiotics include bacterial resistance, obesity, and diabetes10. The adverse effects of using antibiotics may include malabsorption resulting in a celiac-like disease, reduced medicine absorption, altered metabolism and absorption of vitamins, colonization by resistant organisms, and altered susceptibility to infections11. Laser application for bacterial inhibition via optical fibers is gaining attention as an alternative method to address these issues12,13.
A wide range of wavelengths can be applied (from 400 to 1000 nm), and among the wavelengths, blue light has shown effectiveness in reducing the viability of a variety of bacterial species, including methicillin-resistant S. aureus (MRSA), Helicobacter pylori, Porphyromonas gingivalis, and Pseudomonas aeruginosa, while most spectra at high irradiance are able to kill bacteria by photothermal effect even with low irradiance14,15. Another laser source, such as ultraviolet (UV) light, is also well recognized for bactericide. However, due to cytotoxic effects on mammalian tissue, the in vivo use of UV light is restricted16. Dai, et al. discovered a $57\%$ loss of keratinocytes when researching UV light therapy for central line infections14. This cytotoxicity indicates that despite being an effective anti-microbial agent, the application of the UV light may not be the ideal choice for living tissue17. Thus, 405 nm laser light (blue light) has been widely used for bacterial disinfection18,19. Maclean et al. concurred that oxidative damage to the bacteria and photoexcitation of porphyrins at the 405-nm wavelength might generate the highest germicidal activity20,21. In addition, porphyrins and other photoactive substances can decontaminate bacteria by generating reactive oxygen species (ROS) as a result of the absorption of the 405-nm light. ROS is a class of free radicals comprising of singlet oxygen, superoxide anion, hydrogen peroxide, and hydroxyl radicals. During bacterial cytotoxicity, under the violet-blue light exposure (405 nm), endogenous photosensitizing molecules (porphyrins) get photo-excited and generate ROS. The sufficient production of ROS leads to cellular lysis via oxidative stress. *In* general, the production of ROS in bacteria involves two pathways during photodynamic therapy (PDT): Type 1 and Type 2. In Type 1, endogenous porphyrin reacts directly with the cellular components, including protein, lipids, nucleic acids, and other micro molecules, to generate superoxide anion and hydroxyl radical, which, in turn, leads to the production of other ROS molecules. In type 2, porphyrin binds with molecular oxygen to form singlet oxygen. This mechanism subsequently leads to oxidative damage to light-exposed bacterial cells and causes cellular death21,22. Other mechanisms, such as the disruption of bacterial membranes and DNA damage, have been reported to be the effects of blue light23.
Although antibiotics has been used for treating UTIs, the current medication is still associated with various side effects, such as obesity and bacterial resistance. The aim of the current study was to examine an alternative treatment for UTI by irradiating laser light on the bacterial biofilm grown in US via an optical device. It was hypothesized that using 405 nm laser light could inhibit bacterial biofilm as a phototherapy in stented patients to prevent UTIs. This in vitro study was designed as a first-stage investigation for the alternative treatment of UTIs. A 405 nm laser from a basket-integrated optical device was used to deliver light onto the inner surface of the US. For safety evaluations, MTT assays were used to evaluate the effect of 405 nm laser light as a phototherapeutic agent to inhibit bacterial biofilm formation in human cell lines. The proposed optical treatment can be a feasible alternative treatment for bacterial infection in stented patients.
## Bacterial culture in US
The bacterial culture condition in the US was analyzed by using the CV staining method to estimate the approximation of bacterial viability during the culturing time. Each day offers the different biofilm formations, based on Fig. 1a (0–3 day culturing). The CV staining worked as an intercalating dye, which is used for fast biofilm visualization, and the stianing allowed the quantification of cells, as shown in Fig. 1b. The estimation of biofilm formation on day 3 reached more than $80\%$, compared to that on day 0.Figure 1Growth of bacterial biofilm: (a) urethral stents stained with crystal violet at various time points after culturing and (b) bacterial viability versus culture time.
## Evaluation of effect 405 nm laser in human cell lines (CCD-986sk)
An MTT experiment was carried out to evaluate cell viability under both pre-and post-treatment settings to select an alternative method of removing the bacterial biofilm from US with 405 nm laser light. Treated cells showed the decreased cell viabilities of $2\%$ (5 min), $4\%$ (10 min), and $10\%$ (15 min) after 24 h of incubation, in comparison to control ($p \leq 0.05$). The MTT assay results demonstrated that the 15 min laser irradiation caused toxic effect to the cells, leading to more than $10\%$ cell reduction (Fig. 2a). Temperature development in Fig. 2b demonstrates that the maximum reached a temperature of 32 °C for 15-min irradiation. Figure 2Response of normal cells (CCD-986sk) to 405 nm laser irradiation: (a) cell viabilities of various irradiation times (CTRL = control; (*$p \leq 0.05$ vs. CTRL; $$n = 5$$) and (b) temperature development during laser irradiation on surface of CCD-986sk suspension in DMEM media.
## Evaluation of bacterial inhibition in US
CV staining was utilized to calculate the bacterial survival before and after laser treatment in order to evaluate the inhibitory impact of 405 nm laser light on S.aureus. As represented in Fig. 3a, the bacterial viability decreased by $26\%$ in 5 min ($p \leq 0.05$), $59\%$ in 10 min ($p \leq 0.001$), and $68\%$ in 15 min ($p \leq 0.001$), respectively. The maximum temperature on the stent surface reached 37.5 °C (Fig. 3b).Figure 3Inhibition of bacterial biofilm after 405 nm laser irradiation: (a) bacterial viability after exposure of 405 nm laser light at various irradiation times (CTRL = control; *$p \leq 0.05$ and **$p \leq 0.001$ vs. CTRL; $$n = 5$$) and (b) temperature development during laser irradiation on urethral stent surface.
Figure 4a shows the determination of S.aureus in CFU after 405 nm irradiation, and each treatment condition depicts a different number of colonies ($$n = 10$$). The results showed that the total colony decreased after the irradiation and was represented in log CFU/mL (1.2 log, 2.2 log and 3.2 log reduction for 5, 10, and 15 min) (Fig. 4b). Although the maximum inhibition reached a 3.2 log reduction ($p \leq 0.01$) in 15 min irradiation (270 J/cm2), the human cell viability (CCD-986sk) (Fig. 2a) under the same condition also decreased by more than $10\%$. Thus, the 10 min laser irradiation could accomplish the minimum inhibition rate (2–3 log reduction) to prevent the biofilm formation in US with the safety treatment dose. Figure 4S. aureus bacterial inhibition after exposure of 405 nm laser at various irradiation times: (a) bacterial cells on agar plates (CTRL = control) and (b) quantification of bacterial removal in log CFU/mL (*$p \leq 0.05$ and **$p \leq 0.01$ vs. CTRL; $$n = 10$$).
Figure 5a presents the levels of ROS generation determined by NBT staining, which was entered into the cell membrane. The result shows that the NBT could detect the ROS generation in all treated conditions. The results of ROS generation were 21.7, 46.7, and $74.4\%$ ($p \leq 0.05$) for 5, 10, and 15 min, respectively. According to this result, longer irradiation of 405 nm laser could produce more ROS that affected cellular death. Figure 5b demonstates the result of the scavenging activity of antioxidants by using the DPPH assay. The results demonstrates that the free radicals after the ROS production increased, based on the irradiation time of 405 nm laser light. The treatment conditions (5, 10, and 15 min) reached $22\%$ ($p \leq 0.05$), $43\%$ ($p \leq 0.001$), and $62\%$ ($p \leq 0.001$) of antioxidant activity compared to control, respectively. The control group (no laser treatment) showed the smallest antioxidant capacity whereas the treated groups yielded the increasing antioxidant capacities with the increasing light doses. The bacterial group that received the greater dose of 15 min revealed a statistically significant increase in the DPPH scavenging activity ($p \leq 0.05$) in comparison to the control. Figure 5c presents the concentration of protein leakage before and after treatment by using the Lowry method. The observation of the concentration of protein leakage in 5, 10, and 15 min reached 37, 71, and 69 µg /mL, respectively. The largest amount of protein leakage after the laser treatment was measured in 10 min of the laser irradiation and decreased in 15 min. The protein leakage activity reached a stationary state after 10 min of treatment because the protein concentration dropped in 15 min of irradiation, compared to 10 min laser irradiation ($$p \leq 0.92$$). These findings suggested that the 405 nm laser could enhance bacterial death by causing bacteria to undergo the increased oxidative stress and allowing the proteins to leak out of the damaged bacterial membrane. Figure 5Optical inhibition effect of 405 nm laser light against S. aureus in urethral stent: (a) ROS generation during treatment, (b) DPPH scavenging activity, and (c) concentration of protein leakage using Lowry method (*$p \leq 0.05$ and **$p \leq 0.001$ vs. CTRL; ns = not significant; $$n = 5$$), and (d) SEM images of S. aureus after laser treatment (blue, orange, and red arrow represent thick biofilm, bacteria released from biofilm, and bacterial death with morphological damage (scale bar = 2 µm; 20 kX and scale bar of inlet = 20 µm; 2 kX).
The membrane integrity loss of the bacterial biofilms exposed to four different treatment conditions are shown in SEM (Fig. 5d) and fluorescent images in Fig. 6 (CTRL, 5, 10, and 15 min). A significant proportion of bacterial colonies have developed into a thick biofilm architecture made of extracellular polymeric materials (blue arrow) in the untreated control (Fig. 5d (CTRL)). According to Fig. 5d (5 min), the 405 nm laser light destroyed the biofilm and released the biofilm into a single cell (orange arrow). The bacterial colonies in the biofilm deposited in the US showed the absence of intact biofilm morphology. In Fig. 5d (10 and 15 min), some bacteria were damaged (red arrow), indicating cell death, and the bacteria were removed from the US.Figure 6Analysis of bacterial membrane integrity after laser treatment: (a) live bacteria stained with SYTO 9, (b) dead bacteria stained with PI, (c) quantification of pixel intensity for live bacteria (green intensity), and (d) quantification of pixel intensity in dead bacteria (red intensity) (**$p \leq 0.001$ vs. CTRL; ns = not significant; $$n = 7$$; scale bar = 100 µm; 40X).
Fluorescent image assesment indicated that the implementation of 405 nm laser light significantly decreased the number of bacterial cells with few intact cell membranes remaining. The SYTO 9 marked the remaining intact membrane as live bacteria (green color), and the population after the treatment was lower than that of control (Fig. 6a). Propidium iodide (PI) demonstrated that the number of the bacteria after the treatment was higher than that of the control, which means more membrane damage represented in red color after the treatment (Fig. 6b). The quantification of live bacteria (Fig. 6c) represented in green intensity confirmed that the 405 nm laser light could inhibit the bacterial biofilm in US, which agrees well with the results of SEM images (62, 21, and $4\%$ for 5, 10, and 15 min ($p \leq 0.001$), respectively). The quantification of dead bacteria validated that the 405 nm laser light could inhibit the bacteria that obtained higher red intensity after the 405 nm laser exposure (17, 68, and $98\%$ for 5, 10, and 15 min, respectively) (Fig. 6d).
## Discussion
The current study attempted to understand the effect of 405 nm laser light against the bacterial biofilm-developed US. The laser treatment was used as an attempt to replace or assist the use of antibiotics to prevent the side effects. Another treatment to prevent the UTIs is stent removal, but along with that, trauma and other risks, such as major urological complications after stent removal and the urethral obstruction, still occur24,25. Thus, the alternative treatment, such as laser irradiation, can be another option to prevent UTIs.
405 nm laser light has an ability to inhibit bacterial biofilm formation in the US. The mechanism to inhibit the biofilm formation relies on ROS generation, which can affect membrane disintegration and cell death26,27. According to a prior work28, endogenous porphyrins in bacterial cells selectively absorbed 405 nm laser light and produced the intracellular ROS required to effectively kill the bacteria. Additionally, the presence of ROS can result in lipid peroxidation, protein and nucleic acid oxidation, and enzyme inhibition, all of which may destroy microorganisms21. Giuliano et al. reported that a typical patient under a urologic procedure with a positive urine culture has 103 colonies29. The bacterial reduction from the present work confirmed that the inhibition using the 405 nm laser light could reduce more than 3 log reduction after 15 min of laser exposure with an irradiance of 0.3 W/cm2. Although the condition was insignificantly toxic to normal cells (Fig. 2), the safety dose should still be confirmed for this treatment prior to clinical translations. Bauer et al. reported the cytotoxic effects were found at 300 J/cm2 after 405 nm laser exposure30. An MTT assays performed after 15 min laser irradiation (270 J/cm2) also confirmed cytotoxic effects with more than $10\%$ cell reduction. Therefore, the 10-min laser irradiation of 405 nm laser light could be the safe dose (180 J/cm2) with less than $10\%$ cell reduction and 2.2 bacterial log reduction.
ROS generation after 405 nm laser treatment was examined by using NBT staining, which interacts with superoxide ions to produce an insoluble and stable intracellular purple/blue formazan precipitate31. The production of ROS is affected by the exposure time of the 405 nm laser to bacteria. The longer light exposure is applied to the bacteria, the more ROS can be detected in each condition. In addition, scavenging activity affected by ROS was carried out by DPPH assay, which yielded similar results to ROS generation after the laser treatment. The capacity of the 405 nm laser that interacts with bacteria to donate hydrogen to DPPH and convert it to DPPH-H is considered to be a free radical scavenging efficacy. This phenomenon resulted in a decrease in the absorbance value after the radical purple color (DPPH) was changed to yellow color (DPPH-H)32. Moreover, the effect of 405 nm laser light against the bacterial cells was confirmed by using the estimation of protein leakage by Lowry’s method. Interestingly, the concentration of protein leakage after the treatment increased, but for 15 min irradiation, the concentration of protein starting decreased and resulted in the lower amount of protein release possibly because of the disintegration of the protein molecules by the prolonged laser irradiation. The largest amount of the protein concentration occurred in 10 min with 71 µg/mL. Since the protein is a type of essential intracellular component33, protein leakage is considered to be an indication including both cytoplasmic leakage, damaged of lipid layer34 and cell membranes35.
The effect of 405 nm laser on morphological cells were dependent on the 405 nm laser irradiation time, resulting in biofilm removal and cell damages in 10 and 15 min. The structure changes from the perfect circle to shrunk circle, caused by the membrane disintegration which confirmed by SEM images of bacteria. One criterion for determining whether bacterial cells are alive or dead is thought to be cellular and membrane integrity. The live cells are supposed to have the intact and tight cell membranes that are impermeable to some stainings, whereas the dead cells are assumed to have disrupted and/or damaged membranes36. SYTO 9 staining bind to DNA and RNA and emit green flurescence while PI can only enter cells with the compromised membrane, which binds to DNA and RNA and emits the red fluorescence signals37. The membrane integrity loss after 10 and 15 min laser irradiation had a lower green pixel intensity and has a higher red pixel intensity compared to control. It was confirmed that the 405 nm laser caused the membrane disintegration of the bacteria and emited the red fluorescence signals from the dead bacteria after the treatment.
For clinical applications, the proposed method is expected to be used for patients with UTI by inserting a basket-integrated optical device into the urethra and positiong it on the US. The diffusing optical fiber situated in the urethral channel can irradiate 405-nm laser light onto the surface of the in-dwelling US to prevent or minimize the formation of bacterial biofilm on the US with photo-inhibitory effects. Further works should thus test an in vivo model to explore clinical doses and to adapt the optimal dose to a urethral channel and peripheral tissues. The minimum inhibition rate to remove 103 colonies could be different under the real-time condition. Thus, it is necessary to carry out experiments under the conditions that can mimic the urethral channel. In addition to make it one of the treatments for UTI in stented patients, we need to clarify and monitor the photo-inhibitory effect against urological diseases, such as urethral obstruction and urethral stricture. Lastly, a pilot study should be performed to identify and optimize the clinical treatment conditions, such as laser power, irradiation time, and number and period of treatment, as well as to validate the efficacy and safety for clinical translations.
In conclusion, the present work demonstrated the effectiveness of a 405 nm laser light for bacterial inhibition in US with 2–3 log bacterial reduction and the safety margin. The ROS generation after 405 nm laser exposure to the biofilm contributed to protein leakage, membrane disintegration, and affected cell death. Further studies will examine the simultaneous bacterial inhibition using the multi-bacterial in vivo to mitigate the risk of UTI in stented patients by reducing the presence of bacterial resistance caused by the prolonged use of antibiotics.
## Bacterial biofilm formation
Sterile USs were purchased from UVENTA™ Urethral stent; TAEWOONG Medical, South Korea. The US was self-expandable, with 80 mm in length and 10 mm in diameter,and consisting of an implantable full-coverage metallic structure made of nitinol wire and covered with a thin layer of silicone (not biodegradable). S. aureus (KCTC 1916) from bacterial stock culture (− 80 °C) were placed in an agar plate and inoculated for 24 h. Then, the bacteria were subcultured in tryptic soy broth (TSB) (50 mL) and incubated for 24 h in an incubator at 37 °C. To determine the number of bacterial colonies, the McFarland method38 was used to obtain 108 colonies in 10 mL. Initially, fresh medium was pumped (P1 = 1.5 mL/min) in a small silicone tube (inner diameter = 4 mm; outer diameter = 5 mm; Sungjin, South Korea) with bacterial suspension injection (10 mL). Then, the second pump (P2 = 50 mL/min) was turned on when P1 drained the media throughout the silicone tube, and was used to drain the larger silicone media (inner diameter = 10 mm; outer diameter = 12 mm; Sungjin, South Korea) for three days. A US was placed in a larger silicone tube, and the silicone was placed in a water bath at 37 °C. Subsequently, the media that was used flowed into another bottle. Fresh media was pumped every 4 h with P1 to keep the bacteria from growing and forming a uniform biofilm in the US. It should be noted that the current process was focused on the development of the bacterial biofilm in the US in vitro, which hardly mimics clinical conditions. Figure 7A shows the setup. After being cultured for three days, the US was stained with crystal violet (CV) to determine whether the biofilm was formed during the culturing days, as shown in Fig. 7b. Figure 7Schematic illustrations of bacteria preparation: (a) bacterial culturing in urethral stent under dynamic condition, (b) examples of urethral stent before (day 0) and after culturing, and (c) 405 nm laser treatment set up. ( day 3; stained with crystal violet; TSB = tryptic soy broth media; SA = S. aureus suspension; WB = water bath; US = urethral stent; UM = used media;P1, P2 = pump, S1 = small silicone, S2 = large silicone, and DF = diffusing fiber).
## Experimental setup
The basket-integrated optical device used in this study was adapted by modifying the basket shape, diameter (10 mm), and the characterization of the material that was used for the basket device from previous work39. The basket, which was integrated with an optical diffuser, aims to maintain the position of the diffusing fiber in the center when placed in the US, such that the spread of light can be uniform in all directions. Blue light (405 nm; CNI laser, Changchun, P.R. China) was used as the light source with an irradiance of 0.3 W/cm2. The treatment conditions were varied, depending on the irradiation times of 5, 10, and 15 min. Control was a non-treated condition. The corresponding fluences were 90, 180, and 270 J/cm2. For the treatment area, we cut the stent into 10 mm in length and exposed the 405 nm laser light inside the stent. An experimental setup was developed by placing the cultured US in a standing holder, and a basket-integrated device was then positioned in the same direction and inserted into the US at the center. An infrared camera (FLIR A325, 320 × 240 pixels, resolution = 25 μm, spectral range = 7.5–13 μm; FLIR, Wilsonville, Oregon) was placed 30 cm above the US surface and controlled with a personal computer to monitor temperature development during treatment (Fig. 7c).
## MTT assay
The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT; Sigma- Aldrich, St. Louis, MO, USA) assay was used to evaluate safety and toxicity of the 405 nm laser light. The CCD-986sk cell line (procured from the Korean Cell Line Bank) was used as the normal cell line in all treatment conditions. CCD-986sk cells were thawed in a water bath at 37 °C, placed in a 15 mL conical tube, 3 mL of Dulbecco’s modified Eagle’s medium (DMEM, Corning, NY, USA) was added, and the suspension was centrifuged at 1000 × g for 5 min. The supernatant was removed, and the cells were placed in a culture dish with 10 mL DMEM for 48 h. Cell subculture was performed every two or three days to obtain a stable cell line for the MTT assay. The stable cell lines were counted, seeded in 24-wells plates, and incubated for 24 h to allow the cells to grow well before treatment. The cells were treated according to the treatment conditions applied to the US (irradiation times: 5, 10, and 15 min) with an irradiance of 0.3 W/cm2. Treated cells were incubated for 24 h. After the incubation, the medium was removed, and MTT solution was added and incubated for 3 h in the dark at 37 °C. The MTT solution was removed, replaced with dimethyl sulfoxide (DMSO), and incubated for 15 min. The absorbance was determined spectrophotometrically at 570 nm using a microplate reader (Multiskan GO, Thermo Fisher Scientific, Waltham, Massachusetts, USA; $$n = 10$$ per condition).
## Bacterial viability
After treatment, CV staining was used to estimate bacterial viability by washing the US with distilled water and incubating for 20 min in CV solution. Subsequently, a solubilization solution was added ($95\%$ ethanol with $5\%$ distilled water) and sonicated for 10 min. The bacterial solutions were placed in a 96-well plate, and its absorbance at 570 nm was measured using a microplate reader (Multiskan GO, Thermo Fisher Scientific, Waltham, Massachusetts, USA; $$n = 10$$ per condition). In addition, bacterial viability was calculated in terms of colony-forming units (CFU). Ten millimeters of US were washed with 1 mL distilled water and placed in a 15 mL conical tube. US was sonicated for 10 min and vortexed for 5 min. The bacterial suspension was then diluted tenfold, and 100 μL of the suspension was spread on a tryptic soy agar (TSA) plate and incubated for 24 h at 37 °C. After incubation, agar plates were analyzed using OpenCFU40 to calculate the number of bacterial colonies, which is represented by the log number of the bacterial viability on the 10 mm US surface (CFU/mL, $$n = 10$$ per condition).
## Evaluation of oxidative stress
To investigate the dominant mechanism of the proposed treatment, both the control and treated US were placed in a 15 mL conical tube with 1 mL distilled water, sonicated for 20 min, and vortexed for 5 min to detach the bacteria in the US. A 1 mg solution of nitro blue tetrazolium (NBT) was added to the tube and incubated for 30 min in the dark. After 0.1 M HCl was added to the solution to inhibit bacterial interaction with NBT, all tubes were centrifuged at 12,000 × g for 5 min. To release intercellular ROS, the pellets were first treated with 800 mL of saline and 400 mL of dimethyl sulfoxide (DMSO). To estimate ROS generation, 200 μL of each sample was placed in a 96-well plate and measured at 575 nm ($$n = 5$$ per condition). The collected ROS generation was normalized by the absorbance of control samples to exclude any experimental errors caused by the sonication procedure. The following equation was used to determine the level of intercellular ROS amplification (Ri):\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ R_{i} = \left[{\frac{{\left({At - Ac} \right)}}{Ac} \times 100} \right] $$\end{document}Ri=At-AcAc×100where *Ac is* the absorbance of the control and *At is* the absorbance of the treated sample.
## DPPH scavenging activity
1,1-Diphenyl-2- picrylhydrazyl (DPPH) is a stable free radical used to evaluate the general radical-scavenging capabilities of various antioxidants after ROS generation. DPPH assays were performed by adding the DPPH solution (40 µg/mL methanol) to the control and treated cells and incubating for 30 min in the dark. A UV spectrophotometer was used to detect the absorbance at 517 nm, and the experiment was repeated five times. A log dosage inhibition curve was used to obtain the standard’s IC50 value41, which is the concentration of the standard required to inhibit $50\%$ of the DPPH free radical. The reaction mixture’s lower absorbance suggested the increase free radical activity. The following equation was used to calculate the percentage of the DPPH scavenging (Ds) effect.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ D_{s} = \left[{\frac{{\left({Ac - At} \right)}}{Ac} \times 100} \right] $$\end{document}Ds=Ac-AtAc×100where *Ac is* the absorbance of the control, and *At is* the absorbance of the treated sample.
## Protein leakage
The method described by Lowry et al. [ 1951] was used to assess protein released from S. aureus cells (106 CFU/mL) before and after treatment with 405 nm laser light for 24 h. Absorbance was measured spectrophotometrically at 660 nm. The amount of released protein was determined by extrapolating an equation from the calibration curve using bovine serum albumin (BSA), as previously described42.
## Microscopic evaluation of biofilm formation
To evaluate biofilm formation architecture, control and treated US were washed several times with distilled water and fixed using $2.5\%$ glutaraldehyde with pH 7.5 for 4 h at room temperature (26 °C). After incubation, the US was washed with $100\%$ ethanol and left to dry at room temperature. The control and treated US were cut into small pieces (5 mm) and covered with gold–palladium for SEM analysis.
A LIVE/DEAD BacLight Bacterial Viability kit (Bio Probes, Eugene, OR, USA) was used to evaluate live and dead bacteria based on the membrane integrity of the control and treated stents. The bacterial suspension from the control and treated US was centrifuged at 1000 rpm for 5 min to collect the bacterial biofilm attached to the stent. SYTO 9 and propidium iodide (PI) stains were then added at a 1:1 concentration and incubated for 15 min. After incubation, 1 μL suspension was added to a glass slide and covered with a coverslip under a fluorescence microscope. Live and dead bacteria are represented in green and red, respectively. A quantitative analysis of the fluorescence images was performed by using the total number of green and red pixel intensities and calculating the RGB values with Image J (National Institute of Health, Bethesda, MD, USA).
## Statistical analysis
Data are represented as the mean and standard deviation under four conditions: one as a control and three other treatment conditions (irradiation times: 5, 10, and 15 min). Each condition (control and treatment) was repeated five times ($$n = 5$$). CFU analysis was performed in 10 replicates to ensure appropriate data. The Mann–Whitney U test was used to evaluate all conditions by comparing the control and type conditions. SPSS (SPSS, Chicago, USA) was used for statistical analysis, and $p \leq 0.05$ was considered statistically significant.
## Ethics approval
This article does not contain any studies with human participants performed by any of the authors.
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|
---
title: Pre- and peri-natal hurricane exposure alters DNA methylation patterns in children
authors:
- Erin Kello
- Alexandre R. Vieira
- Sona Rivas-Tumanyan
- Maribel Campos-Rivera
- Karen G. Martinez-Gonzalez
- Carmen J. Buxó
- Evangelia Morou-Bermúdez
journal: Scientific Reports
year: 2023
pmcid: PMC9995354
doi: 10.1038/s41598-023-30645-5
license: CC BY 4.0
---
# Pre- and peri-natal hurricane exposure alters DNA methylation patterns in children
## Abstract
Hurricane Maria was the worst recorded natural disaster to affect Puerto Rico. Increased stress in pregnant women during and in the aftermath of the hurricane may have induced epigenetic changes in their infants, which could affect gene expression. Stage of gestation at the time of the event was associated with significant differences in DNA methylation in the infants, especially those who were at around 20–25 weeks of gestation when the hurricane struck. Significant differences in DNA methylation were also associated with maternal mental status assessed after the hurricane, and with property damage. Hurricane Maria could have long lasting consequences to children who were exposed to this disaster during pregnancy.
## Introduction
Puerto Rico (PR) is an archipelago in the Eastern Caribbean with a population of approximately 3.3 million1. It has been an un-incorporated territory of the United States since 1898 characterized by high poverty rate ($43\%$) and high income inequality2. The island is burdened by significant public health problems, including one of the highest prevalence rates of premature births in the United States ($11.8\%$, 2020) and worldwide3,4, and a high incidence of HIV infections. The Zika epidemic also affected the island5.
On September 20 of 2017, PR was devastated by Hurricane Maria, a category 4 storm that affected the entire island. Two weeks earlier, Hurricane Irma, a category 5 storm had passed very close to the island causing significant damage on the infrastructure. The storms completely wiped-out the island’s electric power infrastructure, water supplies, communications, and transportation systems (including all ports and airports), leaving many residents isolated for several weeks. By November 2017, two months after the storm, only $50\%$ of the electric power generation had been restored, and $10\%$ of the citizens were still without water service; by February 2018, only $70\%$ of the electric power generation capacity had been restored5. In October 2017 it was estimated that there were 51 deaths directly caused by the hurricane, and an additional 900 potentially hurricane-related deaths. Subsequent studies estimated that the “excess all-cause deaths” in Puerto Rico during the three months following Hurricane Maria could amount to more than 40006.
Although it is far too soon to estimate the public health consequences of Hurricane Maria in Puerto Rico, pregnant women were an especially vulnerable population during this unprecedented disaster because of the distress from the fearful experience itself, the disruption of prenatal care, nutritional alterations due to lack of electricity and fresh food supplies, and possible exposures to infectious and toxic materials. During the storm the majority of the 69 major hospitals on the island were left without electricity or fuel for their generators, and only three hospitals were functional 3–4 days after the storm2. Although obstetric services had to be available $\frac{24}{7}$, not all women had access to them, and the conditions were far from adequate. The University Hospital was fully operational by day 8, and by day 9 it was estimated that the number of deliveries was $33\%$ higher compared to the same month of the previous year2. The threat to life and multiple causes of a state of loss associated with this hurricane were a source of increased PNMS that can lead to significant maternal mental health problems like depression and post-traumatic stress disorder (PTSD)2,6,7. Dietary changes are also a common consequence in these types of emergencies due to the lack of access to fresh food supplies8. The Island’s prevailing high importation rate added to the direct impact of the hurricane on the ports of entry further aggravated the loss of resources available to address basic needs.
Prenatal maternal stress due to natural disasters has been shown to impact practically all spheres of child development, including birth outcomes, and cognitive, motor, physical, socio-emotional, and behavioral development9,10. Previous experience from natural disasters like Hurricane Andrew [1992], Hurricane Katrina [2005], the Iowa Floods [2008], the Queensland Floods [2011] and the Ice Storm of Québec [1998] have shown that these types of prenatal stressors can lead to adverse pregnancy outcomes, such as prematurity, reduced birth weight and head circumference and other neonatal complications, especially in highly exposed women9,11–14. The long-term health consequences for the children who were prenatally exposed to these events are far more important, and they include effects on early infant temperament and motor development15, early cognitive development16, increased risk for immunologic conditions such asthma17 autism traits18,19 and obesity11.
Prenatal maternal stress may affect the fetus via the release of maternal stress hormones however, many of the long-term effects of prenatal maternal stress on offspring health can also be attributed to epigenetic changes to the fetal genome20,21. Children and adolescents prenatally exposed to the 1998 Québec Ice Storm showed significant differences in methylation levels of thousands of CpG sites, based on their mother’s objective prenatal maternal stress during the storm, and/or her cognitive appraisal of the impact of the storm in her life. The differentially methylated CpGs were affiliated with 1564 different genes and 408 biological pathways, primarily related to immune function22. These epigenetic changes were associated with significant health problems later in the life of these children, including obesity and diabetes23,24, and, more recently, brain development25.
The objective of this study is to evaluate DNA methylation profiles in children who were exposed to Hurricane Maria in Puerto Rico during pregnancy in relation to hurricane-related maternal mental health exposures. Assessments were performed within two years of the exposure to the hurricane using validated measures for objective and subjective maternal stressors. Our hypothesis was that children who were prenatally exposed to maternal psychosocial and environmental stressors associated with Hurricane Maria in Puerto Rico would have an altered DNA methylation profile. We report that the timing of the traumatic maternal event affects methylation patterns differently as expressed in early childhood. Specific measures of maternal stress are also associated with differing methylation patterns. The results of this study provide information for clinical providers working with women who experience a traumatic event during pregnancy. Our findings further inform the current understanding of the temporal dimension of DNA methylation during development by demonstrating differences in methylation levels based on the period of gestation at the time of the major traumatic event.
## Results
A total of 47 significant differentially methylated single probes (DMPs) were associated with all hurricane-related variables tested (Supplemental Table 1).
We identified 30 significant DMPs associated with the gestation stage in weeks at the time of the hurricane (Fig. 1). Almost all DMPs significantly associated with the timing of the exposure are hypermethylated. The most significant hypermethylated probes cluster on chromosomes 1–4.Figure 1Manhattan plot of significant CpG sites associated with the stage of gestation [weeks] at the time of hurricane impact. Red lines represent FDR significance threshold.
Mean methylation levels were calculated for each subject and used to explore the direction of methylation changes dependent on the time of maternal exposure to the hurricane. Maternal exposure between 20- and 25-weeks’ gestation yields higher mean methylation levels (Fig. 2). Average methylation values for women that were exposed in the first part of the third trimester (~ 30 weeks) are lower compared to other exposure times (Fig. 2).Figure 2Mean methylation levels by gestational age at impact. Each dot corresponds to a different child. Colors indicate trimester. [ 0] indicates children conceived within three months after the hurricane.
One significant true differentially methylated region (DMR) with multiple probes was associated with the timing of the hurricane. This DMR contains four probes and is in the promoter region of LLRC39 gene. The FDR adjusted p-value for this region is 0.000355. The probes were in the 5′UTR and the TSS200 (within 200 base pairs of the transcription start site) region of the gene. The probes are in open chromatin. A heatmap of this region shows that higher methylation values are found in the children of women who were in the 2nd half (< 20 weeks) of their pregnancy during the hurricane impact (Supplementary Fig. S1). The LLRC39 gene produces a protein that is a component of the sarcomeric M-band, which plays a role in myocyte response to biomechanical stress26. Downregulation of LRRC39 in spontaneously beating engineered heart tissue results in a lower generation of force. An in-vivo zebrafish knockdown model of LRRC39 resulted in severely impaired cardiac function and cardiomyopathy26.
Of the 10 most significant DMPs associated with the timing of exposure, the most biologically relevant site is the probe located near the SPR (Sepiapterin Reductase) gene. This DMP is located within a CpG island and within 200 bps of the transcription start site in an open chromatin region. The enzyme sepiapterin reductase is involved in the production of the tetrahydrobiopterin (BH4), which converts amino acid precursors into the neurotransmitters serotonin and dopamine27. BH4 is also a necessary cofactor for nitric oxide (NO) synthesis via nitric oxide synthase (NOS). Mutations in this gene have been linked to hypertension and brachycardia, likely because of an imbalance between sympathetic and parasympathetic input and impaired NO production in endothelial cells28. SPR variants have also been linked to a higher susceptibility to bipolar disorder29 and an increased risk of schizophrenia in females of Han Chinese descent30. Other functions represented by genes associated with significant DMPs include basic cellular processes, apoptosis, and cell proliferation, and the epigenetic process of histone demethylation.
A total of five DMPs were associated with the constructed variable “prenatal maternal stress” (PNMS) (Supplementary Fig. S2). The high PNMS group included children of mothers with symptoms of moderate to severe depression and post-traumatic stress disorder (PTSD), while the low PNMS mothers had no symptoms of depression or PTSD (Supplement: Study Sample). One of these DMPs is associated with the gene CRIP2; the same DMP is also significantly associated with the maternal PHQ-9 score. Another DMP significant in the PNMS group, associated with a region on chromosome 10, was also significant in the PTSD group. CRP2, the protein produced by the CRIP2 gene is associated with smooth muscle differentiation. CRP2 forms a complex with serum response factor and GATA proteins which converts fibroblasts to smooth muscle cells31.
Five significant DMPs were associated with the categorical PHQ-9 score, which measures levels of depression symptoms in the mothers (categories: none, mild, moderate, moderately severe, and severe) were identified by the dmrff algorithm (Fig. 3). The significant DMPs map to genes involved in cell proliferation; protein folding, trafficking, prevention of aggregation, and proteolytic degradation; methionine metabolism/ histone methylation, and maintenance of intracellular calcium homeostasis. None of the significant DMPs for the PHQ-9 score are in a CpG-dense region or upstream of the transcription start site. Four of the five significant DMPs are hypermethylated and may represent a pattern of hypermethylation associated with this score. Figure 3Manhattan plot of significant CpG sites associated with PHQ-9 score, which measures symptoms of depression in the mothers. Red lines represent FDR significance threshold.
Five significant DMPs were associated with the categorical PSS-10 score which measures symptoms of perceived maternal stress (categories: High, Moderate, Low) (Fig. 4). None of the significant DMPs are in a CpG dense region or upstream of the transcription start site. The significant DMPs map to genes involved in carbohydrate metabolism, actin cytoskeleton and cell polarity organization, and neuronal migration. Four of the five significant probes are hypermethylated and may represent a pattern of hypermethylation associated with this score. Figure 4Manhattan plot of significant CpG sites associated with PSS-10 score, which measures symptoms of perceived stress in the mothers. Red lines represent FDR significance threshold.
Three significant DMPs were associated with the binary variable of presence/absence of PTSD symptoms (Supplementary Fig. S3). Of the three significant DMPs, two mapped to genes that are involved in RNA processing. Both DMPs were hypermethylated. One probe, associated with the HNRNPF gene is in a CpG island and in the TSS200 or 5′ UTR region depending on alternate splicing variants. The HNRNPF gene is part of the heterogeneous nuclear ribonucleoproteins [hnRNPs] family of genes that regulate mRNA processing and transport. The hnRNP family is also involved in regulating telomerase activity and telomere length32. Individuals with psychiatric disorders have significantly shorter telomeres33. Send et al.34 found higher levels of perceived maternal stress during pregnancy associated with shorter telomeres in newborns. Aberrant methylation of HNRNPF associated with maternal PTSD might represent an epigenetic pathway by which psychological maternal trauma is associated with shorter telomeres in offspring.
One significant DMP was associated with the categorical “property damage” score (Supplementary Fig. S4). It is located on chromosome 9, a CpG shore region, with an adjusted p-value of 0.013865217. This DMP is hypomethylated. The DMP is in the body of the JAK2 gene. The JAK2 protein is important for controlling the production of blood cells from hematopoietic stem cells and gain-of-function mutations have been linked to polycythemia vera and idiopathic erythrocytosis35.
Changes in global methylation at repetitive genomic elements have been linked with genomic instability and disease processes36. To explore if global methylation changes were associated with exposure to the hurricane, a linear model was applied to a subset of 17,730 probes associated with long interspersed nucleotide element-1 (LINE-1) elements. The PNMS variable (yes/no) was used to assess if there were methylation differences between the groups. No significant differences were found.
## Discussion
The Developmental Origins of Health and Disease theory (DOHaD) postulates that exposure to prenatal stressors during critical periods of early development could lead to permanent alterations of the developing tissues. These alterations could lead to a higher risk of developing serious pathological conditions later in life, such as obesity, cardiometabolic disease, immunological conditions, and mental health problems20. Although the biological mechanisms that mediate these effects are not completely understood, recent studies indicate that epigenetic mechanisms are an important “interface” through which the body interprets and responds to stressful experiences early in prenatal and perinatal life by modulating DNA function and gene expression21.
Founded on the principals of the DOHaD this study examined the impact of prenatal stress due to Hurricane Maria on the methylation profiles of children participating in the HELiOS cohort (Hurricane Exposures and Long-term Infant Outcomes Study), which includes 187 children exposed to hurricane Maria in Puerto Rico during pregnancy, and their mothers. The epigenetic analysis only included children who were evaluated within 2 years of the disaster, which is much earlier compared to previous studies on other natural disasters22,37. The timely evaluation of the mother–child dyads is a strength of this study because it allowed us to identify mothers who experienced higher levels of PNMS due to the disaster and to compare them against those with no apparent symptoms of mental distress. The assessments were performed using validated instruments, providing an additional strength to the study.
The timing of maternal exposure to the hurricane during pregnancy was the factor with the strongest impact on the methylation patterns in children. In the absence of a non-exposed control, this finding provides a strong argument that the observed effects were indeed related to the disaster. Property damage, another objective hurricane-related exposure, was associated with one significant DMP. Subjective measures of PNMS, such as maternal depression, stress, and PTSD symptoms were also associated with differences in methylation patterns in children in our study, but the impact was smaller. Previous studies in children prenatally exposed to the Quebec ice-storm in 1998 have also reported that the objective hardship experienced by the mothers during the natural disaster had a stronger association with the DNA methylation patterns of affected children, compared to more subjective maternal distress levels22. These observations reflect the difficulty in accurately quantifying maternal distress levels specifically related to the disaster, which could be due to the confounding effect of multiple other factors, including unrelated personal experiences and previous mental health history.
With respect to the timing of the hurricane, our data indicates that children whose mothers were exposed during the second half of their pregnancy had higher methylation values, in the promoter region of the LLRC39 gene, which is involved in the development of the sarcomeric M-band and may be associated with cardiac function. A recent study on adults who had been prenatally exposed to the Tangshan earthquake in 1976 also reported that those who had been exposed during the second trimester of pregnancy had significantly higher methylation levels in the promoter region of the human glucocorticoid gene NR3C1, and this finding was associated with poorer working memory performance37. Virk et al.38 found that the second trimester of pregnancy (13–27 weeks) was the most developmentally sensitive period for maternal bereavement, which acts through a similar pathway to stress, and this exposure was associated with higher risk for the development of type-2 diabetes later in life. Increased maternal cortisol levels during the second trimester of pregnancy have also been reported to be associated with decreased infant physical and neuromuscular maturation in males39.
HELiOS has collected extensive information on phenotypic characteristics of the children and potential confounders but we did not use these variables in the methylation analysis because of the small sample size. This may have biased our results, however, none of the mothers in the sample smoked either cigarettes or marijuana during pregnancy or heavily consumed alcohol. The small sample size is a limitation in this study, which decreased statistical power. To address this, we used the dmrff algorithm, which is more powerful than other comparable tools, to identify significant probes. The epigenetic findings of this study will guide our ongoing analysis evaluating the associations between the clinical outcomes and hurricane-related stressors within the entire HELiOS cohort, where we will include adjustments for confounders. Future studies are planned to evaluate the long-term clinical impact of hurricane Maria in the HELiOS cohort and to understand the underlying biological mechanisms that mediate the impact of early life stress on children’s health and development.
## Study sample
Project HELiOS (Hurricane Exposures and Long-term Infant Outcomes Study) is a birth cohort that includes children who were prenatally exposed to Hurricane Maria in Puerto Rico or were conceived within three months post-disaster (born $\frac{09}{20}$/2017 and $\frac{09}{21}$/2018) and their mothers. The HELiOS cohort consists of 187 maternal-child dyads. For this study, the research team selected 16 children of mothers with symptoms of moderate to high depression and PTSD [high PNMS group]. These were matched by age and gender to 16 children of mothers with no symptoms of depression or PTSD (low PNMS group). The final sample for this study consisted of 32 children, 26 males, and 6 females, aged between 13 to 23 months at the time of the study (average 17.1 ± 4.3 months). There is a small number of females in the sample. Differences in autosomal DNA methylation by sex have been found. A list of significant probe IDs from this study was compared to the list of probe IDs associated with biological sex found by Grant et al.40. There was no overlap. The age of the mothers at the time of the study was between 22 and 41 years (average 31.2 ± 4.8 years). Children born to mothers with history of depression and PTSD were not excluded as we used the presence of the current symptoms as a proxy of how much stress the mother had during the pregnancy. When considering the effects of maternal mental health status, current evidence suggests that it is irrelevant if the symptoms are related to a first episode of Major Depression Disorder (MDD)/PTSD or if it is a recurrence as post-disaster, as literature shows both increased incidence and recurrence of both disorders. The study was conducted following the ethical principles for medical research involving human subjects as defined by the declaration of Helsinki. The study protocol is approved by the Institutional Review Board of the University of Puerto Rico Medical Sciences Campus (UPR-MSC, Protocol #A0060118). Written consent approved by the IRB of the UPR-MSC was obtained by the mothers or legal guardians.
## Study variables
Project HELiOS utilizes validated instruments to assess objective and subjective hurricane-related exposures, pregnancy outcomes, maternal and child medical histories, and child growth, development, and diet. In this report we included results for temporal, psychological, and selected hurricane exposure variables. Detailed pregnancy history, including pregnancy category and type of delivery was obtained using validated instruments currently used in other genetic cohort studies at the Dental and Craniofacial Genomics Core at the University of Puerto Rico. Gestational age (weeks) at the time of impact was used to assess the temporality of maternal exposure to the storm.
Maternal depressive symptoms were measured using the PHQ-9. The PHQ-9 is a multiple-choice, 4-point Likert scale, self-report inventory composed of nine items, assisting clinicians in screening for depression as well as selecting and monitoring treatment41. The PHQ-9 is a reliable and valid measure (α = 0.90) of depression severity42 and has been validated within primary care settings42,43. Its brevity makes it a useful clinical and research tool42 and it can detect and monitor depression in diverse ethnic/racial populations44. We have used scores of moderate depression or higher (score of 10 or higher) as indicator of depression. The Perceived Stress Scale (PSS-10) was used to assess subject maternal exposure to stress. The PSS-10 is widely used for measuring the degree in which situations in one’s life are perceived as stressful, unpredictable, and uncontrollable45. The PSS-10 was designed for at least a junior high school education. The questions are designed for any subpopulation group but have been previously used with pregnant women in post-hurricane settings46. The PSS-10 involves a 5-point Likert scale, with response ranging from 0 to 40, as higher scores indicate more stress. Scores from 0 to 13 are classified as low stress, 14–26 as moderate stress, and 27–40 as high stress. It has a good validity and reliability, with a α = 0.7644 and is used in Spanish in ongoing studies with Puerto Ricans. Post-traumatic stress disorder symptoms were measured using the PTSD checklist for DSM-5 (PCL-5), which is a 20-item self-report measure that assesses the 20 DSM-5 symptoms of PTSD 14. It takes approximately 5–10 min to complete. The total symptom severity score (range 0–80) is determined by summing the scores for each of the 20 items. We used the recommended cut-off for the PCL-5 of 33 points. We also used the criterion-based score (PCL-5 will be positive if the subject answers “moderately” or higher to all criteria needed for PTSD diagnosis). The PCL-5 has been translated into Spanish and has been used with Puerto Rican samples.
Measuring maternal mental health 1–2 years after Hurricane *Maria is* an ideal marker of PNMS as the moment of peak post-traumatic symptomatology is at this first-year mark47. Most cases of post-traumatic depression and PTSD improve over time, so the mothers that continue to present symptoms between 1 to 2 years after the hurricane are the more severe cases that will probably continue to present symptoms. Administration of certified psychological tests was performed by trained staff supervised by a licensed psychologist/psychiatrist from the Center for the Treatment and Management of Anxiety (CETMA) of the University of Puerto Rico Medical Sciences Campus. Any mothers who exhibited symptoms requiring special mental health evaluation and treatment were referred to CETMA.
To assess objective prenatal exposures related to Hurricane Maria we adapted the Exposure to Disaster Scale48 that was used with Hispanic populations after Hurricane Andrew in Florida and Hurricane Georges49 in PR. This self-report questionnaire includes questions assessing the threat to life or danger encountered during the hurricane, any injury or illness to self or others and the degree of property damage or loss. The adaptation includes specific stressors for Hurricanes Irma and María captured through formal interviews with patients receiving treatment for PTSD at the CETMA. These adaptations include measuring how long the person lived without electricity and communication services and the effect of the hurricane on sleep patterns.
Blood samples were collected by trained nurses/phlebotomists at the Hispanic Alliance for Clinical and Translational Research (Alliance) of the UPR Medical Sciences Campus. Samples were immediately transported to the Alliance core laboratory facility for processing and storage by trained staff. Whole blood samples were aliquoted, snap-frozen in dry ice/ethanol and stored at -80 0C. Dried blood spot (DBS) samples were also prepared in filtered paper for future analysis of environmental exposures, in addition to metabolic and cortisol tests.
## DNA methylation analysis
Coded blood samples were mailed to the Vieira Lab at the University of Pittsburgh for DNA methylation analysis. Samples were processed by the University of Pittsburgh Genomics Core. Genomic DNA (gDNA) was extracted from blood samples using the Qiagen DNeasy blood Mini Kit. Bisulfite modification of 1 µg DNA was conducted using an EZ DNA Methylation Kit (Zymo Research) according to the manufacturer’s procedure. The Infinium Methylation EPIC assay was performed according to Illumina’s standard protocol. Six sample pairs were processed on the same chip to account for chip-to-chip variation. All samples were run in duplicates. Infinium *Methylation data* was processed in R (version 4.0.2). Methylation levels of CpG sites were be calculated as β-values (β = intensity of the methylated allele (M)/ (intensity of the unmethylated allele (U) + intensity of the methylated allele (M) + 100)50. Data was preprocessed and analyzed according to the “A cross-package Bioconductor workflow for analyzing methylation array data”. All samples passed quality control (QC) with detection p-values > 0.05 and CpG coverage > $95\%$ (Supplementary Fig. S5).
CpG sites ($$n = 11$$,648) on the sex chromosomes were not removed for the initial analysis. Data was normalized via the preprocess Funnorm function which performs better than other normalization methods on datasets with global methylation variation51. This function adjusts for known covariates via internal control probes, applies a background correction and corrects dye bias (Supplementary Fig. S6).
Probes with SNPs known to effect methylation at the probe site, or the single nucleotide extension were excluded. Cross-reactive probes were removed52. A cell heterogeneity correction was applied to normalized data53. To evaluate potentially confounding variables, MDS plots were created. Ethnicity and race affect methylation profiles54,55. In this dataset subjects did not cluster by race or ethnicity (Supplementary Fig. S7). The outliers in the MDS plots are samples with higher detection p-values.
Mode of delivery (Vaginal, C-section) may influence newborn methylation profiles, but the evidence is not conclusive56. In this dataset there is some clustering based on mode of delivery for subjects who delivered via un-scheduled C-section (Supplementary Fig. S8). This was used as a covariate in linear models to evaluate differential methylation. Preterm birth is also associated with differential methylation program in children57. In this dataset there was no clustering based on whether a delivery was term or preterm. A linear model was created via limma to assess the differential methylation by variable. All analyses were run using M-values, which have better statistical properties than beta values58. All tests were run using the false discovery rate (FDR) as a significance threshold59. To evaluate differential methylation the dmrff algorithm was used60. Like other popular methods of identifying differentially methylated sites, (bumphunter, comb-p, and DMRcate), dmrff uses EWAS summary statistics. Dmrff was chosen for this study because it combines these summary statistics from probes located in close proximity to each other. This takes into account that methylation marks at these sites is often interdependent, leading to is more power and better control of false positives than other tools. Data for DNase I hypersensitive sites (DHSs) is included for each significant site for which this data is available. DHSs indicate chromatin is not condensed at this location and is transcriptionally available.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-30645-5.
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|
---
title: 'Orally administered Lactiplantibacillus plantarum OLL2712 decreased intestinal
permeability, especially in the ileum: Ingested lactic acid bacteria alleviated
obesity-induced inflammation by collaborating with gut microbiota'
authors:
- Yimei Wang
- Tomohiro Takano
- Yingyu Zhou
- Rong Wang
- Takayuki Toshimitsu
- Toshihiro Sashihara
- Masaru Tanokura
- Takuya Miyakawa
- Haruyo Nakajima-Adachi
- Satoshi Hachimura
journal: Frontiers in Immunology
year: 2023
pmcid: PMC9995389
doi: 10.3389/fimmu.2023.1123052
license: CC BY 4.0
---
# Orally administered Lactiplantibacillus plantarum OLL2712 decreased intestinal permeability, especially in the ileum: Ingested lactic acid bacteria alleviated obesity-induced inflammation by collaborating with gut microbiota
## Abstract
### Introduction
Chronic inflammation caused by dietary obesity has been considered to induce lifestyle-related diseases and functional ingredients with anti-inflammatory effects are attracting attention. Although multiple studies on obesity had proved the anti-inflammatory effects of ingestion of lactic acid bacteria (LAB) and other functional ingredients on adipose tissue, the precise effects on the intestine, especially on the individual intestinal segments have not been made clear. In this study, we elucidated the mechanisms of *Lactiplantibacillus plantarum* (basonym: Lactobacillus plantarum) OLL2712 in suppressing obesity-induced inflammation using high fat diet (HFD)-fed mice obesity model.
### Methods
We orally administered heat-treated LAB to HFD-fed mice model, and investigated the inflammatory changes in adipose tissue and intestinal immune cells. We also analyzed gut microbiota, and evaluated the inflammation and permeability of the duodenum, jejunum, ileum and colon; four intestinal segments differing in gut bacteria composition and immune response.
### Results
After 3-week LAB administration, the gene expression levels of proinflammatory cytokines were downregulated in adipose tissue, colon, and Peyer’s patches (PP)-derived F$\frac{4}{80}$+ cells. The LAB treatment alleviated obesity-related gut microbiota imbalance. L. plantarum OLL2712 treatment helps maintain intestinal barrier function, especially in the ileum, possibly by preventing ZO-1 and Occludin downregulation.
### Discussion
Our results suggest that the oral administration of the LAB strain regulated the gut microbiota, suppressed intestinal inflammation, and improved the gut barrier, which could inhibit the products of obesity-induced gut dysbiosis from translocating into the bloodstream and the adipose tissue, through which the LAB finally alleviated the inflammation caused by dietary obesity. Barrier improvement was observed, especially in the ileum, suggesting collaborative modulation of the intestinal immune responses by ingested LAB and microbiota.
## Introduction
According to the WHO Fact Sheet, worldwide obesity has nearly tripled since 1975, and the number of obese people is still rising due to the increased availability of high-calorie foods and lack of exercise, and it has become one of the most serious problems worldwide [1, 2]. Multiple studies have shown that obesity can cause chronic inflammation (3–5). Persistent inflammatory conditions have been frequently reported to induce an exacerbation of lifestyle diseases, contributing to elevated risks of atherosclerosis, type 2 diabetes, and some cancers (6–8).
Gut microbiota, which represents the microorganisms in the gastrointestinal tracts of the animals, is mainly regulated by digested food. Gut microbiota is essential for the host metabolism, relating to the immune system and the barrier function [9, 10]. Dysbiosis of gut microbiota is one of the key factors regulating obesity-associated disorders [11], as shown in the observation that germ-free mice do not show increased body fat mass or exacerbated insulin resistance when fed a high-fat (HFD) diet, and this phenomenon disappears after gut microbiota transplantation [12, 13]. Multiple studies on gut microbiota in obese patients have suggested that obesity changes the gut microbiota, and excessive accumulation of adipose tissue is correlated with the composition of the gut microbiota. In addition, dietary obesity is known to reduce the diversity of the gut microbiota, followed by a disruption of the metabolic equilibrium, which is normally maintained by diverse components of the gut microbiota [14].
On the other hand, intestinal barrier dysfunction is also considered to be related to the aggravation of chronic inflammation caused by obesity. The gut is connected to the external environment for the absorption of nutrients. In the gastrointestinal tract, especially in the large intestine, there are large amounts of gut bacteria, as well as bacterial pathogens and other harmful substances. The intestinal barrier functions to protect the host from these harmful substances [15, 16]. It has been reported that obesity increases intestinal permeability, which allows the leakage of inflammation inducible foreign substances such as lipopolysaccharide (LPS), which is one of cell component derived from Gram-negative bacteria. The leakage of Gram-negative bacteria and LPS into the bloodstream could induce the infiltration of proinflammatory macrophages in the adipose tissue and the liver tissue, inducing systemic inflammation [17]. Furthermore, recent studies have suggested that dysbiosis results in intestinal inflammation in obesity [18].
Recently, functional ingredients with anti-inflammatory effects have received attention, from which lactic acid bacteria (LAB) are a diverse group of Gram-positive bacteria that produce lactic acid as the major end product of the carbohydrate fermentation, and are often considered as probiotics balancing the gut microbiota. As a LAB strain, *Lactiplantibacillus plantarum* OLL2712 has been selected owing to its capacity to accelerate the production of the anti-inflammatory cytokine interleukin (IL)-10 in murine marrow-derived dendritic cells (DCs) and peritoneal macrophages [19]. Moreover, it has been reported that oral administration of L. plantarum OLL2712 alleviates chronic inflammation of adipose tissue in obese mouse models [20] and reduces fasting plasma glucose and serum proinflammatory cytokine concentrations in prediabetic individuals [21], suggesting that this functional LAB could be used as novel pharmaceuticals.
In this study, our main purpose was to focus on the intestine, especially on the different parts of the digestive tract. The anti-inflammatory functions of OLL2712 on the adipose tissue had been reported [19] but the pathways through which ingested OLL2712 exerted the anti-inflammatory effects on the adipose tissue remained unclear. In this regard, the effects on the intestine were unknown. We hypothesized that OLL2712 alleviated the adipocyte inflammation via the intestine by suppressing inflammation or enhancing the gut barrier, and we presumed that such functions were different among different parts. We investigated the mechanisms of the anti-inflammatory effects of the LAB strain, focusing on the gut microbiota and intestinal function using HFD-fed mouse model. We found a mechanism by which the oral administration of LAB regulated the gut microbiota, suppressed intestinal inflammation, and improved the gut barrier. This could inhibit bacterial harmful components, induced by obesity, from translocating into the bloodstream and adipose tissue, through which the LAB strain alleviated the inflammation caused by dietary obesity. Furthermore, the improvement of barrier function was observed, especially in the ileum of HFD-fed mice under the LAB treatment, suggesting collaborative modulation of the intestinal immune responses by the ingested LAB and microbiota.
## The LAB strain
L. plantarum OLL2712, which had been heat-treated by incubation at 75°C for 60 min and lyophilized, after being cultured in de Man, Rogosa, and Sharpe (MRS) broth (Becton Dickinson, USA), and stored at -20°C.
## Mice and diet
C57BL/6 male mice were purchased from Charles River Laboratories (Japan, RRID: IMSR_CRL:027). Mice were fed sterilized (121°C, 20 min) water and maintained at an appropriate temperature (23 ± 2°C) and humidity (50 ± $5\%$) with a 12-hour light-dark cycle. All experiments were conducted with the approval of the Experimental Animal Ethics Committee of the Graduate School of Agriculture and Life Sciences of the University of Tokyo.
To create obese C57BL/6NCrl mice, mice were fed a HFD ($60\%$ kcal from fat; Oriental Yeast, Japan) from 8-week-old, and mice in the control group were fed a normal chow diet (AIN-93M; Oriental Yeast, Japan) (Figure S1A). 6 individuals were used for each group to investigate the proinflammatory changes induced by a 4-week HFD. The nutrient composition of HFD-60 and AIN-93M is shown in Tables S1, S2.
To investigate the effects of oral administration of L. plantarum OLL2712 in obese mice, C57BL/6NCrl mice were fed a HFD from 8-week-old for 4 weeks. In the last 3 weeks, L. plantarum OLL2712, suspended in the sterilized water to the concentration of 20 mg/mL, was orally administered every day, 4 mg to each mouse (Figure 1A). 8 – 12 individuals were used for each group.
**Figure 1:** *Oral administration of L. plantarum OLL2712 alleviated the inflammation of adipocytes, colon and PP macrophages in mice but changed neither the body weight nor the adipose tissue weight. C57BL/6N male mice were fed a HFD (60% kcal from fat) for 4 weeks from 8 weeks of age. (A) In the last 3 weeks, mice were administered OLL2712 daily (4 mg dissolved in 200 μL water per dose), and mice administered water simultaneously were used as a control group (HFD). (B) Mice were weighed once per week. The weights of the epididymal adipose tissue (EAT) (C) and mesenteric adipose tissue (MAT) (D) were measured after 3 weeks of treatment with OLL2712 and compared with the control group. The SVF cells were isolated from the MAT. The mRNA expression of CCL2 (Ccl2) (E), IL-1β (Il1b) (F), TNF (Tnf) (G), and F4/80 (F4/80) (H) was measured by qPCR. The mRNA expression of CCL2 (Ccl2) (I), IL-1β (Il1b) (J), TNF (Tnf) (K), and F4/80 (F4/80) (L) in colon tissue was measured by qPCR. Macrophages were isolated from PPs in the mice. The mRNA expression of CCL2 (Ccl2) (M), IL-1β (Il1b) (N), TNF (Tnf) (O), and IL-6 (Il6) (P) was measured by qPCR. The results are representative of two independent experiments and are shown as the mean ± standard deviation (n = 8 - 12). *p<0.05; **p<0.01; ***p<0.001 (assessed using Student’s t-test). HFD, high-fat diet; LAB, lactic acid bacteria (L. plantarum OLL2712); MAT, mesenteric adipose tissue; SVF, stromal vascular fraction; PP, Peyer’s patch.*
To investigate the effects of a short-term oral administration of L. plantarum OLL2712 in mice, L. plantarum OLL2712, suspended in the sterilized water to the concentration of 20 mg/mL, was orally administered every day, 4 mg to each C57BL/6NCrl mouse from 9-week-old (Figure S1B). 5 individuals were used for each group.
## Cell preparation
Epididymal adipose tissue (EAT) and mesenteric adipose tissue (MAT) were shredded into 2-3 mm pieces and dissociated with collagenase type II (1 mg/mL; Sigma-Aldrich, USA, Cat#C6885) at 37°C for 45-60 min until the adipose tissue was almost dissolved, and then the reaction was stopped with EDTA (10 mM) for 5 min. After being filtered with a 115 µm nylon mesh (Tokyo Screen, Japan), stromal vascular fraction (SVF) derived from adipose tissue was treated with red blood cell lysis buffer, made from ammonium chloride, potassium carbonate, and EDTA, for 5 min at room temperature. After centrifugation, the EAT SVF and MAT SVF were suspended in $10\%$ FCS-RPMI.
Peyer’s patches (PPs) were treated with collagenase I (1 mg/mL; FUJIFILM Wako Pure Chemical, Japan, Cat#032-22364) and 10 μg/mL DNase I (Roche Diagnostics, Germany, Cat#10104159001) at 37°C for 60–90 min before being filtered with an 86 µm nylon mesh (Tokyo Screen, Japan). The PP cells were centrifuged twice and suspended in $10\%$ FCS-RPMI. $10\%$ FCS-RPMI was prepared using RPMI 1640 (Nissui Pharmaceutical, Japan, Cat#05918), containing 100 U/ml penicillin G potassium (Meiji Seika Pharma, Japan), 100 μg/ml streptomycin sulfate (Meiji Seika Pharma, Japan), 50 μM 2-mercaptoethanol (FUJIFILM Wako Pure Chemical, Japan, Cat#137-06862), $0.03\%$ L-glutamine (FUJIFILM Wako Pure Chemical, Japan, Cat#074-00522), and $0.2\%$ sodium hydrogen carbonate (FUJIFILM Wako Pure Chemical, Japan, Cat#191-01305), and $10\%$ heat-inactivated fetal calf serum (Thermo Fisher Scientific, Germany, Cat#173012).
After F$\frac{4}{80}$ MicroBeads Ultrapure (Miltenyi Biotec, Germany, Cat#130-110-443) were added to PP whole cells, F$\frac{4}{80}$+ cells were isolated using a magnetic-activated cell sorting (MACS) system (Miltenyi Biotec, Germany). The obtained F$\frac{4}{80}$+ cells were used as macrophages.
## Quantitative PCR
The intestinal contents were removed, and the intestinal tract was washed with PBS [-], added to TRIzol (Invitrogen, USA, 15596026), and homogenized using TissueRuptor (QIAGEN, Germany) until the tissue was barely visible. The intestinal tissue was immediately frozen in liquid nitrogen and stored at -80°C.
The intestinal tissue samples were thawed at 4°C. Then, 0.2 mL of chloroform (FUJIFILM Wako Pure Chemical, Japan, Cat#038-02606) was added to 1 mL of the sample in TRIzol reagent, and the mixture was stirred manually and kept at room temperature for 3 minutes. After centrifugation, the upper layer was transferred to a new tube, and 0.5 mL of isopropanol (FUJIFILM Wako Pure Chemical, Japan, Cat#166-04836) was added. After being kept at room temperature for 10 min, the sample was centrifuged. The sample was washed with 1 mL of $75\%$ ethanol and dried at room temperature until the precipitate turned translucent. The RNA solution derived from the intestinal tissue was dissolved in sterilized water, and any DNA was removed using an RNase-Free DNase (QIAGEN, Cat#79254).
Total RNA from cells was isolated using QIAshredder (QIAGEN, Germany, Cat#79656), 2-mercaptoethanol (FUJIFILM Wako Pure Chemical, Japan, Cat#137-06862), and an RNeasy mini kit (QIAGEN, Cat#74106) according to the provided protocol. Complementary DNA (cDNA) was synthesized using SuperScript VILO MasterMix (ThermoFisher Scientific, USA, Cat#11755-050) and the GeneAmp PCR System 9700 (Applied Biosystems).
Synthesized cDNA samples were added to a LightCycler 480 Multiwell Plate 96 (Roche Diagnostics), and quantitative PCR was performed with a QuantiTect SYBR Green PCR Kit (QIAGEN, Cat#204143) using a CFX Connect Real-Time PCR Detection System (Bio-Rad, USA). The relative expression levels of each gene were standardized against the gene expression levels of glyceraldehyde-3-phosphate dehydrogenase (GAPDH). The primer sequences for quantitative PCR (qPCR) are shown in Table S3.
## Measurement of intestinal permeability
Intestinal permeability in vivo, was measured using 4 kDa Fluorescein isothiocyanate-dextran (FITC-Dextran) (Sigma, Cat#46944). 4 hours after FITC-dextran was orally administered to mouse (12 mg per mouse), the serum was collected and diluted in a microplate reader (Greiner bio one, Austria), and the fluorescence was measured at 485(ex)/528(em) nm using a SpectraMax iD5 (Molecular Devices, Japan). The concentration of FITC-dextran was then calculated.
Intestinal permeability ex vivo was investigated according to a previous report [22]. The whole digestive tract from the stomach to the final part of colon was collected. After MAT was removed, specific intestinal sections were collected. A 4 cm segment under the stomach was selected as the duodenum, a segment from the 5th to the 10th centimetre below the stomach as the jejunum, a 5 cm intestinal section proximal to the cecum as the ileum, and a 5 cm segment below the cecum as the colon (Figure 2B). The collected intestinal tracts were washed by PBS [-] and the contents were gently removed without breaking the intestinal tissues. A 1 mg/mL solution of 4 kDa FITC-dextran was injected into the selected intestinal sections tied with surgical sutures, and each segment was moved to DMEM (Sigma-Aldrich, USA, Cat#11965092) and placed at 37°C (Figure S1C). The concentration of FITC-dextran transported from the lumen to the DMEM was measured every 30 minutes, and the cumulative concentration (Qt) of the DMEM, collected at each time point, was calculated using the following formula.
**Figure 2:** *L. plantarum OLL2712 exhibited the ability to decrease intestinal permeability, especially in the distal intestine. (A) C57BL/6N male mice were treated with OLL2712 (4 mg dissolved in 200 μL water for each dose) for 7 days, and mice treated with water simultaneously were used as a control group (Ctrl) (n = 5). The concentration of FITC-dextran in serum was measured and calculated 4 hours after FITC-dextran was orally administered to mice to investigate epithelial permeability in vivo. (B) Specific intestinal sections were collected, and the permeability of each section was assessed. A 4 cm segment under the stomach was selected as the duodenum, a segment from the 5th to the 10th centimetre below the stomach as the jejunum, a 5 cm intestinal section proximal to the cecum as the ileum, and a 5 cm segment below the cecum as the colon. The apparent permeability of the duodenum (C), jejunum (D), ileum (E), and colon (F) in C57BL/6N male mice fed an HFD and treated with OLL2712 (HFD-LAB) were compared with those fed an HFD and treated with sterilized water (HFD) (n = 9 - 12). The relative expression of ZO-1 (ZO1) (G), Occludin (Ocln) (H), and MUC2 (I) in ileal tissues from ND or HFD-fed mice was measured by qPCR (n = 5 - 6). The relative expression of ZO-1 (ZO1) (J), Occludin (Ocln) (K), and MUC2 (L) in ileal tissues from mice fed HFD and treated with OLL2712 (HFD-LAB) were compared with those fed HFD and treated with sterilized water (HFD) (n = 10). The results are representative of two independent experiments and are shown as the mean ± standard deviation. *p<0.05; **p<0.01 (assessed using Student’s t-test). Ctrl, control; LAB, lactic acid bacteria (L. plantarum OLL2712); ND, normal diet; HFD, high-fat diet.*
Qt = (Ct*Vr) + (Ct sum*Vs), where: Qt = Cumulative concentration at time t Ct = Concentration at time t (ng/mL) Vr = Volume at the receiver side (mL)
Ctsum = Sum of all previous Ct Vs = Volume sampled (mL) *Qt versus* time (t) was plotted and the slope (δQt/δt) was calculated. And the apparent permeability (Papp) of each individual intestinal sac was calculated using the following formula: Papp = (δQt/δt)/(A*Co), where:
A = Area of tissue (cm2) Co = Initial concentration (ng/mL)
## Gut microbiota
The cecal contents were collected in 1.5 mL tubes and stored at -80°C. The gut microbiota was analysed with next-generation sequencing and amplicon sequencing by TechnoSuruga Laboratory (Japan). DNA was extracted according to the method previously reported [23], using an automated DNA isolation system (GENE PREP STAR PI-480 KURABO, Japan). The details of the analysis were provided by TechnoSuruga Laboratory (Japan). The 341f/R806 primers and dual-index method was used to amplify the V3-V4 regions of Bacterial 16S rRNA (23–26). And then barcoded amplicons were paired-end sequenced on 2×284-bp cycle using the MiSeq system with MiSeq Reagent Kit version 3 (600 Cycle) chemistry. Paired-end sequencing reads were merged by fastq-join ver 1.3.1 with default setting [27].
FASTX-Toolkit ver 0.0.13 was being used to extract joined-reads, which had quality value score of ≥ 20 for more than $99\%$ of the sequence. After the chimeric sequences were deleted with usearch61 [28, 29], nonchimeric reads were submitted for 16S rDNA-based taxonomic analysis using the Ribosomal Database Project ver 2.11 (RDP, RRID: SCR_006633). Finally, Metagenome@KIN Ver 2.2.1 analysis software (World Fusion, Japan) was used to perform the identification with confidence ≥ 0.8.
## Statistical analysis
The results are given as the mean ± SD, and Student’s t-test was used for statistical analyses. A difference was considered significant at $p \leq 0.05.$
## Four-week intake of a high-fat diet caused inflammation in the SVF cells derived from adipose tissue in mice
It has been frequently reported that diet-induced obesity is related to chronic inflammation. A long period of intake of HFD, usually more than 12 weeks, could cause abnormal cytokine production, a disorder of lipid metabolism, and elevated blood glucose, followed by disruption of the regulatory mechanism of adipocytokine production [30]. To investigate the inflammation in early obesity induced by a short period of HFD ingestion, C57BL/6N mice were fed HFD ($60\%$ kcal from fat) for 4 weeks, and mice fed a normal chow diet (AIN-93 M) were used as a control group (ND) (Figure S1A). The mice were weighed every 7 days, and it was found that the HFD group showed increasing body weights (Figure 3A), followed by increasing weight of their EAT and MAT (Figures 3B, C).
**Figure 3:** *Four weeks of intake of a high-fat diet caused inflammation in the stromal vascular fraction (SVF) cells derived from adipose tissue of mice. C57BL/6N male mice were fed a HFD (60% kcal from fat) for 4 weeks from 8 weeks of age. Mice fed a standard chow diet (AIN-93 M) were used as a control group. (A) Mice were weighed once per week. The weights of the epididymal adipose tissue (EAT) (B) and mesenteric adipose tissue (MAT) (C) were measured after 4 weeks on a HFD and compared with the ND group. The stromal vascular fraction (SVF) cells were isolated from the MAT (D–G). The mRNA expression of CCL2 (Ccl2), IL-1β (Il1b), TNF (Tnf) and F4/80 (F4/80) was measured by qPCR. The results are representative of two independent experiments and are shown as the mean ± standard deviation (n = 6). *p<0.05; **p<0.01; ****p<0.0001 (assessed using Student’s t-test). ND, normal diet; HFD, high-fat diet; EAT, epididymal adipose tissue; MAT, mesenteric adipose tissue; SVF, stromal vascular fraction.*
We isolated SVF, which contained immune cells, from the EAT and MAT, and the gene expression of proinflammatory cytokines in the EAT SVF and MAT SVF of mice was measured by qPCR (Figures S2A–C, 3D–F). And the gene expression of F$\frac{4}{80}$ (F$\frac{4}{80}$), as a marker of macrophages, was measured simultaneously (Figures S2D, 3G). Cytokine chemokine (C-C motif) ligand 2 (CCL2; Ccl2), as a macrophage-specific chemokine, increased with a 4-week HFD diet in EAT SVF and MAT SVF (Figures S2A, 3D). Nevertheless, other major proinflammatory cytokine such as IL-1β (Il1b) and TNF (Tnf), and macrophage marker F$\frac{4}{80}$ (F$\frac{4}{80}$) did not show a remarkable change (Figures S2B–D, 3E–G). These data suggested that a short-term of HFD feeding could induce increases in body weight and fat mass, followed by slight increase in inflammation in the adipose-derived SVF by inducing CCL2 (Ccl2).
## Four-week intake of a HFD induced significant changes in the intestinal microbiota composition of mice
Many previous studies have reported that both obese patients and obese mice show an increase in Firmicutes and a decrease in Bacteroidetes in their gut microbiota [31, 32]. We collected the contents of the cecum from mice fed a HFD for 4 weeks and analysed the gut microbiota composition using next-generation sequencing applications. The relative abundance of Firmicutes was found to increase in the HFD group compared to normal diet (ND) group (Figure 4A), which is a relevant marker of gut dysbiosis, and we detected a descending tendency in the relative abundance of Bacteroidetes (Figure 4B), although there was no change in the Firmicutes/Bacteroidetes ratio (Figure 4C). At the genus level, the mice in the HFD group showed an obvious distinction from the ND group (Figure 4D). The HFD group exhibited a decreasing tendency in the relative abundance of Lactobacillus ($$p \leq 0.06$$, Figure 4E) and the relative abundance of Lactococcus, *Lachnospiracea incertae* sedis, and Peptococcus which was almost absent in the cecum of the control group, was found to increase with a HFD feeding (Figures 4F, H, J). Although Pseudoflavonifractor did not change (Figure 4I), an increasing tendency induced by HFD was detected in the relative abundance of *Clostridium cluster* XIVa ($$p \leq 0.08$$, Figure 4G). These data indicated that a short-term HFD feeding had already resulted in a substantially different gut bacterial flora compared with the ND group, which could be involved in inflammation-associated diseases.
**Figure 4:** *Four weeks of intake of a HFD caused significant changes in several genera derived from the cecum of mice. Male C57Bl/6N mice fed a HFD for 4 weeks were compared with mice fed a normal diet. The cecal contents of the mice were isolated, and the gut microbiota was investigated with next-generation sequencing applications. At the phylum level, the relative abundance of Firmicutes (A) and Bacteroidetes (B) and the ratio of the two (C) were calculated. At the genus level, the composition of the gut microbiota of each mouse was analysed and compared (D). The relative abundance of Lactobacillus
(E), Lactococcus
(F), Clostridium XIVa (G), Lachnospiraceae incertae sedis
(H), Pseudoflavonifractor
(I), and Peptococcus
(J) in the HFD group were calculated and compared with those in the ND group. The results are representative of two independent experiments and are shown as the mean ± standard deviation (n = 6). *p<0.05; **p<0.01 (assessed using Student’s t-test). ND, normal diet; HFD, high-fat diet.*
## Oral administration of L. plantarum OLL2712 alleviated inflammation in SVF cells derived from adipose tissue
To explore the anti-inflammatory effects of L. plantarum OLL2712, we orally administered the heat-treated strain to mice on a HFD during the last 3 weeks (Figure 1A). We were unable to detect a significant difference in body weight or fat mass between mice treated with L. plantarum OLL2712 and those treated with sterilized water (Figures 1B–D). Nevertheless, in the EAT SVF, the gene expression of F$\frac{4}{80}$ (F$\frac{4}{80}$) decreased significantly in the LAB-treated mice (Figure S3D). Although not significant, the gene expression of CCL2 (Ccl2) showed a declining tendency with the LAB treatment ($$p \leq 0.05$$) (Figure S3A), while there was no change found in the gene expression of IL-1β (Il1b) and TNF (Tnf) (Figures S3B, C). Simultaneously, remarkable changes were found in the MAT SVF, as the gene expression of proinflammatory cytokines, CCL2 (Ccl2), IL-1β (Il1b) and TNF (Tnf), and macrophages marker, F$\frac{4}{80}$ (F$\frac{4}{80}$), decreased in the LAB group (Figures 1E–H).
## Colon inflammation was suppressed by LAB treatment
Colonic macrophages play important roles in the induction of obesity-associated insulin resistance. Both macrophage-specific CCR2 knockout and intestinal epithelial cell-specific tamoxifen-inducible CCL2 knockout mice have been observed to be resistant to HFD, showing improved glucose and insulin tolerance [18]. To investigate the colonic macrophage infiltration underlying dietary obesity, we evaluated the changes of CCL2 and F$\frac{4}{80}$ under HFD and LAB treatment. Unexpectedly, we did not find colonic inflammation in mice fed an HFD for 4 weeks (Figures S4A–D). Nevertheless, in macrophages derived from PP cells, the gene expression of CCL2 (Ccl2) and TNF (Tnf) was upregulated by dietary-induced obesity (Figures S4E, G), suggesting that intestinal inflammation was already elicited in the small intestinal compartment, although there was no detected change in the gene expression of IL-1β (Il1b) and IL-6 (Il6) in PP macrophages (Figures S4F, H).
On the other hand, a 3-week oral administration of L. plantarum OLL2712 elicited decreased expression of CCL2 (Ccl2), IL-1β (Il1b) and F$\frac{4}{80}$ (F$\frac{4}{80}$) (Figures 1I, J, L) in the colon, and TNF (Tnf) did not change in the gene expression levels (Figure 1K). Meanwhile, there was no change detected in the duodenum, jejunum, and ileum (data not shown). From these results, we supposed that the LAB strain had an anti-inflammatory effect on the large intestine of mice. Furthermore, with the oral administration of heat-treated OLL2712, the gene expression of the proinflammatory cytokines IL-1β (Il1b) and TNF (Tnf) were found to be downregulated in PP macrophages (Figures 1N, O), although CCL2 (Ccl2) and IL-6 (Il6) did not change (Figures 1M, P).
## Gut microbiota bias caused by HFD intake was alleviated by an oral administration of L. plantarum OLL2712
We analysed the gut microbiota in the cecum of mice treated daily with heat-treated L. plantarum OLL2712 for 3 weeks compared with the control mice treated with water. At the phylum level, Firmicutes and Bacteroidetes did not show a significant change under the OLL2712 treatment (Figures 5A–C). However, at the genus level, the gut bacterial flora displayed a remarkable difference between mice treated with LAB and the control group treated with water (Figure 5D). The relative abundance of Lactobacillus dramatically increased (Figure 5E). Furthermore, the relative abundance of *Clostridium cluster* XIVa, *Lachnospiracea incertae* sedis, and Pseudoflavonifractor, which had been increased by the HFD and are considered to be associated with host inflammation and diseases, showed a significant decrease under OLL2712 treatment (Figures 5G–I). And the relative abundance of Lactococcus showed a declining tendency ($$p \leq 0.05$$) (Figure 5F), while Peptococcus did not change with the LAB treatment (Figure 5J). These data suggested that the oral administration of OLL2712 could modulate the gut microbiota composition related to obesity.
**Figure 5:** *Oral administration of L. plantarum OLL2712 caused changes in the gut microbiota composition of mice. C57BL/6N male mice fed an HFD and treated with OLL2712 were compared with those fed an HFD and treated with sterilized water. The cecal contents of the mice were isolated, and the gut microbiota was investigated with next-generation sequencing applications. At the phylum level, the relative abundance of Firmicutes (A) and Bacteroidetes (B) and the ratio of the two (C) were calculated. At the genus level, the composition of the gut microbiota of each mouse was analysed and compared (D). The relative abundance of Lactobacillus
(E), Lactococcus
(F), Clostridium XIVa (G), Lachnospiraceae incertae sedis
(H), Pseudoflavonifractor
(I), and Peptococcus
(J) in mice treated with OLL2712 were calculated and compared with those treated with water. The results are representative of two independent experiments and are shown as the mean ± standard deviation (n = 6). *p<0.05; ***p<0.001; ****p<0.0001 (assessed using Student’s t-test). HFD, high-fat diet; LAB, lactic acid bacteria (L. plantarum OLL2712).*
## L. plantarum OLL2712 improved gut barrier function in the ileum
According to the experimental results obtained thus far, we confirmed the anti-inflammatory effects of heat-treated OLL2712 on adipose tissue, PP macrophages, and the colon. In addition, the LAB treatment caused a substantial change in the gut microbiome.
Obesity has been reported to increase intestinal permeability, after which the products of obesity-induced gut dysbiosis are allowed to translocate into the bloodstream, adipose tissue, or other organs, as one of the causes of chronic systemic inflammation [15, 33]. Since intestinal inflammation and gut dysbiosis both affect the intestinal barrier (34–36), we next assessed the effects of the LAB strain on intestinal permeability. Seven days orally administration of heat-treated OLL2712 (Figure S1B) induced the lower levels of orally administered FITC-dextran in the serum compared to the control group, supporting our hypothesis (Figure 2A).
Since the proinflammatory cytokines in the colon decreased after 3 weeks of LAB treatment, while those in the jejunum did not, there was a possibility that different parts of the gut played different roles and might respond to the LAB strain in different ways. Assessment of the intestinal permeability in the previous in vivo experiment measured overall gastrointestinal absorption without any site specificity.
To further investigate site specificity of the protective effects of LAB treatment on the gut barrier, we collected duodenum, jejunum, ileum, and colon from the LAB treated mice and assessed the permeability ex vivo (Figures 2B, S1C). After 3 weeks of LAB treatment, the permeability of the ileum derived from HFD mice showed a significant reduction (Figure 2E), while the permeability of other segments had barely changed compared to that of HFD mice treated with water (Figures 2C, D, F). To gain mechanistic insight, we investigated the relative expression of barrier-related genes in each intestinal section. In the ileum, the expression of Occludin (Ocln), as one of the proteins forming tight junctions, decreased with a 4-week HFD feeding (Figure 2H). In addition, the gene expression of a secreted mucin with a physical barrier function, MUC2 (Muc2), also decreased with obesity (Figure 2I). And there was no significant change detected in the gene expression of ZO-1 (ZO1) (Figure 2G). *The* gene expression of ZO-1 (ZO1) and Occludin (Ocln) increased with the LAB treatment (Figures 2J, K), although no significant change was found in MUC2 (Muc2) (Figure 2L). These data suggested that LAB treatment blocked intestinal barrier disruption in the ileum of HFD-fed mice.
## Discussion
This study clarified the mechanism by which oral administration of L. plantarum OLL2712 suppressed obesity-induced inflammation. Ingested OLL2712 might directly regulate the gut microbiota in the large intestine and reduce harmful substances, which are derived from obesity-induced gut dysbiosis and leak into the blood, eventually relieving adipocyte inflammation. Simultaneously, the LAB strain enhanced the intestinal barrier, especially in the ileum, suggesting collaborative modulation of intestinal immune responses by ingested LAB and microbiota. As a result of the enhancement of the gut barrier, the leakage of harmful substances into the bloodstream was reduced, which resulted in anti-inflammatory changes in the adipose tissue.
Obesity, which is usually caused by unhealthy eating habits, can induce chronic inflammation, leading to high risks of metabolic and immunological diseases [37, 38]. The suppression and prevention of obesity-induced chronic inflammation by functional components have been frequently investigated (39–41). As functional ingredients, multiple LAB strains have been proven to be anti-inflammatory probiotics [42, 43], among which L. plantarum OLL2712 was focused on due to its good ability to highly induce the anti-inflammatory cytokine IL-10 [19]. In recent studies, L. plantarum OLL2712 has been shown to hamper obesity-induced inflammation in vivo, reducing proinflammatory cytokines in murine adipose tissue [20] and human serum [21]. In this study, we confirmed the anti-inflammatory effects of the LAB strain in the early period of obesity, focused on the regulatory effects of OLL2712 on the intestinal environment, and investigated the pathway by which this LAB strain exerted anti-inflammatory effects.
First, we fed mice a HFD for 4 weeks to examine the inflammatory responses triggered by early obesity. We found an increasing body weight and an enlarging fat mass in mice, with proinflammatory cytokines increasing in the adipose tissue-derived SVF, such as adipocyte immune cells. With the daily administration of the LAB strain for 3 weeks in the early period of obesity, although there was no alteration found in body weight or fat mass, the expression of macrophage-specific chemokine CCL2 (Ccl2) and proinflammatory cytokine IL-1β (Il1b) decreased, suggesting that the LAB could alleviate the macrophage infiltration and inflammation of adipose tissue caused by obesity.
We treated mice with the heat-treated L. plantarum OLL2712, because in previous studies, it had been found that the strain demonstrated strong anti-inflammatory effects on bone marrow-derived dendritic cells and peritoneal macrophages after being heat-treated to 75°C [19]. We considered that the anti-inflammatory effects of the strain were stabilized by this heat treatment.
We believe that orally administered OLL2712 first reached the intestine and did not exert its effect directly on adipose tissue. It is well known that gut bacterial bias and disruption of the intestinal barrier contribute to chronic inflammation in obesity (33, 44–47). Furthermore, it has been suggested that intestinal inflammation precedes the inflammation in the adipose tissue [18]. Therefore, we focused on gut microbiota and intestinal inflammation, examining the pathways of L. plantarum OLL2712 before it suppressed adipocyte inflammation.
We collected cecal contents and investigated the microbiota alterations caused by early obesity and the LAB treatment. Lactobacillus showed a significant decrease with HFD and an increase with the LAB treatment. We consider there was a possibility that the large increase in Lactobacillus was partly due to the administration of OLL2712 and simultaneously it was also possibly induced by the change of other strains belonging to Lactobacillus genus. Simultaneously, we detected a significant difference in gut microbiota between the short-term HFD group and the ND group, with an increasing trend in Lactococcus, *Clostridium cluster* XIVa, *Lachnospiracea incertae* sedis, and Pseudoflavonifractor. We found those genera changed oppositely in response to treatment with OLL2712. *The* genera we focused on have been reported to be involved in inflammation-associated diseases. An upregulation of bile acids production was detected in the intestines and feces of obese rodents, being related to the host inflammation, and was reported to be correlated to an increase in abundance of Lactococcus [48]. It is known that diet-induced obesity induces the overproduction of *Clostridium cluster* XIVa [49], increasing the levels of deoxycholic acid, a gut bacterial metabolite that can cause DNA damage and is involved in the enhancement of obesity-associated hepatocellular carcinoma development in mice [50, 51]. Lachnospiracea incertae sedis showed an enrichment in faecal samples of NAFLD (nonalcoholic fatty liver disease) patients [52], and Pseudoflavonifractor was reported to increase in the faeces of patients with ulcerative colitis [53]. We cannot give a definite answer about whether OLL2712 was used as a food source by other bacteria or not. Nevertheless, there are multiple studies discussing that components derived from heat-sterilized products of LAB might feed intestinal bacteria and change the gut microbiota (54–56), which might be due to the proliferation of the gut bacteria that could easily utilize the active components of the LAB strain.
Furthermore, we examined intestinal inflammation by investigating the intestinal tissue in relation to their permeability as a hallmark of gut barrier enhancement [22]. Considering that different parts of the gastrointestinal tract differ not only in their immune response but also in their number and composition of intestinal bacteria, we evaluated the inflammation and barrier function of the duodenum, jejunum, ileum, and colon to determine the effects of OLL2712 on each part of the intestine. We found that anti-inflammatory effects and intestinal barrier-enhancing effects of OLL2712 were exerted differently in the individual intestinal segments. The expression of CCL2 (Ccl2) and IL-1β (Il1b) was found to decrease in colon tissue but not in the small intestine after the LAB treatment. The apparent permeability of the ileum significantly decreased in response to the LAB treatment. Meanwhile, the gene expression of Occludin (Ocln) and MUC2 (Muc2) in ileum tissue declined in the HFD mice, while Occludin (Ocln) increased under the LAB treatment.
Occludin is well known as one of the proteins expressed in the intestine, forming tight junctions together with ZO-1 and claudins, which protect the body from harmful substances and pathogenic bacteria [57]. MUC2 is a secreted mucin with a physical barrier function in the intestinal tract. Furthermore, we detected a significant decrease in serum FITC-dextran levels after 7-day treatment with LAB, which suggested that the administration of the LAB strain decreased the overall intestinal permeability [58]. Therefore, it was suggested that OLL2712 could enhance the barrier function and alleviate adipocyte inflammation in obese mice by protecting them from harmful substances derived from the intestinal tract. Moreover, we found that such effects of L. plantarum OLL2712 on intestinal permeability were most noticeable in the ileum.
It was interesting that with the administration of L. plantarum OLL2712, the large intestine showed no change in barrier function, but the colonic inflammation was alleviated. Since there was a high possibility that the ingested LAB strain might not directly induce an immune response in the large intestine, our results suggested that the colonic inflammation might be alleviated by OLL2712 through regulating gut microbiota. On the other hand, there was no inflammatory change found in the small intestine tissue except for the PPs, but the barrier function was improved by the LAB strain in the distal part of the small intestine. The large intestine and small intestine are anatomically and functionally distinct [59]. Most functions of the large intestine rely on gut bacteria [60], which include fermenting dietary fiber, producing SCFAs, and modulating the immune response [61, 62]. On the other hand, the small intestine harbors lower numbers of commensal bacteria, such as segment filamentous bacteria, which mostly participate in the immune response by reacting directly to ingested food [63, 64]. The duodenum is connected directly with the stomach, participating in food digestion [65], while the jejunum is believed to be involved in the immune response, as well as nutrient absorption [66]. Compared to the jejunum, the ileum is closer to the large intestine, both physically and functionally. Meanwhile, unlike the large intestine, which cannot respond to orally ingested ingredients directly, in the ileum, multiple PPs are highly developed [67], and an immune response could be directly triggered by ingested food. Thus, the ileum, which is located in the final part of the small intestine, is the gut tract both easily influenced by gut microbiota and directly affected by the immunomodulatory effects of ingested components [68].
Therefore, we hypothesized that the administered L. plantarum OLL2712 might decrease intestinal permeability by cooperating with the gut microbiota via modulating the intestinal production of SCFAs. SCFAs, such as acetate and butyrate produced by the balanced bacteria, could inhibit the pathways of hyodeoxycholic acid (HDCA) or NF-κB to alleviate the intestinal inflammation and enhance the gut barrier [69]. On the other hand, OLL2712 might also enhance the barrier by inducing the production of IgA, which may protect the intestinal epithelial cells from LPS and pathogenic bacteria and alleviated the intestinal inflammation [70]. Nevertheless, we consider there was still a possibility that OLL2712 function directly on the intestinal epithelial cells via Toll-like receptors (especially TLR2) or Nod-like receptors, well known as the pathways through which the intestinal epithelial cells recognized the bacteria (71–73). Further studies especially in vitro experiments to co-culture SCFAs or the intestinal contents and OLL2712, are needed to confirm the hypothesis.
The results of this research suggested that OLL2712 reached the small intestine, alleviating inflammation and cooperating with the gut bacteria to enhance barrier function, especially in the ileum. This prevented the leakage of harmful substances, thereby suppressing adipocyte inflammation (Figure 6). If intestinal substances that cooperate with OLL2712 and participate in anti-inflammatory effects can be identified, we could elucidate the mechanisms of the health function of LAB to alleviate metabolic diseases and chronic inflammation.
**Figure 6:** *The pathways by which the lactic acid bacteria (LAB) strain exerted its anti-inflammatory effects. Ingested OLL2712 might directly regulate the gut microbiota in the large intestine and reduce harmful substances, which are derived from obesity-induced gut dysbiosis and leak into the blood, eventually relieving adipocyte inflammation. Simultaneously, the LAB strain enhanced the intestinal barrier, especially in the ileum, suggesting collaborative modulation of intestinal immune responses by ingested lactic acid bacteria and microbiota. The enhancement of the gut barrier reduced the leakage of harmful substances into the bloodstream, which resulted in anti-inflammatory changes in the adipose tissue.*
## Data availability statement
The data supporting this study are available from the corresponding author upon request.
## Ethics statement
The animal study was reviewed and approved by the Experimental Animal Ethics Committee of the Graduate School of Agriculture and Life Sciences of the University of Tokyo.
## Author contributions
YW and SH conceived this study. YW designed the research studies. YW, TTa, YZ and RW performed the experiments. YW analyzed the data and wrote the manuscript. TTo and TS prepared materials and reviewed the manuscript. TTa, YZ, RW, HN-A, TM, MT and SH reviewed the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
TTo and TS are employees of Meiji Holdings Co., Ltd. SH received a grant from Meiji Holdings Co., Ltd.
The remaining 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.2023.1123052/full#supplementary-material
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|
---
title: 'Rheumatoid arthritis study of the Egyptian College of Rheumatology (ECR):
nationwide presentation and worldwide stance'
authors:
- Tamer A. Gheita
- Hala A. Raafat
- Samah A. El-Bakry
- Ahmed Elsaman
- Hanan M. El-Saadany
- Nevin Hammam
- Iman I. El-Gazzar
- Nermeen Samy
- Nora Y. Elsaid
- Suzan S. Al-Adle
- Samar Tharwat
- Amira M. Ibrahim
- Samar M. Fawzy
- Nahla N. Eesa
- Rawhya El Shereef
- Faten Ismail
- Mervat I Abd Elazeem
- Enas A. Abdelaleem
- Amany El-Bahnasawy
- Zahraa I. Selim
- Nada M. Gamal
- Maha Nassr
- Samah I. Nasef
- Abdel Hafeez Moshrif
- Shereen Elwan
- Yousra H. Abdel-Fattah
- Marwa A. Amer
- Doaa Mosad
- Eman F. Mohamed
- Dina F. El-Essawi
- Hanan Taha
- Mohamed N. Salem
- Rasha M. Fawzy
- Maha E. Ibrahim
- Asmaa Khalifa
- Nouran M. Abaza
- Ahmed M. Abdalla
- Amany R. El-Najjar
- Noha A. Azab
- Hanan M. Fathi
- Khaled El-Hadidi
- Tahsin El-Hadidi
journal: Rheumatology International
year: 2023
pmcid: PMC9995404
doi: 10.1007/s00296-022-05258-2
license: CC BY 4.0
---
# Rheumatoid arthritis study of the Egyptian College of Rheumatology (ECR): nationwide presentation and worldwide stance
## Abstract
To depict the spectrum of rheumatoid arthritis (RA) in Egypt in relation to other universal studies to provide broad-based characteristics to this particular population. This work included 10,364 adult RA patients from 26 specialized Egyptian rheumatology centers representing 22 major cities all over the country. The demographic and clinical features as well as therapeutic data were assessed. The mean age of the patients was 44.8 ± 11.7 years, disease duration 6.4 ± 6 years, and age at onset 38.4 ± 11.6 years; 209 ($2\%$) were juvenile-onset. They were 8750 females and 1614 males (F:M 5.4:1). $8\%$ were diabetic and $11.5\%$ hypertensive. Their disease activity score (DAS28) was 4.4 ± 1.4 and health assessment questionnaire (HAQ) 0.95 ± 0.64. The rheumatoid factor (RF) and anti-cyclic citrullinated peptide (anti-CCP) were positive in $73.7\%$ and $66.7\%$ respectively. Methotrexate was the most used treatment ($78\%$) followed by hydroxychloroquine ($73.7\%$) and steroids ($71.3\%$). Biologic therapy was received by $11.6\%$ with a significantly higher frequency by males vs females ($15.7\%$ vs $10.9\%$, $$p \leq 0.001$$). The least age at onset, F:M, RF and anti-CCP positivity were present in Upper Egypt ($p \leq 0.0001$), while the highest DAS28 was reported in Canal cities and Sinai ($p \leq 0.0001$). The HAQ was significantly increased in Upper Egypt with the least disability in Canal cities and Sinai ($$p \leq 0.001$$). Biologic therapy intake was higher in Lower Egypt followed by the Capital ($p \leq 0.0001$). The spectrum of RA phenotype in *Egypt is* variable across the country with an increasing shift in the F:M ratio. The age at onset was lower than in other countries.
## Introduction
Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease primarily affecting small synovial joints usually symmetrically. Symptoms for more than 6 months establish the diagnosis of RA [1]. An intricate network of cytokines and cells trigger synovial cell proliferation and cause damage to both cartilage and bone [2].
Alone the laboratory test for RA cannot confirm a diagnosis that is commonly challenging. A complete clinical approach is necessary to diagnose and avoid debilitating joint damage [1]. Yet, auto-antibodies signify a hallmark of RA, with the rheumatoid factor (RF) and anti-cyclic citrullinated (anti-CCP) peptides being the most acknowledged. Seropositive patients present a certain disease course. With the recent improvements in diagnosis and the discovery of new autoantibodies, the group of seronegative patients is persistently shrinking [3]. Using applicable disease activity measures can help in clinical practice to take on treat-to-target strategies in RA patients [4]. There has been a rising importance for the early and demanding diagnosis and treatment of RA with the goal of reducing disability and mortality [5].
To improve the clinical outcome in RA, various therapeutic approaches are required [1], although current management recommendations may still support a 'one-size-fits-all' treatment strategy [6]. Early treatment with disease-modifying anti-rheumatic drugs (DMARDs) is standard, yet many patients progress to disability with substantial morbidity over time [1]. The arrival of biologics has changed the treatment of RA due to their remarkable impact on disease manifestations and their ability to diminish joint damage [5]. With the development of biologics and Janus kinase (JAK) inhibitors [2], these agents are being used by a rising number of patients including those with a mild disease. However, cost and safety issues remain key determinant [2, 5]. Personalized medicine is necessary to select special treatment strategies for certain clinical or molecular phenotypes of patients [6] and key factors of RA disease such as epidemiology, clinical presentations and treatment options should be presented.
In the milieu of the restricted information on the epidemiology and treatment patterns of RA across Egypt, the aim of the present study was to present the spectrum of RA in Egypt and compare it to other studies from around the world to provide broad-based characteristics to this particular population.
## Study population and design
This cross-sectional study included a large cohort of 10,364 adult RA patients (new and existing cases) fulfilling the American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) classification criteria [7] that were recruited from 26 specialized rheumatology departments and centers representing 22 major governorates across the country by members of the Egyptian College of Rheumatology (ECR) during the period from September 2018 till December 2021. Any patients with another rheumatic disease or below the age of 18 were excluded. The patients’ in the corresponding university-teaching hospitals provided informed consents to participate and the study was approved by the local ethics committee, in accordance to the 1964 Helsinki declaration and its later amendments.
## Measures and outcomes
Patients were subjected to full history taking and clinical examination. Juvenile-onset RA (JoRA) cases were considered for those who developed the disease before the age of 18 years. It is noteworthy that co-morbidities or manifestations relied on the records of the files. Presence of rheumatoid factor (RF) and/or anti-cyclic citrullinated peptide (anti-CCP) were determined. The use of medications to treat RA was described. Disease activity score (DAS28) [8] and health assessment questionnaire (HAQ) [9] were assessed.
## Statistical analysis
Data were collected on a standardized data sheet and stored in an electronic database. Data missing completely at random (MCAR) as for the RF, anti-CCP and anti-nuclear antibody (ANA) positivity was handled by running a complete-case analysis (CCA), where all persons with missing values were excluded from the analysis of this test and imputation was not used. Statistical Package for Social Sciences (SPSS) version 25 was used. Variables were presented as frequencies and percentages or mean and standard deviation. A comparison was done using Chi-square test, Mann Whitney U tests or analysis of variance (ANOVA). P value < 0.05 was considered significant.
## Results
The study included 10,364 RA patients recruited from 22 governorates across Egypt. Their mean age was 44.8 ± 11.7 years. They were 8750 females and 1614 males (F:M 5.4:1). Characteristics of the patients and gender differences are presented in Table 1. 209 ($2\%$) were Jo-RA. Steroids were received by $71.3\%$ of the patients. DMARDs were received in the following descending frequency: methotrexate (MTX) ($78\%$), hydroxychloroquine (HCQ) ($73.6\%$), leflunomide (LFN) ($54.8\%$), sulfasalazine (SAZ)($37.2\%$), cyclophosphamide (CYC) ($2.4\%$), azathioprine (AZA)($2\%$), cyclosporine A (CSA)($0.5\%$) and mycophenolate mofetil (MMF)($0.46\%$). Steroids and DMARDs received were comparable between genders except for HCQ (male: $77.6\%$ vs females $73\%$; $$p \leq 0.002$$). Biologic therapy was received by $11.6\%$ with a significantly higher frequency by males vs females ($15.7\%$ vs $10.9\%$, $$p \leq 0.001$$). Biologic therapies received were etanercept ($30.4\%$), adalimumab ($18.4\%$), golimumab ($14\%$), rituximab ($7.9\%$), infliximab ($3.3\%$), tofacitinib ($1.6\%$), certolizumab ($1\%$), upadacitinib ($0.8\%$), baricitinib ($0.39\%$), abatacept ($0.39\%$) and undefined ($17.8\%$). Patients also received low dose aspirin ($4.6\%$), colchicine ($1.3\%$) and oral anticoagulants ($1.1\%$).Table 1Characteristics of the rheumatoid arthritis patients and gender differences: demographic features, co-morbidities, manifestations, laboratory investigations, functional status and disease activityParameter n (%) or mean ± SDRheumatoid arthritis patientspAll($$n = 10$$,364)Females($$n = 8750$$)Males($$n = 1614$$)Age (years)44.8 ± 11.744.4 ± 11.647.1 ± 12.1 < 0.0001Female:Male5.4:1–––Disease duration (years)6.4 ± 6.036.4 ± 66.5 ± 6.40.66Age at onset (years)38.4 ± 11.638 ± 11.440.7 ± 12.3 < 0.0001BMI28.5 ± 5.328.5 ± 5.328.6 ± 5.50.89Smoking849 (8.2)213 (2.4)636 (39.4) < 0.0001Married9458 (91.1)7993 (91.3)1465 (90.8)0.64Comorbidity Diabetes mellitus833 [8]689 (7.8)144 (8.9)0.22 Hypertension1194 (11.5)995 (11.4)199 (12.3)0.36 HCV88 (0.85)72 (0.82)16 [1]0.55 Bronchial asthma68 (0.66)63 (0.72)5 (0.31)0.01 Thyroid dysfunction185 (1.8)178 [2]7 (0.43) < 0.0001Family hx RA (1st degree)155 (1.5)136 (1.6)19 (1.2) < 0.0001Manifestations Rheumatoid nodules413 [4]339 (3.9)74 (4.6)0.16 Ocular1086 (10.5)904 (10.3)182 (11.3)0.37 Sjögren’s syndrome980 (9.5)870 (9.9)110 (6.8) < 0.0001 CNS703 (6.8)593 (6.8)110 (6.8)0.43 Vasculitis77 (0.74)63 (0.72)14 (0.87)0.26 GIT1059 (10.2)860 (9.8)199 (12.3)0.8 CVS619 [6]501 (5.73)118 (7.3)0.14 Chest732 (7.1)605 (6.9)127 (7.9)0.07 FMS760 (7.3)713 (8.1)47 (2.9) < 0.0001 Renal205 [2]137 (1.6)68 (4.2) < 0.0001Laboratory investigations Hemoglobin (g/dl)11.6 ± 1.511.5 ± 1.412.3 ± 1.7 < 0.0001 TLC (× 103/mm3)7.1 ± 2.57 ± 2.57.3 ± 2.5 < 0.0001 Platelets (× 103/mm3)297.1 ± 95.2297.7 ± 94.8293.1 ± 98.20.18 ESR (mm/1st hr)45 ± 27.745 ± 27.545.1 ± 28.70.85 CRP (mg/dl)17.5 ± 22.616.9 ± 21.922.6 ± 26.40.036 ALT (IU/l)21.7 ± 15.321.8 ± 15.221.1 ± 160.22 AST (IU/l)22.6 ± 15.722.8 ± 15.721.3 ± 15.70.02 Urea (mg/dl)19.5 ± 17.619.9 ± 17.617.5 ± 17.10.009 Creatinine (mg/dl)0.71 ± 0.340.71 ± 0.320.76 ± 0.450.001 Cholesterol (mg/dl)195.4 ± 62.4193.1 ± 56.8206 ± 87.90.02 Triglycerides (mg/dl)123.9 ± 55.5124.9 ± 53.7115.8 ± 68.10.11 HDL (mg/dl)55.4 ± 29.355.9 ± 28.851.5 ± 32.20.09 LDL (mg/dl)104.1 ± 40.3104.8 ± 38.798.6 ± 51.10.15 SUA (mg/dl)4.68 ± 1.44.6 ± 1.45 ± 1.5 < 0.0001 RF ($$n = 7992$$, F:M $\frac{6877}{1115}$)5889 (73.7)5022 [73]867 (77.8) < 0.0001 Anti-CCP ($$n = 5433$$,F:M $\frac{4617}{816}$)3623 (66.7)3046 [66]577 (70.7)0.007 ANA ($$n = 2556$$, F:M $\frac{2271}{285}$)330 (12.9)302 (13.3)28 (9.8)0.07HAQ0.95 ± 0.640.95 ± 0.630.97 ± 0.70.56DAS284.43 ± 1.444.4 ± 1.44.3 ± 1.60.015Statistical analysis was done using a Chi-square or Mann Whitney U tests. Bold values are significant at $p \leq 0.05$BMI body mass index, HCV hepatitis C virus, hx history, CNS central nervous system, GIT gastrointestinal tract, CVS cardio-vascular system, FMS fibromyalgia syndrome, TLC total leucocytic count, ESR erythrocyte sedimentation rate, CRP C-reactive protein, ALT alanine transaminase, AST aspartate transaminase, HDL high density lipoprotein. LDL low density lipoprotein, SUA serum uric acid, RF rheumatoid factor, Anti-CCP anti-cyclic citrullinated peptide, ANA antinuclear antibody, HAQ health assessment questionnaire, DAS28 disease activity score Certain variables according to the geo-location are presented in Table 2 and graphically presented in Figs. 1 and 2. The age at onset, gender distribution, disease activity, RF and anti-CCP positivity were significantly varied. The least age at onset, F:M, RF and anti-CCP positivity were present in Upper Egypt, while the highest DAS28 was reported in Canal cities and Sinai. The HAQ was significantly increased in Upper Egypt with the least disability in Canal cities and Sinai. Biological therapy intake was higher in Lower Egypt ($46.3\%$), followed by the Capital ($33.1\%$), Upper Egypt ($20.3\%$) and the Canal cities and Sinai ($0.2\%$) ($p \leq 0.0001$).Table 2Age at onset, gender, disease activity, rheumatoid factor and anti-cyclic citrullinated peptide in rheumatoid arthritis patients according to the geo-locationGeo-locationTotal($$n = 10$$,364)Age at onset(years)F:MDAS28PositiveRFPositiveanti-CCPLower EgyptDelta and N Coast180240.4 ± 10.84.73:14.43 ± $\frac{1.31204}{1502}$ (80.2)$\frac{926}{1270}$ (72.9)1Alexandria23535.8 ± 9.68.4:15.02 ± $\frac{1.588}{137}$ (64.2)$\frac{140}{189}$ (74.1)2Beheira1540.5 ± 9.34:14.01 ± $\frac{1.611}{15}$ (73.3)$\frac{3}{5}$ [60]3Kafr El-Sheikh29837 ± 114.6:14.8 ± $\frac{1.4148}{223}$ (66.4)$\frac{67}{99}$ (67.7)4Damietta5945.2 ± 12.418.7:14.7 ± $\frac{1.247}{59}$ (79.7)$\frac{13}{25}$ [52]5Gharbia55846.4 ± 6.83.5:13.7 ± $\frac{0.82454}{513}$ (88.5)$\frac{469}{499}$ [94]6Dakahlia38937 ± 11.34.9:14.6 ± $\frac{1.5263}{320}$ (82.2)$\frac{171}{263}$ [65]7Sharkia6039.2 ± 10.67.6:14 ± $\frac{1.838}{47}$ (80.6)$\frac{8}{12}$ (66.7)8Menoufiya5344.8 ± 9.29.6:14.8 ± $\frac{1.140}{53}$ (75.5)$\frac{30}{46}$ (65.2)9Kalyoubia13537.5 ± 12.13.5:14.4 ± $\frac{1.03116}{135}$ (85.9)$\frac{25}{133}$ (18.8)Canal citiesand Sinai32040.5 ± 115.27:14.97 ± $\frac{1.4243}{282}$ (86.2)$\frac{200}{273}$ (73.4)10Port-Said641.8 ± 10.7females4.6 ± $\frac{0.145}{5}$ [100]$\frac{1}{2}$ [50]11Ismailia31240.4 ± 10.95.2:15 ± $\frac{1.4237}{275}$ (86.2)$\frac{199}{270}$ (73.7)12South Sinai248.5 ± 30.41:$\frac{141}{2}$ [50]$\frac{0}{113}$ Capital (Cairo)481237.9 ± 11.87.8:14.38 ± $\frac{1.43015}{4039}$ (74.6)$\frac{1498}{2079}$ (72.1)Upper Egypt343037.8 ± 11.43.9:14.4 ± $\frac{1.51426}{2169}$ (65.7)$\frac{999}{1808}$ (55.3)14Fayoum37840.7 ± 11.95.4:15 ± $\frac{1.2273}{365}$ (74.8)$\frac{250}{316}$ (79.1)15Beni-Suef43338 ± 11.43.4:14.9 ± $\frac{1.7240}{397}$ (60.5)$\frac{184}{313}$ (58.8)16Minia51836.8 ± 10.74.6:13.7 ± $\frac{1.4322}{516}$ (62.4)$\frac{227}{468}$ (48.5)17Assiut82337.7 ± 11.44.9:14.7 ± $\frac{1.5449}{706}$ (63.6)$\frac{260}{597}$ (43.6)18Sohag113237 ± 11.63:14.2 ± $\frac{1.635}{55}$ (63.6)$\frac{8}{16}$ [50]19Qena4242.7 ± 11.049.5:14.2 ± $\frac{0.9720}{30}$ (66.7)$\frac{7}{15}$ (46.7)20Aswan9839.2 ± 9.43.9:14.2 ± $\frac{0.9781}{94}$ (86.2)$\frac{62}{83}$ (74.7)21Red Sea246.5 ± 7.8Females5.7 ± $\frac{1.12}{2}$ [100]–22New Valley443.3 ± 11.1FemalesNA$\frac{4}{4}$ [100]$\frac{1}{2}$ [50]$p \leq 0.0001$ < 0.0001 < 0.0001 < 0.0001 < 0.0001Statistical analysis was done using analysis of variance (ANOVA) tests. Bold values are significant at $p \leq 0.05$F:M female to male ratio, DAS28 disease activity score, RF rheumatoid factor, ACPA: anti-cyclic citrullinated peptideFig. 1Rheumatoid factor and anti-cyclic citrullinated peptide (anti-CCP) in rheumatoid arthritis patients across different geographic regions in Egypt. LE lower Egypt, C and S Canal and Sinai, UE Upper Egypt. Statistical analysis was done using analysis of variance (ANOVA) testsFig. 2The age at onset, gender distribution, disease activity, rheumatoid factor and anti-cyclic citrullinated peptide positivity as well as the main medications received by rheumatoid arthritis patients from the four main regions across Egypt. Lower Egypt (North coast and Delta); ALX: Alexandria, BH: Beheira, KS: Kafr El Sheikh, DM: Damietta, GB: Gharbia, DK: Dakahlia, SK: Sharkia, MNF: Menoufiya, KB: Kalyoubia. Canal cities and Sinai; PS: Port-Said, IS: Ismailia, SZ: Suez. Upper Egypt; FM: Fayoum, BS: Beni-Suef, MN: Minia, AST: Assuit, SO: Sohag, QN: Qena, LX: Luxor, ASW: Aswan. HCQ: hydroxychloroquine, MTX: methotrexate, LFN: leflunomide
## Discussion
This cross-sectional study presented the socio-demographic, clinical, and therapeutic profile of 10,364 RA patients recruited across Egypt. In the present work the mean age at onset of RA patients in Egypt was 38 years which was significantly lower in females. The F:M was 5.4:1. The age at onset, gender distribution and disease characteristics of RA patients in countries from different continents were compared to the current study (Table 3). Interestingly, the age at onset was lower than that in other countries and nations [10–15] while it was comparable with that from Arab countries [16] and Turkey [17]. A potential explanation could be related to the lower average age of the populations in the Middle Eastern countries [18]. However, genetic and environmental factors cannot be excluded. The higher F:M was comparable to large registries from Latin America [13, 19, 20] thus raising the subject about an increasing shift in the ratio. Once more, the BMI in the RA patients of the current study were similar to that reported from Turkey [17]. RA, the most common inflammatory rheumatic disease, is no exception, with a F:M > 4 before 50 years old and < 2 after the age of 60 [21]. Furthermore, with the increasing incidence of spondyloarthritis (SpA) worldwide, it could have been that more male patients were misdiagnosed as having RA.Table 3The age at onset, gender distribution and disease characteristics of the rheumatoid arthritis patients in countries from different continents compared to the current studyParameterEuropeUSALatinAsiaECR[10][11][5, 12][13, 20][19][29][17][14, 15]no14,438389842,00081,38637174721103830,50110,364CountriesUKEUCanadaUSAColombiaBrazilArgentinaSouth KoreaTurkeyChinaEgypt Centres189 registries8339regions812336500 + 26 regions26Age atonset (ys)≈ 43≈45.1≈47≈49≈4443.9 ± 1341.4 ± 13.5≈48.638.4 ± 11.6F:M3.2:14.1:13.5:15.3:15.7:15.8:14.2:14.1:15.4:1Smoking$21.8\%$$17.6\%$$12\%$$11\%$$8.01\%$$16.8\%$$8.2\%$DAS286.5 ± 14.1 ± 13.5 ± 1.62.4(1.8–3.3)4.9 ± $2.746.9\%$(3.2–5.1)3.7 ± 1.65.1 ± 1.74.4 ± 1.4RF–$42.5\%$$75\%$$76\%$$92\%$$86.8\%$$83.6\%$$73.7\%$ACPA–$32.7\%$$24\%$$83.9\%$$66.7\%$MTX$56.6\%$–––$84.4\%$$81.4\%$$64.4\%$$55.9\%$$78\%$cDMARDs$65.1\%$$30.3\%$$97.5\%$$90.8\%$$89.7\%$Steroids$50.3\%$$38\%$$19\%$$56.7\%$$74\%$$50.4\%$$40.6\%$$71.3\%$Biologics$100\%$$100\%$$100\%$$15.5\%$$69.7\%$$5.8\%$$10.5\%$$8.3\%$$11.6\%$UK United Kingdom, EU Europe, USA United States of America, FM female to male, DAS28 disease activity score, RF rheumatoid factor, ACPA anti-citrullinated peptide antibody, MTX methotrexate, cDMARDs disease modifying anti-rheumatic drugs *The misdiagnosis* of SpA as RA leads to a delayed SpA diagnosis and inadequate therapeutic outcomes. Typical SpA-related clinical manifestations were present in RA patients. The advancements and accessibility of imaging modalities pave way for a more precise classification [12]. In this work, associated bronchial asthma and thyroid dysfunction, a family history of RA, Sjögren's syndrome, fibromyalgia syndrome and disease activity were significantly increased in females. It is notable that a lower frequency of females was receiving biologic therapy. On the contrary, males were significantly more smoking, had more renal manifestations, higher serum uric acid, more frequent positivity of RF and anti-CCP. Regarding the various clinical manifestations reported in this work, they were further compared to those from other countries.
Interstitial lung disease (ILD) is a well-known potentially life-threatening complication in RA [22]. The enduring appraisal of the complex relationships between smoking, COPD, and other factors in RA-associated ILD is important [23]. In this work, the reported frequency of smoking in RA patients was lower ($8.2\%$) than that from other studies from the UK ($21.8\%$) [10], European Union (EU) and Canada ($17.6\%$)[11] as well as Turkey ($16.8\%$) [17].
In this work, neurological manifestations were reported at a low frequency. The frequencies of depression and anxiety were doubled in early RA than in long-standing disease. RA patients with short disease duration and functional limitation were more likely to suffer from depression and anxiety [24].
In this study, the reported frequency of cardiovascular manifestations was low. However, there is a considerable rise in mortality and morbidity in RA due to cardiovascular disease (CVD). The augmented risk for heart disease is related to disease activity and chronic inflammation with traditional risk factors and RA-related characteristics playing a central role [25]. RA patients had higher rates of obesity than the general population and this was strongly associated with physical dysfunction [26]. The BMI in this work was higher than that reported from other nations such as the UK [10] and EU [11]. Compared to osteoarthritis (OA), RA patients were significantly more frequently diabetic and smokers but had lower prevalence of obesity and dyslipidemia [27]. The frequency of metabolic syndrome in RA patients is doubled and raises the risks of stroke and heart disease [28]. The frequency of diabetes mellitus in this work was similar to the USA [12] and Latin American [19] registries, CVD was comparable to the USA CORRONA study [12] and chest involvement was in line to the Korean registry (KORRONA)[29].
In this work, the RF was positive in $73.7\%$ while the anti-CCP was positive in $66.7\%$. The frequency of RF was comparable to that from a large Colombian study on 68,247 cases [13] and to the CORONNA study from USA [12]. It was lower than Asian studies from Korea ($86.8\%$)[29] and China ($84.7\%$)[14]. Moreover, the frequency of anti-CCP positivity was lower than that reported in a Korean work ($83.9\%$)[29] but higher than the registries from Colombia ($24\%$)[13] and from the EU ($32.7\%$)[11]. Anti-CCP and RF combined detection improves the diagnostic efficiency of RA, providing a potential strategy for early clinical screening [30]. The frequency of remission is three times higher in sero-negative patients with RA. However, the rate of remission does not depend on the serological status as almost two thirds of patients achieve remission in the first 6 months of DMARDs therapy. Anti-CCP and RF titers at the onset of the disease do not influence remission [31].
There was moderate disability in the present cases as measured by the HAQ. The functional capacity (physical and psychosocial) is a central treatment aspect to consider when the RA therapeutic strategy is personalized [32]. The average HAQ score reported in a population-based study was 0.49, and in RA was 1.2 [9]. The disease activity score in the present work was similar to that reported from the EU [11], higher than that from Turkey [17] and the USA [12] while it was lower than that from the UK [10] and China [14].
The medications received by the patients of the current study were diverse. In this study, more males were receiving HCQ and biologic therapy and with a lower disease activity. In early RA, targets can be achieved when the baseline level of diseases activity is low, with male gender and shorter disease duration [33]. In this work, MTX was received by $77.9\%$. Using MTX before initiating biologic therapy may contribute to a cost-effective RA care [34]. Variables related to MTX failure such as female gender, higher BMI, smoking, higher disease activity and diabetes can aid in predicting the disease process and outcome of treatment [35]. $54.8\%$ of cases received leflunomide while $37.1\%$ received sulfasalazine. Leflunomide is comparable to sulfasalazine in MTX-failed RA patients with similar safety profile [36]. $11.6\%$ of the current patients were on biologics while in *Korea a* 6 times fold usage was reported [37].
Across the country there was a significant difference in the age at onset, gender distribution, disease activity, RF and anti-CCP positivity. A potential converse causal link between educational accomplishment and the risk of RA has been noticed [38].
National Registries are essential to direct current practice. RA registries in the Middle East and North Africa (MENA) region are rarely presented [39]. On comparing the findings to countries from other continents, variations were easily noted.
In a study from Morocco on 225 RA cases, the age of onset (44 years), F:M (7.1:1), DAS28 (5.2 ± 1), RF positivity ($90.5\%$), anti-CCP positivity ($88.8\%$) were higher than the current findings however, those patients were all receiving biologic therapy [40].
In a study on 300 RA patients from Palestine, treatment with biologic therapy, younger age, having work, higher income, absence of morning stiffness and absence of co-morbidities were significantly associated with better quality of life and less disability [41]. In the work from a tertiary care hospital in KSA on 288 RA patients, the majority ($88\%$) were females with a F:M 7.3:1. In agreement to this work, hypertension was the most common co-morbidity followed by diabetes and almost all of their patients had high disease activity at presentation time [42]. Compared to patients in Western countries, South Korean patients with RA, even those with better physical function, seem to have a lower quality of life [43]. In a study conducted by the Korean College of Rheumatology (KCR) on 2422 patients with a F:M 6.8:1, $19.4\%$ were overweight and $16.1\%$ obese, $13.6\%$ smoked, $11.6\%$ had dyslipidemia, $28\%$ were hypertensive and $4.5\%$ were diabetic. RF and anti-CCP were positive in $82.6\%$ and $86.9\%$, respectively. The mean DAS28 was 4.7 ± 1.6, $79.9\%$ were receiving steroids, $93.2\%$ MTX, $68.8\%$ HCQ and $46.3\%$ LFN while $61.7\%$ were on biologics [37].
In a large RA registry in the UK, of 27,607 patients, $70.6\%$ were female (F:M 2.4:1) and their mean BMI was 27.3 [44]. In a study from 11 registries from 9 European countries: France, Sweden, Czech, UK, Denmark, Italy, Germany and Portugal on 130,315 RA patients; for biologic naive patients the age at onset was 56.4 years and F:M 2.6:1 and for those who received anti-TNF the age at onset was 46.5 years and F:M was 3:1 [45].
In a large nationwide US study, the F:M was 2.4:1. Obesity was present in $15.1\%$, diabetes in $20.4\%$ and dyslipidemia in $48\%$ [46].
Although this is currently the largest data of RA patients from across Egypt, there is a desperate need for effective and applicable national management strategies and guidelines. It seems that still across the country the diagnostic tests are not strictly considered for all patients. In spite that the medications received are mostly alike among the major cities, there is a disperse intake of biologic therapy being higher along a North to South gradient.
In conclusion, the spectrum of RA phenotype in *Egypt is* variable across the country with an increasing shift in the F:M ratio. The age at onset was lower than in other countries.
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|
---
title: Implication of apoptosis and oxidative stress in mitigation of ivermectin long-term
hazards by zinc nanoparticles in male rabbits
authors:
- Set A. El-Shobokshy
- Magda I. Abo-Samaha
- Ferial M. Sahwan
- Samia M. Abd El-Rheem
- Mohamed Emam
- Asmaa F. Khafaga
journal: Environmental Science and Pollution Research International
year: 2022
pmcid: PMC9995419
doi: 10.1007/s11356-022-24095-1
license: CC BY 4.0
---
# Implication of apoptosis and oxidative stress in mitigation of ivermectin long-term hazards by zinc nanoparticles in male rabbits
## Abstract
Ivermectin is the medication of choice for treating human onchocerciasis and is used in veterinary medicine to treat a variety of ectoparasites and endoparasites. This study was designed to investigate the effects of zinc nanoparticles (ZnNPs) on the fertility of male rabbits exposed to experimental ivermectin (IVM) intoxication. A total of 72 mature male rabbits were equally divided into 4 groups ($$n = 18$$). The first group (CTR) served as control; the second group (IVM) received subcutaneous injection of IVM (0.2 mg/kg body weight); the third group (ZnNPs) fed on zinc nanoparticles (60 mg/kg diet); and the fourth group (ZnNPs + IVM) were administered IVM and zinc nanoparticles at the same doses. The experiment lasted for 9 weeks. Results revealed that IVM-intoxicated rabbits showed impaired growth performance parameters, including body weight, total body weight gain (TBWG), total feed intake (TFI), and feed conversion ratio (FCR). Moreover, carcass characteristic and fertility parameters (including semen quality parameters and testosterone levels) were also impaired after IVM administration. Additionally, testicular malondialdehyde (MDA) and antioxidant (reduced glutathione, superoxide dismutase, catalase) levels as well as the histopathology and immunohistochemical expression of caspase 3 and PCNA in the testes and epididymis were detrimentally affected. On the contrary, ZnNP administration efficiently improved most of these parameters in IVM-intoxicated rabbits. In conclusion, ZnNPs exhibited promising ability for improving the growth and fertility status of rabbits and reducing the deleterious effects of IVM possibly through the suppression of apoptotic and oxidative pathways.
## Introduction
Commercial rabbits have recently gained considerable attention due to their high prolificacy and rapid growth rate compared to broiler chicken (Azazi et al. 2018). Rabbits are an important source of protein for humans because of its high quality and low fat and cholesterol content (PARA et al., 2015). With the increase in rabbit production, the need for animal supplements has become a necessary part of their daily diet. As such, studies into many areas of rabbit production are urgently needed (Abdel-Wareth et al. 2015; Al-Sagheer et al. 2017).
In commercial rabbit production, parasitic infestation, particularly by Sarcoptes scabiei, which causes mange, has been a major concern (El-Ashram et al. 2020). Ivermectin (IVM) is a drug extensively used to treat and prevent sarcoptic mange, a dangerous condition with serious health implications, including rabbit death (Joshi et al. 2021). This particular drug is an acaricide and anthelmintic developed from avermectin B1, which originates from Streptomyces avermitilis. The subcutaneous route has been identified as the most efficient and recommended route of administration, promoting better absorption than the oral and topical routes (Khan Sharun et al. 2019).
IVM is generally well tolerated in mammals (Johnson-Arbor 2022); however, in female rabbits, repeated IVM doses caused pathological alterations in hepatic tissue, including vacuolation of hepatocytes and fibrosis (Al-Jassim et al. 2015). In male rats, therapeutic and double therapeutic doses reduced the total sperm count and induced sperm mortality, as well as pathological abnormalities in the liver, kidneys, and testis. Congestion of blood vessels, degenerative changes (e.g., vacuolar, hydropic, and even necrotic changes), and functional problems of the liver and kidneys are some of the pathogenic changes (Elzoghby et al. 2015). Injecting IVM every 2 weeks has been the standard prophylactic regimen used by rabbit breeders. In addition, IVM is reported to cause excessive generation of reactive oxygen species from the mitochondria, which could potentially interfere with the proper release of cytochrome c, which subsequent activation of caspase-3 and induction of cell apoptosis (Ali et al., 2017). Moreover, it can reduce the expression of proliferating cell nuclear antigen (PCNA), which plays a pivotal role in the S-phase of the cell cycle via DNA synthesis and replication (Strzalka and Ziemienowicz 2011). Therefore, the reduction in PCNA expression may promote suppression of DNA synthesis with subsequent DNA damage (Moshari et al. 2017).
Several metabolic processes and physiological functions of animals rely heavily on trace minerals (Underwood 2012). Zinc (Zn) is the mammalian body’s second most abundant trace element (Dosoky et al. 2022). It cannot be stored in the body and must be consumed on a regular basis to meet physiological requirements (Fairweather-Tait and de Sesmaisons 2019). Zn is an essential component of around 300 enzymes involved in the production and degradation of proteins, lipids, carbohydrates, and nucleic acids, as well as in the metabolism of other micronutrients (Abd El-Hack et al. 2017). It is also a required component of the superoxide dismutase (SOD) enzyme, which plays an important role in the antioxidant defense system (Azad et al. 2017). Additionally, *Zn is* essential for polynucleotide transcription, which leads to genetic expression, and plays an important role in immune system function, affecting humoral and cellular immunity (Chasapis et al. 2020). Zn boosts T cell production by increasing thymus gland secretion of thymulin. Hence, Zn deficiency results in thymus malfunction, which has a major impact on proper immune function (Mocchegiani et al. 2013). Zn is also required for optimal human physiological activities, such as regular growth and reproduction (Swain et al. 2016).
Recently, several studies have concluded that animals fed zinc nanoparticles (ZnNPs) had better growth, reproduction, and immunity compared to those who were not. To date, however, no available studies have investigated the impact of ZnNPs on the side effects of IVM. Therefore, the present study aimed to determine the possible impact of ZnNPs on the growth and fertility of male rabbits receiving IVM treatment, focusing specifically on the possible mechanisms of action.
## Ethical statement
The Institutional Animal Care and Use Committee of University of Alexandria approved the experimental protocol used in this study (Permit #$\frac{2021}{013}$/97).
## Chemicals
Zn oxide $99.99\%$ (ZnO powder; containing $80.32\%$ Zn, as an inorganic form of Zn) was purchased as a commercial product from Sigma Company, Egypt. Nano-Zn oxide $97\%$ (nZnO powder; containing $77.92\%$ Zn, as a nano-form of Zn) was purchased as a commercial product from Sigma Company, Egypt. The size of Zn oxide nanoparticles was ˂ 50 nm according to the manufacturer company. IVM was purchased from Alfasan International BV Company, the Netherlands.
## Experimental design
A total number of 72 V-line male rabbits (3 months old) were used in this experiment. Rabbits were individually reared in batteries (width × length × height; 44 cm × 50 cm × 35 cm, respectively) of galvanized wire net, equipped with an automatic drinker and a manual feeder. Rabbits were reared in an open house system (naturally ventilated room by windows and ceiling fans). The temperature was adjusted to 19–23 °C. Relative humidity was nearly $60\%$ with a 16-h light and 8-h dark cycle. Fresh tap water was continuously available for consumption via stainless steel nipples located inside each cage. Rabbits were acclimated for 2 weeks before the beginning of experimental procedures.
After acclimatation, animals were divided into four groups (18 bucks/group), and each group was subdivided into three replicates (6 bucks/replicate). Bucks were fed a balanced basal diet containing all the required nutrients according to NRC [1977] (Table 1). The treatment groups were as follows: [1] the first group served as control (CTR) and received a basal diet without ivermectin (IVM) injection; [2] the second group (IVM) received a basal diet with IVM injection; [3] the third group (ZnNPs) received a basal diet with inorganic Zn replacement through ZnNPs (60 mg/kg diet) without IVM injection; and [4] the fourth group (ZnNPs + IVM) received a basal diet with inorganic Zn replacement through ZnNPs (60 mg/kg diet) with IVM injection. IVM was administrated via subcutaneous injection at dose of 0.2 mg/kg body weight (BW). IVM injection was initiated when bucks were 14 weeks old and repeated weekly for five consecutive weeks until 19 weeks old. A pelleted diet was provided ad libitum for animals during the whole experiment. Table 1The component of basal experimental dietIngredientskg/ton Corn50 Barley150 Wheat bran250 Oil30 Molasses30 B hay322 SBM 42.9147 Meth2 Limestone2 MCP9 Salt5 Mineral premix11 Vitamin premix21.5 Anti-mycotoxin0. 5 Total1000Calculated analyses (NRC 1977) Digestible energy; kcal/kg2588.5 Crude protein %17.05 Ether extract %5 Crude fiber %11.97 Starch15.71Mineral premix provided per kilogram of diet: manganese, 8.5 mg; iron, 100 mg; copper, 10 mg; cobalt, 0.3 mg; iodine, 0.2 mg; selenium, 0.3 mg; zinc, 60 mg. Each 1-kg mineral premix contains the following: Mn sulfate (34.55 g), iron carbonate (207.47 g), copper oxide (12.52 g), cobalt oxide (0.42 g), pot iodide (0.26 g), sodium selenite (0.657 g), Zn oxide (74.63 g), and carrier (limestone) up to 1 kg2Each 1 kg of vitamins premix contained the following: vitamin A, 10,000 IU; vitamin D3, 1800 UI; vitamin E, 15 mg; vitamin K3, 4.5 mg; vitamin B1, 0.5 mg; vitamin B2, 4 mg; vitamin B12, 0.001 mg; folic acid, 0.1 mg; pantothenic acid, 7 mg; nicotinic acid, 20 mg
## Growth performance parameters
Individual live BWs were recorded as the initial BW at 12 weeks old and every 2 weeks until the end of the experiment (the final BW was obtained at 21 weeks old). Total weight gain and total feed intake were recorded, after which the average feed conversion ratio, that is, the amount of total feed intake/total body gain, was calculated.
## Assessments of carcass characteristics
For the assessment of carcass characteristics, nine rabbits from each treatment were randomly selected. Rabbits were weighed in a fasted state before slaughtering to determine the live body weight. After the slaughtered rabbits were bled, the skin, genitals, urinary bladder, gastrointestinal tract, and the distal part of the legs were removed. Hot carcasses (with the head, thoracic cage organs, liver, kidneys, and perirenal and scapular fat) were weighed. The dressing percentage and the ratio of thigh, skin (with head skin), skin (without head skin), head with skin, head without skin, heart, lung, liver, spleen, abdominal fat, and kidney relative to the live BW were calculated.
## Dimensions and relative weight of the reproductive organs
Organ dimensions (scrotum, penile length, and testicular circumference, length, and width) were measured after slaughter. Circumference was measured using a measuring tape, whereas testicular length was measured using an obstetrical pelvimeter.
## Epididymal sperm preparation
Immediately after slaughtering, the animals were dissected and the epididymis was collected as quickly as possible and placed in a clean Petri plate. Thereafter, the cauda epididymis was separated from the whole epididymis, cut into several pieces, immersed in 3 mL pre-warmed phosphate buffer saline (PBS) solution, and incubated for 10 min at 37 °C to allow for sperm release from the epididymal lumen (Mangoli et al. 2013). The sperm count was evaluated using a hemocytometer chamber (count × 106) at 40 × magnification using light microscopy (Olympus Co., Tokyo, Japan). The percentages of progressive motility and viability were evaluated for at least 200 spermatozoa from each buck. Assessment of motile sperm at the warm stage showed progressive forward movement under 100 × magnification using a light microscope. Assessment of live and dead sperm was performed by counting 200 sperm cells using an eosin-nigrosin staining mixture. Complete or partial purple-stained sperm cells were considered non-viable, whereas non-stained sperm cells were considered viable.
## Plasma testosterone measurement
Using an indirect enzyme immunoassay assay kit (Monobind, 100 North point Drive, Lake Forest, CA), plasma testosterone levels were estimated following the methods described by Tietz [1995].
## Antioxidant indicators
At the end of the experimental period, rabbits were slaughtered and dissected. The testes were carefully removed, cut into small pieces, and preserved at − 20 °C for further analysis of Zn, reduced glutathione (GSH), superoxide dismutase (SOD), catalase (CAT), and malondialdehyde (MDA). Testicular Zn (CAT. Zn 21 20, Biodiagnostic), SOD (CAT. SD 25 21, Biodiagnostic), and CAT (Cat. CA 2517, Biodiagnostic) were assessed using spectrophotometric procedures (Hitachi spectrophotometer, Tokyo, Japan) with commercially available kits (Biodiagnostic Co., Dokki, Giza, Egypt) according to the manufacturer’s instructions. GSH (CAT. No. GR 25 11, Biodiagnostic) and MDA (Cat. MD 2529, Biodiagnostic) testicular contents were examined using the colorimetric method according to Beutler et al. [ 1963] and Okhawa et al. [ 1979], respectively.
As shown in Table 6, dietary replacement of inorganic Zn with ZnNPs significantly enhanced testicular Zn concentrations as compared to control. The CAT enzyme was significantly lower in the IVM group compared to the control group. In contrast, GSH concentrations were significantly greater in the ZnNPs group compared to the control group. Interestingly, SOD concentrations were significantly higher in the ZnNPs and ZnNPs + IVM groups compared to the control group. Meanwhile, the same marker (SOD) was significantly lower in the IVM group compared to the control group. Table 6Effects of dietary supplementation of inorganic zinc or zinc nanoparticles with or without ivermectin (IVM) treatment on zinc level in testicles and antioxidants in testicles of rabbit malesVariableGroupP valueCTRIVMZnNPsZnNPs + IVMZn (µg/g)2.03 ± 0.05b1.75 ± 0.36b2.90 ± 0.14a2.05 ± 0.03b0.0017CAT (µg/g tiss)41.50 ± 1.01a18.33 ± 0.88b45.33 ± 1.69a44.00 ± 1.44a <.0001GSH (mg/dl)32.33 ± 6.09bc23.33 ± 1.36c45.50 ± 0.43a38.50 ± 1.01ab0.0002SOD (µg/g tiss)50.00 ± 0.29b28.33 ± 1.30c63.67 ± 1.64a62.00 ± 1.73a <.0001MDA (Nmol/g tiss)3.65 ± 0.13b9.27 ± 0.29a2.87 ± 0.07c3.40 ± 0.06b <.0001The data presented as mean ± standard error. Means bearing different superscript letters within the same row are significantly different ($P \leq 0.05$) Our findings showed that the level of testicular MDA (an index of oxidative process) was significantly improved in the IVM group and significantly lower in ZnNPs group compared to the control group. Meanwhile, rabbits in ZnNPs + IVM group showed no significant difference in MDA levels compared to the control group.
## Histopathological study
Immediately after slaughtering, specimens were collected from testes of control and treated bucks. The collected samples were washed and immersed for 48 h in a $10\%$ neutral-buffered formalin solution for fixation. Fixed samples were prepared using the routine paraffin-embedding technique (Bancroft 2013). Briefly, fixed samples were dehydrated in ascending grades of ethanol, cleared using several changes of xylene, prepared in paraffin blocks, and microtomed into 3–5-µm-thick sections. Prepared sections were routinely stained with hematoxylin and eosin (H & E staining). Thereafter, blinded examination and image capture were performed by an experienced pathologist. Representative photomicrographs were obtained using a digital camera (Leica EC3; Leica, Germany) connected to a microscope (Leica DM500). Scoring for spermatogenesis was performed via Johnsen’s scoring system. The presence or absence of the primary cell types and/or lesions was scored from 1 to 10 according to Johnsen [1970] and Hassan et al. [ 2019]. Twenty seminiferous tubules were randomly selected in each cross-section and scored under a light microscope (× 400); the mean score was determined for each group.
## Immunohistochemical study
For immunohistochemical evaluation, each paraffin block was cut into several 4-µm-thick sections and rehydrated in decreasing concentrations of ethanol. Antigen retrieval was done in citrate-buffered saline (0.01 mol/L, pH 6.0), and endogenous peroxidase activity was quenched in phosphate-buffered saline with H2O2 $0.3\%$ (v/v) (PBS). Thereafter, sections were incubated for 1 h with $10\%$ (v/v) normal goat serum to block non-specific immunologic reagent binding, and tissue sections were incubated overnight at 4 °C with anti-Caspase-3 antibody rabbit monoclonal [EPR18297] (Cat, ab184787, Abcam, Cambridge, UK) and Anti-PCNA Mouse monoclonal antibody [24/PCNA] (Cat, ab280088 Abcam, Cambridge, UK). Afterwards, sections were washed in PBS and treated for 60 min with biotin-conjugated goat anti-rabbit IgG antiserum (Histofine kit, Nichirei Corporation, Japan). The sections were then rewashed in PBS and treated for 30 min with streptavidin-peroxidase conjugate (Histofine kit, Nichirei Corporation, Japan). The streptavidin–biotin complex was observed for 3 min with a 3,3′-diaminobenzidine tetrahydrochloride (DAB)-H2O2 solution. Finally, Mayer’s hematoxylin solution was used to counterstain the sections. Several original micrographs were obtained from five high-power fields/sections/organs at random and used for quantitative histomorphometric examination of immunostaining. Caspase-3 and PCNA positive brown color cells were counted in each micrograph (HPF, × 40) using manual computer-assisted cell counting (ImageJ plug-in-cell counter.jar) with ImageJ (v1.46 r, NIH, Bethesda, MD, USA) (Schneider et al. 2012) as reported by Powell et al. [ 2014]. For each group, the mean count of immunological positive cells was computed and analyzed using non-parametric statistics (Saleh et al. 2020).
## Statistical analyses
Statistical analyses of the data were performed using SAS software (SAS 2014). One-way analysis of variance (ANOVA) was used for data analysis. Duncan’s test was used when treatment effects were significant. The overall significance level was set at $P \leq 0.05.$ All values were expressed as mean ± standard error. Histomorphometric analysis of caspase-3 and PCNA immune expressions were analyzed using the non-parametric analysis using Kruskal–Wallis test to assess the significance between mean scores obtained from Wilcoxon rank-sum test.
## Growth performance
The effects of dietary inorganic Zn replacement through ZnNPs with or without IVM injection on the growth performance of buck rabbits are presented in Table 2. The statistical analysis of our data revealed no significant differences between the live BW of different experimental groups at baseline and 2 weeks later (12 and 14 weeks old). However, rabbits in the IVM and IVM + ZnNPs groups showed a significant ($P \leq 0.0001$) reduction in live BW at 18, 20, and 21 weeks old compared to the control group. In addition, IVM-treated rabbits had significantly lower total BW gain (TBWG) and total feed intake (TFI) ($P \leq 0.0001$) compared to control group, which caused worst FCR. Conversely, rabbits in the ZnNPs group had significantly higher TFI ($P \leq 0.0001$) compared to control rabbits. Interestingly, rabbits in the ZnNPs + IVM group showed significant greater improvement in live BW at 16, 18, and 20 weeks old and TFI, with a subsequent improvement in FCR, compared to the IVM group. Table 2Effects of dietary supplementation of inorganic zinc or zinc nanoparticles with or without ivermectin (IVM) treatment on growth performance of rabbit malesVariableGroupP valueCTRIVMZnNPsZnNPs + IVMInitial bwt (kg) (12 weeks)1.24 ± 0.051.22 ± 0.021.24 ± 0.031.22 ± 0.09NS14 weeks (kg)1.85 ± 0.071.89 ± 0.031.80 ± 0.031.91 ± 0.03NS16 weeks (kg)2.25 ± 0.00a2.08 ± 0.01b2.26 ± 0.06a2.26 ± 0.04a0.006618 weeks (kg)2.51 ± 0.01a2.19 ± 0.02c2.51 ± 0.07a2.37 ± 0.04b <.000120 weeks (kg)2.70 ± 0.01a2.34 ± 0.02c2.71 ± 0.06a2.52 ± 0.04b <.0001Final bwt (kg) (21 weeks)2.77 ± 0.02a2.46 ± 0.00b2.89 ± 0.10a2.60 ± 0.04b <.0001TBWG (kg)1.53 ± 0.07ab1.23 ± 0.03c1.65 ± 0.06a1.38 ± 0.03bc <.0001TFI (kg)7.01 ± 0.09b6.11 ± 0.44c7.40 ± 0.06a6.67 ± 0.11b <.0001AFCR (kg)4.58 ± 0.204.95 ± 0.064.48 ± 0.1354.83 ± 0.06NSThe data presented as mean ± standard error. Means bearing different superscript letters within the same row are significantly different ($P \leq 0.05$)NS non-significant, TG total body weight gain, TFI total feed intake, AFCR average feed conversion ratio
## Carcass quality
The effects of dietary ZnNP supplementation with or without IVM injection on dressing percentage and carcass quality of buck rabbits are shown in Table 3. Tabulated results revealed that the dressing percentage was significantly increased ($P \leq 0.0001$) in the ZnNPs group and significantly lower ($P \leq 0.0001$) in the IVM group compared to the control group. However, the relative weight of the thigh was significantly improved ($P \leq 0.0001$) in the ZnNPs group compared to the control group. Conversely, skin (with or without head skin) and abdominal fat showed no significant change ($P \leq 0.0001$) compared to the control group. In addition, the relative weights of the head with skin, heart, and lungs were significantly lower ($P \leq 0.0001$) compared to the control group. The relative weights of the head without skin and spleen were significantly decreased in IVM group ($P \leq 0.0001$) compared to the control group. Additionally, the relative weights of the liver were significantly greater ($P \leq 0.0001$) in the IVM group and significantly lower ($P \leq 0.0001$) in ZnNPs and ZnNPs + IVM groups compared to control group. The relative weight of the kidney was significantly lower ($P \leq 0.0001$) in the IVM and ZnNPs + IVM groups compared to the control group. Table 3Effects of dietary supplementation of inorganic zinc or zinc nanoparticles with or without ivermectin (IVM) treatment on dressing percentages of rabbit malesVariableGroupP valueCTRIVMZnNPsZnNPs + IVMLive body wt. ( g)2536.22 ± 17.68c2508.00 ± 12.12c2635.00 ± 10.10b2793.89 ± 32.54a <.0001Dressing (%)52.39 ± 0.20b51.02 ± 0.45c53.71 ± 0.36a53.36 ± 0.28ab <.0001Thigh (%)21.24 ± 0.32b21.59 ± 0.05b22.43 ± 0.02a21.24 ± 0.13b0.0022Skin (with head skin) (%)16.30 ± 0.6116.42 ± 0.1316.12 ± 0.2816.02 ± 0.26NSSkin (without head skin) (%)12.76 ± 0.6312.87 ± 0.1513.52 ± 0.2412.90 ± 0.30NSHead with skin (%)19.34 ± 0.21a18.53 ± 0.03b16.88 ± 0.09d18.01 ± 0.01c <.0001Head without skin (%)6.63 ± 0.15a6.14 ± 0.09b6.43 ± 0.08a6.50 ± 0.05a0.0105Heart (%)0.39 ± 0.01a0.34 ± 0.02b0.28 ± 0.00c0.30 ± 0.01c <.0001Lung (%)0.64 ± 0.05a0.54 ± 0.01b0.42 ± 0.00c0.46 ± 0.02c <.0001Liver (%)2.58 ± 0.08b2.87 ± 0.03a2.33 ± 0.01c2.41 ± 0.10bc <.0001Spleen (%)0.06 ± 0.005a0.04 ± 0.00b0.06 ± 0.00a0.05 ± 0.00a0.0049Abdominal fat (%)0.10 ± 0.1071.03 ± 0.1221.06 ± 0.111.00 ± 0.17NSKidney (%)0.66 ± 0.02a0.56 ± 0.01b0.51 ± 0.01c0.62 ± 0.01a <.0001The data presented as mean ± standard error. Means bearing different superscript letters within the same row are significantly different ($P \leq 0.05$)NS non-significant
## Dimensions and relative weight of reproductive organs
Data in Table 4 shows that the diameter of the scrotum was significantly lower in the IVM group and significantly greater ($P \leq 0.0001$) in ZnNPs group compared to the control group. However, the penile length of the different groups differed significantly ($P \leq 0.0001$) compared to that of the control group, with ZnNPs group and CTR group having the greatest and least values (1.95 and 1.50 cm, respectively). In addition, testicular circumference and length were significantly lower ($P \leq 0.0001$) in all treatment groups compared to the control group. However, testicular width was significantly higher ($P \leq 0.0001$) in ZnNPs + IVM compared to control group. Table 4Effects of dietary supplementation of inorganic zinc or zinc nanoparticles with or without ivermectin (IVM) treatment on organ size, sperm motility, viability, and concentration of rabbit malesVariableGroupP valueCTRIVMZnNPsZnNPs + IVMOrgan dimensions Scrotum (cm)5.45 ± 0.01bb5.33 ± 0.08c5.70 ± 0.06a5.63 ± 0.12ab0.008 Penis length (cm)1.50 ± 0.00c1.77 ± 0.07b1.95 ± 0.01a1.90 ± 0.08ab <.0001 Testis circumference (cm)4.25 ± 0.07a3.63 ± 0.08c3.85 ± 0.04b4.03 ± 0.06b <.0001 Testis length (cm)4.05 ± 0.04a3.30 ± 0.16b3.10 ± 0.12b3.03 ± 0.07b <.0001 Testis width (cm)1.20 ± 0.00b1.23 ± 0.02b1.35 ± 0.04b1.67 ± 0.09a <.0001Relative organ weight Testis (%)0.10 ± 0.000.10 ± 0.010.10 ± 0.000.10 ± 0.00NS Epididymis (%)0.09 ± 0.000.09 ± 0.000.10 ± 0.000.09 ± 0.00NS Pituitary gland (%)0.01 ± 0.000.01 ± 0.000.01 ± 0.000.01 ± 0.00NS Accessory gland (%)0.16 ± 0.01b0.18 ± 0.01b0.25 ± 0.02a0.22 ± 0.01a <.0001Semen quality parameters Sperm motility (%)51.67 ± 3.63b50.00 ± 5.77b83.33 ± 3.33a60.00 ± 2.89b <.0001 Sperm livability (%)62.33 ± 6.50b60.33 ± 5.05b87.00 ± 3.00a67.67 ± 3.61b0.0013 Sperm concentration (× 106/ml)583.33 ± 58.33b325.00 ± 50.52c733.33 ± 8.33a675.00 ± 7.22ab <.0001The data presented as mean ± standard error. Means bearing different superscript letters within the same row are significantly different ($P \leq 0.05$) Conversely, the weight of the testis, epididymis, and pituitary gland did not differ significantly among groups. However, the weight of the accessory glands was significantly increased ($P \leq 0.0001$) in ZnNPs and ZnNPs + IVM group compared to the control group (Table 4).
## Semen quality parameters
As shown in Table 4, sperm motility and livability were significantly higher in ZnNPs group compared to the control group. However, sperm concentrations were significantly lower in the IVM and ZnNPs compared to the control group.
## Testosterone concentration and age at puberty
In Table 5, serum testosterone concentrations at 16 weeks old were significantly ($P \leq 0.0026$) higher in ZnNPs group compared to the control group. Meanwhile, serum testosterone concentration at 18 weeks old was significantly higher ($P \leq 0.0001$) in ZnNPs and ZnNPs + IVM groups than in the control group. At 20 weeks old, serum testosterone concentrations did not significantly differ ($P \leq 0.0001$) among the groups. Table 5Effects of dietary supplementation of inorganic zinc or zinc nanoparticles with or without ivermectin (IVM) treatment on testosterone hormone level (pg/ml) in rabbit malesVariableGroupP valueCTRIVMZnNPsZnNPs + IVMTestosterone (pg/ml) at 16 weeks0.62 ± 0.10b0.55 ± 0.06b2.09 ± 0.44a0.94 ± 0.10b0.0026Testosterone (pg/ml) at 18 weeks0.13 ± 0.02b0.97 ± 0.04b4.33 ± 1.34a3.85 ± 1.08a0.0001Testosterone (pg/ml) at 20 weeks2.29 ± 0.401.98 ± 0.162.94 ± 0.372.65 ± 0.74NSAge at puberty (weeks)19 ± 0.29ab20 ± 0.00a18 ± 0.58b18 ± 0.58b0.0066The data presented as mean ± standard error. Means bearing different superscript letters within the same row are significantly different ($P \leq 0.05$)NS non-significant Additionally, the age at puberty was recorded for all groups; ZnNPs and Zn-NPs + IVM groups reached puberty (around 18 weeks of age) significantly earlier ($P \leq 0.0066$) than the other groups. Meanwhile, the CTR and IVM groups reached puberty at 19 and 20 weeks old, respectively (Table 5).
## Histopathologic findings
As shown in Fig. 1, testicular tissues of rabbits from the CTR and ZnNPs group showed normal histoarchitecture of seminiferous tubules, interstitial tissues, spermatogenic cells, Sertoli cells, and Leydig cells (Fig. 1A, D). In contrast, those that received IVM (IVM group) showed abnormal arrangement, degeneration, and vacuolization of spermatogenic cells. Moreover, necrosis of the epithelial cell lining the seminiferous tubules and multifocal separation of basement membrane were observed. Furthermore, necrotic spermatocytes and/or eosinophilic proteinaceous materials were identified with cellular debris occupying the central regions of the tubular laminae (Fig. 1B) and characteristic formation of a high number of sperm giant cells (Fig. 1C). Moreover, Sertoli cells were apparently reduced, whereas the intertubular tissues were widened and contained sparse distribution of Leydig cells with congested intertubular blood vessels. These lesions were ameliorated after inorganic Zn replacement through Zn nanoparticles in ZnNPs + IVM group (Fig. 1E, F).Fig. 1Representative photomicrograph of testicular tissues of rabbits from control (A), IVM (B, C), ZnNPs (D), and ZnNPs + IVM (E, F) showed normal histoarchitecture of seminiferous tubules, interstitial tissues, spermatogenic cells, Sertoli cells, and Leydig cells (A, D). Abnormal arrangement and vacuolization of spermatogenic cells, eosinophilic proteinaceous materials (black arrow) and necrotic debris (blue arrow) occupying the central regions of the tubular laminae (B), formation of sperm giant cells (arrows) (C), and mild to moderate vacuolization and degeneration of spermatogenic cells and intertubular congestion (arrow) (E, F). H&E, scale bar = 50 µm (A, C, D, F), scale bar = 200 µm (B, E) Epididymal tissues showed normal histologic limits of the epididymal tubules in the CTR and ZnNPs groups (Fig. 2A, C). Meanwhile, tissues from the IVM group showed necrotic eosinophilic proteinaceous materials and cellular debris occupying the central regions of the tubular laminae, degeneration and necrosis of epididymal tubule epithelial cells, and interstitial tissue fibrosis (Fig. 2B). Conversely, tissues from ZnNPs + IVM group exhibited amelioration of the most of these lesions. Interstitial tissue fibrosis and intertubular capillary congestion were evident in this group (Fig. 2D). The scoring of spermatogenesis was significantly ($P \leq 0.05$) declined in IVM group compared to control rats. However, oral administration of ZnNPs in ZnNPs + IVM group significantly ($P \leq 0.05$) improved the score of spermatogenesis compared to rats in IVM group (Fig. 3).Fig. 2Representative photomicrograph of epididymal tissues of rabbits from control (A), IVM (B), ZnNPs (C), and ZnNPs + IVM (D) showed normal histologic limits of epididymal tubules (A, C), necrotic eosinophilic proteinaceous materials (black arrow) and cellular debris (blue arrow) occupying the central regions of the tubular laminae, degeneration and necrosis in epithelial lining (red arrow) (B), and amelioration of the most of these lesions with interstitial fibrosis and congestion of intertubular capillaries (arrow) (D). H&E, scale bar = 50 µm (A, C, D), scale bar = 200 µm (B)Fig. 3Ameliorating effect of oral administration of ZnNPs (60 mg/kg diet) on spermatogenesis score of male rats intoxicated with IVM (0.2 mg/kg bwt) for 5 weeks. Data are expressed as the mean ± SEM. Different letters are significant at $P \leq 0.05$ with respect to the control group as a negative control (ANOVA with Dunnett’s multiple comparison test)
## Immunohistochemical findings
Apoptotic activity of testicular spermatogenic cells and columnar epithelium of epididymal tubules were evaluated via immunohistochemical localization of caspase-3 (Figs. 4 and 5). In the testes, the immunoreactivity of caspase-3 was greatest in the IVM group followed by ZnNPs + IVM compared to CTR and ZnNPs groups. The highest apoptotic activity of the testicular cells was detected in the IVM group. In the epididymis, the immune staining of caspase-3 was significantly greatest in the IVM group followed by ZnNPs + IVM group compared to the CTR and ZnNPs groups. Figure 7 shows the non-parametric analysis for the mean count of immunological positive cells in the testes and epididymis. Fig. 4Representative photomicrograph of testicular tissues of rabbits from control (A), IVM (B), ZnNPs (C), and ZnNPs + IVM (D) (sections stained with anti-PCNA immune staining; scale bar = 50 μm) showed strong (A, C, D) and moderate immunoreactivity of PCNA in testicular spermatogenic cellsFig. 5Representative photomicrograph of testicular tissues of rabbits from control (A), IVM (B), ZnNPs (C), and ZnNPs + IVM (D) (sections stained with anti-PCNA immune staining; scale bar = 50 μm) showed strong (A, C) and mild to moderate immunoreactivity of anti-PCNA in lining epithelium of epididymis and strong immunoreactivity of intraluminal debris and interstitium (B) On the contrary, as shown in Figs. 5 and 6, the proliferative activity of testicular and epididymal cells in the experimental groups was assessed using immunohistochemical localization of the proliferating cellular nuclear antigen (PCNA). In the testes (Fig. 6), the immunoreactivity of PCNA was mildly lower in the IVM and ZnNPs + IVM groups compared to the control groups (CTR and ZnNPs). Meanwhile, in the epididymal tubules, the proliferative activity was significantly decreased in the lining epithelium and increased in intraluminal tissue debris (Fig. 7). Figure 8 shows the non-parametric analysis for the mean count of immunological positive cells in testes and epididymis. Fig. 6Representative photomicrograph of testicular tissues of rabbits from control (A), IVM (B), ZnNPs (C), and ZnNPs + IVM (D) (sections stained with anti-caspase-3 immune staining; scale bar = 50 μm) showed strong (B), moderate (D), and mild to negative immunoreactivity of anti-caspase-3 in testicular spermatogenic cellsFig. 7Representative photomicrograph of testicular tissues of rabbits from control (A), IVM (B), ZnNPs (C), and ZnNPs + IVM (D) (sections stained with anti-caspase-3 immune staining; scale bar = 50 μm) showed strong (B), moderate (D), and negative immunoreactivity of anti-caspase-3 in lining epithelium of epididymis and strong immunoreactivity of intraluminal debris and interstitium (B, D)Fig. 8Histomorphometric analysis of immune expression of a caspase-3 and b PCNA in testicular and epididymal tissues of rabbits; all scores were subjected to non-parametric analysis using Kruskal–Wallis test to assess the significance between mean scores obtained from Wilcoxon rank-sum test (P > chi-square < 0.05)
## Discussion
The present study found that the ZnNPs group that received nano-Zn replacement had the highest live BW, total body gain, and total feed intake and the best average feed conversion ratio compared to the control group that received inorganic Zn (CTR). Our results are consistent with those presented in Tag-El Din [2019], which showed that nano-Zn supplementation at 60 mg/kg improved the final BW, gain, FI, and FCR in growing rabbits. Consistent with this data, Hassan et al. [ 2017] reported that rabbits fed a diet supplemented with nano-Zn (30 and 60 mg/kg) had increased BW, gain, and FCR compared to the control group. Other reports revealed that the dietary supplementation of 60 mg/kg of nano-Zn/kg promoted the highest BW gain and better feed conversion ratio than the control in broilers (Zhao et al. 2014) or dual-purpose chickens (Siddhartha et al. 2016). These results can be explained by the smaller size of nano-Zn, which allows for easier and faster passage through the cell membrane, thereby increasing its bioavailability and efficiency in promoting proper physiological functions, including DNA and protein syntheses, leading to better growth (Case and Carlson 2002; Onuegbu et al. 2018). Zn also participates in several enzymatic and metabolic functions, including carbohydrate, lipid, and protein metabolism (MacDonald 2000; Prasad and Kucuk 2002). Moreover, *Zn is* essential for free radical scavenging, immune system enhancement, and protection of the pancreatic tissue against oxidative damage, thereby ensuring optimal pancreatic function in secreting the digestive enzymes and subsequently improving nutrient digestibility (Zhao et al. 2014; Saleh et al. 2018).
On the contrary, our findings revealed that weekly IVM injections significantly deteriorated the growth and feed intake among rabbits receiving dietary inorganic Zn. Similarly, El-Shobokshy et al. [ 2022] found that repeated injection of IVM in female rabbits caused a decrease in BW, TBWG, and TFI. Similar to our findings, Khaldoun-Oularbi et al. [ 2015] and Khaldoun Oularbi et al. [ 2017] studied the adverse effects of emamectin benzoate in rats and concluded that the final BW and BWG decreased significantly due to a significant decrease in FI caused by the loss of appetite with decreasing in gastrointestinal tract nutrient absorption and deterioration of food conversion efficiency (Ball and Chhabra 1981; Sheriff et al. 2002). Our results also agree with those of Chahrazed et al. [ 2020] who observed that the repeated injection of high doses of IVM (2 mg/kg BW subcutaneously, 3 doses per week) for three consecutive weeks in rabbits significantly ($P \leq 0.05$) decreased the TFI due to the decreased in appetite, which significantly decreased the BW and total gain. Fortunately, the present study revealed that replacement of inorganic Zn with nano-Zn could overcome the negative effects of repeated IVM injections on the growth and FI. Hence, the co-administration of nano-Zn had ameliorative effects by reversing the negative effects of IVM on the BW, gain, and feed consumption in rabbits.
The present study revealed that nano-Zn significantly enhanced the dressing percentage and had no significant effect on the relative weights of the skin, head, and spleen. Meanwhile, rabbits receiving nano-Zn had significantly lower ($P \leq 0.0001$) relative weights of the heart, lungs, kidneys, and livers compared to those receiving inorganic Zn (CTR). Consistent with our results, Tag-El Din [2019] found that supplementation with nano-Zn at 60 mg/kg non-significantly decreased the relative weight of the head; however, they found that nano-Zn had no significant effect on the kidney, liver, and heart percentage. Similar to our findings, El-Katcha et al. [ 2017] reported that nano-Zn (45 and 60 mg/kg) improved the dressing percentage, lowered the relative weight of the liver, and had no significant effect on the relative weight of the spleen in broiler chickens compared to dietary inorganic Zn. Moreover, Lina et al. [ 2009] stated that nano-Zn significantly elevated the dressing percentage of boiler chickens.
Our data clarified that repeated IVM injections caused the worst dressing percentage and organ relative weights, whereas dietary nano-Zn ($P \leq 0.0001$) improved the dressing percentage and reversed the negative effects of IVM injection on organ relative weights, especially of the spleen and liver. Similarly, previous studies by Khaldoun-Oularbi et al. [ 2013], El Zoghby et al. [ 2015], and Khaldoun Oularbi et al. [ 2017] revealed that IVM significantly increased rat liver weight, whereas IVM co-treatment with, for instance, vitamin C decreased the absolute and relative weight of the liver. Moreover, Chahrazed et al. [ 2020] found that repeated high-dose injections of IVM in rabbits increased the relative weight of the liver and decreased lung weight due to IVM accumulation in lung tissue and generation of oxidative stress due to continuous IVM injection (Al-Jassim et al. 2016), whereas vitamin C reversed the aforementioned changes and protected the weights of the internal organs from the negative effects of IVM hazards.
Testosterone is a steroid hormone from the androgen group in mammals, reptiles, birds, and other vertebrates. In mammals, testosterone is primarily secreted in the testicles of males and the ovaries of females, although small amounts are also secreted by the adrenal glands (Vodo et al. 2013; El-Far 2013). Free testosterone, the serum testosterone not bound to sex hormone-binding globulin or albumin, is biologically active and able to exert its effects by permeating into cells and activating its receptor (Kevin et al. 2012; El-Far 2013). From the obtained data, we can clearly observe a significant increase in serum testosterone in the ZnNPs group at 16 weeks old, which may have been due to the role Zn plays in several biochemical processes and physiological functions. Reports have shown that *Zn is* required for the normal function of numerous structural proteins, enzymes, and hormones necessary for growth and development (Bao et al. 2009; Abdel-Wareth et al. 2020). The improved concentrations of testosterone in response to nano-Zn replacement might be due to the increased number of Leydig cells in the testis, which increases testosterone production (El-Masry et al. 1994; Imam et al. 2009; Abdel-Wareth et al. 2020). At 18 weeks old, no significant difference in testosterone concentration was observed between the ZnNPs and ZnNPs + IVM groups, with both groups reaching puberty at the same age and earlier than the other groups. These results highlight the vital role of ZnNPs as a powerful antioxidant that stimulates the process of steroid genesis and release of GnRH hormones from the anterior pituitary gland. Moreover, Zn acts as a scavenger for excessive superoxide radicals, thereby exhibiting antioxidant-like activities (Gavella and Lipovac 1988; Baiomy et al 2018). Chia et al. [ 2000] suggested that Zn may bind with free radicals in the seminal plasma, produced by abnormal spermatozoa, thereby decreasing the concentration of this element. Zn deficiency causes a lowering in testosterone levels (Hadwan et al. 2012; Chia et al. 2000).
In line with this, the present study revealed that dietary nano-Zn replacement significantly enhanced Zn concentrations in the testes of ZnNP-treated group. Our results were consistent with those obtained by Zhao et al. [ 2014] who found that serum Zn concentrations were significantly higher in broilers receiving 60 or 100 mg/kg nano-Zn compared to those receiving inorganic Zn. Moreover, Hassan et al. [ 2017] found that nano-Zn supplemented groups had significantly greater hepatic and serum Zn contents ($P \leq 0.001$) compared to the inorganic Zn groups. Moreover, similar findings in Japanese quails were reported by Reda et al. [ 2020]. Given the increased concentration of Zn in ZnNPs group and its antioxidant role, it was unsurprising that rabbits treated with ZnNPs showed the best oxidative status. Zn is a fundamental component in SOD and is involved in the cellular scavenging of free radicals and reactive oxygen species (Prasad 2008; Abd El-Hack et al. 2018). MDA is an important index for lipid peroxidation and oxidative damage caused by reactive oxygen species (Bin-Jumah et al. 2020; El-Far et al. 2020). Our results revealed that GSH, SOD, and CAT activities had significantly increased in the testes of the ZnNPs group, whereas MDA concentrations had decreased. The depressed MDA levels in our study resulted from the suppressive effects of dietary nano-Zn replacement on reactive oxygen species generation, thereby decreasing MDA levels. These results are consisted with that reported by Reda et al. [ 2021] who found that dietary nano-Zn level significantly ($P \leq 0.001$) enhanced serum SOD and glutathione peroxidase (GPX) but reduced MDA levels. Moreover, Kamel et al. [ 2020] stated that the addition of nano-Zn into the diets of rabbits significantly increased glutathione and superoxide dismutase activities and decreased MDA levels. Moreover, Tag-El Din [2019] reported that plasma CAT content was slightly elevated following high-dose nano-Zn supplementation (60 mg/kg) in the diet of growing rabbits.
In contrast, rabbits in the IVM groups showed lower enzymatic activity of GSH, SOD, and CAT and higher MDA levels. This result highlights the harmful and immunosuppressive effects of IVM injection in rabbits. Omshi et al. [ 2018] stated that repeated IVM administration was associated with oxidative degradation in male rats. GabAllh et al. [ 2017] and Al-Jassim et al. [ 2015] also reported that IVM injection adversely affects the immune status of rabbits. However, the present study showed that IVM injection slightly affected the nano-Zn supplemented group. This result suggests the potential role of nano-Zn supplementation in alleviating the stressful effects of IVM injection. Such findings could be attributed to the enhanced Zn concentration resulting from nano-Zn supplementation, which plays a vital role in inhibiting seminal oxidative stress (Marzec-Wróblewska et al. 2012). These results are in agreement with those of Kamel et al. [ 2020] who reported that dietary nano-Zn supplementation improved the immune status of heat-stressed rabbits. Moreover, Saleh et al. [ 2018] reported similar findings in heat-stressed birds.
The effect of IVM on genital organ dimensions was demonstrated in the IVM group, especially with regard to the size and weight of the testis plus epididymis and weight of accessory glands. This result was in agreement with that reported by El-Far [2013] who demonstrated that therapeutic and double therapeutic doses of IVM in male rats significantly decreased testes size. The mentioned study attributed these changes to the degenerative changes observed in double therapeutic dose of IVM associated with complete necrosis and complete absence of spermatogenesis in most seminiferous tubules. The epididymis showed degeneration and necrosis in the epithelial cells of the epididymal tubules. These results are supported by our histopathological findings, which revealed severe testicular damages in the form of abnormal spermatogenic cell arrangement, focal degenerative and necrotic changes, and cellular debris occupying the tubular lumen in groups treated with IVM. Similar results have been previously reported by several authors (Elzoghby et al. 2015; Ahmed et al. 2020). Similarly, GabAllh et al. [ 2017] noticed degeneration and vacuolation of spermatogenic cells at therapeutic doses of IVM after 4 and 8 weeks. These lesions were associated with necrosis of the epithelial cells lining the seminiferous tubules with pyknotic nuclei and formation of a few sperm giant cells at a therapeutic dose after 8 weeks. Meanwhile, the same degenerative changes were also observed at double therapeutic dose wherein complete necrosis and complete absence of spermatogenesis in most of seminiferous tubules with high number of sperm giant cells were noted. In the current study, immunohistochemical staining with PCNA revealed a reduction in testicular tissue immune expression among groups treated with IVM, with PCNA playing a pivotal role in the S-phase of the cell cycle via DNA synthesis and replication (Strzalka and Ziemienowicz 2011). Therefore, the reduction in PCNA expression may promote suppression of DNA synthesis with subsequent DNA damage and reduced cellularity and spermatogenesis (Moshari et al. 2017). Similar results were obtained by Ahmed et al. [ 2020]. In the current study, the immunocytochemical staining of caspase-3 revealed strong immunoreactivity in testicular tissues of IVM-exposed rabbits. Meanwhile, a significant reduction in the number of caspase-3 immune positive cells was observed in rabbits with a ZnNP-supplemented diet. The excessive reactive oxygen species production from the mitochondria of testicular cells could potentially interfere with the proper release of cytochrome c, which subsequently activates caspase-3 and induces cell apoptosis (Simon et al. 2000; Chung et al. 2003). IVM had been reported to collapse the mitochondrial membrane, leading to cell death and activation of various apoptotic markers, including caspase-3 (Khafaga and El-Sayed 2018). Similar results were previously obtained by Ahmed et al. [ 2020].
IVM affected semen parameters, such that the IVM group had significantly lower semen motility, livability, and concentration than the other groups. This result was in agreement with those reported by Elzoghby et al. [ 2015] who found that therapeutic and double therapeutic doses of IVM in male rats significantly decreased total sperm count and mortality. The accumulation of free radicals has been associated with a significant decrease in sperm motility and sperm plasma membrane integrity and a significant increase in sperm abnormality and DNA damage, leading to infertility (Potts et al. 2000). ZnNP-treated group had significantly better semen parameters than the other groups given that Zn plays a pivotal role in sperm cell function, including lipid flexibility, cell membrane stabilization (Chia et al. 2000), sperm capacitation, and acrosomal reaction (Eggert-Kruse et al. 2002). Moreover, reports had shown that Zn increased semen volume, total live sperm count, sperm motility, and conception in heat-stressed rabbits (El-Masry et al. 1994). Our findings clearly showed that ZnNPs + IVM group had better semen parameters than the IVM group, indicating the vital role of Zn in over 200 proteins and enzymes essential for male fertility (Kumar et al. 2006). Research has shown that Zn supplementation enhances the physical characteristics of semen, including ejaculate volume, sperm count, motility, seminal plasma antioxidants, and fertility rate (Amen and Muhammad 2016; Rahman et al. 2014; El-Speiy and El-Hanoun 2013; Rafique et al. 2010; Ghasemi et al. 2009; Oliveira et al. 2004; Maldjian et al. 1998). Rats treated with Zn showed an increase in sperm count, sperm motility, and testosterone levels, as well as improved testicular structure and spermatogenesis abnormalities caused by obesity (Ma et al. 2020). However, other studies have suggested no significant association between Zn and sperm quality (Eggert-Kruse et al. 2002; Lin et al. 2000).
## Conclusion
Replacement of inorganic Zn with Zn nanoparticles enhanced the BW, weight gain, and FCR of male rabbits. In addition, fertility and oxidative parameters, as well as histopathologic findings, were also improved in ZnNP-treated rabbits. Dietary supplementation of ZnNPs for IVM-intoxicated rabbit ameliorated the negative impact of IVM and improved the performance of male rabbits potentially via its antioxidant and antiapoptotic pathways. Hence, we recommend including ZnNPs in the diets of rabbit exposed to IVM injections.
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|
---
title: The interaction between age and parity on adverse pregnancy and neonatal outcomes
authors:
- Jiayang Dai
- Ya Shi
- Yinshuang Wu
- Lu Guo
- Dan Lu
- Ying Chen
- Yuanyuan Wang
- Hanpeng Lai
- Xiang Kong
journal: Frontiers in Medicine
year: 2023
pmcid: PMC9995429
doi: 10.3389/fmed.2023.1056064
license: CC BY 4.0
---
# The interaction between age and parity on adverse pregnancy and neonatal outcomes
## Abstract
### Background
Although age and parity are recognized as associated factors for adverse pregnancy outcomes, there are no studies exploring the interaction between the two during pregnancy. This study aimed to investigate the impact of the interaction between age and parity on adverse pregnancy outcomes.
### Methods
This was a retrospective cohort study with 15,861 women aged ≥20 years. All women were grouped according to age, parity, and a mix of the two. The data were analyzed using multivariate logistic regression analysis.
### Results
Age, parity, and interaction between the two were related with the risk of gestational hypertension, eclampsia/pre-eclampsia, placenta previa, placental implantation, postpartum hemorrhage, preterm birth, cesarean section, and Apgar score <7 within 5 min of birth. The risk of gestational diabetes mellitus and transfer to the neonatal unit was linked with age and the interaction between age and parity, but the impact of parity was not statistically significant. The risk of anemia, placental abruption, premature rupture of the membrane, oligohydramnios, and macrosomia was only associated with parity; the risk of fetal distress was only associated with age.
### Conclusion
The interaction between advanced age and parity might results in more adverse outcomes for both puerpera and infants, necessitating additional prenatal screening and health education throughout pregnancy.
## Introduction
The International Federation of Gynaecology and Obstetrics (FIGO) defines advanced maternal age (AMA) as age ≥35 years at the time of expected delivery [1]. At present, the definition of very advanced maternal age (vAMA) is rather debatable, with some researchers defining ≥40 years at the time of expected delivery as very advanced maternal age (vAMA) [2, 3].
Currently, international research indicated that the proportion of AMA and vAMA increased with year [4, 5]. In China, studies have shown that the proportion of AMA increased from $7.4\%$ in 2013 to $15.9\%$ in 2018 [6]. In addition, as assisted reproductive technology becomes more prevalent, the proportion of AMA is expected to increase in the coming years [7]. There is a gradual increase in the number of AMA in numerous countries. According to a survey conducted by the World Health Organization (WHO) of 308,149 mothers and newborns covering 29 countries in Africa, Asia, Latin America, and the Middle East indicated that the proportion of AMA reached $12.3\%$ [8]. The Centers for Disease Control and Prevention (CDC) reported that the fertility rate for women aged 35–44 years increased from 19.8‰ in 1980 to 52.6‰ in 2018 [9].
There are several risk factors for adverse pregnancy outcomes, with advanced age and parity being the most significant [1, 10]. Many studies have been conducted to investigate the relationship between advanced age or parity and adverse pregnancy outcomes [1, 11]. It has been demonstrated that advanced maternal age is associated with numerous adverse pregnancy outcomes. Vandekerckhove discovered that the risk of maternal and fetal complications increased steadily with age and was particularly high after 35 years [12]. Guarga Montori also discovered that women >35 years had worse perinatal outcomes than younger women, with the disparity being more pronounced in patients >40 [13]. It is debatable if parity is a risk factor for adverse pregnancy outcomes. Shechter-Maor G indicated that pregnancy complications were much more likely in nulliparous women of advanced maternal age than multiparous women of the same age [14], and Schimmel MS also found similar conclusions [15]. Muniro Z, on the other hand, discovered that grand multiparity was associated with increased risks of adverse pregnancy outcomes, such as postpartum hemorrhage, gestational hypertension, gestational diabetes mellitus, and high perinatal mortality [16]. Therefore, the relationship between parity and adverse pregnancy outcomes remains to be studied. Advanced age and parity have been studied more frequently in relation to adverse pregnancy outcomes; nevertheless, to our knowledge, there has been no investigation into the impact of the interaction between advanced age and parity on adverse pregnancy outcomes and neonatal outcomes, which requires further investigation.
Overall, this study conducted retrospective analyzes on the interaction between age and parity on adverse pregnancy and neonatal outcomes to fill the gap in this area. We will identify trends in the risk of pregnancy outcomes and neonatal outcomes across age and parity, which will give obstetric healthcare professionals with more detailed clinical evidence for more informed clinical consultation and decision-making.
## Study population and design
The study population was women aged ≥20 years who had a singleton birth at the Northern Jiangsu People’s Hospital in Yangzhou City, Jiangsu Province, China, between January 2016 and December 2020.
The inclusion criteria were ≥28 weeks gestational week of delivery, age ≥20 years, and singleton live birth. The exclusion criteria were induction of labor, intrauterine fetal death, viral myocarditis, congenital heart disease, liver, kidney, lung, and other important organ pathologies, serious primary diseases, combined with serious infectious diseases, mental disorders, or cognitive dysfunction. Fifteen thousand eight hundred and sixty-one mothers met the criteria.
## Data collection
The Northern Jiangsu People’s Hospital’s electronic information system was used to collect the data for this investigation. Two researchers independently collected case information of all participants (Collecting time: May 2022–July 2022), including age at delivery, height, pre-pregnancy body mass, education, residence, mode of conception, parity, mode of previous deliveries, and captured data on maternal pregnancy outcomes and neonatal outcomes via the electronic information system. They then checked each other’s work, and if a discrepancy was discovered, a third person reviewed the data.
## Diagnostic criteria
All women were grouped according to three bases: age, parity, and a mixture of age and parity. According to age, pregnant women were divided into five groups: the first appropriate age group (20–24 years, A1 group), the second appropriate age group (25–29 years, A2 group), the third appropriate age group (30–34 years, A3 group), the advanced maternal age group (35–39 years old, AMA group), and the very advanced maternal age group (≥40 years, vAMA group). For parity, pregnant women were divided into two groups: a nulliparous group (parity = 1) and a multiparous group (parity ≥ 2). With regard to the mixture of age and parity, pregnant women were divided into 10 groups, combining the two previous groupings. Education was categorized into bachelor’s degree or above and no bachelor’s degree. Residence was divided into urban and rural areas. Marital status was divided into married and unmarried. Smoking was divided into Yes and No. Pre-pregnancy BMI was calculated by dividing pre-pregnancy weight (kg) by the square of height (m2). BMI was divided into four categories using Asian-specific cut-offs [17]: <18.5, 18.5–23, 23–27.5, and ≥27.5 kg/m2. Gestational weight gain (GWG) was classified following the 2009 Institute of Medicine (IOM) guidelines, the standard divides GWG into Inadequate, Adequate, and Excessive according to different prenatal weight standards for pregnant women [18]. Pregnancy was divided into two categories: assisted reproduction and natural conception. Gestational weeks were separated into three categories: 28–31, 32–36, and 37 weeks and above, with deliveries at less than 37 weeks being considered as preterm births. For multiparas, the form of the previous birth was classified into cesarean section and vaginal delivery.
The outcome indicators were maternal pregnancy outcomes and neonatal outcomes. Maternal pregnancy outcomes included gestational hypertension, eclampsia/pre-eclampsia, gestational diabetes mellitus (GDM), intrahepatic cholestasis of pregnancy (ICP), anemia, placenta previa, placental abruption, placental implantation, premature rupture of membranes, postpartum hemorrhage, oligohydramnios, preterm birth, and cesarean section. Neonatal outcomes included macrosomia, fetal distress, transfer to the neonatal unit, neonatal jaundice, and an Apgar score <7 within 5 min of birth. All outcome indicators were diagnosed according to the International Classification of Diseases 10th edition (ICD-10).
## Statistical analysis
Microsoft Excel 2007 was used to record and organize the data. The SPSS 26.0 statistical program was utilized for data collection and analysis. Quantitative data were described as x¯±s, and qualitative data were expressed as frequencies (percentage). The χ2 test was used for comparisons among groups. Single-factor analysis was used to compare the prevalence of adverse pregnancy outcomes and adverse neonatal outcomes of pregnant women in the single age group, single parity group, and mixed group. Significant factors identified by the single-factor analysis were incorporated into a multivariate logistic regression analysis. After adjusting for possible confounding factors, the adjusted OR (aOR) and $95\%$ confidence interval ($95\%$ CI) were used to show the risk of pregnancy and neonatal adverse outcomes in the single age group, single parity group, and combination group. All p values were two-sided tests, with $p \leq 0.05$ indicating statistical significance.
## Ethical considerations
Due to the absence of an ethical statement component in our research, an ethics application is unnecessary. This data collection was approved by the Obstetrics and Gynecology Department of the Northern Jiangsu People’s Hospital.
## Sociodemographic data and pre-pregnancy characteristics
A total of 15,861, women were included in this study. Of these, 2,586 ($16.3\%$) women were aged 20–24 years, 8,057 ($50.80\%$) women were aged 25–29 years, 3,636 ($22.92\%$) women were aged 30–34 years, 1,314 ($8.28\%$) women were aged 35–39 years, and 268 ($1.69\%$) women were aged ≥40 years. There were 12,002 nulliparous women ($75.67\%$) and 3,859 multiparous women ($24.33\%$). $50.11\%$ of these women have a bachelor’s degree or above, $60.38\%$ of women living in urban areas. $96.65\%$ of women are married, and $1.08\%$ of women have a bad habit of smoking. $34.81\%$ of women had a pre-pregnancy BMI in the normal range, $3.35\%$ of women weighed less than the normal range, and $61.85\%$ of pregnant women weighed more than the normal range. $69.97\%$ of women GWG at an appropriate level, $9.95\%$ of women gained less gestational weight, and $20.08\%$ of women gained more during pregnancy. In terms of mode of conception, $5.43\%$ of women using assisted reproductive technologies. In terms of the gestational week of delivery, $88.21\%$ of women gave birth at full term and $11.79\%$ of women gave birth prematurely. $68.26\%$ of women had a cesarean section at their previous birth, and $31.74\%$ of women had a vaginal delivery (Table 1).
**Table 1**
| Variables | Total | Nulliparas | Nulliparas.1 | Nulliparas.2 | Nulliparas.3 | Nulliparas.4 | p | Multiparas | Multiparas.1 | Multiparas.2 | Multiparas.3 | Multiparas.4 | p.1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Variables | Total | A1 (%) | A2 (%) | A3 (%) | AMA (%) | vAMA (%) | p | A1 (%) | A2 (%) | A3 (%) | AMA (%) | vAMA (%) | p |
| n | 15861 | 2541 | 7821 | 1374 | 230 | 36 | | 45 | 236 | 2262 | 1084 | 232 | |
| Education | | | | | | | 0.00*** | | | | | | 0.00*** |
| Bachelor’s degree or above | 7,948 (50.11) | 1,240 (48.80) | 4,536 (58.00) | 721 (52.47) | 70 (30.43) | 10 (27.78) | | 7 (15.56) | 32 (13.56) | 898 (39.7) | 375 (34.59) | 59 (25.43) | |
| Undergraduate | 7,913 (49.89) | 1,301 (51.20) | 3,285 (42.00) | 653 (47.53) | 160 (69.57) | 26 (72.22) | | 38 (84.44) | 204 (86.44) | 1,364 (60.3) | 709 (65.41) | 173 (74.57) | |
| Place of residence | | | | | | | 0.00*** | | | | | | 0.00*** |
| Urban | 9,577 (60.38) | 1,321 (51.99) | 5,005 (63.99) | 901 (65.57) | 132 (57.39) | 22 (61.11) | | 18 (40.00) | 95 (40.25) | 1,359 (60.01) | 605 (55.81) | 119 (51.29) | |
| Rural | 6,284 (39.62) | 1,220 (48.01) | 2,816 (36.01) | 473 (34.43) | 98 (42.61) | 14 (38.89) | | 27 (60.00) | 141 (59.75) | 903 (39.92) | 479 (44.19) | 113 (48.71) | |
| Marital status | | | | | | | 0.92 | | | | | | 0.85 |
| Married | 15,329 (96.65) | 2,454 (96.58) | 7,563 (96.70) | 1,326 (96.51) | 220 (95.65) | 35 (97.22) | | 44 (97.78) | 231 (97.88) | 2,184 (96.55) | 1,048 (96.68) | 224 (96.55) | |
| Unmarried | 532 (3.35) | 87 (3.42) | 258 (3.30) | 48 (3.49) | 10 (4.35) | 1 (2.78) | | 1 (2.22) | 5 (2.12) | 78 (3.45) | 36 (3.32) | 8 (3.45) | |
| Smoking | | | | | | | 0.89 | | | | | | 0.95 |
| Yes | 172 (1.08) | 28 (1.10) | 82 (1.05) | 15 (1.09) | 2 (0.87) | 1 (2.78) | | 1 (2.22) | 3 (1.27) | 26 (1.15) | 12 (1.11) | 2 (0.86) | |
| No | 15,689 (98.92) | 2,513 (98.90) | 7,739 (98.95) | 1,359 (98.91) | 228 (99.13) | 35 (97.22) | | 44 (97.78) | 233 (98.73) | 2,236 (98.85) | 1,072 (98.89) | 230 (99.14) | |
| Pre-pregnancy BMI (kg/m2) | | | | | | | 0.07 | | | | | | 0.72 |
| <18.5 | 531 (3.35) | 126 (4.96) | 274 (3.50) | 49 (3.57) | 7 (3.04) | 1 (2.78) | | 1 (2.22) | 4 (1.69) | 42 (2.92) | 23 (1.20) | 4 (1.72) | |
| 18.5–23 | 5,521 (34.81) | 890 (35.03) | 2,660 (34.01) | 470 (34.21) | 89 (38.70) | 14 (38.89) | | 18 (40) | 92 (38.98) | 823 (36.38) | 398 (36.72) | 67 (28.88) | |
| 23–27.5 | 6,320 (39.85) | 1,017 (40.02) | 3,128 (39.99) | 549 (39.96) | 87 (37.83) | 14 (38.89) | | 18 (40) | 92 (38.98) | 899 (39.74) | 418 (38.56) | 98 (42.24) | |
| ≥27.5 | 3,489 (22.00) | 508 (19.99) | 1,759 (22.49) | 306 (22.27) | 47 (20.43) | 7 (19.44) | | 8 (17.78) | 48 (20.34) | 498 (22.02) | 245 (22.60) | 63 (27.16) | |
| Gestational weight gain (GWG) | | | | | | | 0.54 | | | | | | 0.98 |
| Inadequate | 1,578 (9.95) | 240 (9.45) | 780 (9.97) | 140 (10.19) | 33 (14.35) | 2 (5.56) | | 3 (6.67) | 25 (10.59) | 218 (9.64) | 112 (10.33) | 25 (10.78) | |
| Adequate | 11,098 (69.97) | 1,796 (70.68) | 5,483 (70.11) | 957 (69.65) | 151 (65.65) | 27 (75.00) | | 33 (73.33) | 163 (69.07) | 1,584 (70.03) | 742 (68.45) | 162 (69.83) | |
| Excessive | 3,185 (20.08) | 505 (19.87) | 1,558 (19.92) | 277 (20.16) | 46 (20.00) | 7 (19.44) | | 9 (20.00) | 48 (20.34) | 460 (20.34) | 230 (21.22) | 45 (19.40) | |
| Assisted reproduction | | | | | | | 0.00*** | | | | | | 0.00*** |
| Yes | 862 (5.43) | 5 (0.20) | 385 (4.92) | 273 (19.87) | 79 (34.35) | 16 (44.44) | | 0 | 5 (2.12) | 25 (1.11) | 58 (5.35) | 16 (6.90) | |
| No | 14,999 (94.57) | 2,536 (99.80) | 7,436 (95.08) | 1,101 (80.13) | 151 (65.65) | 20 (55.56) | | 45 (100) | 231 (97.88) | 2,237 (98.89) | 1,026 (94.65) | 216 (93.10) | |
| Week of pregnancy (weeks) | | | | | | | 0.31 | | | | | | 0.11 |
| 28–31 weeks | 199 (1.25) | 31 (1.22) | 94 (1.20) | 15 (1.09) | 3 (1.30) | 1 (2.78) | | 1 (2.22) | 3 (1.27) | 32 (1.41) | 15 (1.38) | 4 (1.72) | |
| 32–36 weeks | 1,671 (10.54) | 255 (10.04) | 780 (9.97) | 122 (8.88) | 34 (14.78) | 5 (13.89) | | 6 (13.33) | 30 (12.71) | 244 (10.79) | 159 (14.67) | 36 (15.52) | |
| ≥37 weeks | 13,991 (88.21) | 2,255 (88.74) | 6,947 (88.82) | 1,237 (90.03) | 193 (83.91) | 30 (83.33) | | 38 (84.44) | 203 (86.02) | 1,986 (87.8) | 910 (83.95) | 192 (82.76) | |
| Previous delivery | | | | | | | | | | | | | 0.23 |
| Cesarean section | 2,634 (68.26) | —— | —— | —— | —— | —— | | 28 (62.22) | 170 (72.03) | 1,526 (67.46) | 759 (70.02) | 151 (65.09) | |
| Vaginal delivery | 1,225 (31.74) | —— | —— | —— | —— | —— | | 17 (37.78) | 66 (27.97) | 736 (32.54) | 325 (29.98) | 81 (34.91) | |
## Incidence of pregnancy outcomes and neonatal outcomes in women of different ages and parities
The incidence of adverse pregnancy outcomes ranged from 1.07 to $22.91\%$, with gestational diabetes mellitus ($22.91\%$), premature rupture of membranes ($17.92\%$), and transfer to the neonatal unit ($14.48\%$) being the three most prevalent diseases.
The incidence of adverse pregnancy and neonatal outcomes for single age groups is shown in Table 2. In terms of a single age group, the difference in the following outcomes among the five age groups was statistically significant, including the prevalence of gestational hypertension, eclampsia/pre-eclampsia, gestational diabetes mellitus, placenta previa, placental implantation, postpartum hemorrhage, preterm birth, cesarean section, vaginal delivery, fetal distress, transfer to neonatal unit, and Apgar score <7 within 5 min of birth, while the difference in the remaining indices was not statistically significant.
**Table 2**
| Variables | Total | A1 (%) | A2 (%) | A3 (%) | AMA (%) | vAMA (%) | p |
| --- | --- | --- | --- | --- | --- | --- | --- |
| n | 15861 | 2586 | 8057 | 3636 | 1314 | 268 | |
| Gestational hypertension | 740 (4.67) | 68 (2.63) | 317 (3.93) | 177 (4.87) | 136 (10.35) | 42 (15.67) | 0.00*** |
| Eclampsia/pre-eclampsia | 537 (3.39) | 31 (1.20) | 230 (2.85) | 146 (4.02) | 97 (7.38) | 33 (12.31) | 0.00*** |
| Gestational diabetes mellitus | 3,633 (22.91) | 481 (18.60) | 1,736 (21.55) | 883 (24.28) | 436 (33.18) | 97 (36.19) | 0.00*** |
| Intrahepatic cholestasis of pregnancy | 374 (2.36) | 59 (2.28) | 201 (2.49) | 77 (2.12) | 29 (2.21) | 8 (2.99) | 0.70 |
| Anemia | 262 (1.65) | 36 (1.39) | 118 (1.46) | 74 (2.04) | 27 (2.05) | 7 (2.61) | 0.06 |
| Placenta previa | 279 (1.76) | 20 (0.77) | 81 (1.01) | 80 (2.20) | 77 (5.86) | 21 (7.84) | 0.00*** |
| Placental abruption | 174 (1.10) | 27 (1.04) | 83 (1.03) | 43 (1.18) | 17 (1.29) | 4 (1.49) | 0.83 |
| Placental implantation | 170 (1.07) | 28 (1.08) | 71 (0.88) | 33 (0.91) | 29 (2.21) | 9 (3.36) | 0.00*** |
| Premature rupture of membrane | 2,843 (17.92) | 478 (18.48) | 1,492 (18.52) | 618 (17.00) | 211 (16.06) | 44 (16.42) | 0.09 |
| Postpartum hemorrhage | 665 (4.19) | 82 (3.17) | 304 (3.77) | 164 (4.51) | 89 (6.77) | 26 (9.70) | 0.00*** |
| Oligohydramnios | 741 (4.67) | 116 (4.49) | 371 (4.60) | 168 (4.62) | 64 (4.87) | 22 (8.21) | 0.09 |
| Preterm birth | 1,870 (11.79) | 293 (11.33) | 907 (11.26) | 413 (11.36) | 211 (16.06) | 46 (17.16) | 0.00*** |
| Cesarean section | 8,878 (55.97) | 1,123 (43.43) | 4,133 (51.30) | 2,338 (64.30) | 1,063 (80.90) | 221 (82.46) | 0.00*** |
| Macrosomia | 549 (3.46) | 90 (3.48) | 258 (3.20) | 137 (3.77) | 55 (4.19) | 9 (3.36) | 0.32 |
| Fetal distress | 120 (0.76) | 33 (1.28) | 65 (0.81) | 15 (0.41) | 6 (0.46) | 1 (0.37) | 0.002** |
| Transfer to neonatal unit | 2,296 (14.48) | 309 (11.95) | 1,120 (13.90) | 540 (14.85) | 256 (19.48) | 71 (26.49) | 0.00*** |
| Neonatal jaundice | 778 (4.91) | 119 (4.60) | 394 (4.89) | 171 (4.70) | 73 (5.56) | 21 (7.84) | 0.14 |
| Apgar score <7 within 5 min of birth | 278 (1.75) | 33 (1.28) | 127 (1.58) | 72 (1.98) | 36 (2.74) | 10 (3.73) | 0.00*** |
The incidence of adverse pregnancy outcomes and neonatal outcomes for single parity group are shown in Table 3. In terms of the single parity group, the difference in the following outcomes among the two parity groups was statistically significant, including the prevalence of gestational hypertension, eclampsia/pre-eclampsia, gestational diabetes mellitus, anemia, placenta previa, placental abruption, placental implantation, premature rupture of membranes, postpartum hemorrhage, oligohydramnios, preterm birth, cesarean section, macrosomia, fetal distress, transfer to the neonatal unit, and Apgar score <7 points within 5 min of birth, whereas the difference in the incidence of intrahepatic cholestasis of pregnancy and neonatal jaundice was not statistically significant.
**Table 3**
| Variables | Nulliparas (%) | Multiparas (%) | p |
| --- | --- | --- | --- |
| n | 12002 | 3859 | |
| Gestational hypertension | 487 (4.06) | 253 (6.56) | 0.00*** |
| Eclampsia/pre-eclampsia | 349 (2.91) | 188 (4.87) | 0.00*** |
| Gestational diabetes mellitus | 2,627 (21.89) | 1,006 (26.07) | 0.00*** |
| Intrahepatic cholestasis of pregnancy | 296 (2.47) | 78 (2.02) | 0.11 |
| Anemia | 176 (1.47) | 86 (2.23) | 0.00*** |
| Placenta previa | 133 (1.11) | 146 (3.78) | 0.00*** |
| Placental abruption | 128 (1.07) | 70 (1.81) | 0.00*** |
| Placental implantation | 114 (0.95) | 56 (1.45) | 0.01* |
| Premature rupture of membrane | 2,226 (18.55) | 418 (10.83) | 0.00*** |
| Postpartum hemorrhage | 452 (3.77) | 213 (5.52) | 0.00*** |
| Oligohydramnios | 946 (7.88) | 103 (2.67) | 0.00*** |
| Preterm birth | 1,340 (11.16) | 530 (13.73) | 0.00*** |
| Cesarean section | 6,056 (50.46) | 2,822 (73.13) | 0.00*** |
| Macrosomia | 393 (3.27) | 156 (4.04) | 0.02* |
| Fetal distress | 107 (0.89) | 13 (0.34) | 0.00*** |
| Transfer to neonatal unit | 1,688 (14.06) | 608 (15.76) | 0.00*** |
| Neonatal jaundice | 573 (4.77) | 205 (5.31) | 0.18 |
| Apgar score <7 within 5 min of birth | 182 (1.52) | 96 (2.49) | 0.00*** |
The incidence of pregnancy outcomes and neonatal outcomes for 10 groups of mixed age and parity are shown in Table 4. The difference in the following outcomes among these 10 groups was statistically significant: prevalence of gestational hypertension, eclampsia/pre-eclampsia, gestational diabetes mellitus, placenta previa, placental implantation, postpartum hemorrhage, preterm birth, cesarean section, transfer to neonatal unit, and Apgar score <7 points within 5 min of birth, while the difference in the remaining indices was not statistically significant.
**Table 4**
| Variables | Total (%) | Nulliparas | Nulliparas.1 | Nulliparas.2 | Nulliparas.3 | Nulliparas.4 | Multiparas | Multiparas.1 | Multiparas.2 | Multiparas.3 | Multiparas.4 | p |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Variables | Total (%) | A1 (%) | A2 (%) | A3 (%) | AMA (%) | vAMA (%) | A1 (%) | A2 (%) | A3 (%) | AMA (%) | vAMA (%) | p |
| n | 15861 | 2541 | 7821 | 1374 | 230 | 36 | 45 | 236 | 2262 | 1084 | 232 | |
| Gestational hypertension | 740 (4.67) | 66 (2.60) | 308 (3.94) | 77 (5.60) | 30 (13.04) | 6 (16.67) | 2 (4.44) | 9 (3.81) | 100 (4.42) | 106 (9.78) | 36 (15.52) | 0.00*** |
| Eclampsia/pre-eclampsia | 537 (3.39) | 30 (1.18) | 223 (2.85) | 72 (5.24) | 20 (8.70) | 4 (11.11) | 1 (2.22) | 7 (2.97) | 74 (3.27) | 77 (7.10) | 29 (12.50) | 0.00*** |
| Gestational diabetes mellitus | 3,633 (22.91) | 474 (18.65) | 1,689 (21.60) | 362 (26.35) | 88 (38.26) | 14 (38.89) | 7 (15.56) | 47 (19.92) | 521 (23.03) | 348 (32.10) | 83 (35.78) | 0.00*** |
| Intrahepatic cholestasis of pregnancy | 374 (2.36) | 58 (2.28) | 196 (2.51) | 35 (2.55) | 5 (2.17) | 2 (5.56) | 1 (2.22) | 5 (2.12) | 42 (1.86) | 24 (2.21) | 6 (2.59) | 0.80 |
| Anemia | 262 (1.65) | 35 (1.38) | 112 (1.43) | 22 (1.60) | 5 (2.17) | 2 (5.56) | 1 (2.22) | 6 (2.54) | 52 (2.30) | 22 (2.03) | 5 (2.16) | 0.07 |
| Placenta previa | 279 (1.76) | 20 (0.79) | 77 (0.98) | 23 (1.67) | 10 (4.35) | 3 (8.33) | 0 | 4 (1.69) | 57 (2.52) | 67 (6.18) | 18 (7.76) | 0.00*** |
| Placental abruption | 174 (1.10) | 26 (1.02) | 80 (1.02) | 14 (1.02) | 3 (1.30) | 1 (2.78) | 1 (2.22) | 3 (1.27) | 29 (1.28) | 14 (1.29) | 3 (1.29) | 0.95 |
| Placental implantation | 170 (1.07) | 28 (1.10) | 70 (0.90) | 12 (0.87) | 3 (1.30) | 1 (2.78) | 0 | 1 (0.42) | 21 (0.93) | 26 (2.40) | 8 (3.45) | 0.00*** |
| Premature rupture of membrane | 2,845 (17.94) | 473 (18.61) | 1,454 (18.59) | 253 (18.41) | 42 (18.26) | 6 (16.67) | 7 (15.56) | 38 (16.10) | 365 (16.14) | 169 (15.59) | 38 (16.38) | 0.14 |
| Postpartum hemorrhage | 665 (4.19) | 80 (3.15) | 296 (3.78) | 60 (4.37) | 12 (5.22) | 4 (11.11) | 2 (4.44) | 8 (3.39) | 104 (4.60) | 77 (7.10) | 22 (9.48) | 0.00*** |
| Oligohydramnios | 741 (4.67) | 114 (4.49) | 360 (4.60) | 62 (4.51) | 11 (4.78) | 2 (5.56) | 2 (4.44) | 11 (4.66) | 106 (4.69) | 53 (4.89) | 20 (8.62) | 0.79 |
| Preterm birth | 1,870 (11.79) | 286 (11.26) | 874 (11.18) | 137 (9.97) | 37 (16.09) | 6 (16.67) | 7 (15.56) | 33 (13.98) | 276 (12.20) | 174 (16.05) | 40 (17.24) | 0.00*** |
| Cesarean section | 8,878 (55.97) | 1,102 (43.37) | 3,980 (50.89) | 749 (54.51) | 194 (84.35) | 31 (86.11) | 21 (46.67) | 153 (64.83) | 1,589 (70.25) | 869 (80.17) | 190 (81.90) | 0.00*** |
| Macrosomia | 549 (3.46) | 88 (3.46) | 250 (3.20) | 48 (3.49) | 7 (3.04) | 0 | 2 (4.44) | 8 (3.39) | 89 (3.93) | 48 (4.43) | 9 (3.88) | 0.55 |
| Fetal distress | 206 (1.30) | 32 (1.26) | 99 (1.270) | 18 (1.31) | 3 (1.30) | 1 (2.78) | 1 (2.22) | 3 (1.27) | 30 (1.33) | 15 (1.38) | 4 (1.72) | 0.1 |
| Transfer to neonatal unit | 2,296 (14.48) | 305 (12.00) | 1,095 (14.00) | 227 (16.52) | 48 (20.87) | 13 (36.11) | 4 (8.89) | 25 (10.59) | 313 (13.84) | 208 (19.19) | 58 (25.00) | 0.00*** |
| Neonatal jaundice | 778 (4.91) | 117 (4.60) | 382 (4.88) | 62 (4.51) | 11 (4.78) | 1 (2.78) | 2 (4.44) | 12 (5.08) | 109 (4.82) | 62 (5.72) | 20 (8.62) | 0.37 |
| Apgar score <7 within 5 min of birth | 278 (1.75) | 32 (1.26) | 123 (1.57) | 22 (1.60) | 4 (1.74) | 1 (2.78) | 1 (2.22) | 4 (1.69) | 50 (2.21) | 32 (2.95) | 9 (3.88) | 0.01* |
## Logistic regression analysis of adverse pregnancy outcomes and neonatal outcomes at different ages and parities
The risk of adverse pregnancy outcomes and neonatal outcomes for the single age group is shown in Figure 1. After correcting for confounding factors such as education, place of residence, pre-pregnancy BMI, assisted reproduction, and week of pregnancy, with increasing age, the risk of gestational hypertension, eclampsia/pre-eclampsia, gestational diabetes mellitus, placenta previa, placental implantation, postpartum hemorrhage, preterm birth, cesarean section, transfer to the neonatal unit, and Apgar score <7 within 5 min of birth showed an upward trend, while only the A3 group of women (30–34 years) had a reduced incidence of fetal distress.
**Figure 1:** *Forest plot for risk of adverse pregnancy outcomes and neonatal outcomes by single age group. *p < 0.05, statistically difference, **p < 0.01, statistically significant difference, and ***p < 0.001, highly statistically significant difference.*
The risk of adverse pregnancy outcomes and neonatal outcomes for the single parity group is shown in Figure 2. After correcting for confounding factors, such as education, place of residence, pre-pregnancy BMI, assisted reproduction, and week of pregnancy, multiparous women showed a higher risk of gestational hypertension, eclampsia/pre-eclampsia, anemia, placenta previa, placental abruption, placental implantation, postpartum hemorrhage, preterm birth, cesarean section, macrosomia, and Apgar score <7 within 5 min of birth than nulliparous women, whereas only the probability of premature rupture of membranes and oligohydramnios was less than that of nulliparous women.
**Figure 2:** *Forest plot for risk of adverse pregnancy outcomes and neonatal outcomes by single parity group. *p < 0.05, statistically difference, *p < 0.01, statistically significant difference, and ***p < 0.001, highly statistically significant difference.*
The risk of adverse pregnancy outcomes and neonatal outcomes for the combination group with mixed age and parity is shown in Table 5 and Figure 3. The 20–24-year-old nulliparous women were taken as the control group, after correcting for confounding factors such as education, place of residence, and assisted reproduction. Regarding age, mothers with increasing age showed an increased risk of gestational hypertension, eclampsia/pre-eclampsia, gestational diabetes, placenta previa, placenta implantation, postpartum hemorrhage, cesarean section, transfer to the neonatal unit, and Apgar score <7 within 5 min of birth. From the perspective of parity, multiparous women showed a higher risk of placental previa, postpartum hemorrhage, transfer to the neonatal unit, and Apgar score <7 within 5 min of birth than nulliparous women of the same age. Only the risk of nulliparous women with AMA suffering from gestational hypertension, eclampsia/pre-eclampsia, gestational diabetes mellitus, and cesarean section and the risk of nulliparous women with vAMA suffering from gestational diabetes and cesarean section were greater than those of multiparous women of the same age; however, the risk of preterm birth did not vary regularly with age and parity.
## Discussion
Our study discovered that advanced age and multiple parities led to adverse pregnancy outcomes, while the interaction between advanced age and multiple parities further increased the risk of these outcomes. The relationship between different pregnancy outcomes and the three categories of age, parity, and the interaction between age and parity are not entirely consistent. According to our findings, the risk of gestational hypertension, eclampsia/pre-eclampsia, placenta previa, placental implantation, postpartum hemorrhage, preterm birth, cesarean section, and Apgar score <7 within 5 min of birth was associated with age, parity, and the interaction between the two. The risk of gestational diabetes mellitus and transfer to the neonatal unit was associated with age and the interaction between age and parity, but the impact of parity was not statistically significant; the risk of anemia, placental abruption, premature rupture of membranes, oligohydramnios, and macrosomia was only associated with parity; the risk of fetal distress was only associated with age.
We discovered that the risk of placenta previa, placental implantation, postpartum hemorrhage, and Apgar score <7 within 5 min of birth increased with age and parity, and that the interaction between age and parity enhanced the risk of these adverse outcomes even further. Both placenta previa and placental implantation are frequent placental abnormalities in mothers. A previous meta-analysis showed that AMA were 3.16 times more likely to have placenta previa than women of normal age [19]. According to Ozdemirci’s study, parity increased the chance of placenta previa [20]. Consistent with our findings, another study found that women with a history of numerous cesarean procedures had a higher likelihood of placental implantation [21]. Our study further proved that the interaction between age and parity had a negative influence placental disease, possibly due to the reduced physiological function of the placenta in women who are older and have more parities and due to the history of poor childbirth in some women, such as multiple miscarriages and short intervals between cesarean sections, which can lead to adverse outcomes such as placenta previa and placental implantation. Placental problems predispose women to postpartum hemorrhage. Guarga demonstrated that AMA and vAMA were 1.13 times and 1.85 times more likely to have postpartum hemorrhage than those at an appropriate age [13]. According to Ozdemirci’s study, parity increases the likelihood of placenta previa, which, in turn, increases the risk of postpartum hemorrhage [20]. Our study also confirmed that the interaction between age and parity further increased the risk of postpartum hemorrhage, probably due to a prolonged third stage of labor or incomplete delivery of the placenta as a result of decreased placental function, which predisposes patients to postpartum hemorrhage. An Apgar score <7 within 5 min of birth is a common adverse neonatal outcome. The study by Mehari MA revealed that the risk of an Apgar score <7 within 5 min of birth was 7.51 times higher in older mothers than in those of appropriate age [22]. There are few articles examining the relationship between an Apgar score <7 within 5 min of birth and parity, but our analysis revealed that either age or parity was a risk factor, and that the interaction between age and parity also increased its risk.
The interaction between age and parity further increased the risk of partial pregnancy outcomes; however, it was not very regular on some pregnancy outcomes. Gestational hypertension (HDP) is a leading global cause of maternal morbidity and mortality [23]. Gestational hypertension includes gestational hypertension, pre-eclampsia, eclampsia, and chronic hypertension in pregnancy and chronic hypertension complicated by pre-eclampsia, which predispose mothers to increased vascular endothelial damage with age, inflammatory immune hyperactivation, and uteroplacental, resulting in an increase in blood pressure, systemic small artery spasm, a decrease in blood flow to the uterus and placenta, placental imbalance, and even placental abruption. Kahveci B observed that the prevalence of gestational hypertension in AMA was 1.55 times higher than that in women of normal age and that the prevalence increased with age, with the prevalence in vAMA being 1.68 times higher than that in women of normal age [2]. The prevalence of eclampsia and pre-eclampsia was 2.39 and 9.92 times greater in the vAMA group compared to the normal age group, respectively. In terms of the relationship between gestational hypertension and age or parity, our findings are consistent with previous studies indicating that either age or parity was a risk factor for gestational hypertension. However, in terms of the interaction between age and parity, our result showed that the prevalence of nulliparous women in the AMA group was greater than that of multiparous women, which was inconsistent with previous studies and may be due to weak awareness of blood pressure control in nulliparous women of AMA. The cesarean section is a negative pregnancy outcome that has received increased attention in recent years. Our study identified age and parity as risk factors for cesarean section. However, in terms of the interaction between age and parity on cesarean section, the risk of cesarean section was higher in multiparous women of appropriate age than in nulliparous women of appropriate age, but it was higher in nulliparous women in the AMA and vAMA groups than in multiparous women. The risk of cesarean section was significantly elevated in nulliparous women of AMA; recent research suggests that this is likely attributable to faster changes in the risk of adverse outcomes in nulliparous women of AMA and an increased proportion of elective maternal cesarean section [24]. Regarding preterm birth, the pathophysiology is still poorly known and may involve infection, hemorrhage, and maternal-fetal stress. Previous studies have demonstrated that the relationship between preterm birth and age follows a U-shaped curve, with the lowest risk at 30–34 years and increased risk at both younger than 24 years and older than 40 years [25]. In the single age group of our study, we found a greater risk of preterm birth in advanced and very advanced maternal age than in appropriate age women, and in the single parity group, we found a greater risk of preterm birth in multiparous women than in nulliparous women, which is consistent with previous findings, but in the mixture of age and parity group, we found no regular relationship between the risk of preterm birth and changes in age and parity, possibly due to the interference of additional confounding factors. Therefore, the impact of the interaction between age and parity on the risk of preterm birth remains inconclusive.
Moreover, the risk of gestational diabetes mellitus and transfer to the neonatal unit was associated with age and the interaction between age and parity, according to our study. Gestational diabetes mellitus was the most prevalent condition among AMA in this study, and multiple investigations indicate the independent relationship between AMA and gestational diabetes mellitus [7, 26]. Regarding the interaction between age and parity, our results showed that the prevalence of gestational diabetes mellitus increased with age, with a greater prevalence of multiparous women in the age-appropriate group than in the nulliparous age group. However, nulliparous women in the AMA group and vAMA group showed a higher risk of gestational diabetes mellitus than multiparous women in the AMA group and vAMA group, similar with the findings of Kahveci B and possibly attributable to abnormal glucose and lipid metabolism in advanced maternal age [27]. Our study found no correlation between gestational diabetes mellitus and parity. Casagrande SS demonstrated that the risk of gestational diabetes mellitus was 1.57 times higher in women with ≥4 parities than in nulliparous women [28]. In terms of neonatal outcomes, transferring to the neonatal unit is an adverse neonatal outcome that has been less well studied. Similar to our findings, Vandekerckhove’s study revealed an increased risk of fetal transfer to the neonatal unit in older women [12]. Our study also revealed that the interaction between age and parity resulted in a higher rate of transfer to the neonatal unit in multiparous women than in nulliparous women.
In addition, the risk of certain pregnancy outcomes in this study was solely connected with a single factor. We found that the risk of anemia, placenta abruption, premature rupture of membrane, oligohydramnios, and macrosomia was exclusively connected with parity, while the risk of fetal distress was only associated with age. More research on anemia in pregnancy has been conducted in developing nations, likely because the majority of anemia in pregnancy is connected with maternal malnutrition. Lebso’s study showed that parity was a risk factor for anemia in pregnancy [29], and our study came to a similar conclusion. Lin’s study indicated that the risk of anemia in AMA is 1.386 times higher than that of women of appropriate age [30], but our study did not find a relationship between anemia and age or anemia and the interaction between age and parity, likely because our study population involved women who gave birth in tertiary care hospitals in a developed province in China. Future research should focus on anemic women residing in different locations and hospital levels. Placenta abruption and premature rupture of the membrane are common placental problems during pregnancy. Numerous studies have shown the relationship between placental abruption and parity, especially in women with previous cesarean sections, and a previous meta-analysis confirmed that advanced age was also a risk factor for placental abruption [19]. Our study merely confirmed the link between placental abruption and parity, while the etiology of placental abruption is still poorly known. Premature rupture of membranes was one of the few pregnancy outcomes in our study, for which the risk was higher in nulliparous women than in multiparous women. It emerges from intricate, multiple pathways and predisposes women to premature births [31]. Claramonte Nieto et al. found that the risk of premature rupture of membranes was 1.25 times higher in AMA than in women of appropriate age [32]. However, neither age nor the interaction between age and parity were found to be related to premature membrane rupture. In addition, few studies have verified the relationship between oligohydramnios and age or parity; our study only verified that parity was a risk factor for oligohydramnios, possibly due to decreased placental function in multiparous women. Maternal hyperglycemia stimulates the secretion of insulin in large quantities, resulting in faster fetal growth and the formation of macrosomia [33]. Lei showed that the risk of macrosomia in multiparous women was 1.26 times that of nulliparous women [34], which is similar to our findings. A Brazilian study showed that the odds of macrosomia in AMA were 1.22 times higher than in the appropriate age group [35], but our study did not find a relationship between macrosomia and age, and we did not find influence of the interaction between age and parity on macrosomia, which may be due to the low prevalence of macrosomia in our cohort. Fetal distress is a common adverse neonatal outcome. A Chinese study showed that the incidence of fetal distress increased in pregnant women >45 years old [36]. The combination of gestational hypertensive disease in AMA results in changes in the small systemic vascular arteries and impaired circulation to the uterus and placenta, which leads to an inadequate supply of oxygen and nutrients to the fetus, thereby adversely affecting normal fetal development and even stillbirth. This leads to a lack of oxygen and nutrient supply to the fetus, thus adversely leading to fetal distress. Currently, the connection between fetal distress and advanced age is still controversial. Our study found that the incidence of fetal distress at 30–34 years old was reduced, and the incidence of other ages and parities was not statistically significant. This may be due to the low incidence of fetal distress in the cohort, and future cohort studies with bigger samples could be performed to determine the association between fetal distress and age and parity.
## Conclusion
This study focused on exploring the impact of the interaction between age and parity on adverse pregnancy and neonatal outcomes, which may fill the current gap in the mixed role of advanced age and parity on pregnancy outcomes. The following are the paper’s strengths: large sample size, 15,861 maternal cases collected from tertiary care hospitals over 5 years; wide distribution of maternal age, maternal age ≥20 years, including very advanced maternal age ≥40 years; detailed grouping based on three levels (age, parity, age, and parity); comprehensive comparison of the interaction between age and parity on adverse pregnancy outcomes and neonatal outcomes; comprehensive adverse outcomes, including neonatal jaundice, transfer to neonatal unit, and other adverse outcomes investigated less frequently in the past; comprehensive confounding factors include pre-pregnancy BMI, gestational weight gain and assisted reproduction, etc. The limitations are that the data were only from one tertiary care hospital in Yangzhou, China, which may be subject to selection bias and not representative of all advanced maternal age in China due to geographical differences, economic level differences, etc. In addition, due to the limitations of data administration in Chinese hospitals and the secrecy of certain topics, such as personal income, certain confounding factors, such as a family history of illness, medications taken during pregnancy, etc., may be excluded. There is still an increasing trend of advanced maternal age and very advanced maternal age, so a multicenter large sample study could be designed to further investigate the current status of pregnancy and the risk of adverse pregnancy outcomes among women with different ages and parities. In our study, the interaction between age and parity on adverse pregnancy outcomes such as intrahepatic cholestasis of pregnancy, preterm birth, and anemia was not clear, and further studies should be conducted to investigate these pregnancy outcomes. On the whole, all these results will provide clinicians, midwives, and obstetric nurses with more detailed information on the risk of adverse maternal outcomes and how to safeguard the health of the mother and fetus.
## Data availability statement
The data analyzed in this study are subject to the following licenses/restrictions: *Our data* are derived from the obstetrics and gynecology system of Northern Jiangsu People’s Hospital in Yangzhou, China, and cannot be disclosed to the public due to the confidentiality of personal data. Requests to access these datasets should be directed to Jiayang Dai, [email protected].
## Author contributions
JD and YS contributed to the conception or design of the study, and drafted the manuscript. JD, YiW, YC, and LG collected data and conducted data analysis. DL, YuW, and HL reviewed the literature and conducted some data analysis. JD, YS, XK, and DL revised the article and provided final approval of the manuscript. 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.
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|
---
title: Alpha fetoprotein promotes polarization of macrophages towards M2-like phenotype
and inhibits macrophages to phagocytize hepatoma cells
authors:
- Minni Zhang
- Kun Liu
- Qiuyue Zhang
- Junnv Xu
- Jinchen Liu
- Haifeng Lin
- Bo Lin
- Mingyue Zhu
- Mengsen Li
journal: Frontiers in Immunology
year: 2023
pmcid: PMC9995430
doi: 10.3389/fimmu.2023.1081572
license: CC BY 4.0
---
# Alpha fetoprotein promotes polarization of macrophages towards M2-like phenotype and inhibits macrophages to phagocytize hepatoma cells
## Abstract
Alpha-fetoprotein(AFP) is a cancer biomarker for the diagnosis of hepatocellular carcinoma(HCC); however, its role in macrophage polarization and phagocytosis remains unclear. In the present study, we explored the correlation between AFP regulation of macrophage function and the possible regulatory mechanisms. Human mononuclear leukemia cells (THP-1) and monocytes from healthy donors were used to analyze the effect of AFP on the macrophages’ phenotype and phagocytosis. THP-1 cells and healthy human donor-derived monocytes were polarized into M0 macrophages induced by phorbol ester (PMA), and M0 macrophages were polarized into M1 macrophages induced by lipopolysaccharide(LPS) and interferon-γ(IFN-γ). Interleukin-4(IL-4) and interleukin-13(IL-13) were used to induce M0 macrophage polarization into M2 macrophages. Tumor-derived AFP(tAFP) stimulated M0 macrophage polarization into M2 macrophages and inhibited M1 macrophages to phagocytize HCC cells. The role of AFP in promoting macrophage polarization into M2 macrophages and inhibiting the M1 macrophages to phagocytize HCC cells may be involved in activating the PI3K/Akt signaling pathway. AFP could also enhanced the migration ability of macrophages and inhibited the apoptosis of HCC cells when co-cultured with M1-like macrophages. AFP is a pivotal cytokine that inhibits macrophages to phagocytize HCC cells.
## Introduction
The incidence and mortality of hepatocellular carcinoma (HCC) is increasing annually. The characteristics of high recurrence, easy metastasis, and a high degree of malignancy lead to high mortality, as well as great obstacles in improving the efficacy and survival rate [1]. Chronic hepatitis virus infection, biliary tract diseases, long-term alcoholism, aflatoxin exposure, and drug use are important risk factors for the development of liver cancer [2, 3]. Liver fibrosis and cirrhosis caused by hepatitis B virus(HBV) infection are the most important risk factors in China. In the case of cirrhosis, gene mutation, epigenetic dysregulation and abnormal molecular signal transduction are important causes of hepatocyte carcinogenesis [4]. Alpha-fetoprotein (AFP), a single-chain serum glycoprotein belonging to the albumin family, is mainly synthesized by the fetal liver and yolk sac during embryonic development, and its concentration in serum decreases rapidly a few months after birth. Therefore, the concentration of AFP levels in normal adults is very low (<20 ng/mL) [5]. Abnormal elevation of AFP is common in patients with chronic or active hepatitis, liver cirrhosis, liver cancer, genital tumors, and pregnancy, and approximately $70\%$ of patients with liver cancer have elevated serum AFP levels. In clinical practice, AFP is regarded as a specific tumor biomarker for the screening and diagnosis of primary liver cancer, and it plays an important role in judging the degree of malignancy, evaluation of efficacy, detection of recurrence after surgery, liver transplantation, and guidance for clinical medication [6].
Tumor-associated macrophages (TAMs) in the tumor microenvironment (TME) can promote tumor development, invasion, metastasis, and recurrence [7]. Generally, TAMs are M2 macrophages with phenotypic characteristics. Macrophages are mainly differentiated from circulating monocyte precursors and are characterized by high heterogeneity and functional diversity. The polarization of macrophages is an important factor affecting its anti-tumor activity [8], according to secreted cytokines and functions, macrophages can be divided into classical activated macrophages (M1 type) and alternatively activated macrophages (M2 type) [9, 10]. M1-type macrophages are mainly induced and activated by lipopolysaccharide(LPS) and interferon-γ (IFN-γ). M1 macrophages can secrete proinflammatory cytokines, such as interleukin(IL)-12, IL-6, IL-1β, IL-23, and tumor necrosis factor-α(TNF-α), and also produce chemokines, such as CXCL9 and CXCL10. High expression of co-stimulatory molecules, such as CD86 and CD80, can also participate in inflammation by producing nitric oxide(NO), reactive oxygen species(ROS), and inducible nitric oxide synthase(iNOS) and by mediating the Th1-type cell immune response. It is mainly involved in the positive immune response through antigen presentation, and has strong phagocytosis and anti-tumor activity, thus exerting an immune surveillance function (11–14). M2-type macrophages are primarily induced and activated by IL-4, IL-13, and IL-10. The secretion of the inflammatory cytokines IL-10, transforming growth factor(TGF-β), and Arginase-1(Arg-1) and the expression of high levels of scavenger receptor (CD163), mannose receptor(CD206), CCL-17, CCL-22, and other chemokines downregulate the immune response. It mainly mediates the Th2-type immune response and plays a key anti-inflammatory role by accelerating tumor cells activation, invasion, angiogenesis, tissue remodeling, and inhibition of adaptive anti-tumor immunity (15–17).
Previous studies in our laboratory have shown that AFP could induce the expression of downstream target genes by activating the PI3K/Akt signaling pathway and regulating the growth, proliferation, invasion, metastasis, generation of stem cells, and other malignant behaviors of liver cancer cells (18–20). TAMs also play a key role in promoting HCC invasion and metastasis, immune escape, matrix remodeling, epithelial-mesenchymal transition(EMT), lymphangiogenesis and angiogenesis, and drug resistance [21]. At present, there are few studies on whether AFP can regulate the PI3K/Akt signaling pathway to affect the polarization and phagocytosis of macrophages, reverse the macrophages phenotype, or reshape TAMs in vivo. In the present study, we investigated the role of AFP in the polarization and phagocytosis of macrophages, explored the inhibitory effect of AFP on cellular immunity, and identified a new function of AFP in stimulating HCC cells to escape the surveillance of immune cells.
## Cell lines and cell culture
The human mononuclear leukemia cell line, THP-1 was purchased from Wuhan Punoxai Life Technology Co., Ltd., while the human HCC cell lines Bel7402, HepG2 and HLE were purchased from Wuhan Boshude Bioengineering Co., Ltd. Cell resuscitation was performed using a UV lamp on a sterile ultraclean table. Cells were cultured in RPMI 1640 medium supplemented with $10\%$ heat-inactivated fetal calf serum(FCS) and incubated at 37°C in a humidified atmosphere containing $5\%$ CO2, as previously described [22, 23]. Fresh medium (5 mL) was added to the new cell flask, the name of the cell was labeled with the name of the operator and the date, and then placed in the cell incubator for preheating. Cell status was observed, and the liquid was changed the next day.
## Inducing macrophage polarization
THP-1 and cells were centrifuged at 800 RPM/min for 5 min, the supernatant was discarded, and the number of cells was adjusted to 1×106/mL by adding an appropriate amount of fresh medium. Phorbol ester(PMA) was added to the cell suspension to a final concentration of 50 ng/mL, gently blown and mixed, and the cell suspension was seeded into a six-well plate at a volume of 2 mL per well and placed in an incubator under light. After treatment with PMA for 48 h, the cells turned from a suspension to adherent cells with protruding pseudopodia and were viewed under a microscope. THP-1 cells differentiated into M0 macrophages. M0 macrophages were washed twice with an appropriate amount of sterile PBS and cultured in fresh medium containing 50 ng/mL LPS+20 ng/mL IFN-γ for 24 h to obtain M1-type macrophages. Fresh medium containing 20 ng/mL IL-4+20 ng/mL IL-13 was added, and M0 macrophages were cultured for 72 h to obtain M2-type macrophages.
## Lentiviral infection and screening of stable expression cell lines
For adherent Bel7402 cells, the cell number was diluted to 1.2×105 cells/mL, and the cells were seeded into a 24-well plate at 500 μL/well. The culture was continued, and viral infection was performed when the degree of cell fusion reached $40\%$. For suspended THP-1 cells, The number of cells was diluted to 1×105/mL, and 500 μL/well was seeded into a 24-well plate for direct viral infection. Then, 250 μL of fresh medium containing 1×Hitans GP or 1×Hitans GA was added, and the corresponding virus volume was converted according to the selected multiplicity of infection(MOI) gradient (10, 24–26) and added to fresh medium containing the viral infection booster solution. Cell culture plates were shaken using the crossing method. After 4 h, 250 μL of fresh medium containing the infection booster solution was added again for 15 h. The cells were washed twice with sterile PBS, and fresh virus-free medium was added. The efficiency of the viral infection was observed after 48 and 72 h. After the puromycin concentration was screened, cells in the blank group were seeded in a 24-well plate, and the culture medium was replaced with fresh medium containing puromycin after 24 h. The puromycin screening gradients was 0.6, 1.2, 1.8, 2.4, and 3.0 μg/mL. The fresh medium was replaced according to the cell state, and the minimum puromycin concentration that killed all cells in the blank group for 3-4 days was selected as the experimental concentration of infected lentiviral cell lines. To screen stable virus-infected cell lines, 72 h after lentiviral infection, the concentration of puromycin found in the pre-experiment was used to simultaneously screen the lentivirus-infected and blank groups. After the cells in the blank group died completely, the concentration of puromycin in the lentivirus-infected cells was reduced to $50\%$ of the original concentration and the culture was maintained. After 3 days, the medium was replaced with fresh medium without puromycin, and the obtained cells were considered THP-1 stable cells.
## Real-time polymerase chain reaction (PCR)
Total RNA extraction, RNA-free centrifuge tubes, EP tubes, PCR tubes, and pipetting nozzles were used in this experiment. The macrophages were washed twice with sterile PBS, the PBS was aspirated, an appropriate amount of ACCUTASE cell digestion solution was added, and the cells were shaken gently and placed in a cell incubator. When the cells gradually fell off the bottle, sterile PBS was added, the adherent cells were blown with a pipette gun, and the cell suspension was centrifuged at 1500 RPM/min for 10 min. The temperature of the water bath was maintained at 70°C. The supernatant was discarded and 300 μL of the lysate was added to the cell precipitate and gently aspirated using a pipette gun. Next, 300 μL of RNA diluent was added, mixed well, and placed in a water bath at 70°C for 3 min to improve the RNA yield. The procedure was as follows: 600 μL of RNA wash solution was added to the column, centrifuge at 12000 RPM/min for 1 min, the filtrate obtained in the collection tube was discarded, and 50 μL DNase I incubation solution was added to the middle of the adsorption membrane of each tube of the centrifugation column and incubated at room temperature for 15 min. Add 600 μL RNA solution, centrifuge at 12000 RPM/min for 1 min, discard the filtrate obtained in the collection tube, and repeat the above procedure twice. The concentration and purity of RNA were determined using an ultramicro spectrophotometer, and the RNA was stored at -80°C until further analysis. The mRNA levels of the target genes were detected by real-time PCR as previously described [27] and the primers were shown in Table 1.
**Table 1**
| Gene name | primer sequences (5’ to 3’) |
| --- | --- |
| GAPDH | Forward: TGATGACATCAAGAAGGTGGTGAAGReverse: TCCTTGGAGGCCATGTGGGCCAT |
| CD86 | Forward: CTGCTCATCTATACACGGTTAReverse: GGAAACGTCGTCAGTTCTGTG |
| TNF-α | Forward: TGGCCCAGGCAGTCAGAReverse: GGTTTGCTACAACATGGGCTACA |
| CD163 | Forward: TTTGTCAACTTGAGTCCCTTCACReverse: TCCCGCTACACTTGTTTTCAC |
| Arg-1 | Forward: ACGGAAGAATCAGCCTGGTGReverse: GTCCACGTCTCTCAAGCCAA |
## Western blotting analysis
To estimate the polarization of macrophages induced by PMA, lipopolysaccharide(LPS), interferon-γ(IFN-γ), IL-4, and IL-13, the expression of the markers of M1-type macrophages, CD86 and inducible nitric oxide synthase(iNOS), and the markers of M2-type macrophages, CD163 and IL-10, were detected by Western blotting. The influence of AFP on the expression of these marker proteins and PI3K/Akt signaling pathway-related proteins was analyzed in M0-type macrophages. M0 macrophages were infected with the AFP-expressed lentiviral vectors and treated with the PI3K inhibitor Ly294002 (final concentration:20 μM) for 48 h. The expression of these proteins was analyzed by Western blotting. Briefly, these proteins were probed with the following primary antibodies: mouse anti-CD86 (1:500), anti-iNOS (1:500), anti-CD163 (1:500), anti-IL-10 (1:500), anti-β-actin (1:1000), rabbit anti-PI3K (1:400), anti-Akt (1:400), or anti-p-Akt(Ser473) (1:400) (all from eBioscience and Abcam Inc.). The detailed procedure has been previously described [20, 28].
## Flow cytometry studies
To analyze the macrophage phenotype, THP-1 cells were induced to differentiate into macrophages of various phenotypes, according to the method described above. The cells were washed twice with pre-cooled PBS, PBS was added, and an appropriate amount of ACCUTASE cell digestion solution was added. The cells were gently shaken to evenly cover the cell surface and then placed in a cell incubator. When the cells gradually fell off, 3 mL of fresh medium was added to blow the adherent cells, and the cell suspension was centrifuged at 1500 RPM/min for 10 min. THP-1 cells were centrifuged at 800 RPM/min for 5 min, without digestion. Next, 100 μL of fixation solution was added to 100 μL of the cell suspension by blowing, and the tube was mixed by pulsed vortexing and incubated for 30 min at room temperature in the dark. At the end of fixation, 2 mL of 1×membrane breaking solution was added and centrifuged for 5 min at 1500 RPM/min at room temperature. The supernatant was discarded, the cell precipitate was resuspended in 100 μL of 1×membrane breaking solution, and the corresponding flow antibody or the respective isotype control was added according to the manufacturer’s instructions. The cells were incubated at room temperature for 30 min in the dark, and the cell suspension was shaken every 10 min during the incubation period to allow for full reaction with the antibody. At the end of the incubation period, 2 mL of 1×membrane-breaking solution or pre-cooled PBS was added to each tube and centrifuged for 5 min at 1500 RPM/min at room temperature. Finally, the stained cells were resuspended in 300 μL of flow cytometry staining solution and transferred to a 5 mL flow cytometry tube with a cell filter for operation. The cell apoptosis analysis procedure was as follows: a noncontact co-culture system was established using a Transwell chamber (pore size:0.4 μm). Macrophages (approximately 1×106 cells/well) were inoculated in the upper chamber and placed above the six-well plate of HCC cells (approximately 1×106 cells/well) for 48 h. RPMI 1640 medium was added at 1.5 mL and 2.6 mL inside and outside the small chamber, respectively. The HCC cells were digested, centrifuged, and resuspended twice in pre-cooled PBS. The cell number was adjusted to 1×106 cells/mL by adding 1×Annexin V binding buffer, and 100 μL of the cell suspension was placed into a 1.5 mL EP tube. The Annexin V-PE/7-AAD Apoptosis Detection Kit(Beijing Biolaibo Technology Co., LTD, Beijing, China) was used to detect cell apoptosis. Five microliters of PE annexin V and 5 μL of 7-AAD solution were added, and the solution was gently blown with a pipette gun, mixed, and incubated at room temperature in the dark for 15 min. At the end of the incubation, 400 μL of 1×Annexin V binding buffer was added and then transferred to a 5 mL flow tube with a filter screen for loading. The procedure for the analysis of phagocytosis of macrophages was as follows: THP-1 cells were stimulated into M0-type macrophages using PMA in a six-well plate, the supernatant was discarded, the cells were washed with sterile PBS, and serum-free medium was added for 6 h. Carboxylate-modified polystyrene latex beads were prepared as follows: 100 μL of polystyrene latex bead suspension was added to 10 mL of $1\%$ BSA and incubated in a 37°C water bath for 30 min, ultrasonicated for 5 min, and then used as prepared. Analysis of macrophage phagocytosis: The serum-free medium in the six-well plate was replaced with complete medium, and a certain volume of latex bead suspension was added and incubated in the cell incubator for 3 h and 6 h, respectively, in the dark. The six-well plate was removed according to the set phagocytosis time, pre-cooled PBS was added, and the plate was washed several times to remove the latex beads that had not been phagocytosed. The macrophages that had phagocytosed latex beads in the six-well plate were digested and centrifuged, resuspended in flow cytometry staining solution, and transferred to a 5 mL flow tube with a cell filter. The phagocytic ability of macrophages was detected using flow cytometry, and the fluorescence intensity represented the relative number of latex beads were phagocytosed by macrophages.
## Laser confocal microscopy
M0-type macrophages (2×105 cells/well) were cultured and infected with AFP-expressing negative control lentivirus vector(M0-NC) or AFP-expressing lentivirus vector(M0-AFP). Then, the phagocytic experiment with polystyrene latex beads was carried out by laying plates in laser confocal microscope Petri dishes, as described above. The cells were treated with carboxylate-modified polystyrene latex beads for 3 or 6 h, and then the un-engulfed polystyrene latex beads were removed by washing several times with precooled PBS. The cells were treated with 400 μL $4\%$ paraformaldehyde for 20 min and washed twice with PBS containing $0.1\%$ Triton X-100. Actin-Tracker Green was diluted in the staining working solution with an immunofluorescence secondary antibody diluent, and 300 μL was added to each well and incubated at room temperature for 40 min in the dark. The cells were washed twice with PBS containing $0.1\%$ Triton X-100 for 4 min each wash. Next, 300 μL of DAPI staining solution was added to each well, and the staining was shielded from light for 5 min. The samples were washed three times with PBS containing $0.1\%$ Triton X-100, and polystyrene latex beads engulfed by macrophages were observed and photographed using a laser confocal microscope.
## Cell migration assay
Macrophages in each group were digested with ACCUTASE cell digestion solution, and the number of cells was adjusted to 1×106/mL using medium without serum. A Transwell chamber (pore size, 8 μm) was used, and 500 μL of fresh RPMI 1640 medium containing $20\%$ FBS was added to each well in the lower chamber. Two hundred microliters of cell suspension were added to each upper chamber, and the cells were cultured for 72 h in a cell incubator. The cells in the upper layer of the Transwell chamber were gently wiped with a cotton ball, washed twice with PBS, and treated with $4\%$ paraformaldehyde for 20 min. The fixed Transwell chamber was cleaned twice with PBS and stained with $0.1\%$ crystal violet for 20 min. After staining, the residual staining solution was cleaned with PBS and the plate was allowed to dry. Images were captured under an inverted microscope, and five fields were randomly selected for each group to calculate the average value.
## Healthy donor-derived monocytes were induced to polarize into macrophages
Healthy human(donors) (aged 22-26 years, four males and two females) were selected, and 50mL of blood was collected from each donor. Monocyte separation was performed as previously described [29, 30]. Then PMA was added to the cell for 24h, and then the cells were treated with LPS+IFN-γor IL-4+IL-13 for 24h, respectively, to obtain M0-like macrophages, M1-like macrophages and M2-like macrophages.
## Phagocytosis assay with tumor-derived AFP (tAFP)
A patient with liver cancer was recruited from the Department of Hepatobiliary Surgery, the First Affiliated Hospital of Hainan Medical College. The serum AFP concentration of the patient was >5000 ng/mL. 100 ml of blood was collected and the tumor-derived AFP(tAFP) was purified following a previously described procedure [31, 32]. M1-like macrophages derived from THP-1 and M1- or M2-like macrophages derived from monocytes were treated with tAFP(final concentration 20μg/mL) for 24h. The cells were resuspended in 1 mL of PBS, and 1 μL of 5-(and -6)-carboxyfluorescein diacetate succinimidyl ester (CFSE). Laser confocal microscopy and intelligent living-cell high-throughput imaging analyzer were used to observe macrophages phagocytizing polystyrene latex beads or liver cancer cells(HLE cells). The following describes the experimental operation of observing M1-like macrophages that phagocytize HCC cells using an intelligent living-cell high-throughput imaging analyzer.
## Intelligent living-cell high-throughput imaging analyzer studies
To improve the fluorescence expression effect of HCC cells infected with lentivirus, Bel 7402 cells (untreated or infected with the interference AFP-expressing vector) were digested and centrifuged for cell counting. The cells were resuspended in 1 mL of PBS, and 1 μL of CFSE storage solution was added for blowing and mixing. The cells were incubated for 30 min at room temperature and then washed three times with PBS. M1-type macrophages were digested and centrifuged for cell counting. The stained liver cancer cells were co-cultured with M1-type macrophages in a six-well plate at a ratio of 1:1.5, and placed in a cell incubator for 2 h until the cells adhered to the wall. The bright field channel of the intelligent living-cell high-throughput imaging analyzer and the RFP red fluorescence channel (to reduce the strong green fluorescence background) were used to dynamically track and image the cells in contact with the co-culture. Five different visual fields were randomly selected for each group and photographed every 30 minutes for 24 hours.
## Statistical analysis
Each experiment was repeated more than three times. All experimental data were analyzed using the GraphPad Prism software (version 9.0). A t-test was used to compare the differences between two groups, the Mann Whitney U-test was applied to compare the differences in macrophage marker; one-way ANOVA was used to compare the differences between multiple groups, and the data are expressed as the mean ± standard deviation (x ± SD). Differences were considered statistically significant at $P \leq 0.05.$
## Morphological characteristics to identify THP-1 monocytes and M0-, M1- and M2-type macrophages
THP-1 cells in good growth state were uniform in size, regular in shape, arranged in a circle under the microscope, and grown in suspension with clear cell boundaries and good refraction. After stimulation with RPMI 1640 medium containing 50 ng/mL PMA for 48 h, THP-1 cells gradually changed from suspension to adherent growth, lost their proliferative ability, increased in size, extended pseudopodia, and turned into tightly aligned oval or irregular M0-type macrophages. After stimulation of M0 macrophages with RPMI 1640 medium containing 50 ng/mL LPS+20 ng/mL IFN-γ for 24 h, the pseudopodia of M0-type macrophages changed more significantly, becoming M1-type macrophages with a long spindle shape. After stimulation of M0 macrophages with RPMI 1640 medium containing 20 ng/mL IL-4 + 20 ng/mL IL-13 for 72 h, the cell volume increased and the cells became polygonal M2-type macrophages (shown in Supplementary materials: S-Figure 1). The mRNA expression levels of macrophage markers were analyzed to identify the M1 and M2 types. M0 macrophages were induced to polarize toward M1- or M2-type macrophages using IFN-γ+LPS and IL-4+IL-13, respectively. Real-time PCR was used to detect the Ct values of the M1-type macrophage markers CD86 and TNF-α, and the M2-type macrophages markers CD206 and Arg-1. The expression of each target gene in the M0-type macrophages was used as a control. The relative mRNA expression levels of each target gene in the M1- and M2-type macrophages were calculated using the 2-△△Ct method. The experimental results showed that the melting curves of the reference gene GAPDH and the target genes CD86, TNF-α, CD206, and Arg-1 all had single peaks (Figure 1A), indicating that the amplification products had high specificity. The relative mRNA expression levels of CD86 and TNF-α in M1-type macrophages were higher than those in M0- and M2-type macrophages. The relative mRNA expression levels of CD206 and Arg-1 in M2-type macrophage were higher than those in M0- and M1-type macrophages (Figure 1B). These results indicated that CD86 and TNF-α were highly expressed in M1 macrophages upon stimulation with IFN-γ+LPS, but CD206 and Arg-1 were highly expressed in M2 macrophages upon stimulation with IL-4+IL-13. M0-type macrophages were induced to polarize toward M1- or M2-type macrophages using IFN-γ+LPS and IL-4+IL-13, respectively. The protein expression levels of the M1 macrophages markers CD86 and iNOS and the M2 macrophages markers CD163 and IL-10 were detected by Western blotting. The experimental results showed that the protein expression levels of CD86 and iNOS in M1 macrophages were higher than those in M0 and M2 macrophages, and the protein expression levels of CD163 and IL-10 in M2 macrophages were higher than those in M0 and M1 macrophages (Figure 1C). These results indicated that M1-type macrophages overexpressed CD86 and iNOS under the stimulation of IFN-γ+LPS, and that M2-type macrophages overexpressed CD163 and IL-10 under the stimulation of IL-4+IL-13.
**Figure 1:** *THP-1 monocytes were induced to polarize toward M0, M1 and M2 macrophages. (A) M0-type macrophages were induced to polarize toward M1- or M2-type macrophages using LPS+IFN-γ and IL-4+IL-13, respectively, for 72 h. Real-time PCR was used to detect the Ct values of the M1-type macrophage markers CD86 and TNF-α and the M2-type macrophage markers CD206 and Arg-1. Melting curve comparison of the relative mRNA expression levels of each target gene in M0, M1 and M2 macrophages. (B) The expression of each target gene in M0 macrophages was used as a control. The relative mRNA expression levels of each target gene in M1 and M2 macrophages were calculated by the 2-△△Ct method. The right column diagrams display the statistical differences in relative mRNA expression levels in each group, **P<0.01. (C) Protein expression of the M1-type macrophage markers CD86 and iNOS, and the M2-type macrophage markers CD163 and IL-10 detected by Western blotting; the right column diagrams display the expression levels of proteins in each group that were statistically analyzed by gray scanning, **P<0.01. (D) Expression of CD68 in the IgG isotype control and THP-1 cells; expression of CD68, CD86 and CD206 in M0, M1 and M2 macrophages detected by flow cytometry; the right column diagrams display statistical analysis of the expression of CD68, CD86 and CD206, **P<0.01. (E) The expression of the macrophage markers M0 (CD68), M1 (iNOS) and M2 (Agr-1) was observed by laser confocal microscopy. The results of the at least three independent experiments are shown.*
CD68, as a 110-kDa highly glycosylated transmembrane protein, is the most specific and widely used marker for macrophages to distinguish monocytes from lymphocytes. The Flow cytometry results showed that the expression rates of CD68 in M0, M1, and M2 macrophages were 81.94 ± $4.34\%$, 88.69 ± $3.59\%$ and 86.06 ± $4.41\%$, respectively (Figure 1E). The expression rate of CD68 in these macrophages was significantly higher than that in THP-1 cells (9.58 ± $2.25\%$) (Figure 1E), indicating that CD68 was highly expressed in M0, M1, and M2 macrophages. D86, a 60 kDa molecule expressed on antigen-presenting cells, belongs to the type I membrane protein of the immunoglobulin superfamily and is a biomarker of M1 macrophages. M0-type macrophages were induced to polarize toward M1- or M2-type macrophages using IFN-γ+LPS and IL-4+IL-13, respectively. Flow cytometry results showed that the expression rate of CD86 in M1 macrophages was 64.85 ± $3.39\%$, which was higher than that in M0 macrophages (30.61 ± $3.19\%$) and M2 macrophages (23.23 ± $5.73\%$) (Figure 1D), indicating that M0 macrophages highly expressed CD86 after IFN-γ+LPS stimulation. CD206, also known as the macrophage mannose receptor (MMR), is a 175 kDa type I single-chain transmembrane glycoprotein with a multilectin receptor structure, which is a biomarker of M2 macrophages. M0 macrophages were induced to polarize toward M1 or M2 macrophages using IFN-γ+LPS and IL-4+IL-13, respectively. Flow cytometry results showed that the expression rate of CD206 in M2 macrophages was 50.59 ± $4.25\%$, which was higher than that in M0 macrophages(7.61 ± $3.07\%$) and M1 macrophages(3.06 ± $4.48\%$) (Figure 1D), indicating that M0 macrophages highly expressed CD206 after stimulation with IL-4+IL-13. Laser confocal microscopy was used to observe the fluorescence expression of the M0-type macrophage marker CD68 (green fluorescence), the M1-type macrophage marker iNOS (green fluorescence), and the M2-type macrophage marker Arg-1 (green fluorescence). DAPI (blue fluorescence) staining was used to determine the location of nuclei. The merged images represent fluorescence expression on the surface of macrophages when the two types of fluorescence were fused (Figure 1E). The results showed that the pseudopodia of the M1 macrophages were more obvious than those of the M0 and M2 macrophages. CD68, iNOS, and Arg-1 were expressed in the M0, M1, and M2 macrophages, respectively.
## Establishment of cell lines stably overexpressing or interfering with AFP expression, and the influence of AFP on the expression of macrophage markers
THP-1 cells were infected with AFP-expressing lentivirus vectors and the expression of green fluorescent protein (GFP) in the cells was observed under an inverted fluorescence microscope for 72 h (Figure 2A). The GFP expression rate in THP-1 cells was approximately $80\%$. After puromycin screening, the culture was continued, and cellular proteins were extracted. The protein expression levels of AFP in the cells were detected by Western blotting (Figure 2B). The results showed that the protein expression of AFP in THP-1 cells while infected with AFP-expressing lentivirus vectors was significantly higher than that in the control group THP-1-NC (negative control, NC), proving that a THP-1-AFP cell line stably overexpressing AFP was successfully constructed. The Bel7402 cell line was infected with short hairpin RNA to suppress AFP(shAFP) lentiviral vectors, and the expression of green fluorescent protein (GFP) in the cells was observed under an inverted fluorescence microscope after 72 h (Figure 2C). The positive rate of GFP expression in the Bel7402 cells was approximately $90\%$. After puromycin screening, the culture was continued, and cell proteins were extracted. Western blotting was performed to detect the protein expression (Figure 2D). The results indicated that the protein expression of AFP in Bel7402-shAFP cells was significantly lower than that in the control group Bel7402-shNC (negative control, NC), proving that the Bel7402-shAFP cell line stably interfering with AFP expression was successfully established. THP-1 cells stably expressing AFP were stimulated with PMA to polarize them into M0 macrophages. Then, the cells were treated with LPS+IFN-γ or IL-4+IL-13, and the expression levels of the M1 macrophage markers CD86 and iNOS, and the M2 macrophage markers CD163 and IL-10 were detected by Western blotting. The results showed that the expression of CD163 and IL-10 in the macrophages of the M0-AFP (infected with AFP-expressing lentivirus vectors) group was higher than that in the M0 and M0-NC (infected with unexpressed lentivirus vectors) groups, and the protein expression levels of CD86 and iNOS in the macrophages of the M0-AFP group were lower than those in the M0 and M0-NC groups (Figure 2E). These results indicate that AFP overexpression in macrophages could upregulate the protein expression of the M2-type macrophage markers CD163 and IL-10 and downregulate the protein expression of the M1-type macrophage markers CD86 and iNOS. These results indicated that AFP overexpression promotes polarization of macrophages into the M2 type.
**Figure 2:** *Establishment of stable overexpression of AFP in the THP-1-cell line and stable interference with AFP expression in the Bel7402 cell line, and AFP influences the expression of macrophage markers. (A) Fluorescence microscope was applied to observe the expression of GFP in THP-1 cells while infected with AFP-expressing negative control lentivirus vectors (M0-NC) or AFP-expressing lentivirus vectors (M0-AFP) after 48 h. (B) The expression of AFP was detected by Western blotting; the low column diagram shows statistical analysis of the expression levels of AFP in each group by gray scanning, **P<0.01. (C) Fluorescence microscope was applied to observe the expression of GFP in Bel7402 cells while infected with interference AFP-expressing vectors after 48 h. (D) The expression of AFP was detected by Western blotting; the low column diagram shows statistical analysis of the expression levels of AFP in each group by gray scanning, **P<0.01. (E) The expression of the M1-type macrophage markers CD86 and iNOS, and the M2-type macrophage markers CD163 and IL-10 was detected by Western blotting; the right column diagram displays the expression levels of macrophage markers in each group, which were statistically analyzed by gray scanning, **P<0.01. (F) Effect of AFP overexpression on the expression of the M1-type macrophage marker CD86, and the M2-type macrophage marker CD206 detected by flow cytometry; the right column diagram shows statistical analysis of the expression levels of CD86 and CD206, **P<0.01. The pictures are representative photos of three independent experiments.*
THP-1 cells stably expressing AFP were stimulated with PMA to polarize them into M0-type macrophages. M0-AFP cells were then treated with LPS+IFN-γ or IL-4+IL-13. Flow cytometry was used to detect the expression of the M1-type macrophage marker CD86 and M2-type macrophage marker CD206. The results showed that the expression rate of the M2-type macrophage marker CD206 in the M0-AFP group was 31.59 ± $1.99\%$, which was higher than that in the M0-NC group (9.71 ± $2.38\%$). The expression rate of the M1-type macrophage marker CD86 in macrophages of the M0-AFP group was 16.30 ± $2.04\%$, which was lower than that in the M0-NC group (32.30 ± $1.74\%$) (Figure 2F). These results indicated that AFP overexpression in macrophages could upregulate the expression of the M2-type macrophage marker CD206 and downregulate the expression of the M1-type macrophage marker CD86.
## AFP activates the PI3K/Akt signaling pathway and stimulates macrophages to polarize into M2 type
Changes in the expression of M1- and M2-type macrophage markers after treatment with the PI3K inhibitor Ly294002 were detected by Western blotting. To investigate whether the PI3K/Akt signaling pathway is involved in the effect of AFP on the macrophages phenotype, changes in the expression of PI3K, Akt, and p-Akt(Ser473) in macrophages of the M0-NC and M0-AFP groups (treated with LPS+IFN-γ or IL-4+IL-13, respectively) were detected by western blotting. The results showed that the expression of PI3K and Akt in macrophages of the M0-NC and M0-AFP groups did not change significantly, but the expression of p-Akt(Ser473) in macrophages of the M0-AFP group was higher than that in macrophages of the M0-NC group (Figure 3A). To further verify whether AFP regulates the PI3K/Akt signaling pathway and affects the macrophage phenotype, the PI3K/Akt pathway inhibitor Ly294002 was used to treat the cells for 24 h before PMA was used to induce THP-1 polarization into the above two groups of macrophages. Western blotting was used to detect changes in the protein expression of the M1-type macrophage markers CD86 and iNOS, and the M2-type macrophage markers CD163 and IL-10. The results indicated that, compared with the macrophages in the M0-AFP group without Ly294002 treatment, the expression of CD86 and iNOS significantly increased, but the expression of p-Akt(Ser473), CD163, and IL-10 significantly decreased when the cells were treated with Ly294002(Figure 3B). These results indicated that AFP could promote the expression of CD163 and IL-10 proteins in macrophages; treatment with the pathway inhibitor Ly294002 successfully blocked the PI3K/Akt signaling pathway and the effect of AFP on macrophage polarized phenotype was inhibited.
**Figure 3:** *The effects of AFP and the PI3K inhibitor Ly294002 on the expression of M1- and M2-type macrophages’ markers. The M0 macrophages were infected with AFP-expressing negative control lentivirus vectors (M0-NC) or AFP-expressing lentivirus vectors (M0-AFP) for 48 h and then treated with LPS+IFN-γ or IL-4+IL-13 for 24 h, followed by treated with Ly294002 (final concentration: 20 μmol/L) for 24 h. (A) The expression of PI3K, Akt and p-Akt(Ser473) proteins in the M0-NC and M0-AFP groups was analyzed by Western blotting; the right column diagram displays the expression levels of proteins in each group statistically analyzed by gray scanning, **P<0.01; (B) The expression of the M1-type macrophage markers CD86 and iNOS and the M2-type macrophage markers CD163 and IL-10 in the M0-NC and M0-AFP groups analyzed by Western blotting; the right column diagram displays the expression levels of proteins in each group statistically analyzed by gray scanning, **P<0.01. (C) The expression of the M1-type macrophage marker CD86 and the M2-type macrophage marker CD206 in macrophages of the M0-NC and M0-AFP groups was detected by flow cytometry; the right column diagram displays statistical analysis of the expression of CD86 and CD206 in each group (NS: P >0.05). The results of the at least three independent experiments are shown. v-NC: negative control lentivirus vectors; v-AFP: AFP-expressing lentivirus vectors.*
Changes in the M1 and M2 macrophages phenotype markers after treatment with Ly294002 were detected using flow cytometry. After PMA was used to induce THP-1 cells into macrophages in the M0-NC and M0-AFP groups, the PI3K/Akt pathway inhibitor Ly294002 was added and incubated for 24 h. Flow cytometry was used to detect the expression of the M1-type macrophage phenotype marker CD86 and the M2-type macrophage phenotype marker CD206. The flow cytometry results showed that the expression rate of the M1-type macrophages phenotype marker CD86 in the M0-NC group was 31.36 ± $3.29\%$, and there was no significant difference between the M0-NC and M0-AFP groups (30.85 ± $2.88\%$). The expression rate of the M2-type macrophage phenotype marker CD206 in the M0-NC group was 8.19 ± $2.48\%$, which was not significantly different from that in the M0-AFP group (10.60 ± $1.95\%$) (Figure 3C). The above results showed that after blocking the PI3K/Akt signaling pathway with Ly294002, there was no significant difference in the expression levels of CD86 and CD206 in the macrophages of the M0-AFP and M0-NC groups ($P \leq 0.05$). These results indicated that the effect of AFP on the macrophage polarized phenotype was inhibited after blocking the PI3K/Akt signaling pathway, which proves that AFP promotes macrophage polarization toward the M2-type phenotype by activating the PI3K/Akt signaling pathway.
## Overexpression of AFP in macrophages was able to enhance the migratory ability of macrophages and inhibit apoptosis of HCC cells.
To detect the effect of AFP on the migratory ability of macrophages, the M0, M0-NC, and M0-AFP groups of macrophages were subjected to a 72 h migration assay in a Transwell chamber (pore size:8 μm). The results showed that the migratory numbers of macrophages in the M0-AFP group of macrophages were higher than those in the M0 and M0-NC groups (Figure 4A), indicating that AFP promoted the migratory ability of macrophages. The M0-NC and M0-AFP groups of macrophages were co-cultured with Bel7402 and HepG2 cells without contact in a Transwell chamber (pore size:0.4 μm) for 48 h, and apoptosis of HCC cells was detected by flow cytometry. The experimental results showed that the apoptosis rate of Bel7402 cells co-cultured with M0-AFP cells was 2.94 ± $0.48\%$, which was lower than that of Bel7402 cells co-cultured with M0-NC cells (8.39 ± $0.55\%$) (Figure 4B). The apoptosis rate of HepG2 cells co-cultured with M0-AFP group was 3.10 ± $0.33\%$, which was lower than that of HepG2 cells co-cultured with M0-NC group (8.24 ± $0.25\%$) (Figure 4B). These results indicated that AFP-overexpressing macrophages in the co-culture system could inhibit the apoptosis of HCC cells.
**Figure 4:** *Effect of AFP on the migration of macrophages and apoptosis of HCC cells. M0 macrophages were infected with AFP-expressing negative control lentivirus vectors(M0-NC) or AFP-expressing lentivirus vectors(M0-AFP) for 48 h and then treated with LPS+IFN-γ for 24 h to induce M0 macrophage polarize into the M1-like phenotype. (A) The number of cells crossing the Transwell chamber membrane in each group was observed by inverted microscope after 72 h, and 5 fields were randomly selected to calculate the mean value of cells crossing the membrane; the right column diagram displays the statistical analysis of the number of invaded cells in each group, **P<0.01. (B) Bel7402 and HepG2 hepatoma cells were co-cultured with M0-NC or M0-AFP, and the effect of macrophages on apoptosis of HCC cells was detected by flow cytometry; the right column diagram displays the statistical analysis of the apoptosis rate of liver cancer cells, **P<0.01. The images are from the at least three independent experiments.*
## Macrophage-derived AFP inhibits macrophages to phagocytize polystyrene latex beads by activating the PI3K/Akt pathway
The effects of AFP overexpression on macrophages engulfing polystyrene latex beads were observed using laser confocal microscopy. To study the effect of AFP on the phagocytosis of macrophages, the actin-Tracker Green-stained M0-NC and M0-AFP groups engulf carboxylate-modified polystyrene latex beads with a diameter of 0.5 μm (red fluorescence) was observed by laser confocal microscopy. The cell nucleus were stained with DAPI (blue fluorescence) staining (Figure 5A). The results showed that macrophages engulfed polystyrene latex beads were mainly located at the irregular membrane edge of the macrophage processes, and a few were located around the nucleus. In M0, M0-NC, and M0-AFP groups, macrophages engulfed significantly more polystyrene latex beads at 6 h than at 3 h. At the same phagocytosis time, macrophages engulfed the number of polystyrene latex beads in the M0 and M0-NC groups was higher than that in the M0-AFP group.
**Figure 5:** *The effects of AFP and Ly294002 (PI3K inhibitor) on macrophages enggulfing polystyrene latex beads. M0 macrophages were infected with an AFP-expressing negative control lentivirus vector(M0-NC) or AFP-expressing lentivirus vector(M0-AFP) for 48 h and then treated with LPS+IFN-γ for 24 h to induce M0 macrophage polarization into M1-like phenotype. (A) Macrophages engulfed polystyrene latex beads in the M0, M0-NC and M0-AFP groups at 3 h and 6 h were observed by laser confocal microscopy; the fluorescence intensity represents the numbers of phagocytoses. Blue: cell nucleus(DAPI stained); Green: cytoplasm(5-(and -6)-carboxyfluorescein diacetate succinimidyl ester(CFSE) incorporation of the intracellular fluorescent dye); Red: polystyrene latex beads. (B) Macrophages engulfed the numbers of polystyrene latex beads in the M0, M0-NC and M0 AFP groups at 3 h and 6 h, detected by flow cytometry; the lower column diagram shows the statistical analysis of the phagocytosis rate of each group, **P<0.01. (C) After treatment with the PI3K inhibitor Ly294002 for 24 h, macrophages engulfed the numbers of polystyrene latex beads in the M0, M0-NC and M0-AFP groups at 3 and 6 h were detected by flow cytometry; the low column diagram shows the statistical analysis of the phagocytosis rate of each group, P>0.05. The images are from the at least three independent experiments.*
The effect of AFP overexpression on the macrophages engulfing polystyrene latex beads was detected by flow cytometry. To further verify the effect of AFP on the phagocytic ability of macrophages to engulf polystyrene latex beads, flow cytometry was used to detect the fluorescence intensity after M0, M0-NC, and M0-AFP macrophages engulfed polystyrene latex beads in vivo. The fluorescence intensity indicates the relative numbers of polystyrene latex beads in the macrophages; the stronger fluorescence intensity of the polystyrene latex beads, the larger the quantity. The results of flow cytometry showed that the macrophages phagocytosis rates at 3 h and 6 h in the M0-AFP group were 32.75 ± $3.39\%$ and 53.38 ± $2.28\%$, respectively; in the M0 group, they were 43.14 ± $3.12\%$ and 66.59 ± $3.36\%$, respectively; in the M0-NC group, they were 44.31 ± $2.98\%$ and 67.89 ± $3.47\%$, respectively. The experimental results showed phagocytosis of macrophages in each group at 3 h and 6 h, and the numbers of polystyrene latex beads in macrophages of the M0-AFP group was significantly lower than that in macrophages of the M0 and M0-NC groups (Figure 5B), indicating that the phagocytic ability of macrophages engulfed with polystyrene latex beads was decreased in macrophages which overexpressing AFP.
After PMA was used to induce THP-1 to become macrophages in the M0-NC and M0-AFP groups, the cells were treated with the PI3K/Akt pathway inhibitor Ly294002 for 24 h. Macrophages engulfed polystyrene latex beads in the M0-NC, M0-NC, and M0-AFP groups were detected by flow cytometry at 3 and 6 h. The results showed that the macrophages phagocytosis rate in M0 groups were 40.39 ± $3.2\%$ and 65.63 ± $4.15\%$ at 3 and 6 h, respectively, and the macrophages phagocytosis rates in the M0-NC group were 41.94 ± $2.82\%$ and 67.61 ± $3.37\%$ at 3 and 6 h, respectively. The macrophages phagocytosis rates in the M0-AFP group were 40.20 ± $3.04\%$ and 62.11 ± $2.4\%$ at 3 h and 6 h, respectively (Figure 5C). Statistical analysis of the macrophages phagocytosis rates in each group showed no significant differences ($P \leq 0.05$). Compared to that without Ly294002 treatment, the macrophage phagocytosis rate in the M0 and M0-NC groups did not change significantly, but the phagocytic ability of the M0-AFP group was enhanced, indicating that AFP could activate the PI3K/Akt signaling pathway to inhibit the phagocytic ability of macrophages to engulf polystyrene latex beads.
## Inhibition of AFP expression could enhance the phagocytic effect of macrophages on HCC cells
M1 macrophages can kill tumor cells and inhibit the growth of tumor cells through phagocytosis and Th1 response. To study the phagocytosis of M1 macrophages on HCC cells in the process of contact and co-culture with Bel7402-shAFP and Bel7402-shNC HCC cells, five different visual fields were randomly selected for the M1 macrophages co-cultured with Bel7402-shAFP group, and M1 macrophages co-cultured with Bel7402-shNC group using an intelligent living cell high-throughput imaging analyzer to conduct dynamic tracking and imaging. The morphology and position of M1 macrophages changed continuously during the contact and co-culture of the two cell types. Contact with Bel7402-shAFP cells, the morphology of M1 macrophages changed, the cell membrane invaginated, and Bel7402-shAFP cells were gradually phagocytosed and lysed (Figures 6A, B). When exposed to Bel7402-shNC cells, M1 macrophages only swam around the edges of Bel7402-shNC cells, without phagocytosis (Figures 6C, D). The dynamic video of M1 macrophage phagocytize HCC cells showed in attachment (Supplementary Videos 1, 2). These results indicated that the number of M1 macrophages that phagocytosed Bel7402-shAFP cells in the co-culture process was higher than that in the Bel7402-shNC group, proving that AFP expression in HCC cells may inhibit the phagocytic ability of macrophages.
**Figure 6:** *The effect of AFP on macrophages phagocytize HCC cells. M0 macrophages were administered LPS+IFN-γ for 24 h to induce M0 macrophage polarization into the M1-like phenotype. (A) A bright field channel was used to photograph the M1 macrophages co-cultured with Bel7402-shNC group. (B) A bright field channel was used to photograph the M1 macrophages co-cultured with Bel7402-shAFP group; (C) Bel7402-shNC cells were photographed using the RFP channel; (D), Bel7402-shAFP cells were photographed using the RFP channel. The macrophages phagocytized HCC cells was observed by an intelligent living-cell high-throughput imaging analyzer, and images were taken every 30 min for 24 h. The low column diagram shows statistical analysis of the numbers of phagocytic cells in 5 randomly selected fields, **P<0.01. The images are from the at least three independent experiments. Red: Bel7402 cells; gray: THP-1 derived M1-type macrophages.*
## Tumor-derived AFP(tAFP) inhibited macrophages to phagocytize polystyrene latex beads or HCC cells
In order to observe the effect of AFP on macrophages phagocytizing polystyrene latex beads or HCC cells, tumor-derived AFP(tAFP) from liver cancer loading patient was purified, and healthy human(donor) monocytes were collected. First, we observed the influence of tAFP in THP-1 derived M1-like macrophages phagocytizing polystyrene latex beads or liver cancer cells(HLE(non-AFP expressed line)), the results indicated that when treated with tAFP(final concentration 20μg/mL), the THP-1 derived M1-like macrophages phagocytized polystyrene latex beads or HLE cells was significantly decreased (Figures 7A, B). Second, monocytes from healthy donors were collected and treated with PMA for 24 h, followed by stimulation with LPS+IFN-γ or IL-4+IL-13 for 24 h, Monocytes were then induced to polarize into M0, M1, and M2(shown in Supplementary materials: S-Figure 2); then, the polarized monocytes were co-cultured with polystyrene latex beads or HLE cells. The results showed that tAFP was able to significantly inhibit the monocytes derived M1-like macrophages to phagocytize polystyrene latex beads or HLE cells (Figures 7C, D); Moreover, an intelligent living-cell high-throughput imaging analyzer was used in this study to dynamically track observe the effect of tAFP on M1-like macrophages phagocytizing HLE cells, the dynamic video of M1-like macrophages phagocytize HLE cells shows in attachment (Supplementary Videos 3, 4). These results further proved that tAFP could significantly inhibit M1-like macrophages to phagocytize HCC cells.
**Figure 7:** *The effect of tAFP on macrophages phagocytizing polystyrene latex beads or HCC cells. Monocytes from health donors were treated with PAM to induce the polarization into M0 macrophage, then M0 macrophages were administered with LPS+IFN-γ or IL-4+IL-13 for 24 h to induce M0 macrophage polarize towards M1-like phenotype or M2-like phenotype(showed in
Supplementary materials: S-Figure 2
). The THP-1 derived M1-like macrophage(gray) or health monocytes derived macrophages(gray) co-cultured with polystyrene latex beads(red) or HLE cells(red), then treated with tAFP(final concentration 20μg/mL). (A) THP-1 derived M1-like macrophage co-culture with polystyrene latex beads and treated with tAFP, the images of macrophages phagocytize polystyrene latex beads were taken by laser laser confocal microscopy after co-culture for 3h or 6h. Blue: cell nucleus(DAPI stained); Green: cytoplasm(CFSE incorporation of the intracellular fluorescent dye); Red: polystyrene latex beads. (B) THP-1 derived M1-like macrophages co-cultured with HLE cells and treated with tAFP, the macrophages phagocytosis was observed by an intelligent living-cell high-throughput imaging analyzer, the bright field channel was used to photograph, and images were taken every 30 min for 24 h. (C) Health monocytes-derived macrophage co-cultured with polystyrene latex beads and treated with tAFP, the images of macrophages phagocytize polystyrene latex beads were taken by laser laser confocal microscopy after co-culture for 3h or 6h. (D) Health monocytes derived M1-like macrophages co-cultured with HLE cells, then treated with tAFP, the M1-like macrophages phagocytosis was observed by an intelligent living-cell high-throughput imaging analyzer, the bright field channel was used to photograph, and images were taken every 30 min for 24 h. The images are from the at least three independent experiments.*
## Discussion
In early tumor microenvironment (TME) tissues, M1 macrophages, which inhibit angiogenesis and activate tumor immunity, are the main macrophage type. After tumor progression, the proportion of M2-type macrophages becomes the dominant type of infiltration in the TME, and M2-type macrophages can promote angiogenesis and inhibit immune response. In the early stages of tumor metastasis, macrophages are recruited from distant organs and improve the tissue microenvironment by secreting cytokines, thus providing a suitable microenvironment for tumor survival and distant metastasis [24]. Owing to the plasticity of macrophages, the transformation of tumor-promoting M2 macrophages into tumor-killing M1 macrophages is closely associated with the prognosis of patients with liver cancer. Recently, Mantovani A, et al. [ 33] has suggested that macrophages could be used as an effective tool for targeted cancer therapy. As the first line of defense against exogenous invading pathogens, macrophages are an important bridge between the innate and adaptive immunity. Macrophages can recognize and phagocytose invading pathogens or cell debris in the form of fixed or free cells by expressing several receptors and secreting various bioactive substances. Macrophages can activate lymphocytes and other immune cells to kill pathogens [34, 35]. Phagocytic receptors, such as Fc receptors(FcRs), exist on the membranes of macrophages and participate in phagocytic processes, they can be roughly divided into diverse types [36, 37]. The study of the anti-tumor effects of macrophages has become a new focus in the exploration of tumor immunotherapy.
The phosphatidylinositol 3-kinase (PI3K)/Akt pathway has been reported to regulate cell growth, proliferation, differentiation, and metabolism, and its abnormal activation plays an important role in the occurrence and development of cancers, cardiovascular diseases, and diabetes (38–40). PI3K is a member of the intracellular lipid kinase family and can be divided into three classes according to its substrate specificity. Among them, class I, consisting of the catalytic subunit p110 (α,β,γ) and regulatory subunit P85, can be activated by signaling factors that promote the phosphorylation of PIP2 to PIP3 [41]. PIP3 recruits Akt to bind to its PH domain with high affinity, inducing the phosphorylation of serine/threonine residues (Thr308/Ser473). Fully activated Akt can activate downstream signaling molecules to regulate cell function [42]. The PI3K/Akt signaling pathway also plays an important role in the phenotypic polarization of macrophages, and activation of the PI3K/Akt pathway promotes the polarization of M2-type macrophages (43–45). As the most significant effector of PI3K, Akt comprises serine/threonine protein kinases (Akt1, Akt2, and Akt3) [46, 47]. Knockdown of Akt1 can upregulate the expression of miRNA-155 and induce macrophage polarization toward the M1 type. Knockdown of Akt2 can upregulate the expression of miRNA-146a and induce polarization of macrophages into the M2 type [48, 49]. THP-1 cells were isolated from the peripheral blood of a one-year-old boy with acute myelocytic leukemia(AML). THP-1 cells are convenient for stable subculture and cryopreservation in vitro. THP-1 cells are similar to human monocytes in terms of cell morphology, membrane surface antigens, and secreted cytokines; therefore, THP-1 cells are widely used in the study of related mechanisms, drug functions, and signaling pathways in monocytes and macrophages [50, 51]. Therefore, THP-1 cells were used in the subsequent experiments. Lipopolysaccharide(LPS)+IFN-γ and IL-4+IL-13 were used according to the classic induction method, and the appropriate concentration was found to stimulate macrophages to polarize into M1- or M2- type [25, 52]. THP-1 cells, stimulated by the differentiation inducer phorophester(PMA), activate the protein kinase C(PKC) pathway, stop proliferating, gradually turn from suspended cells to adherent cells, extend pseudopodia, generate phagocytic vesicles, and become macrophages with irregular nuclei [53]. AFP is used as a specific index for the screening and diagnosis of primary liver cancer and has many important biological functions, including as a growth regulating factor with a two-way adjustment function, regulating the expression of oncogenes, promoting the growth and proliferation of tumor cells, invasion and metastasis of autophagy, inhibiting tumor cell apoptosis, suppressing the activation of T cells and DC cells, inducing apoptosis of these cells, and thus escaping immune surveillance, further causing malignant cancer transformation (54–56). At present, the relationship between AFP and macrophage polarization, and the influence of AFP on the TME has rarely been reported. To investigate whether AFP could affect the phenotype and phagocytic effect of macrophages, we established a THP-1 cell line stably overexpressing AFP by constructing an AFP-expressing lentiviral vector and stimulated it with PMA to polarize into M0 macrophages. After stable AFP expression in M0 macrophages, THP-1 cells were treated with IFN-γ+LPS or IL-4+IL-13. Flow cytometry and Western blotting were used to detect the expression of M1-type macrophage- and M2-type macrophage-related markers in M0 macrophages stably expressing AFP and to explore the relationship between changes in the macrophage phenotype and the PI3K/Akt signaling pathway. The effects of AFP overexpression on the migratory ability of macrophages and apoptosis rate of HCC cells in the co-cultured system were evaluated. The phagocytic function of macrophages is key to the body’s defense, immunity maintenance, and tissue homeostasis. To investigate whether AFP could inhibit the phagocytic ability of macrophages, we studied the effect of AFP overexpression on the phagocytic ability of macrophages. Macrophages phagocytize tumor cells is one of the anti-tumor mechanisms of macrophages, and M1 macrophages play an important role in the antigen presenting and phagocytizing tumor cells. In the present study, co-cultured M1 macrophages with Bel7402-shNC and Bel7402-shAFP cells, and an intelligent living-cell high-throughput imaging analyzer was used to dynamically track and image AFP-expressing cells to observe phagocytosis by macrophages. In-depth exploration of the relationship between AFP and phagocytosis of macrophages, and targeted intervention of AFP could provide new therapeutic hope for patients with liver cancer.
At present, the efficacy of surgical resection, transcatheter arterial chemoembolization, and radiofrequency ablation in the treatment of liver cancer is not satisfactory, and targeted therapy for liver cancer is an urgent problem to be solved [57, 58]. With the development of tumor immunology, new immunotherapies have become a hot and challenging topic in basic cancer research. Immune checkpoint inhibitors targeting programmed death receptor-1(PD-1), programmed cell death ligand-1(PD-L1), cytotoxic T-lymphocyte-associated antigen-4 (CTLA-4), and chimeric antigen receptor T-cell immunotherapy(CAR-T) have been developed. Emerging immunotherapies such as these have been approved by the FDA and have entered the clinical application stage, they have been proven to have remarkable efficacy in the treatment of some advanced malignant tumors, and can prolong the overall survival of tumor patients compared with traditional treatment methods [59, 60]. However, despite bringing new hope to cancer treatment, these immunotherapies still have certain limitations. Therefore, it is necessary to identify new therapeutic targets for immune intervention to develop more effective clinical immunotherapies for liver cancer [61, 62].
To investigate whether AFP could affect the macrophage phenotype, we constructed THP-1 cell lines stably expressing AFP and stimulated them with PMA to induce polarization into M0-type macrophages. Flow cytometry and Western blotting analysis showed that the M2-type macrophage markers CD163, CD206, and IL-10 were significantly expressed in AFP-overexpressing M0 macrophages, whereas the M1-type macrophage markers iNOS and CD86 were expressed at lower levels. It has been proven that AFP could promote macrophage polarization from M0 to M2. Evidence has been shown that the PI3K/Akt signaling pathway can regulate the phenotypic polarization of macrophages and thus affect the occurrence, development, and prognosis of tumors [63]. In this study, we found that the effect of AFP on the macrophage phenotype was inhibited after treatment with Ly294002, an inhibitor of the PI3K/Akt signaling pathway, indicating that AFP promotes macrophage polarization toward the M2-type by activating the PI3K/Akt signaling pathway.
AFP, a G protein-coupled receptor (GPCRs) agonist, can regulate cell migration and invasion. Human recombinant AFP protein can increase the mRNA and protein levels of matrix metalloproteinase 9(MMP9), thereby enhancing the invasive ability of THP-1 cells in a concentration-dependent manner [26]. To explore whether AFP could affect the migratory ability of THP-1-derived macrophages, we conducted Transwell chamber analyses and found that the migratory ability of M0 macrophages which overexpressing AFP was stronger than that of the control group, indicating that AFP overexpression could also enhance the migratory ability of THP-1-derived macrophages. M0 macrophages overexpressing AFP and macrophages in the control group were co-cultured with Bel7402 and HepG2 cells in a Transwell chamber (pore size:0.4 μm) without contact. Flow cytometry results showed that co-cultured with AFP-overexpressing macrophages reduced the apoptosis rate of Bel7402 and HepG2 cells. In this study, we found that AFP could promote the transformation of M0 macrophages into M2 macrophages. Therefore, it is speculated that the decreased apoptosis rate of HCC cells may be related to the secretion of pro-tumor cell growth factors by macrophages.
It has been reported that purified human recombinant AFP protein could inhibit the phagocytic ability of macrophages in chicken red blood cells by binding to mouse peritoneal macrophages, and this inhibition could be relieved to varying degrees after AFP removal [64]. In this study, laser confocal microscopy and flow cytometry analysis indicated that the phagocytic ability of M0 macrophages which overexpressing AFP for polystyrene latex beads was significantly lower than that of the control group, indicating that AFP could inhibit the phagocytic ability of macrophages for polystyrene latex beads. Signal regulatory protein α (SIRPα) on the surface of macrophages can help macrophages recognize “myself” and “non-me” cells, and the surface of tumor cells usually highly express the CD47 molecule. CD47 can bind to SIRPα on the surface of macrophages to convey the “don’t eat me” signal to macrophages, forming the antiphagocytic signaling axis, inhibiting phagocytosis of macrophages on tumor cells [65, 66]. In addition to CD47, the macrophage surface leukocyte immunoglobulin-like receptor B1(LILRB1) protein can specifically recognize major histocompatibility complex I(MHC I) and microglobulin-like β2 (β2M) on the surface of tumor cells, allowing tumor cells to directly escape macrophage phagocytosis [67]. In a tumorigenesis experiment in nude mice, it was found that the survival time of nude mice was prolonged by nearly $70\%$ after inhibiting the expression of MHC I on the surface of cancer cells. When the β-chains of CD47 and MHC I were simultaneously targeted, anti-tumor activity was significantly enhanced compared to that of CD47 or MHC I alone [68]. Phagocytosis of macrophages in tumor cells is an anti-tumor mechanism. To study the differences in the phagocytic ability of macrophages in AFP-expressing HCC cells, we used an intelligent living-cell high-throughput imaging analyzer to contact and co-cultured M1 macrophages with Bel7402-shAFP and Bel7402-shNC cells and conducted dynamic tracking photography. M1 macrophages gradually phagocytized Bel7402-shAFP cells and lysed them by changing the cell morphology and position. When M1 macrophages came into contact with Bel7402-shNC cells, macrophages only swam around the edge of Bel7402-shNC cells without phagocytosis, indicating that AFP expression in HCC cells may inhibit macrophages to phagocytize hepatoma cells.
This study initially explored the effects of exogenous AFP overexpression in THP-1 cells on the macrophage phenotype, migration, and phagocytosis. Previous studies have reported that there is a specific AFP receptor on the cell membrane of THP-1 cells that may be involved in physiological regulation of the immune response [26, 69]. Because immunotherapy of liver cancer is a hot scientific issue in research on the prevention and treatment of liver cancer, and AFP is a highly specific protein expressed by liver cancer cells, the immunosuppressive function of AFP has been widely studied [70], and the immunomodulatory function of AFP in hepatoma cells should be evaluated. To further study the effect of AFP on the phagocytosis of macrophages, in this study, liver cancer-derived AFP(tAFP) was treated in THP-1- derived M1 cells and monocyte-derived macrophages from healthy donors, and the effect of tAFP on macrophages phagocytizing, the phagocytosis of THP-1-derived M1 and monocytes-derived macrophages was observed. The results showed that tAFP not only inhibited THP-1-derived M1 cells phagocytizing polystyrene latex beads and HCC cells, but also inhibited monocytes-derived macrophages phagocytizing HCC cells. These results suggest that tAFP inhibits macrophages phagocytosis. In the future, normal AFP(nAFP) and tAFP proteins will be used to treat THP-1-derived macrophages and monocyte-derived macrophages from healthy donors, and to test the different effect of nAFP and tAFP on phagocytosis of the macrophage phenotype. In the present study, the results indicated that AFP has a novel function in stimulating M0-type macrophages polarization into M2-type macrophages and inhibiting M1-type macrophages to phagocytize HCC cells, implying that AFP plays a key role in anti-inflammation, accelerating HCC cells to escape from the attack of macrophages, and inhibiting adaptive anti-tumor immunity. AFP may be used in immunotherapy for patients with HCC.
AFP promotes polarization of M0 macrophages into M2-type and attenuates macrophages to phagocytize polystyrene latex beads. AFP also inhibited the ability of M1 macrophages to phagocytize HCC cells. The role of AFP in suppressing the phagocytic ability of M1 macrophages involves the activation of the PI3K/Akt signaling pathway. tAFP may be used as a novel biotarget for immunotherapy in HCC patients.
## 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.
## Ethics statement
All experiments were approved by the committee of Hainan Medical College, Haikou, Hainan Province, China
## Author contributions
MZg, KL, QZ, and JX designed the experiments. JL, BL, and M Zg performed experiments. ML and MZu designed and supervised the study and analyzed the data. ML wrote the manuscript. 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.2023.1081572/full#supplementary-material
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|
---
title: The ameliorating effect of withaferin A on high-fat diet-induced non-alcoholic
fatty liver disease by acting as an LXR/FXR dual receptor activator
authors:
- Varsha D. Shiragannavar
- Nirmala G. Sannappa Gowda
- Lakshana D. Puttahanumantharayappa
- Shreyas H. Karunakara
- Smitha Bhat
- Shashanka K. Prasad
- Divya P. Kumar
- Prasanna K. Santhekadur
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC9995434
doi: 10.3389/fphar.2023.1135952
license: CC BY 4.0
---
# The ameliorating effect of withaferin A on high-fat diet-induced non-alcoholic fatty liver disease by acting as an LXR/FXR dual receptor activator
## Abstract
Introduction: Non-alcoholic fatty liver disease (NAFLD) incidence has been rapidly increasing, and it has emerged as one of the major diseases of the modern world. NAFLD constitutes a simple fatty liver to chronic non-alcoholic steatohepatitis (NASH), which often leads to liver fibrosis or cirrhosis, a serious health condition with limited treatment options. Many a time, NAFLD progresses to fatal hepatocellular carcinoma (HCC). Nuclear receptors (NRs), such as liver X receptor-α (LXR-α) and closely associated farnesoid X receptor (FXR), are ligand-inducible transcription factors that regulate various metabolism-associated gene expressions and repression and play a major role in controlling the pathophysiology of the human liver. Withaferin A is a multifaceted and potent natural dietary compound with huge beneficial properties and plays a vital role as an anti-inflammatory molecule.
Methods: In vivo: Swill albino mice were fed with western diet and sugar water (WDSW) for 12, 16, and 20 weeks with suitable controls. Post necropsy, liver enzymes (AST, ALT, and ALP) and lipid profile were measured by commercially available kits using a semi-auto analyzer in serum samples. Liver histology was assessed using H&E and MTS stains to check the inflammation and fibrosis, respectively, using paraffin-embedded sections and mRNA expressions of these markers were measured using qRT-PCR method. TGF-β1 levels in serum samples were quantified by ELISA. In vitro: Steatosis was induced in HepG2 and Huh7 cells using free fatty acids [Sodium Palmitate (SP) and Oleate (OA)]. After induction, the cells were treated with Withaferin A in dose-dependent manner (1, 2.5, and 5 μM, respectively). In vitro steatosis was confirmed by Oil-Red-O staining. Molecular Docking: Studies were conducted using Auto Dock Vina software to check the binding affinity of Withaferin-A to LXR-α and FXR.
Results: We explored the dual receptor-activating nature of Withaferin A using docking studies, which potently improves high-fat diet-induced NAFLD in mice and suppresses diet-induced hepatic inflammation and liver fibrosis via LXR/FXR. Our in vitro studies also indicated that Withaferin A inhibits lipid droplet accumulation in sodium palmitate and oleate-treated HepG2 and Huh7 cells, which may occur through LXR-α and FXR-mediated signaling pathways. Withaferin A is a known inhibitor of NF-κB-mediated inflammation. Intriguingly, both LXR-α and FXR activation inhibits inflammation and fibrosis by negatively regulating NF-κB. Additionally, Withaferin A treatment significantly inhibited TGF-β-induced gene expression, which contributes to reduced hepatic fibrosis.
Discussion: Thus, the LXR/ FXR dual receptor activator Withaferin A improves both NAFLD-associated liver inflammation and fibrosis in mouse models and under in vitro conditions, which makes Withaferin A a possibly potent pharmacological and therapeutic agent for the treatment of diet-induced NAFLD.
## Introduction
Non-alcoholic fatty liver disease (NAFLD) is an intricate malady that starts from steatosis (the accumulation of fats) and develops into non-alcoholic steatohepatitis (NASH) (Younossi et al., 2019). It constitutes early-stage inflammation and late-stage fibrosis, which leads to severe and irreversible terminal-stage hepatic complications like cirrhosis and malignancy of the liver, which is called hepatocellular carcinoma (HCC) (Allen et al., 2018). NAFLD is a significant risk factor for metabolic syndrome, which includes obesity and frequently associated co-maladies like type 2 diabetes mellitus (T2DM), cardiovascular disease (CVD), and other closely associated disorders (Byrne and Targher, 2015). NAFLD is remediable in the preliminary stages and can be addressed through lifestyle changes and medical help (Zeng et al., 2021). A lack of specific symptoms, knowledge, and awareness about the disease among patients makes the early-stage detection of NAFLD challenging (Cleveland et al., 2019; Busetto et al., 2021). In addition, there are few approved and available drugs for NAFLD on the market and they possess many side effects (Takahashi et al., 2015; Bhave and Ho, 2021). Therefore, looking for natural dietary compounds with minimal or no side effects for NAFLD treatment is greatly needed.
The pathogenesis of NAFLD is mainly linked to the excess and abnormal accumulation of free fatty acids (FFAs) in liver cells due to insulin resistance. It elevates the usage and production of FFAs in the liver and sensitizes hepatocytes to oxidative stress, mitochondrial dysfunction, and endoplasmic reticulum stress (ER stress), which further triggers various transcription factors and induces the secretion of TNF-α, TGF-β, MCP1, and the production of other closely associated inflammatory cytokines (Nascè et al., 2022; Akbari et al., 2021). This triggers macrophage recruitment and activates hepatic star-like cells or commonly known stellate cells (HSCs) and results in liver inflammation and fibrosis (Oates et al., 2019). Targeting the inflammation and fibrosis-associated transcription factors and their target genes and cytokines aids in NAFLD treatment.
Cellular nuclear receptors act as potential therapeutic targets for various clinical conditions like type 2 diabetes and NAFLD (Ballestri et al., 2016). Liver X receptor alpha (LXRα, NR1H3) plays a major role in regulating liver diseases (Tanaka et al., 2017). An associate of ligand-activated nuclear receptor-related transcription factors and the bile acid-binding nuclear receptor farnesoid X receptor (FXR) also has a potential function in human metabolism and an ameliorating effect against NAFLD (Ali et al., 2015). Obeticholic acid (OCA), which is a well-known bile acid and accepted drug for NASH, is known to significantly improve liver fibrosis via FXR (Younossi et al., 2022). In our recent study, we showed that withaferin A, which is a multifaceted drug from the ashwagandha plant, has an inhibitory effect on hepatocellular carcinoma via LXR-α activation and inhibits the NF-κB transcription factor (Santhekadur, 2017; Shiragannavar et al., 2021; Shiragannavar et al., 2022).
Our study indicates that withaferin A suppresses high-fat diet-induced metabolic features, NASH, and fibrosis acting as a ligand for both LXR-α and FXR. This study supports that the dual LXR/FXR activating nature of withaferin A could be used in treating human NASH.
## Materials
Withaferin A was purchased from Xenon Biosciences (India), and the chow diet was procured from Adita Biosys Pvt Ltd. The high-fat diet was procured from VRK Nutritional Solutions, India. Glucose and fructose were procured from Sisco Research Laboratories Pvt. Ltd. Commonly used liver function test enzymes, like aspartate transaminases or aspartate aminotransferase (AST), alanine transaminase or alanine aminotransferase (ALT), and alkaline phosphatase (ALP), and lipid molecules like total cholesterol, triglycerides (TG), and high-density lipoproteins (HDL) kits were purchased from Agape Diagnostics Ltd. TRIzol reagent, sodium palmitate, oleate and Oil Red O stain solution were purchased from Sigma Aldrich, St. Louis, Missouri, United States. cDNA synthesis and SYBR green kits were purchased from Thermo Fisher Scientific. A TGF-β1 ELISA kit was purchased from Krishgen Biosystems. HepG2 and Huh7 cells were purchased from NCCS Pune, India. The cell culture media Minimum Essential Medium Eagle (MEM), Ham DMEM/F-12, 1:1 mixture, bovine serum albumin, and hematoxylin were purchased from HiMedia, India. Fetal bovine serum (FBS) and antibiotics like penicillin/streptomycin were purchased from Gibco. 25-Hydroxycholesterol was a gift provided by Dr. Perumal Madan Kumar, CSIR-CFTRI, Mysuru. Taurochenodeoxycholic acid and deoxycholic acid were a gift from Dr. Ramprasad Talahalli, CSIR-CFTRI, Mysuru.
## Diet composition
Male Swiss albino mice (4–6 weeks old) were fed ad libitum sugar water (SW) containing glucose (18.9 g/L) and fructose (23.1 g/L) and a high-fat diet (Western Diet, WD) containing $42\%$ kcal from fat and $0.1\%$ cholesterol. A standard chow diet and normal water were given to the control mice.
## Experimental animals and study design
All animal experiments were conducted following the ethical clearance and approval from the Jagadguru Sri Shivarathreeshwara Academy of Higher Education and the Research Institutional animal ethics committee (JSSAHER/CPT/IAEC/$\frac{019}{2020}$), JSS AHER, Mysore, Karnataka, India. In this study, male Swiss albino mice (weighting 15–20 g) were selected and separated equally into five groups of six animals as follows: Group 1: control, standard chow diet, and normal water (CDNW); Group 2: western diet (high-fat diet) and sugar water (WDSW); Group 3: WDSW with withaferin A up to 12 weeks (treatment of withaferin A from 8 to 12 weeks); Group 4: WDSW with withaferin A up to 16 weeks (treatment of withaferin A from 8 to 16 weeks); and Group 5: WDSW with withaferin A up to 20 weeks (treatment of withaferin A from 8 to 20 weeks). Every 3 days, the treatment groups received withaferin A (1.25 mg/kg body weight, DMSO $0.1\%$), while the control group received DMSO ($0.1\%$) intraperitoneally before the dark cycle of each day. To compare the treatment groups, Group 1—CDNW and Group 2—WDSW mice also received diet specifications, as previously mentioned for a duration of 12, 16, and 20 weeks. Briefly, Group 3 mice [mice that received WDSW (until 12 weeks) + withaferin A treatment for 4 weeks (from the 8th week until the 12th week)] were compared with the mice group that received CDNW and WDSW for 12 weeks. Group 4 mice [mice that received WDSW (16 weeks) + Withaferin A treatment for 8 weeks (from the 8th week until the 16th week)] were compared with the mice group that received CDNW and WDSW for 16 weeks. Group 5 mice [mice that received WDSW (20 weeks) + Withaferin A treatment for 12 weeks (from the 8th week until the 20th week)] were compared with the mice group that received CDNW and WDSW for 20 weeks. Furthermore, images of individual groups of mice that received the WDSW + Withaferin A treatment for 12, 16, and 20 weeks along with their corresponding controls and WDSW groups are depicted in Supplementary Figures S6A–C.
## Serum biochemical measurements
Hepatic function enzymes, such as ALT, AST, and ALP, and the lipid profile comprising triglycerides, cholesterol, and HDL were determined using commercially available kits and is based on the associated manuals (Agappe Diagnostics Ltd.). The traditional GOD-POD method was used to measure the level of serum glucose. LDL was calculated using a simple mathematical formula as follows:LDL = Total cholesterol–HDL–Triglycerides/5
## Histopathological estimation
Mice were sacrificed at the following time intervals (12, 16, and 20 weeks) to collect the liver tissue. The tissue samples were immediately formalin-fixed and stored at room temperature for subsequent processing and embedded by a standard technique in paraffin blocks for future usage. H&E and Trichome Masson’s (TMS) staining were performed to assess inflammation and visualize fibrosis, respectively.
## Quantitative polymerase chain reaction
The liver tissue was preserved at −80 °C. The TRIzol reagent (Thermo Fisher Scientific) was used to extract total RNA from frozen livers. The verso cDNA synthesis kit was used to create cDNA from 1 μg of total RNA. qPCR was performed on a Rotor-Gene Q (Qiagen) PCR system using the SYBR green kit. These qPCR results were expressed as a fold change relative to the control group, and values were normalized to β-actin mRNAs. The primer sequences used in our experiments were as follows: TNF-α forward: 5′-ATGGCCTCCCTCTCATCAGT-3′ TNF-α reverse: 5′-TTTGCTACGACGTGGGCTAC-3′ IL-6 forward: 5′-GTCCTTCCTACCCCAATTTCCA-3′
IL-6 reverse: 5′-TAACGCACTAGGTTTGCCGA-3′ IL-1β forward: 5′-TGCCACCTTTTGACAGTGATG-3′ IL-1β reverse: 5′-AAGGTCCACGGGAAAGACAC-3′ MCP1 forward: 5′-AGGTGTCCCAAAGAAGCTGT-3′
MCP1 reverse: 5′-AAGACCTTAGGGCAGATGCAG-3′ COL1A1 forward: 5′-CGATGGATTCCCGTTCGAGT-3′ COL1A1 reverse: 5′-GCTGTAGGTGAAGCGACTGT-3′ COL3A1 forward: 5′-GAGGAATGGGTGGCTATCCG-3′
COL3A1 reverse: 5′-TTGCGTCCATCAAAGCCTCT-3′ α-SMA forward: 5′-GCCGAGATCTCACCGACTAC-3′ α-SMA reverse: 5′-ATAGGTGGTTTCGTGGATGC-3′ β-actin forward: 5′-TGGATCAGCAAGCAGGAGTATG-3′
β-actin reverse: 5′-GCATTTGCGGTGGACGAT-3′
## ELISA for serum TGF-β1
An ELISA kit (Krishgen Biosystems) was used to quantify TGF-β1 in the plasma from the study animals following the manufacturer’s instructions. The results are presented as pg/mL after sample values were calculated using a standard curve (created by serially diluting known standards).
## Cell culture
Human hepatoma cells, namely, HepG2 and Huh7 cell lines, were cultured in MEM medium and Ham DMEM/F-12, 1:1 mixture, respectively, complemented with $10\%$ penicillin (100 U/mL)/streptomycin (100 mg/mL) antibiotics in a humid incubator with $5\%$ CO2 at 37 °C.
## In vitro steatosis induction
Stock solutions of sodium palmitate (SP) and oleate (OA) (Sigma-Aldrich, United States) were prepared, as previously described (Römer et al., 2021; Cao et al., 2012). Briefly, 100 μM of SP and OA were incubated for 30 min at 50 °C. Later, fatty acids were mixed with BSA in a culture medium (the fatty acid to BSA molar ratio was 4:1). To induce steatosis, HepG2 and Huh7 cells were exposed to SP and OA conjugated with fatty acid-free BSA. After incubation for 24 h, the cells were treated for 24 h with various concentrations of withaferin A (1, 2.5, and 5 μM). Cells used as controls were treated with fatty acid-free media containing ethanol as a vehicle.
## Oil Red O staining
Following treatment, the cells were fixed for 1 h in $10\%$ formalin. The fixative solution was removed and rinsed with PBS once and then treated with $60\%$ isopropanol for 15 s to facilitate the staining of neutral lipids. Cells were then incubated with a 6:4 diluted Oil Red O solution for 1 min and then washed with PBS to remove the excess stain before being counter-stained for 1 min with hematoxylin stain. Then, cells were washed with distilled water to remove excess stains. Pictures of the lipid droplets were taken using an inverted microscope.
## Oil Red O quantification
Oil Red O staining was measured semi-quantitatively after staining with Hematoxylin, washing with dH2O, and additionally washing with $60\%$ isopropanol. The extracted Oil Red O stain was then treated with $100\%$ isopropanol and gentle rocking. Then, the red color absorbance was measured at 492 nm.
## Molecular docking
The binding affinity of withaferin A with the LXR and FXR was assessed using AutoDock Vina (Eberhardt et al., 2021; Trott et al., 2010). The crystal structure of the LXR/FXR complexed with withaferin A was used as the target structure in the docking study. Water molecules were eliminated from the docking study and checked for prior attachment to the ligand (withaferin A) before being removed from the dimensional structure using version 2.4 of the PyMOL tool. Discovery Studio software, which offers near-able binding residues, was used to further visualize the favored possessions from the findings obtained from the docking technique. This verified the selected ligands’ ideal docking postures, and their binding affinities were recorded. Docking poses and score calculations were used to determine the binding affinity of withaferin A as a ligand with LXR and FXR.
## Statistical analysis
The one-way ANOVA (Bonferroni post hoc test) test was used for the data average value calculation of the results and for statistical analysis, where $p \leq 0.05$ was considered significant. The significance of different groups was expressed using mean ± SEM values. * $p \leq 0.05$, **$p \leq 0.01$, and ***$p \leq 0.001$
## Withaferin A inhibited diet-induced obesity
Swiss Albino mice nourished with WDSW showed advancement in weight and adipose tissue mass contrary to mice nourished with a chow diet and regular water (Figures 1A–D). In addition to progression in diet-induced obesity, nourishment with WDSW showed an elevation in blood glucose levels. Another group of mice fed with WDSW was treated with withaferin A for 12–16 weeks, which showed a considerable reduction in the total body weight (including fat mass) compared to WDSW-fed mice. Withaferin A also reduced blood glucose levels in mice with a high-fat diet and sugar water (Figure 1E). Our data are strongly supported by an elegant study from Harvard Medical School, which clearly shows the anti-obesity and anti-diabetic properties of withaferin A through its leptin sensitizing action, and it may also impact the appetite of the animal (Lee et al., 2016).
**FIGURE 1:** *Withaferin A (WA) inhibited diet-induced obesity. (A) Experimental treatment pattern for testing the therapeutic effects of WA in the WDSW-induced NAFLD mouse model. (B) Images represent the reduction in body and adipose tissue weights in a withaferin A-treated WDSW-induced NAFLD mouse model. (C,D) Graphical representation depicting the decrease in body and adipose tissue weights in withaferin A-treated groups at different time intervals compared to the WDSW group. (E) Serum glucose levels. Data are expressed as mean ± SEM for six animals per group.*
## Withaferin A decreased diet-induced liver injury and dyslipidemia
Liver enzymes AST, ALT, and ALP increased in mice fed WDSW contrary to the control group (Figure 2A). The treatment of withaferin A reduced liver enzyme levels in different time courses. Withaferin A effectively lowered the lipid profile parameters in mice fed WDSW (Figure 2B). These data provide preliminary evidence showing the hepatoprotective effect of withaferin A.
**FIGURE 2:** *Withaferin A decreased diet-induced liver injury and dyslipidemia. Serum liver function tests: (A) aspartate aminotransferase (AST), alanine aminotransferase (ALT), and alkaline phosphatase (ALP). Serum lipid profile: (B) triglycerides (TG), total cholesterol (TC), high-density lipoproteins (HDL), and low-density lipoproteins (LDL).*
## Withaferin A ameliorates steatosis and steatohepatitis both in vivo and in vitro
Mice nourished with a chow diet and regular water showed normal liver anatomy and weight (Figures 3A, B). In contrast, mice on WDSW developed major hallmarks of steatohepatitis (grade 3 macrovesicular steatosis, immune cell infiltration, and hepatocellular ballooning along with some microvesicular steatosis), as confirmed by H&E staining (Figure 3C). The withaferin A treatment also inhibited inflammatory markers, such as TNF-α, IL-6, IL-β, and MCP1 expression, in diet-induced obese mice (Figure 3D) compared to the high-fat diet-fed mice in our qPCR data. In vitro steatosis was confirmed by routine and common Oil Red O staining using HepG2 and Huh7 cells (Figures 4A, C). The withaferin A treatment inhibited sodium palmitate- and oleate-induced lipid droplet accumulation in human Huh7 and HepG2 cells, and the graphs depict a quantitative decrease of lipid accumulation in the cells (Figures 4B, D). This experimental evidence strongly shows the anti-steatohepatitis effect of withaferin A.
**FIGURE 3:** *Withaferin A ameliorates steatosis and steatohepatitis both in vivo. (A,B) Representative images and graphs depicting a reduction in fat accumulation in the liver and liver weights in a withaferin A-treated WDSW-induced NAFLD mouse model. (C) Representative liver sections stained with hematoxylin-eosin (H&E) with a scale bar of 50 μM. (D) Relative mRNA expression levels of TNF-α, IL-6, IL-1β, and MCP1 were evaluated in the liver.* **FIGURE 4:** *Withaferin A ameliorates steatosis and steatohepatitis in vitro. (A,C) Representative microphotographs of sodium palmitate and oleate-induced steatotic HepG2 and Huh7 cells treated with withaferin A in a dose-dependent manner. The cells were treated with the test materials for 24 h, and images were taken after ORO staining at 40X magnification. A semi-quantitative analysis of lipid accumulation in the cells (B,D).*
## Withaferin A inhibited diet-induced fibrosis and fibrogenic signaling
Mice fed a regular low-calorie chow diet and normal water revealed normal liver architecture, whereas mice fed with high-calorie WDSW for 16–20 weeks revealed early fibrotic characteristics such as deposition of collagen and activation of TGF-β signaling. The withaferin A treatment inhibited diet-induced liver fibrosis in WDSW-fed mice (Figure 5A). The withaferin A treatment also inhibited TGF-β secretion and its target genes, Collagen 1 and Collagen 3, expression in WDSW-fed mice and was confirmed by ELISA and qPCR data (Figures 5B, C).
**FIGURE 5:** *Withaferin A inhibits diet-induced fibrosis and fibrogenic signaling. (A) Representative liver sections stained with Masson’s trichome stain (MTS) with a scale bar of 50 μM. (B) Secretion of TGF-β1 by the liver in the withaferin A-treated WDSW-induced NAFLD mouse model measured by ELISA. (C) Relative mRNA expression of TGF-β1 target genes: COL1A1, COL3A1, and α-SMA were analyzed using qPCR.*
## Withaferin A revealed a dual LXR/FXR receptor-activating nature
Our study revealed that withaferin A acts as a potent molecular ligand for LXR-α in HCC and inhibits NF-κB target genes (Shiragannavar et al., 2021; Shiragannavar et al., 2022). To determine the possible molecular mechanism of the hepatoprotective nature of withaferin A in the NAFLD model, we conducted a docking study and our results indicated a strong binding of withaferin A to both LXR-α and FXR, which is a known bile acid nuclear receptor (Figures 6A, B). Some of these common genes and signaling pathways are mutually regulated by ligand-dependent activation of both LXR-α and FXR (Ding et al., 2014), (Dong et al., 2019). Both inflammatory and fibrotic signaling is negatively regulated by LXR-α and FXR through their cognate ligands (Rudraiah et al., 2016; Wang et al., 2008). Based on our docking study results and the previously published evidence, we inferred that withaferin A acts as a dual LXR/FXR receptor activator and inhibits diet-induced steatosis, steatohepatitis, and fibrosis. To support our claim, we validated the expression of LXR-α and FXR and their target genes in both in vitro and in vivo NAFLD models treated with withaferin A. Along with withaferin A, we used the LXR-α specific agonist 25-hydroxycholesterol (25HC) and the FXR-specific agonist taurochenodeoxycholate (TCDC) and TGR5 (bile acid membrane receptor)-specific agonist deoxycholic acid (DCA). Our results showed that withaferin A activated both LXR-α and FXR and induced their canonical target genes (ABCA1, ApoE, ABCB11, and ApoCII) (Supplementary Figures S1–S3, S5, S7).
**FIGURE 6:** *Withaferin A acts as a dual LXR/FXR receptor activator. (A,B) 2D and 3D visualization of the protein–ligand interaction of LXR (A) and FXR (B) with withaferin A; the table represents the binding affinity and RMSD values of the LXR/FXR receptor activator docked with withaferin A.*
## Discussion
Obeticholic acid, which is a known agonist for FXR, has lately been under clinical trial for the management of NAFLD (Younossi et al., 2022). The molecular pathways for the therapeutic effects of obeticholic acid in FXR agonism are poorly understood and need to be elucidated. Another nuclear receptor, LXR-α, has a potential role in cholesterol homeostasis, and the ligand-dependent activation of LXR-α has an anti-inflammatory effect through repressing NF-ҡB-mediated signaling (Wu et al., 2009; Endo-Umeda and Makishima, 2019). Our study elucidated that withaferin A suppresses HCC proliferation, migration, and invasion via the activation of LXR-α and negatively regulates NF-ҡB target genes (Shiragannavar et al., 2021; Shiragannavar et al., 2022).
Although FXRs respond to bile acids and LXRs to oxysterol molecules inside the cellular nucleus, the ligand-specific coordinated actions of LXR and FXR activate transcription and modulate the expression profiles of several genes (Ding et al., 2014). In particular, genes are responsible for cholesterol, lipid, bile acid, and carbohydrate metabolism and control overall liver function (Dong et al., 2019). Along with their role in cellular metabolism, LXR and FXR activation also inhibits inflammation and fibrosis-associated gene expression through a transcription repression mechanism (Shiragannavar et al., 2021; Shiragannavar et al., 2022; Wang et al., 2008; An et al., 2020).
T0901317, a known LXRα [NR1H3] and LXRβ [NR1H2] agonist, also stimulates FXR more effectively than natural bile acid (Bonafide FXR ligand) and acts as a dual LXR/FXR agonist (Houck et al., 2004). However, T0901317 molecular action on pathophysiology remains elusive. There are reports that suggest T0901317 inhibits obesity and induces a fatty liver in mice (Gao and Liu, 2013a; Gao and Liu, 2013b). Also, numerous reports have shown the beneficial lipid-lowering effect of ligand-mediated FXR activation (Fang et al., 2015; Laffitte et al., 2003). Inflammation and fibrosis are major hallmarks of NAFLD-associated chronic conditions like HCC, where NF-κB acts as the master regulator of inflammation and inflammatory cytokine production (Elsharkawy and Mann, 2007; Santhekadur et al., 2012; Santhekadur et al., 2014). In addition, both LXR-α and FXR activation negatively regulates the activity of NF-κB (Wu et al., 2009; Shiragannavar et al., 2021; Wang et al., 2008).
Our docking studies clearly show that withaferin A acts as a bona fide ligand for both LXR-α and FXR and may activate both LXR-α and FXR and induce the expression of their target genes in NAFLD as a dual LXR/FXR receptor activator. In this study, we found the anti-obesity effect of withaferin A in diet-induced obesity mouse models and under in vitro steatotic conditions. FXR activation by its agonists promotes the browning of adipose tissue, induces thermogenesis, and reduces diet-induced obesity and insulin resistance (Fang et al., 2015). The activation of LXR improves glucose tolerance and plays an important role in regulating glucose metabolism in the liver and adipose tissue (Laffitte et al., 2003). This supports our data and shows the LXR/FXR dual receptor-activating nature of withaferin A and its therapeutic role in diet-induced obesity.
Overall, withaferin A treatment decreased ALP, AST, and ALT levels in diet-induced Swiss albino mouse serum in a time-dependent fashion in contrast to the WDSW-fed mice. Total cholesterol, non-HDL cholesterol, and circulating triglyceride levels were decreased in withaferin A-treated WDSW-fed mouse serum when compared with WDSW mice. Withaferin A also decreased hepatic triglyceride content in withaferin A-treated WDSW mice liver tissue when compared to WDSW mice (Supplementary Figure S8). This showed that withaferin A decreased diet-induced liver injury and dyslipidemia in Swiss albino mice. A few supporting studies have shown that FXR pharmacological activation prevents liver injury (Peng et al., 2021; Cui et al., 2009). Activation of LXRs also inhibits liver injury (Beyer et al., 2015). These studies strongly support our hypothesis that LXR/FXR dual activation prevents liver injury.
We also found the anti-NASH and anti-fibrotic effects of withaferin A in our diet-induced NAFLD model (Supplementary Figure S4). It is already well-established and known that withaferin A has anti-inflammatory effects and inhibits NF-κB activation (Shiragannavar et al., 2021). The NF-κB activity is negatively regulated by LXR-α (Shiragannavar et al., 2022). Our immunohistochemistry data showed the anti-steatotic, anti-NASH, and anti-fibrotic effects in our diet-induced obese mice. Also, withaferin A inhibited IL-6, TNF-α, IL-1β, MCP1, COL1A1, COL3A1, and α-SMA expression in liver tissue. Additionally, past published studies have shown that LXR activation exerts a potent anti-inflammatory effect in immune cell types, particularly Kupffer cells/macrophages (Beyer et al., 2015). This may be due to the inhibition of MCP1 expression and suppression of Kupffer cell recruitment by withaferin A. Additionally, ligand-dependent LXR activation reduces acute hepatic inflammation, which is mostly mediated by macrophages that are unique to the liver (Kupffer cells) (Endo-Umeda and Makishima, 2019). LXR-deficient mice also revealed acute liver injury, steatohepatitis, and fibrosis due to excess hepatic cholesterol accumulation-mediated inflammation. Ligand-dependent LXR stimulation also suppressed primary stellate cell activation-mediated fibrosis. Additionally, Lxrαβ (−/−) stellate cells showed increased production and secretion of inflammatory mediators. The treatment of conditioned media from these Lxrαβ (−/−) cells to wild-type cells increased fibrogenic signaling and activated fibrosis (Beaven et al., 2011). A recent study demonstrated that the novel non-bile acid EDP-305 serves as a powerful and highly selective FXR agonist and potently inhibits the liver injury and fibrosis caused by a methionine/choline-deficient diet (An et al., 2020). It also inhibits NF-κB activity and suppresses the expression of TNF-α, IL-1β, COL1A2, COL3A1, α-SMA, and CCL2 (Chau et al., 2019). Another FXR agonist and known bile acid, obeticholic acid, protects against hepatic injury and fibrosis in a NASH mouse model (Goto et al., 2018). Withaferin A ameliorates bile duct ligation-induced liver injury and fibrosis by inhibiting NF-κB signaling (Sayed et al., 2019). Additionally, withaferin A therapeutically reduces fibrosis in HFD-treated leptin-deficient ob/ob mice (Sayed et al., 2019). In support of these classic studies, we found that withaferin A treatment inhibited fatty acid synthesis genes, such as sterol regulatory element binding protein 1c (SREBP1c) and fatty acid synthase (FASN), in both in vitro and in vivo NAFLD models (Supplementary Figure S3). It has been reported that the LXR-α agonist 25HC prevents NAFLD through the regulation of known cholesterol metabolism and inflammatory signaling (Wang et al., 2022). Increased levels of cholesterol 25-hydroxylase (Ch25 h) and its enzymatic by-product 25HC in the liver prevent high-fat diet-induced hepatic steatosis. This beneficial mechanism involves the regulation of enterohepatic circulation of bile acids by the induction of the CYP7A1 gene via activation of LXR-α (Dong et al., 2022). Our data show that withaferin A also activates LXR-α and similarly induces its canonical target genes to that of 25HC and mimics and exerts similar effects on NAFLD. Previous studies show that hepatic overexpression of the canonical FXR target gene ABCB11 reduces hepatosteatosis (Figge et al., 2004). Ligand-mediated FXR activation also ameliorates NAFLD mainly through a bile acid-dependent mechanism (Clifford et al., 2021). Interestingly, even in our study, the withaferin A treatment induced ABCB11 expression via FXR and mimicked the known FXR agonist taurochenodeoxycholate (TCDC) but not the TGR5-specific agonist deoxycholic acid (DCA) (Supplementary Figure S5). These experimental validations of withaferin A along with LXR-α- and FXR-specific agonists and previously published studies add strong support to our work. Based on our research, we propose a possible molecular mechanism involving the dual receptor-activating nature of withaferin A on LXR/FXR activation that ameliorates high-calorie diet-induced NAFLD (Figure 7).
**FIGURE 7:** *Schematic representation of the inhibiting effect of withaferin A on high-fat diet (Western diet sugar water)-induced fibrosis by acting as an LXR/FXR dual receptor activator. This figure was created in part using Servier Medical Art, which is licensed under a Creative Commons Attribution 3.0 Unported license.*
In summary, we show that withaferin A not only activates LXR-α but also stimulates FXR, which is another similar nuclear receptor. Therefore, it exerts dual LXR and FXR ligand properties and confers protective effects against diet-induced animal models of obesity and NAFLD. These beneficial properties of withaferin A appear to be the consequences of decreased adipose tissue mass and glucose levels along with the hepatoprotective nature and anti-inflammatory and anti-fibrotic properties. Our data strongly suggest that the dual LXR/FXR ligand withaferin A might be used in the treatment of NAFLD and associated maladies.
## Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, and further inquiries can be directed to the corresponding author.
## Ethics statement
The animal study was reviewed and approved by the JSS AHER Institutional Animal Ethics Committee.
## Author contributions
VS performed the experiments, analyzed the data, and drafted the manuscript. NS, LP, and SK helped during in vivo and in vitro experiments. SB and SP aided in the docking studies. DK provided intellectual inputs for the manuscript. PS designed the project, provided overall supervision and intellectual guidance, and drafted the manuscript. All authors read and approved the submitted final 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/fphar.2023.1135952/full#supplementary-material
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|
---
title: Protective effect of antihypertensive drugs on the risk of Parkinson’s disease
lacks causal evidence from mendelian randomization
authors:
- Zheng Jiang
- Xiao-Jing Gu
- Wei-Ming Su
- Qing-Qing Duan
- Yan-Lin Ren
- Ju-Rong Li
- Li-Yi Chi
- Yi Wang
- Bei Cao
- Yong-Ping Chen
journal: Frontiers in Pharmacology
year: 2023
pmcid: PMC9995445
doi: 10.3389/fphar.2023.1107248
license: CC BY 4.0
---
# Protective effect of antihypertensive drugs on the risk of Parkinson’s disease lacks causal evidence from mendelian randomization
## Abstract
Background: Evidence from observational studies concerning the causal role of blood pressure (BP) and antihypertensive medications (AHM) on Parkinson’s disease (PD) remains inconclusive. A two-sample Mendelian randomization (MR) study was performed to evaluate the unconfounded association of genetic proxies for BP and first-line AHMs with PD.
Methods: Instrumental variables (IV) from the genome-wide association study (GWAS) for BP traits were used to proxy systolic BP (SBP), diastolic BP, and pulse pressure. SBP-associated variants either located within encoding regions or associated with the expression of AHM targets were selected and then scaled to proxy therapeutic inhibition of angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, β-blockers, calcium channel blockers, and thiazides. Positive control analyses on coronary heart disease (CHD) and stroke were conducted to validate the IV selection. Summary data from GWAS for PD risk and PD age at onset (AAO) were used as outcomes.
Results: In positive control analyses, genetically determined BP traits and AHMs closely mimicked the observed causal effect on CHD and stroke, confirming the validity of IV selection methodology. In primary analyses, although genetic proxies identified by “encoding region-based method” for β-blockers were suggestively associated with a delayed PD AAO (Beta: 0.115; $95\%$ CI: 0.021, 0.208; $$p \leq 1.63$$E-2; per 10-mmHg lower), sensitivity analyses failed to support this association. Additionally, MR analyses found little evidence that genetically predicted BP traits, overall AHM, or other AHMs affected PD risk or AAO.
Conclusion: *Our data* suggest that BP and commonly prescribed AHMs may not have a prominent role in PD etiology.
## Introduction
Parkinson’s disease (PD) is one of the most prevalent neurodegenerative diseases lacking any neuroprotective treatments (Pringsheim et al., 2014), while hypertension ranks among the leading risk factors for all-cause death and disability-adjusted life-years worldwide (GBD 2017 Risk Factor Collaborators, 2018). Since the prevalence of PD and hypertension increases with age, their coexistence in the elderly is not uncommon. Therefore, understanding whether hypertension and antihypertensive medications (AHM) were causal for PD will make the medical decision more reasonable in clinical practice.
The role of hypertension and AHMs in PD has long been debated (Simon et al., 2007; Ton et al., 2007). Summary meta-analyses of epidemiologic studies indicated hypertension might increase the risk for PD (Hou et al., 2018; Chen et al., 2019). Meanwhile, some AHMs, such as calcium channel blockers (CCB), have emerged as prioritized repurposing options for PD prevention (Swart and Hurley, 2016; Katsi et al., 2021). However, considering the limited number of prospective cohort studies, these findings should be cautiously interpreted. Additionally, traditional observational studies are prone to residual confounding and reverse causation (Lawlor et al., 2008) and lack insights into the role of drug targets for specific AHMs.
Mendelian randomization (MR) is an analytical tool proposed to overcome some weaknesses associated with conventional observational studies on estimating the causal effects of risk factors (Davies et al., 2018). In this approach, genetic alleles are randomly assorted during meiosis and thus reducing bias resulting from conventional confounding factors or reverse causality. Meanwhile, analogous to a randomized controlled trial (RCT), the MR method has also been applied to develop a novel indication of the existing drugs by applying randomly assorted variants in the drug target gene (Storm et al., 2021).
Without preventive or disease-modifying interventions (Sardi et al., 2018), prevention strategies targeting modifiable risk factors and repurposing the existing drugs to novel indications are promising for PD. Hence, by using two-sample MR analyses, the aims of this study were to 1) investigate the direct causal link between blood pressure (BP) traits and PD risk and age at onset (AAO) and 2) examine the causal effect of different AHM classes on PD risk and AAO, all of which will benefit for PD in diagnosis, intervention, and prognostic assessment.
## Instrument selection for blood pressure
We extracted single nucleotide polymorphisms (SNP) of BP based on summary statistics in a genome-wide association study (GWAS) meta-analysis of 757,601 individuals from the International Consortium of Blood Pressure database and UK Biobank (Evangelou et al., 2018). In this study, BP traits incorporated systolic BP (SBP), diastolic BP (DBP), and pulse pressure (PP), which have been adjusted for AHM use by adding 15 and 10-mmHg to SBP and DBP, respectively. We restricted the set of SNPs to be significantly associated with the exposure with a p-value reaching genome-wide significance ($p \leq 5$ × 10–8), and the threshold of linkage disequilibrium (LD) was set at R 2 = 0.001 using the 1,000 Genomes European reference panel. In addition, the above study adjusted effect estimates for body mass index (BMI), potentially introducing collider bias as BMI is causal for both elevated BP and PD, so a sensitivity analysis was performed using alternative UK Biobank GWAS summary statistics of SBP ($$n = 436419$$) and DBP ($$n = 436424$$) not adjusted for BMI(Mitchell et al., 2019).
## Instrument selection for antihypertensive medications
Based on recent work by (Gill et al., 2019) and (Walker et al., 2019), we further chose genetic variants as proxies for the SBP lowering effects of first-line drugs for hypertension (Wright et al., 2018), including angiotensin-converting enzyme inhibitors (ACEI), angiotensin receptor blockers (ARB), β-blockers (BB), CCB, and thiazide diuretic agents (Supplementary Table S1).
In the main analyses (Gill et al., 2019), encoding or regulatory regions (promoters and enhancers) of pharmacologically active targets for these drugs were identified through the DrugBank database (Wishart et al., 2018) (http://www.drugbank.ca/; DrugBank V5.1.9) (67 target genes in total, Supplementary Table S1) and the GeneCards online platform (Fishilevich et al., 2017) (https://www.genecards.org/; GenHancer V5.9) (Supplementary Table S2). For all the identified variants in each gene, only variants significantly associated with SBP ($p \leq 5$ × 10–8) and clumped to an LD threshold of R 2 < 0.4 were considered candidate proxies for each drug class (Burgess et al., 2018; Nowak and Arnlov, 2018; Georgakis et al., 2020). More stringent LD thresholds (R 2 < 0.1 and R 2 < 0.001, respectively) were conducted in sensitivity analyses.
In the additional analyses (Walker et al., 2019), cis-expression quantitative trait loci (eQTL) of AHM target genes identified above by DrugBank for each tissue were extracted from the latest GTEx dataset (GTEx Consortium et al., 2017) (https://www.gtexportal.org/home/datasets; release V8, dbGaP Accession phs000424.v8.p2) (67 target genes in 49 tissues, Supplementary Table S3). In the positive control and instrument validation step, we excluded SNPs with a null effect on SBP for each AHM target gene in two-sample MR analyses ($p \leq 0.05$). The selection strategy of LD thresholds was similar to the main analyses.
For all the selected instrumental variables (IV) in this study, F-statistics were above 10, indicating that weak instrumental bias is minimal (Bowden et al., 2016).
## Positive control analysis
Positive control analyses were performed to validate the IV selection in our study. Firstly, for the IVs of blood traits and AHM targets identified by “encoding region-based method”, we examined the association of exposures of interest with coronary heart disease (CHD) and stroke because hypertension is an established risk factor for both CHD and stroke. In addition, for the IVs of AHM targets identified by “eQTL-based method,”, we examined the causative association of exposures of interest with SBP since the BP-lowering effect is the well-proven effect of AHMs.
## Data for outcome
For the main outcome of PD risk, we used the largest and most comprehensive summary statistics data from a meta-analysis GWAS performed by the International Parkinson’s Disease Genomics Consortium, including 33,674 PD cases and 449,056 controls (Nalls et al., 2019). In addition, previous studies indicate that the genetic risk of PD is correlated with PD AAO (Escott-Price et al., 2015; Smolders et al., 2021), so PD AAO was used as the secondary outcome from a GWAS meta-analysis of 28,568 cases (Blauwendraat et al., 2019).
For the positive control outcomes, GWAS summary data for CHD were based on the CARDIoGRAMplusC4D Consortium, which conducted a meta-analysis of 60,801 CHD cases and 123,504 controls (Nikpay et al., 2015). Summary data of stroke were drawn from a recent large-scale meta-analysis of GWAS (MEGASTROKE) confined to European populations of 40,585 cases and 406,111 controls (Malik et al., 2018).
## Primary analysis
Two-sample MR analyses were performed in this study. Three assumptions were established, including that the genetic instruments were associated with the exposure of interest, were independent of potential confounders, and could only affect the outcome through the exposure of interest and not through other pathways. Firstly, we examined the causal effect of BP traits on PD (Figure 1A). Second, we examined the causal effect of overall AHM and different AHMs on PD (Figure 1B). The Wald ratio test was used to calculate the causative effect when a single IV was available, while the multiplicative random effects inverse variance weighted (IVW) method was performed as the main analysis when multiple IVs were available (Lee, 2020). IVW generalized the Wald ratio through a meta-analysis process, and it is the most efficient analysis method with valid IVs because it accounts for heterogeneity in the variant-specific causal estimates (Burgess et al., 2019; Lee, 2020).
**FIGURE 1:** *Flow diagram of the process for the two-sample MR analyses of blood pressure (A) and antihypertensives (B) with Parkinson’s disease. Abbreviations: IV, instrumental variable; SBP, systolic blood pressure; DBP, diastolic blood pressure; PP, pulse pressure; ACEI, angiotensin-converting enzyme inhibitors; ARB, angiotensin receptor blockers; BB, β-blockers; CCB, calcium channel blockers; CHD, coronary heart disease; MR, Mendelian randomization; IVW, inverse variance weighted; LD, linkage disequilibrium; PD, Parkinson’s disease; AAO, age at onset.*
## Sensitivity analysis
Sensitivity analyses accounting for certain violations of the MR assumptions due to different pleiotropy scenarios, including the MR-Egger regression, weighted median, simple mode, and weighted mode methods, were conducted to assess the robustness of the findings (Burgess et al., 2019). Furthermore, we used the MR Egger intercept and Cochran Q statistic to test the presence of directional pleiotropy and heterogeneity, respectively. If outlier IVs were detected using the MR pleiotropy residual sum and outlier (MR-PRESSO) test (Verbanck et al., 2018), the IVW MR analysis was performed again after removing outliers. The leave-one-out analysis was conducted within the IVW method to assess the influence of individual variants on the observed association. To identify specific drug targets driving the causal effect, we also examined the causal effect of each drug target within different AHM classes on PD.
## Statistical analysis
The causal effect of SBP, DBP, and PP on outcomes was scaled to a 10-mmHg increment in BP levels. In contrast, associations of AHMs with outcomes were scaled to a 10-mmHg decrease in SBP to represent the therapeutic inhibition of different AHM classes. The association is considered to be significant after Bonferroni correction for BP traits [$p \leq 0.016$ ($\frac{0.05}{3}$)] and AHMs [$p \leq 0.008$ ($\frac{0.05}{6}$)]. A p-value above $\frac{0.016}{0.008}$ but below 0.05 was considered suggestive of evidence for a potential association. False-discovery rate was used to correct for multiple testing when calculating the effect of a single drug target within different AHMs on PD, and an adjusted p-value of IVW or Wald ratio less than 0.05 is considered to be significant. The main statistical analyses were performed using ‘TwoSampleMR’ (V.0.5.6) in the R package (V.4.1.3) (Hemani et al., 2018).
## Positive control analyses
As shown in Figure 2A, genetically elevated SBP, DBP, and PP were all positively associated with the risk of CHD and stroke (per 10-mmHg increment, all p values <0.016), consistent with previous evidence (Georgakis et al., 2020; Wan et al., 2021). When looking into AHMs (Figure 2B), overall AHM, BB, CCB, and Thiazides were inversely associated with the risk of CHD (Gill et al., 2019) (per 10-mmHg lower, all p values <0.008) except for ACEI. Besides, overall AHM, ACEI, BB, CCB, and Thiazides were associated with a reduced risk of stroke (per 10-mmHg lower, all p values <0.05). In summary, positive control analyses confirmed the validity of the predefined IV selection methodology.
**FIGURE 2:** *Positive control analyses investigating the effects of blood pressure (A) and antihypertensive medications (B) on coronary heart disease and stroke. The linkage disequilibrium thresholds of R
2
were set as 0.001 for BP and 0.4 for AHMs. OR and 95% CIs were scaled to each 10-mmHg increment for BP traits and 10-mmHg lower in SBP for AHMs by ‘encoding region-based method’. p-value less than 0.05 was depicted in bold. Abbreviations: SBP, systolic blood pressure; DBP, diastolic blood pressure; PP, pulse pressure; CHD, coronary heart disease; IVW, inverse variance weighted; AHM, antihypertensive medications; ACEI, angiotensin-converting enzyme inhibitors; BB, β-blockers; CCB, calcium channel blockers; SNP, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval.*
## Genetically determined BP with PD risk (main outcome)
We found no association of genetically elevated SBP, DBP, or PP with PD risk, with p values greater than 0.05 in all MR analyses (per 10-mmHg increment) (Figure 3A and Supplementary Table S4). The effects of SBP and DBP on the risk of PD were also similar using alternative genetic instruments derived from the UK Biobank, which were not adjusted for BMI (Supplementary Table S5).
**FIGURE 3:** *MR analyses between genetically predicted blood pressure and Parkinson’s disease risk (A) and age at onset (B). The linkage disequilibrium threshold of R
2
was set as 0.001. OR/Beta & 95% CI was scaled to each 10-mmHg increment in BP traits. p-value less than 0.05 was depicted in bold. Abbreviations: MR, Mendelian randomization; SBP, systolic blood pressure; DBP, diastolic blood pressure; PP, pulse pressure; PD, Parkinson’s disease; IVW, inverse variance weighted; AAO, age at onset; SNP, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval.*
## Genetically determined BP with PD AAO (secondary outcome)
There was little evidence of an association between either genetically predicted SBP or PP with PD AAO (all p values >0.05, per 10-mmHg increment) (Figure 3B and Supplementary Table S6). Using secondary GWAS statistics of SBP and DBP from the UK Biobank, we get similar results that each 10-mmHg increment in DBP was suggestively associated with a younger PD AAO only by MR Egger method (Beta: −3.734; $95\%$ CI: −6.686, −0.783; $$p \leq 1.38$$E-02) (Supplementary Table S7).
## Genetically therapeutic inhibition of AHM with PD risk (main outcome)
In the main analyses using ‘encoding region-based method’, there was no evidence that reducing SBP affected the risk of PD via the protein targets in overall AHM or different AHM classes (Figure 4 and Supplementary Table S8). When more stringent LD thresholds were set at R 2 < 0.1 and R 2 < 0.001, the results showed consistently null associations with the primary analyses for overall AHM and different AHMs (Supplementary Table S9).
**FIGURE 4:** *MR analyses of genetically predicted antihypertensive medications with Parkinson’s disease risk by “encoding region-based method”. Genetic proxies for AHMs were selected by “encoding region-based method”. The linkage disequilibrium threshold of R
2
was set as 0.4. OR and 95% CI was scaled to each 10-mmHg lower in SBP. p-value less than 0.05 was depicted in bold. Abbreviations: MR, Mendelian randomization; AHM, antihypertensive medications; ACEI, angiotensin-converting enzyme inhibitors; ARB, angiotensin receptor blockers; BB, β-blockers; CCB, calcium channel blockers; PD, Parkinson’s disease; IVW, inverse variance weighted; SNP, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval.*
In the additional analyses using “eQTL-based method”, neither genetic proxies for overall AHM nor those of different AHMs were associated with PD risk (all p values >0.05, Supplementary Figure S1 and Supplementary Table S8). Additionally, MR analyses using more stringent LD thresholds (R 2 < 0.1 and R 2 < 0.001, respectively) remained non-significant for associations of overall AHM and different AHMs with PD risk (all p values >0.05, Supplementary Table S10).
Furthermore, individual target analyses only identified CACNA1H (OR: 1.431; $95\%$ CI: 1.215–1.686; Adjusted $$p \leq 5.37$$E-04; per 10-mmHg lower) by “eQTL-based method” as a significant target that may drive the causal effect of CCB on PD risk (Supplementary Table S11).
## Genetically therapeutic inhibition of AHM with PD AAO (secondary outcome)
In the primary analyses using “encoding region-based method”, although it was suggestive that genetic proxies for BB were associated with a delayed PD AAO (Beta: 0.115; $95\%$ CI: 0.021,0.208; $$p \leq 1.63$$E-2; per 10-mmHg lower; R 2 < 0.4) (Supplementary Figure S2 and Supplementary Table S12), more stringent LD thresholds (R 2 < 0.1 and R 2 < 0.001, respectively) failed to support this association in all MR methods (all p values >0.05, Supplementary Table S13).
In the secondary analyses using ‘eQTL-based method’, the results were suggestive of an association between thiazides and PD AAO only by IVW method (Beta: −0.287; $95\%$ CI: −0.545, −0.030; $$p \leq 2.86$$E-02, Supplementary Figure S3 and Supplementary Table S12). However, restricting LD thresholds to R 2 < 0.1 or R 2 < 0.001 made the results null (Supplementary Table S14).
Analyzing individual targets identified ADRB1 (Beta: 0.192; $95\%$ CI: 0.070, 0.314; adjusted $$p \leq 3.19$$E-02; per 10-mmHg lower) by ‘encoding region-based method’ and KCNH2 (Beta: −0.650; $95\%$ CI: −0.983, −0.318; adjusted $$p \leq 3.64$$E-03; per 10-mmHg lower) by “eQTL-based method” as significant targets that may drive the causal effect of BB on PD AAO (Supplementary Table S15).
## Heterogeneity and horizontal pleiotropy
Although there was limited evidence of heterogeneity and horizontal pleiotropy in MR analyses for BP traits and different AHMs, the MR-PRESSO approach and leave-one-out analyses indicated that our estimates were overall stable (Table 1 and Supplementary Figures S4–S7).
**TABLE 1**
| Exposure | Outcome | SNPs | Heterogeneity | Heterogeneity.1 | Horizontal pleiotropy | MR-PRESSO |
| --- | --- | --- | --- | --- | --- | --- |
| Exposure | Outcome | SNPs | MR Egger | IVW | P | Global test P |
| SBP | PD risk | 447 | 4.4E-04 | 4.9E-04 | 0.825 | 6.0E-04 a |
| DBP | PD risk | 444 | 3.1E-12 | 3.7E-12 | 0.793 | < 1.0E-04 b |
| PP | PD risk | 385 | 0.012 | 0.013 | 0.875 | 0.014 a |
| SBP | PD AAO | 429 | 0.002 | 0.002 | 0.221 | 0.0019 a |
| DBP | PD AAO | 431 | 0.224 | 0.222 | 0.286 | 0.2136 |
| PP | PD AAO | 385 | 0.107 | 0.113 | 0.722 | 0.1156 |
| AHM | PD risk | 101 | 0.139 | 0.116 | 0.123 | 0.117 |
| ACEI | PD risk | 1 | | | | |
| ARB | PD risk | 1 | | | | |
| BB | PD risk | 20 | 0.926 | 0.895 | 0.222 | 0.897 |
| CCB | PD risk | 60 | 0.088 | 0.049 | 0.050 | 0.06 |
| Thiazides | PD risk | 20 | 0.170 | 0.199 | 0.605 | 0.222 |
| AHM | PD AAO | 95 | 0.403 | 0.427 | 0.670 | 0.417 |
| ACEI | PD AAO | 1 | | | | |
| BB | PD AAO | 19 | 0.555 | 0.552 | 0.327 | 0.562 |
| CCB | PD AAO | 58 | 0.518 | 0.555 | 0.924 | 0.579 |
| Thiazides | PD AAO | 17 | 0.308 | 0.343 | 0.522 | 0.342 |
| AHM* | PD risk | 73 | 0.215 | 0.222 | 0.414 | 0.233 |
| ACEI* | PD risk | 2 | | 0.593 | | |
| ARB* | PD risk | 6 | 0.745 | 0.808 | 0.592 | 0.813 |
| BB* | PD risk | 17 | 0.432 | 0.415 | 0.280 | 0.392 |
| CCB* | PD risk | 31 | 0.041 | 0.050 | 0.687 | 0.066 |
| Thiazides* | PD risk | 17 | 0.302 | 0.367 | 0.937 | 0.339 |
| AHM* | PD AAO | 71 | 0.482 | 0.389 | 0.054 | 0.378 |
| ACEI* | PD AAO | 2 | | 0.877 | | |
| ARB* | PD AAO | 6 | 0.490 | 0.537 | 0.462 | 0.578 |
| BB* | PD AAO | 17 | 0.156 | 0.034 | 0.035 | 0.034 b |
| CCB* | PD AAO | 30 | 0.901 | 0.912 | 0.513 | 0.910 |
| Thiazides* | PD AAO | 16 | 0.460 | 0.484 | 0.418 | 0.546 |
## Discussion
To the best of our knowledge, the current study firstly assessed the causal effects of BP and antihypertensive medications on PD using two-sample MR analyses. However, our results failed to support that BP was causally associated with PD risk and AAO. There was also limited evidence that lowering SBP via the targets of first-line antihypertensive drugs affected PD. In summary, our study could help validate the role of BP in the pathogenesis of PD and avoid overestimating the repurposing role of antihypertensive drugs in PD prevention.
Whether hypertension represents a risk factor for PD has not been fully elucidated for a long time, and observational studies in the literature that examined the risk for PD yielded conflicting results. Two studies reported that the risk of PD was not significantly related to high BP(Simon et al., 2007; Tan et al., 2008). In comparison, two other studies suggested that high BP slightly increased the risk of PD (Qiu et al., 2011; Lai et al., 2014), at least in women with arterial hypertension (Qiu et al., 2011). In comparison, another study instead reported that high BP exerted a protective role against the development of PD (Paganini-Hill, 2001). However, all these studies differed in sample size, geographic origin, and duration of the follow-up of the cohort population. Furthermore, concerning the role of antihypertensive drugs in the risk of developing PD, studies showed that among different classes of AHMs, dihydropyridine CCB, but not non-dihydropyridine CCB, may be associated with a reduced risk of PD (Tseng et al., 2021). For β-Adrenoceptor acting agents, a recent meta-analysis of epidemiologic studies suggests that the intake of β-adrenoceptor antagonists, including propranolol and metoprolol, may serve as a risk factor for PD development (Saengphatrachai et al., 2021).
However, PD may have a long prodromal stage up to decades before PD diagnosis, characterized by non-motor dysfunction such as SBP drop, sleep disturbances, and constipation (Postuma et al., 2013; Chen and Ritz, 2018). The prodromal population may be more susceptible to the diagnosis of abnormal BP and an altered tendency to use AHMs, resulting in the inability to accurately define the history of hypertension and AHMs before PD diagnosis in previous cohort studies. Apart from reverse causation, earlier epidemiologic studies could have been subject to unmeasured confounders, such as socioeconomic status, mood disorders, physical activity, and other drug use.
Due to the limitations of observational studies, MR analyses provide an attractive prospect to identify risk or protective factors. Although we provide robust results for the null association of BP and AHMs with PD using primary and secondary MR analyses, we should interpret these results cautiously. In this study, we assumed that the estimated effect of the targets of overall AHM or different AHMs on PD risk or AAO acted through SBP-lowering, but there is potentially an alternative mechanism by which the targets can affect PD. Hence, our null results for overall AHM and common AHM classes do not rule out possible benefits via competing mechanisms by using this drug for PD prevention. Future larger GWAS will be needed to verify that our results did not generate by accident. Furthermore, more precise mapping and mechanistic studies of targets for AHMs will also help elucidate the pathogenesis of PD.
The present study has some strengths. First, positive control analyses were performed to validate the IV selection strategies and confirmed that the approach was appropriate. Additionally, genetic proxies for AHMs were hypothesized to act through the SBP-lowering effect, which would help us understand the antihypertensive role in PD etiology. Thirdly, we included pharmacologically active targets with known biological functions to proxy each AHM class, contributing to better homogeneity of selected IVs. Last, we included AAO as a secondary outcome for PD risk, further supporting the findings from the main outcome.
This study has several limitations. First, MR analysis assumes that the SNPs selected as IVs for BP traits and AHMs influence PD only through the exposure of interest (no pleiotropic effects). Although there did exist some pleiotropic effects for genetic proxies of BP traits, BB, and CCB in our study, multiple methods confirmed the stability of our results. Second, considering the limited genetic proxies identified, the power of MR analyses for ACEI and ARB may be limited. Third, since all of the participants are mainly of European ancestry, the results of this study are not necessarily valid for other ethnic groups.
In conclusion, this study found little evidence that BP and antihypertensive drugs would affect PD risk and AAO. Future studies should consider our research, combined with other sources of evidence, to obtain a reliable answer about the role of hypertension and the potential repurposing of antihypertensives for PD prevention.
## 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
ZJ, X-JG, BC, and Y-PC conceived and designed the study. ZJ, X-JG, W-MS, Q-QD, Y-LR, J-RL, and L-YC extracted the data. ZJ and X-JG contributed to the statistical analysis. ZJ wrote the first draft of the manuscript. ZJ, X-JG, YW, BC, and Y-PC revised and discussed the final edition.
## 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/fphar.2023.1107248/full#supplementary-material
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|
---
title: Association between pre-stroke sarcopenia risk and stroke-associated infection
in older people with acute ischemic stroke
authors:
- Xiaodong Song
- Xufeng Chen
- Jie Bai
- Jun Zhang
journal: Frontiers in Medicine
year: 2023
pmcid: PMC9995446
doi: 10.3389/fmed.2023.1090829
license: CC BY 4.0
---
# Association between pre-stroke sarcopenia risk and stroke-associated infection in older people with acute ischemic stroke
## Abstract
### Background
Stroke-associated infection (SAI) is a common complication after a stroke. The incidence of infection was higher in people with sarcopenia than in the general population. However, the relationship between pre-stroke sarcopenia risk and SAI in older patients has not been confirmed. This study aimed to investigate the association between pre-stroke sarcopenia risk and SAI in older patients with acute ischemic stroke (AIS).
### Methods
This retrospective study was conducted by the Peking University People’s Hospital. We evaluated the pre-stroke sarcopenia risk by applying the SARC-F questionnaire. Multivariate logistic regression was applied to explore the association between pre-stroke sarcopenia risk and SAI.
### Results
A total of 1,002 elder patients with AIS (592 men; 72.9 ± 8.6 years) were enrolled in our study. Pre-stroke sarcopenia risk was found in $29.1\%$ of the cohort. The proportion of patients with pre-stroke sarcopenia risk was larger in the SAI group than in the non-SAI group (43.2 vs. $25.3\%$, $p \leq 0.001$). In multivariate logistic analysis, pre-stroke sarcopenia risk was shown to be independently associated with SAI (OR = 1.454, $95\%$ CI: 1.008–2.097, $$p \leq 0.045$$) after adjusting for potential factors. This association remained consistent across the subgroups based on age, sex, body mass index, smoking status, drinking status, diabetes, hypertension, and dyslipidemia.
### Conclusion
Pre-stroke sarcopenia risk was independently associated with SAI in older patients with AIS. Our findings highlight the significance of pre-stroke sarcopenia identification in the prevention and management of SAI in this population.
## Introduction
Stroke-associated infection (SAI) is a common complication after stroke [1]. The rate of SAI was reported to be $30\%$ ($95\%$ CI: 24–$36\%$), with pneumonia and urinary tract infections (UTI) being the most common [2]. SAI is associated with short-term mortality and adverse neurological functions [3, 4]. It also has long-lasting negative effects on patients’ survival [5].
Sarcopenia was estimated in $15.8\%$ of patients with acute stroke [6]. Notably, pre-stroke sarcopenia is independently associated with severe disability and high mortality rates [7, 8]. In addition, skeletal muscle has been considered an essential regulator of immune system function [9]. Developing sarcopenia increased 3.88 times the risk of community-acquired pneumonia [10]. Moreover, the risk of infection was increased by $49.6\%$ [odds ratio (OR) = 1.496, $95\%$ confidence interval (CI): 1.102–2.031] in patients with diabetes and sarcopenia than in those without [11]. Therefore, it is necessary to pay attention to the risk of SAI in patients with sarcopenia and acute ischemic stroke (AIS).
Muscle mass and quality evaluation occupy an important position in diagnosing sarcopenia [12]. However, acute stroke often led to a sudden disorder of physical function and consciousness. The diagnosis of sarcopenia is difficult to be performed in such a clinical setting. SARC-F is a brief questionnaire for sarcopenia screening and frees researchers from measuring muscle function (13–15). Therefore, we aimed to determine whether the sarcopenia risk defined by SARC-F is a risk factor for SAI in older patients after AIS.
## Patient selection
We retrospectively enrolled the older patients suffering from AIS in the Peking University People’s Hospital from September 2019 to February 2022. The inclusion criteria were as follows: age ≥ 60 years, admission within 24 h of symptom onset, and a neurological deficit symptom with infarction lesion on imaging. The exclusion criteria included the following: [1] intracranial hemorrhage or subarachnoid hemorrhage; [2] cannot complete the SARC-F questionnaire due to dementia, aphasia, and loss of consciousness; [3] active infection within the last 2 weeks; [4] premorbid stroke-related disability; [5] active malignancy; and [6] recent history of trauma or surgery.
## Data collection
Data were abstained at the time of presentation, including age, sex, body mass index (BMI), smoking status, drinking status, stroke etiology, National Institutes of Health Stroke Scale (NIHSS) score, diabetes, hypertension, dyslipidemia, hyperuricemia, coronary artery disease, chronic heart failure, and chronic obstructive pulmonary disease. According to the Trial of Org 10,172 in Acute Stroke Treatment classification, AIS was categorized into five etiologies: large-artery atherosclerosis, cardioembolism, small vessel occlusion, other determined etiologies, and undetermined etiology [16]. Blood samples were taken within 24 h of admission. Laboratory data were collected, including hemoglobin (HB), fast blood glucose, serum creatinine, estimated glomerular filtration rate (eGFR), uric acid, total bilirubin, direct bilirubin, serum album (ALB), alanine aminotransferase, total cholesterol, total glyceride, and D-dimmer.
## Pre-stroke sarcopenia risk assessment
To evaluate pre-stroke sarcopenia risk, patients were asked to complete the SARC-F questionnaire by recalling within 2 days of admission. SARC-F is a simple questionnaire for rapidly identifying people at risk of sarcopenia [13]. It has five items: strength, assistance in walking, rising from a chair, climbing stairs, and falls. Each item will be assigned a score of 0–2 [15], depending on the individual physical performance before stroke onset. The SARC-F score range is 0 to 10. Patients were categorized as having pre-stroke sarcopenia risk if the SARC-F score ≥ 4 [15].
## Outcome definition
Stroke-associated infection was defined as any new infection within the first week of AIS onset [17, 18]. Patients diagnosed with the infection must meet the modified Centers for Disease Control and *Prevention criteria* [19]. SAI was classified into three types, including pneumonia, UTI, and other infection. Pneumonia was suspected when there were relevant clinical symptoms and leukocytosis (>11 × 109/L) and confirmed with an infiltrate on the chest radiograph. UTI was diagnosed according to the urinary tract symptoms and positive microbiological cultures (using midstream urine). Other infection diagnoses were made based on their corresponding diagnostic criteria.
## Statistical analysis
Statistical analyses were conducted using IBM SPSS Statistics version 22.0 (IBM Corp), R project for Statistical Computer version 4.1.2,1 and MedCalc Statistical Software version 20.1.0 (MedCalc Software Ltd). A p-value of <0.05 was defined as statistically significant. The differences between the two groups in continuous variables were evaluated by the t-test or Mann–Whitney U test. The comparisons of categorical variables were performed using Fisher’s exact test and Pearson’s chi-square test. Univariate logistic regression analysis was used to screen for the potential factors associated with SAI. Then, the factors with a value of p of <0.05 were added to stepwise multivariate logistic regression to explore the independent factors of SAI.
## Patient characteristics
A total of 1,002 patients were enrolled in our research cohort after applying the selection criteria (Figure 1). There were 215 ($21.5\%$) patients suffering from SAI during hospitalization, 104 of which were diagnosed with pneumonia, 66 with UTI, 42 with other infections, and 3 with both pneumonia and UTI. Patient’s characteristics classified by pre-stroke sarcopenia risk are shown in Table 1. The sarcopenia risk group was older ($p \leq 0.001$) and had a higher NIHSS score ($p \leq 0.001$) than its counterpart. Patients with sarcopenia risk had a higher level of serum creatinine and lower levels of HB, eGFR, and ALB than those without (all p ≤ 0.05). In addition, the proportion of coronary artery disease was bigger in the sarcopenia risk group ($$p \leq 0.005$$).
**Figure 1:** *Flow diagram of patients’ selection. AIS, acute ischemic stroke.* TABLE_PLACEHOLDER:Table 1
## Association between pre-stroke sarcopenia and SAI
Figure 2 shows the distribution of the SARC-F score before stroke onset in patients. Pre-stroke sarcopenia risk identified by the SARC-F questionnaire (≥4 points) was found in $29.2\%$ of older patients with AIS. The proportion of patients with pre-stroke sarcopenia risk was larger in the SAI group than in the non-SAI group (43.2 vs. $25.3\%$, $p \leq 0.001$). Similarly, the ratios of pre-stroke sarcopenia risk were higher in both patients with pneumonia (41.1 vs. $25.3\%$) and UTI (47.8 vs. $25.3\%$) than those without SAI (all p ≤ 0.05). Patients with other infections had a higher rate of sarcopenia risk compared with those without infection (38.1 vs. $25.3\%$), though the difference was not significant ($$p \leq 0.065$$).
**Figure 2:** *Distribution of SARC-F scores in patients with and without SAI. SAI, stroke-associated infection.*
In univariate logistic regression analysis, the OR of SAI was 2.252 ($95\%$ CI, 1.645–3.084, $p \leq 0.001$) for patients with pre-stroke sarcopenia risk. The association between pre-stroke sarcopenia risk and SAI persisted (OR = 1.454, $95\%$ CI: 1.008–2.097, p ≤0.05) after adjustment for age, BMI, NIHSS score at admission, diabetes, and ALB (Table 2). Subgroup analyses were performed to investigate whether the effect of sarcopenia risk on SAI occurrence was consistent among different patients (Figure 3). Pre-stroke sarcopenia risk did not increase the risk of SAI in patients with NIHSS score > 16 ($$p \leq 0.605$$). However, pre-stroke sarcopenia risk was significantly associated with SAI among diverse groups categorized by age, sex, BMI, smoking status, drinking status, diabetes, hypertension, and dyslipidemia (all $p \leq 0.05$) (Figure 3).
## Discussion
Our research revealed that pre-stroke sarcopenia risk was independently associated with SAI in older patients with AIS. Moreover, the association remained significant in diverse subgroups categorized by clinical characteristics. These results indicate the significance of pre-stroke sarcopenia identification in the prevention and management of SAI in elders.
In Chinese hospitalized elder adults, the prevalence of sarcopenia for men and women was $29.7\%$ ($95\%$ CI 18.4–$41.1\%$) and $23\%$ ($95\%$ CI 17.1–$28.8\%$), respectively [20]. Consistently, pre-stroke sarcopenia risk was found in $29.2\%$ of older patients with AIS in the present study. Sarcopenia is an age-related process occurring in older adults [21]. Hence, it is not surprising that patients with pre-stroke sarcopenia risk were older than those without sarcopenia risk. Additionally, patients with sarcopenia risk had lower levels of ALB and HB compared with their counterparts in our study. The low level of ALB and HB tends to suggest malnutrition, which is an independent risk factor for sarcopenia in the elderly [22].
Age, NIHSS score, diabetes, and ALB were confirmed to be associated with SAI in the previous research [17, 23]. After adjusting for these potential factors, pre-stroke sarcopenia risk was still an independent predictor (OR = 1.454, $95\%$ CI: 1.008–2.097) for SAI in the present study. In line with our results, sarcopenia was shown to be an essential predictor for postoperative infections (24–26). In addition, sarcopenia doubles the risk of nosocomial infection in elder people admitted to acute care after 3 weeks of hospitalization [27]. Skeletal muscle is increasingly accepted as a regulator of the immune system [9]. It can modulate immune functions by secreting myokines, such as IL-7 and IL-15 [9]. IL-7 is considered an essential signal for the survival and expansion of mature T cells [28]. IL-15 plays a key role in the proliferation, activation, and distribution of natural killer cells and CD8 T cells [29]. Moreover, stroke can result in an impairment of the defense mechanisms [1]. Thus, we presumed that sarcopenia might aggravate the impaired immune response to pathogens in older patients with AIS.
Apart from impaired immune function, the decline in muscle quantity and quality might be another non-negligible cause of SAI. The tongue muscle is considered an essential swallowing-related muscle. Sarcopenia is associated with swallowing disorder in older adults [30, 31], partly due to a decrease in the mass of tongue muscles [32]. Moreover, swallowing dysfunction increased the incidence of aspiration in the elderly, leading to airway colonization of Gram-negative bacteria [33]. Thus, sarcopenia may contribute to pneumonia in patients with AIS through the same pathogenesis. Besides pneumonia, UTI ($6.9\%$) was the second most common infection in our cohort. Urinary incontinence rate was elevated in older women with sarcopenia, with the declining function of pelvic muscles being the possible reason [34]. Notably, urinary incontinence may increase the risk of UTI in the older population [35, 36]. Hence, patients with sarcopenia risk were more likely to have UTI in our study.
There are some limitations to our study. First, we eliminated the patients with aphasia, dementia, and consciousness disorder since they could not complete a questionnaire. The selected bias may limit the generalizability of our findings. Second, the pre-sarcopenia risk was diagnosed based on the SARC-F questionnaire. It was considered a screening tool with low sensitivity but high specificity [15]. For this reason, the number of sarcopenia in our cohort may be underestimated. Third, this is a single-center retrospective study with a limited sample size. Multicenter prospective studies are expected to validate the association between pre-stroke sarcopenia risk and SAI in older patients with AIS.
## Conclusion
Pre-stroke sarcopenia risk was independently associated with SAI in older patients with AIS. Our findings highlight the significance of pre-stroke sarcopenia identification in the prevention and management of SAI in older people.
## 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.
## Ethics statement
The studies involving human participants were reviewed and approved by the Ethics Committee of Peking University People’s Hospital. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
JB was involved in the conception and design of the study. JZ contributed to the data analysis and interpretation. XS and XC collected the clinical data and wrote the manuscript. All authors discussed and approved the final manuscript.
## 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.
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---
title: Fine mapping and accurate prediction of complex traits using Bayesian Variable
Selection models applied to biobank-size data
authors:
- Gustavo de los Campos
- Alexander Grueneberg
- Scott Funkhouser
- Paulino Pérez-Rodríguez
- Anirban Samaddar
journal: European Journal of Human Genetics
year: 2022
pmcid: PMC9995454
doi: 10.1038/s41431-022-01135-5
license: CC BY 4.0
---
# Fine mapping and accurate prediction of complex traits using Bayesian Variable Selection models applied to biobank-size data
## Abstract
Modern GWAS studies use an enormous sample size and ultra-high density SNP genotypes. These conditions reduce the mapping resolution of marginal association tests–the method most often used in GWAS. Multi-locus Bayesian Variable Selection (BVS) offers a one-stop solution for powerful and precise mapping of risk variants and polygenic risk score (PRS) prediction. We show (with an extensive simulation) that multi-locus BVS methods can achieve high power with a low false discovery rate and a much better mapping resolution than marginal association tests. We demonstrate the performance of BVS for mapping and PRS prediction using data from blood biomarkers from the UK-Biobank (~300,000 samples and ~5.5 million SNPs). The article is accompanied by open-source R-software that implement the methods used in the study and scales to biobank-sized data.
## Introduction
Genome-Wide Association Studies (GWAS) have reported large numbers of variants associated with many important traits and diseases; however, for complex traits many small-effect risk-loci remain unmapped. In the last decade, several public (e.g., UK-Biobank [1], Million Veteran Program [2], TOPMed, All of Us) and private (e.g., 23andMe®) initiatives have generated unprecedently large biomedical data sets comprising genotype data linked to extensive phenotype/disease data. These advances in data availability have not been fully matched with adequate changes in the analyses-methods used.
Single-marker-regression (SMR) remains the method most frequently used for mapping in GWAS. SMR tests for the marginal association between a phenotype (or a disease indicator) and individual SNPs and does not account for linkage disequilibrium (LD) between variants. Therefore, it can lead to significant associations of phenotypes with SNPs that are physically distant from causal variants–we refer to this phenomenon as poor mapping resolution. Importantly, the mapping resolution of SMR deteriorates with sample size because a large sample size increases the power to detect weak marginal associations between SNPs and phenotypes (Supplementary Data, Section 1). Therefore, for fine mapping, most genetic studies adopt some form of local variable selection approach to refine (SMR) GWAS-peaks to a smaller number of locally independent signals [3, 4]. However, these methods may reduce power due to cancellation of marginal effects (e.g., [5], this could happen if variants have effects with signs opposite to the sign of the covariance of the reference alleles at the two loci) and makes accurate error control challenging.
Bayesian variable selection (BVS) models [6, 7] offer a one-stop solution for fine mapping and Polygenic Risk Score (PRS) prediction, with the clear advantage that Bayesian models can provide accurate error control. However, the adoption of these methods in GWAS remained limited in part because achieving high power with these methods requires using a large sample size and because the computational burden of implementing BVS methods with ultra-high density SNP panels and biobank size data is substantial.
We implemented an efficient algorithm to generate samples from the posterior distribution of BVS models for problems involving hundreds of thousands of samples–the software is part of the BGLR R-package [8]. In this study, we use this software to study the power-FDR performance of BVS for mapping very small-effect risk loci. We compared the performance of a BVS method with a prior from the Spike-Slab (SS) family known as BayesC [9], with marginal-association testing (SMR), two other BVS methods, SuSiE [10] and FINEMAP [11], and two non-Bayesian variable selection procedures (LASSO, and a forward (FWD) regression). Furthermore, we used BayesC and SMR to map risk variants for six blood biomarkers related to metabolic syndrome. The empirical analysis shows that BayesC identifies most of the regions identified by SMR (and a many more) with a much finer mapping resolution than SMR.
## Materials and methods
We used data from the UK-Biobank [1] comprising genotypes and phenotypes of distantly related (pairwise genomic relationships smaller than 0.05) individuals of European background ($$n = 315$$,874). From the imputed genotype SNPs, after filtering (for a minor minor-allele-frequency >0.001 and a calling rate >0.95) and LD-pruning (R-squared <0.9), we retained 5,593,953 SNPs (see Supplementary Methods more details).
For the evaluation of power and FDR, we simulated complex traits with 500 (randomly chosen) casual variants and a trait heritability of 0.5 (i.e., on average a causal locus explained $\frac{1}{10}$th of $1\%$ of the phenotypic variance). We conducted 10 whole-genome simulations, each involving 500 causal loci and 5,593,453 SNPs without effects. We also considered a second simulation scenario with the same heritability and a smaller number of causal variants [50]; thus, with larger SNP-effect sizes.
We evaluated six regression methods: marginal association testing (via SMR) and five variable selection methods (LASSO, FWD, and three Bayesian variable selection procedures). The SMR was a simple linear regression fitted via ordinary least squares using the phenotype as the response and one SNP as the predictor.
The Variable Selection methods were multiple regression models of the form1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$y = X\beta + \varepsilon$$\end{document}y=Xβ+εwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$y = \left({y_1,y_2, \ldots,y_n} \right)\prime$$\end{document}y=y1,y2,…,yn′ is a vector of phenotypes, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$X = \left\{ {x_{ij}} \right\}$$\end{document}X=xij is a matrix of genotypes, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta = \left({\beta _1,\beta _2, \ldots,\beta _p} \right)^\prime$$\end{document}β=β1,β2,…,βp′ is a vector of SNP effects and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varepsilon = \left({\varepsilon _1,\varepsilon _2, \ldots,\varepsilon _n} \right)^\prime$$\end{document}ε=ε1,ε2,…,εn′ is a vector of error terms.
## Local regressions
To apply variable selection methods on a whole-genome scale, we leveraged the fact that LD decays within relatively short distances; therefore, following Funkhouser et al. [ 12], we applied the variable selection method to overlapping segments containing 7000 contiguous SNPs (~4 Mbp for the imputed genotypes). This window of SNPs was displaced by 5000 SNPs, thus producing local regressions with a core of 3000 SNPs and flanking regions, each of ~2000 SNPs. From each regression we retrieved results from the core only (Supplementary Methods for more details).
The LASSO [13] regressions were fitted using the glmnet [14] R-package. The software produces a sequence of solutions \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\{ {\hat \beta _{\lambda _1},\hat \beta _{\lambda _2}, \ldots.} \}$$\end{document}{β^λ1,β^λ2,….} over a grid of values of the regularization parameter (λ). We formed a grid with 1000 values that was evenly spaced in the log-scale. The same grid of values of λ was used across each of the segments to which LASSO regression was applied (see Local Regressions above). For each λ in the sequence we obtained a discovery set and a rejection set consisting of the SNPs with non-zero and zero effect in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat \beta _\lambda$$\end{document}β^λ, respectively. We ranked SNPs based on the value of λ at which the SNP becomes active in the model; these ranks were used to evaluate power and FDR over the regularization path.
The Forward regression also produces a sequence of solutions \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\{ {\hat \beta _{FWD_1},\hat \beta _{FWD_1}, \ldots.} \}$$\end{document}{β^FWD1,β^FWD1,….} starting from the null model (no SNPs), then adding to the model one SNP at a time, at each step adding the SNP that produces the largest reduction in the residual sum of squares. The FWD regressions were applied to overlapping segments (see Local Regression above) and SNPs were ranked based on the reduction on the RSS produced when the SNP entered the model. These ranks were then used to evaluate power and FDR along the forward path.
For the Bayesian Variable Selection regression, we first used a model from the Spike-Slab family known as BayesC [9]. Briefly, the model assumes that the error terms in [1] are iid Normal \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varepsilon _i\sim ^{iid}N\left({0,\sigma _\varepsilon ^2} \right)$$\end{document}εi~iidN0,σε2; therefore, the conditional distribution of the data given the model parameters \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\theta = \left\{ {\beta,\sigma _\varepsilon ^2} \right\}$$\end{document}θ=β,σε2 was:2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p\left({y|\theta } \right) = MVN\left({y|X\beta,I\sigma _\varepsilon ^2} \right) \propto \left({\sigma _\varepsilon ^2} \right)^{ - \frac{n}{2}}Exp\left\{ { - \frac{1}{{2\sigma _\varepsilon ^2}}\left({y - X\beta } \right)^\prime \left({y - X\beta } \right)} \right\}$$\end{document}py∣θ=MVNy∣Xβ,Iσε2∝σε2−n2Exp−12σε2y−Xβ′y−Xβwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MVN\left({y|X\beta,I\sigma _\varepsilon ^2} \right)$$\end{document}MVNy∣Xβ,Iσε2 represents a multivariate normal density with mean Xβ and (co)variance matrix \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$I\sigma _\varepsilon ^2$$\end{document}Iσε2.
In a Bayesian models, priors that assign non-zero probabilities to null effects also specifies probabilities over possible models; this plays a very important role in error control [15]. Therefore, we consider a prior for SNP effects that has a point of mass at zero and a Gaussian slab3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p\left({\beta _j|\sigma _\beta ^2,\pi } \right) = \pi N\left({\beta _j|0,\sigma _\beta ^2} \right) + \left({1 - \pi } \right)1\left({\beta _j = 0} \right)$$\end{document}pβj∣σβ2,π=πNβj∣0,σβ2+1−π1βj=0where π \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left({0 \le \pi \le 1} \right)$$\end{document}0≤π≤1represents the proportion of loci with non-null effects and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma _\beta ^2$$\end{document}σβ2 is the variance of effects (other common choices for the slab are the scaled-t and double-exponential). The prior used in BayesC [9] is equivalent to the one earlier proposed by George & McCulloch’s [16] with a Gaussian spike replaced with a point of mass at zero.
The hyper-parameters (π, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma _b^2$$\end{document}σb2 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma _\varepsilon ^2$$\end{document}σε2) are unknown; thus, for the variance parameters we use scaled-inverse chi-square priors and for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pi$$\end{document}π we use a Beta prior, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pi \sim B\left({\alpha _1,\alpha _2} \right)$$\end{document}π~Bα1,α2 with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha _1 = 1.1$$\end{document}α1=1.1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha _1 = 99$$\end{document}α1=99, implying \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$E\left[\pi \right] = $\frac{1.1}{100}$$$\end{document}Eπ=$\frac{1.1}{100.}$
We compared the power-FDR performance of BayesC with that of SuSiE [17] and FINEMAP [11]. FINEMAP was developed to refine peaks detected in GWAS; therefore, we applied FINEMAP to segments detected through marginal association testing. The segments consisted of SNPs with single-marker-regression p-value smaller than 5e-8 that were at a distance of each other smaller than 1 Mbp. SuSiE was applied in a whole-genome scale using the same local regression approach used to implement BayesC.
## Bayesian FDR
We used the samples from the posterior distribution to estimate SNP-specific probabilities of association: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pi _j = p\left({\beta _j \,\ne\, 0|data} \right)$$\end{document}πj=pβj≠0∣data. The “local” FDR (LFDR [18]) for the jth SNP with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\pi _j$$\end{document}πj is simply \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$LFDR_j = 1 - \pi _j$$\end{document}LFDRj=1−πj. A decision rule that rejects \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$H_{0j}\,if\,\pi _j \, > \, \tau$$\end{document}H0jifπj>τ (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau \in [0,1]$$\end{document}τ∈[0,1]) has an expected proportion of false discoveries equal to the average LFDR of the SNPs in the discovery set:4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$BFDR(\tau) = 1 - \frac{1}{{p_\tau }}\mathop {\sum }\limits_{j:\pi _j > \tau }^{} \pi _j,$$\end{document}BFDR(τ)=1−1pτ∑j:πj>τπj,where pτ is the number of SNPs in the discovery set. Expression [4] was evaluated for each SNP using the BFDR() function of the BGLR R-package [19].
## Software
SNP filtering was done using PLINK [20], genomic relationships were computed using the getG() function of the BGData R-package [21]. Single-marker regressions were performed using the GWAS() function of the BGData R-package. BayesC and SuSiE were implemented using the BGLR [19] (function BLRXy()) and susieR [17] R-packages, respectively. FINEMAP was fitted using the FINEMAP command line tool [11]. The Forward regressions were implemented using the FWD() function available in the BGData R-package, and LASSO regressions were fitted using the glmnet [14] R-package. Plots were generated using ggplot2 [22].
## Power and FDR determination
To estimate power-FDR curves, for each of the simulation scenarios and method we ranked SNPs based on the evidence for association produced by each method: (i) the p-values for the SMR (from smallest to larger), (ii) single-SNP posterior probabilities of inclusion for the BVS method (from largest to smallest, this was used for all the Bayesian models, (iii) the value of λ at which the SNP entered in the model for the LASSO regressions (from largest to smallest), and (iv) the reduction in the RSS produced when the SNP entered in the model in the FWD regression (from largest to smallest). We then produced discovery and rejection sets for each method by selecting the top-k SNPs of each of the ranks ($k = 1$, 2, …). For each discovery set we estimated the proportion of the 500 causal loci recovered in the discovery set and the proportion of SNPs in the discovery set that were not causal loci (i.e., the false discovery proportion).
To evaluate the ability of each method to fine-map causal variants we estimated the power-FDR performance at different mapping resolutions. Specifically, for an x-kbp mapping resolution ($x = 10$ kbp, 100 kbp, …, 1 Mbp), a discovery was considered true (false) if the distance with the closest causal variant was smaller (larger) than x-kbp.
## Analysis of six blood biomarkers
The simulation study demonstrated that the FWD and the BVS methods BayesC and SuSiE had the best performance. Furthermore, the performance of BayesC and SuSiE were very similar and better than that of FINEMAP; therefore, for analysis of the real data we used BayesC and SMR, which is the method most used in GWAS.
The biomarkers that we analyzed (glucose, serum urate (SU), serum creatinine, low- and high-density lipoprotein cholesterols (LDL and HDL, respectively), and triglycerides) are often monitored in medical checkups and are related to metabolic syndrome (see Table S1 of the Supplementary Data for sample size and descriptive statistics by trait).
Analyses were performed using the same genotypes used in the simulation (~5.6 million SNPs). All the traits were adjusted by the effects of sex, age, center, and with the top-10 SNP-derived eigenvectors. For rejection we used p-value < 5e-8 for the SMR and BFDR ≤0.05 or ≤0.10 for the BVS method. In regions of high-LD there may be multiple SNPs with elevated posterior probability of non-zero effect, with none of them reaching the single-SNP BFDR threshold (see Section I of the Supplementary Data for examples of this). Therefore, after identifying individual SNPs that cleared the BFDR thresholds mentioned above, we also identified short segments that had elevated inclusion probability but did not clear the BFDR-threshold. For these segments we estimated the posterior probability of the segment (i.e., the frequency at which at least one SNP from the segment was active in the model) and included that segment in the discovery set if the segment BFDR was smaller than 0.05 or 0.1. Therefore, the discovery sets for the BVS method consisted of individual SNPs and short segments that cleared one of the two BFDR thresholds.
## Polygenic risk scores
To evaluate the prediction accuracy of polygenic risk scores (PRS) we set aside data from 10,000 individuals for testing. As a baseline PRS we used one based on GWAS-significant SNPs (p value < 5e-8) with SNP effects estimated from SMR. These estimates do not account for LD; therefore, we considered a second PRS in which SNPs where selected based on SMR p-values and then SNP effects were estimated using BayesC. For these PRSs, we used p-value thresholds for SNP selection ranging from 1e-12 to 1e-4. Finally, we considered a whole-genome PRS derived using the estimates of effects from the local Bayesian regressions implemented using model BayesC (the same approach used for mapping). These local Bayesian regressions covered all the available SNPs (~5.6 million); however, to simplify the computation of the PRS we only used the SNPs with posterior inclusion probability greater than $\frac{1}{1000.}$
## Results
The power-FDR curves estimated from the simulation scenario with heritability 0.5 and 500 causal loci are displayed in Fig. 1 (and File S1 of the Supplementary Data). For a sample size of 10,000 and a mapping resolution of 100 kbp (top-left panel of Fig. 1) all the methods had relatively low power–this was expected because individual SNPs with non-null effect explained only $\frac{1}{1000}$ of the phenotypic variance. Increasing sample size improved the power-FDR performance of all the methods; however, the variable selection methods improved their performance much more than the SMR. Among the variable selection procedures, the BVS methods (including BayesC, SuSiE, and FINEMAP) and the FWD regression were the best performing ones. Importantly, with a large sample size these methods had a very sharp phase-transition in the power-FDR curve showing that, with a large sample size, both methods can achieve high power with very low FDR even for very small effect variants. This was evident even with a mapping resolution of 10 kbp (see top-right plot in Fig. 1). On the other hand, the SMR only achieve a comparable power-FDR performance with a mapping resolution of 1 Mbp (see lower-right plot) demonstrating that with a large sample size mapping based on SMR p-values produces a large proportion of discoveries that are more than 100 kbp apart from the causal variants. Among the Bayesian methods, SuSiE and BayesC performed very similarly and FINEMAP had a slightly lower power for an FDR of 0.1 (see Fig. 1, top two panels for sample size 50,000 and 100,000). This small reduction in power may result from some of the small-effect causal variants not reaching GWAS-significant values; thus, not making it to the second step. Fig. 1Power-FDR (False Discovery Rate) curves by sample size, mapping resolution, and statistical method used. For a mapping resolution of x-kbp, a SNP in a discovery set was considered a true discovery if its distance to the closest simulated causal variant was closer than x-kbp.
The results from the simulation scenario with larger effect sizes (heritability 0.5, 50 causal variants, Fig. S4) were similar to the ones obtained in the simulation scenario with 500 causal variants in that FWD, SuSiE, and BayesC achieved the best power-FDR performance and had very sharp power-FDR transitions. However, as expected, for any given sample size and FDR in this scenario these three methods achieved higher power than in the scenario with smaller effect sizes (500 causal variants). On the other hand, the power-FDR performance of SMR was worst in the scenario with larger effects (50 causal variants) than in the scenario with smaller effects. This happens because large effect loci can generate marginal association significant results even for a very weak LD (i.e., at a long physical distance) between the marker and the causal variant.
## Bayesian FDR-control
We used the results from the most challenging simulation scenario (heritability 0.5, 500 causal variants) to evaluate the empirical FDR of standard decision rules including SMR p-value ≤ 5e-8 and BFDR ≤ 0.10 or 0.05. The results are summarized in Fig. 2, Figs. S5, S6. For a 1 Mbp mapping resolution the standard rule used in GWAS SMR p-value ≤5e-8 leads to an FDR of ~0.08, comparable to a decision rule using BFDR ≤ 0.1, and a bit higher than using BFDR ≤ 0.05 (lower panel of Figs. 2 and S5, S6). However, for finer mapping resolutions (e.g., 125 kbp) a decision rule rejects if SMR p-value ≤ 5e-8 can produce a rate of false discoveries greater than $50\%$. Importantly, for the SMR, the exponential growth of the FDR with increasingly finer mapping resolution was more marked with large sample size, illustrating once again how the mapping resolution of SMR deteriorates with sample size. On the other hand, while the BVS model also had an increasing FDR with finer mapping resolution, the slope of the curves was very small compared with that of the SMR suggesting that the prior provide reasonably effective (albeit not perfect) error control. We conclude from these results that, for data from unrelated white Europeans, using a BFDR < 0.05 as a decision rule leads to an FDR ≤ 0.1 for a mapping resolution of ~125 kbp. Fig. 2Empirical False Discovery Rate (FDR) by decision rule, sample size, and mapping resolution. Top panel: Empirical FDR versus Bayesian FDR threshold used to determine significance, by sample size. Bottom panel: Empirical FDR by mapping resolution for three decision rules: SMR p value < 5e-8, BFDR < 0.05 and BFDR < 0.1. All the results in this figure are based on the simulation scenario involving a heritability of 0.5 and 500 causal variants. For decision rules using BFDR, results were obtained using model BayesC. For a mapping resolution of x-kbp, a SNP in a discovery set was considered a true discovery if its distance to the closest simulated causal variant was closer than x-kbp.
## High resolution mapping of risk loci associated with six metabolic syndrome-associated blood biomarkers
Table 1 and Fig. 3 display the results of the SMR and of BayesC. The number of variants with SMR-significant marginal association ranged from 469 (Glucose) to 5991 (serum urate). We grouped the SMR-significant variants into non-overlapping chromosome segments, each including all the SMR-significant variants that were at a distance smaller than 1000 Mbp. The number of segments harboring SMR-significant variants ranged from 43 (Glucose) to 225 (HDL-Cholesterol); these regions are displayed in yellow-red scale in Fig. 3.Table 1Number of independent segments discovered (and the number of SNPs included in those segments) by method and overlap between them. DiscoveriesOverlapcBayesCaSMRbBFDR ≤ 0.05BFDR ≤ 0.10BFDR ≤ 0.05BFDR ≤ 0.10Glucose41 [46]54 [60]43 [469]$67.4\%$$76.7\%$Serum Urate194 [216]244 [264]175 [5991]$69.1\%$$81.1\%$Serum Creatinine228 [264]296 [331]225 [4394]$75.1\%$$84.9\%$HDL-Cholesterol246 [274]307 [330]177 [5909]$77.4\%$$86.4\%$LDL-Cholesterol129 [139]161 [168]99 [3802]$77.8\%$$87.9\%$Triglycerides200 [213]246 [264]158 [5679]$71.5\%$$84.2\%$aTotal number of discoveries, in between parenthesis the number of discoveries that were single-SNPs clearing the Bayesian FDR (BFDR) threshold.bTo map individual variants into chromosome segments, we merged all the discoveries (SNPs with p-value < 5e-8) that were at a 1000 kbp or shorter distance of each other.c% of the segments detected by SMR that had at least one Bayesian discovery inside the segment. Fig. 3Regions assciated to each of the six blood biomarkers studied. Ideogram displaying segments identified through single-marker regression (red-yellow bands corresponding to -log10(pvalues)) and by a Bayesian Variable Selection (BayesC) model (blue lines correspond to variants and segments with BFDR < 0.1).
BayesC identified a much smaller number of variants than the SMR; however, the number of independent segments identified by BayesC were typically higher than those identified by SMR except for Glucose. Most often BayesC selected one or a few variants within each of the segments (Fig. 3). The segments identified by BayesC were often very short–the median length was about 30 kbp–36 kbp. On the other hand, the SMR-segments had a median length of 142.5 kbp.
## Polygenic prediction
Figure 4 and Table S2 show the prediction correlations obtained in testing sets. A PRS based on GWAS-significant SNPs (SMR p-value < 5e-8) and with SNP effects estimated from SMRs achieved prediction correlations ranging from 0.09 (+/− 0.01, Glucose) to 0.302 (+/− 0.01, HDL Cholesterol)–the results from these PRSs are represented in blue in Fig. 4 (see also Table S2). The estimates of effects from SMR do not account for LD; re-estimating the SNP effects of GWAS-significant SNPs using BayesC led to significant increases in prediction correlations. The gains in prediction correlation achieved by re-estimating the effects of GWAS-significant SNPs using BayesC ranged from $17\%$ (glucose) to $47\%$ (triglycerides). The PRS that used the estimates of effects from the whole-genome Bayesian regressions (horizontal dashed black lines in Fig. 4, see also Table S2) were very similar to the ones obtained by a PRS based on GWAS-significant SNPs with effect estimates derived using BayesC. Furthermore, for all traits but creatinine, the prediction accuracy achieved by the whole-genome Bayesian regression were within the margin of error of the maximum prediction accuracy that one could obtain in this data set by selecting SNPs using p-values from SMR and then estimating the effects of the SNPs using BayesC (i.e., the maximum of the salmon curve in Fig. 4).Fig. 4Prediction correlation in testing set for various polygenic risk scores. The blue dots are the prediction correlations obtained with GWAS-significant SNPs (p-value < 5e-8) and SNP effects estimated from single-marker regressions (SMR). The pink-salmon curve shows the prediction accuracy of sets of SNPs selected using the log10(p-value) threshold given in the horizontal axis, with SNP effects estimated using BayesC. The horizontal dashed black line gives the prediction accuracy of a whole-genome Bayesian regression (BayesC) applied using overlapping local regressions.
## Discussion
*Modern* genetic studies use a very large sample size and ultra-high-density genotypes (potentially millions of SNPs). In principle, the large sample size and the high-marker density should improve our ability to map risk variants. However, these conditions deteriorate the mapping resolution of SMR–the most frequently used methodology used in GWAS. We illustrated this problem with extensive simulations and with the analysis of six blood biomarkers. With a sample size of ~300,000 and high marker density, SMR can lead to significant associations for variants that are up to 300–1000 kbp apart from the causal variant depending on the effect size, and the extent of LD in the region (Figs. S1, S2). This results in poor power-FDR performance (Fig. 1, Fig. S4); thus, when marginal association testing is applied to biobank-size data and ultra-high-density genotypes, high power can only be achieved at the price of a very high FDR.
To address the poor mapping resolution of SMR several methods have been proposed. One approach is to ‘weight’ the evidence of association of SNPs within a region to estimate an approximate posterior probability of association [3, 23]. However, this approach assumes that only one SNP (in the region) has an effect and do not fully account of multi-locus LD in the region. Another common approach is to use two-steps procedures in which first a marginal-association test is used to identify chromosome segments harboring GWAS-significant variants and then, in a second step, the GWAS-summary statistics obtained in the first step are used, in conjunction with an LD-reference panel, to identify independent signals. However, in the first step the procedure may miss important signals due to “unfaithfulness” or cancellation of marginal effects [5]. Additionally, the use of a reference panel to approximate LD patterns may not accurately reflect the LD-patterns of the data set used to derive the GWAS summary statistics in the first place. The slightly worse performance of FINEMAP is likely reflecting a loss of power due to the use of a 2-step procedure. Furthermore, we note that our results are likely giving an optimistic view of the performance of two step procedures because, here, the LD-matrix was computed using the same data set that was used to obtain the SMR summary statistics. If, as often done, the LD-matrix is computed from a reference panel (with possibly different LD patterns than the data set used to derive the summary statistics) the loss of power may be higher.
To address limitations of two-steps procedures, here we considered four variable selection methods (FWD, LASSO, and two variable selection procedures: BayesC and SuSiE priors) that account for multi-locus LD. These methods are not new; however, the adoption of these methods in human GWAS has been limited in part because achieving high power with variable selection methods often requires a very large sample size. The advent of Big Data in genomic research has opened new opportunities for the use of these methods in GWAS.
Among the four variable selection methods considered, the FWD regression and the BVS methods (both SuSiE and BayesC) were the ones that achieved the best power-FDR performances. With a large sample size (n ≥ 100,000) these two methods can achieve high power with low FDR and very fine mapping resolution, even for very-small-effect variants.
BayesC, a Bayesian method with a Spike-Slab prior, and the FWD regression achieved a very good (and remarkably similar) power-FDR performance. This is not surprising considering the links that exist between these two methods and subset selection. The FWD regression is an approach developed to approximate subset selection constraining the search to a path that adds one predictor at a time [24]. Furthermore, the objective function of subset selection, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat \beta = argmin\left\{ {RSS\left({y,X,\beta } \right) + \lambda \Sigma _j1\left({\beta _j \,\ne\, 0} \right)} \right\},$$\end{document}β^=argminRSSy,X,β+λΣj1βj≠0, can be seen as the logarithm of the kernel of the posterior distribution of a Bayesian model with a Gaussian likelihood and a prior on SNP effects with a point of mass and a flat slab, which is similar to the prior used in BayesC.
Collecting samples from the posterior distribution of high dimensional Bayesian models is computationally demanding. However, advances in hardware and in algorithms has made the application of BVS to biobank-size data feasible. As a reference we provide in Supplementary Fig. S7 the estimated computing time required for BLRXy() to generate 10,000 posterior samples as a function of number of SNPs in the model (from 1000 to 10,000 SNPs) and sample size (we evaluated up to $$n = 300$$,000). The information in the appendix also provides the computing times required for up to 100 iterations of SuSiE and SuSiE-sufficient statistics. It took on average 17 min for BLRXy() to generate 10,000 posterior samples for a model involving 10,000 SNPs and a sample size of 300,000. The computing times of BLRXy() were similar to those of SuSiE-sufficient statistics and considerably lower than those of SuSiE when sample size was large. These results show that it is doable to apply Bayesian regressions using the local-overlapping segments approach used in Funkhouser et al. [ 12] and adopted here.
In this study we focused on a specific BVS model that uses a prior with point of mass at zero and a Gaussian slab. Our simulation results suggest that the power-FDR performance of different BVS methods (e.g., BayesC, SuSiE) is very similar (see Fig. 1) provided that the prior induces some form of variable selection. There are many other variable selection priors that we anticipate will perform similarly, including priors from the spike slab family that use non-Gaussian slabs (e.g., scaled-t [25], or double-exponential [26, 27]).
One concern that is often raised about Bayesian models is the need of specifying prior hyper-parameters and the influences that these may have on inferences. In the case of BayesC there are two hyper-parameters: the prior proportion of non-zero effects and the variance of the slab. To avoid specifying these hyper-parameters a-priori, we treated them as unknown and assigned priors to each of them. For the variance, we choose a scaled-inverse chi-square with small DF which results in limited influence of the prior on inferences when sample size is large. For the proportion of non-zero effects, we used a Beta prior with a prior mean of $\frac{1}{100}$ (i.e., assuming a prior that $1\%$ of the SNPs have none-zero effect). One could use a uniform prior (which is a special case of the Beta); however, adequate FDR control and stringent variable selection can be better achieved by using priors that are informative; this can be particularly important for studies involving a much smaller sample size than the one presented here.
In regions of high LD collinearity may lead to many SNPs with elevated inclusion probability without any of them reaching stringent FDR thresholds (e.g., BFDR < 0.1); thus, reducing power. In our analysis of blood biomarkers, we illustrated how this problem can be addressed using methods which identify sets of variants that are jointly associated with a phenotype.
Finally, we evaluated various strategies to build PRSs; our results suggest that the prediction accuracy that can be achieved using a whole-genome BVS procedure implemented using local regressions is similar to the highest prediction accuracy that can be achieved fitting a BVS to SNPs filtered based on marginal association tests. Therefore, we conclude that BVS applied using local Bayesian regressions can be used for both fine mapping and accurate PRS prediction.
## Supplementary information
Supplementary Methods Supplementary Data The online version contains supplementary material available at 10.1038/s41431-022-01135-5.
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|
---
title: Dysregulated low-density granulocyte contributes to early spontaneous abortion
authors:
- Hongxia Ye
- Lan Li
- Yajun Dong
- Qu Zheng
- Yulin Sha
- Li Li
- Panyu Yang
- Yan Jia
- Jiang Gu
journal: Frontiers in Immunology
year: 2023
pmcid: PMC9995479
doi: 10.3389/fimmu.2023.1119756
license: CC BY 4.0
---
# Dysregulated low-density granulocyte contributes to early spontaneous abortion
## Abstract
Spontaneous abortion (SA) is a common adverse pregnancy event with unclarified pathogenesis and limited therapeutic efficiency. Although most SA cases with the euploid embryo(s) are associated with immunological factors, the contribution of low-density granulocyte (LDG) in SA pathogenesis is rarely reported. This study aimed to investigate the serial characteristics and possible contribution of LDG and their subpopulations in early pregnancy, especially in early SA. Unpregnant (UP), normally pregnant (NP), and SA women were recruited, and the peripheral blood and endometrium/decidua were collected for LDG isolation and histological observation. The percentage, phenotype, and subpopulations of LDG were analyzed via flow cytometric analysis, and the ability of Nets formation was assessed by immunofluorescent and immunohistochemical assays. As a result, 43 participants were enrolled, including 10 UP, 15 NP, and 18 SA women. Compared with the UP group, the LDG percentage in peripheral blood mononuclear cells (PBMCs) and decidual immune cells (DICs) increased in the NP group, while the loss of this increase was observed in the SA group. Meanwhile, CD16int/− cell percentage in peripheral blood LDG (PB-LDG) increased in the NP and SA groups, and insufficient activation of CD16hi PB-LDG characterized by reduced CD11b expression was discovered in the SA group. Moreover, the LDG percentage in DICs was higher than that in PBMCs, and the decidual LDG (D-LDG) showed a surface marker expression profile that is easier to be activated in the pregnant cohort (NP + SA women). Finally, increased decidual Nets formation was observed in the SA group compared with the NP group, and more Nets formation was detected in D-LDG of NP and SA women following PMA stimulation. Overall, LDG participates in the maintenance of early pregnancy, while dysregulated LDG is responsible for early SA, providing novel potential targets for further exploration of SA pathogenesis and therapeutics.
## Introduction
From the perspective of immunology, the embryo can be considered a semi-homograft, carrying half of the genes of both parents and interacting with the maternal immune system without being rejected [1]. Spontaneous abortion (SA) is a common adverse pregnancy event, but the etiology is not well described, and therapeutic efficiency is also far from satisfactory. Growing evidence suggests that most SA cases in the euploid embryo(s) are associated with immune factors. Except for the known autoimmune disorders, such as antiphospholipid syndrome and positive anti-thyroid antibodies, the immunological mechanisms of a considerable part of SA are still unclear [2]. Meanwhile, the known endocrine, metabolic, and infectious factors, such as polycystic ovarian syndrome, hypothyroidism, and chronic endometritis, could perturb the decidualization process. However, a defect in decidualization can result from changes in immune cells, at least partially [2, 3]. Therefore, expanding the understanding of pregnancy-related immune changes and potential mechanisms is urgently needed to explore SA pathogenesis and therapeutic strategies.
Various immune cells infiltrate the maternal-fetal interface in early pregnancy and regulate immune balance. As a critical part of innate immunity, neutrophils are involved in pregnancy and delivery, especially in fertilized egg formation and embryo implantation [4]. Neutrophils kill pathogens via multiple antimicrobial mechanisms, such as phagocytosis, reactive oxygen species (ROS), and releasing bactericidal enzymes and neutrophil extracellular traps (Nets) [5]. Nets are extracellular web-like DNA decorated with histones and antimicrobial proteins released from activated neutrophils and form a powerful antimicrobial mechanism. Nets contain various damage-associated molecular patterns, including DNA, so uncontrolled Nets formation can sustain inflammation and cause tissue damage or dysfunction in the host [6]. In addition to immune response, NETs are also involved in angiogenesis, thrombosis, and tissue remodeling [7, 8].
Although the underlying mechanisms are debated, abnormal changes in neutrophil activity are associated with pregnancy complications, including spontaneous abortion (SA) [9, 10]. Previous studies have confirmed that neutrophils can be isolated from decidua in the first three months of pregnancy, and decidual neutrophils show an activated phenotype and increased anti-apoptotic ability [9, 11]. However, the phenotypes, functional characteristics, and interrelationships among the subtypes of neutrophils in SA, both in peripheral blood (PB) and decidua, have not been clarified. Low-density granulocyte (LDG) were first reported in systemic lupus erythematosus (SLE), and their subsets coexisted with monocytes after gradient centrifugation. Most autoimmune or infectious diseases studies showed that LDG is a group of pro-inflammatory cells. Meanwhile, compared with normal-density neutrophils (NDG), LDG is easier to be activated and exhibits stronger NETosis [12, 13], which reflects the state of neutrophils with NETs formation and contributes to the host defense against pathogens.
LDG is mainly reported in peripheral blood of autoimmune diseases, tumors, infections, thrombosis, and other conditions. Croxatto et al. reported that leukocytes isolated from decidua contained, in the purified mononuclear cell frequency, a population of LDG [11], indicating that LDG may play a role in pregnancy. However, the role and potential mechanisms of LDG in pregnancy and adverse pregnancy events (such as SA) are rarely reported. Therefore, this study aimed to investigate the serial characteristics of LDG, including the percentage, phenotype and subpopulations, and ability to form Nets (Netosis), in peripheral blood and endometrium/decidua of unpregnant, normally pregnant, and SA women and analyze the possible contribution of LDG in early pregnancy and SA, which have not been reported yet. As a result, the findings contributed new evidence of the involvement of LDG in pregnancy and highlighted the participation of dysregulated LDG and its subpopulations in SA pathogenesis, laying a foundation for further study of SA pathogenesis and therapeutics.
## Participants
This study was approved by the Reproductive Medicine Ethics Committee of Chengdu Jinjiang Hospital for Maternal & Child Health Care (approval number: 2019003) and followed the Helsinki Declaration. All participants were informed of the nature of the study and signed a written informed consent before participation.
The participants were recruited between Oct 2020 and Mar 2022, including unpregnant (UP), normally pregnant (NP), and SA women. Intrauterine pregnancy was diagnosed through serum and urine β-human chorionic gonadotropin (β-hCG) tests and Doppler ultrasound, and the SA was diagnosed as the unintentional end of pregnancy. The inclusion criteria were: [1] less than 35 years old, body mass index (BMI) < 28 kg/m2, and no history of smoking and drinking in the three months before endometrial collection (UP women) or during this pregnancy (NP and SA women); [2] for participants in UP and NP groups, no history of adverse pregnancy (such as SA, premature delivery, pregnancy-induced hypertension, placental abruption), hypertension, cryptorrhea (such as diabetes), and immune and infectious diseases; for participants in SA group, experienced at least one SA before this pregnancy, no normal childbearing history, and no use of immunomodulators and anticoagulants during this pregnancy; [3] no anatomical abnormalities of uterine, such as septate, unicornate, bicornate and didelphis uteri, were observed in the ultrasound examination before participation; [4] the UP volunteers agreed to donate endometrial tissue one week after ovulation, the NP women chose to terminate the healthy pregnancies voluntarily, and the SA women selected induced abortion after the demised fetus was confirmed; [5] no chromosomal abnormalities were identified in the aborted tissues; [6] the days of pregnancy (DOP) in NP and SA women were 42 to 70 days. As a result, 43 participants were recruited, including 10, 15, and 18 in the UP, NP, and SA groups, respectively, and the general characteristics are shown in Table S1.
## Collection of peripheral blood and decidual/endometrial tissues
The peripheral blood in all groups was collected. A part was used for blood routine and D-dimer tests, a part was used for plasma separation and dsDNA level determination, and the rest was used to separate peripheral blood mononuclear cells (PBMCs) and normal-density granulocyte (NDG).
For NP and SA participants, fetal heartbeat was confirmed again with a Doppler ultrasound before the induced abortion. After vacuum aspiration, the endometrial/decidual tissues were picked out from the aspirated tissues and repeatedly washed with normal saline to minimize blood contamination. The non-decidual tissue was carefully examined and removed. Part of the endometrial/decidual tissues was fixed and paraffin-embedded for immunofluorescent and immunohistochemical staining, and the remainder was stored in a culture medium for isolation of endometrial/decidual immune cells (EICs/DICs).
## Isolation of PBMCs and EICs/DICs
PBMCs were isolated with Histopaque-1119 and Histopaque-1077 (Sigma, USA) gradient centrifugation procedure. Briefly, Histopaque-1119, Histopaque-1077, and whole blood samples were sequentially added into a 50ml centrifuge tube and centrifuged at 700×g for 30 mins at room temperature. The stratified liquid from top to bottom is plasma, PBMCs (including LDG), Histopaque-1077, peripheral blood NDG (PB-NDG), Histopaque-1119, and red blood cells. The PBMCs and PB-NDG were collected, and the former was used for subsequent peripheral blood LDG (PB-LDG) screening and isolation.
Endometrial and decidual tissues were cut into about 1 mm3 pieces and digested with 1 mg/mL ($0.1\%$) collagenase type IV (Sigma, USA) and 150 U/ml DNase I (Sigma) at 37°C for about 60 mins with gentle agitation. The cell suspension was passed through 100μm, 70μm, and 40μm cell strainer and centrifuged at 700×g in a discontinuous Percoll gradient ($20\%$, $40\%$, and $60\%$) for 30 mins. The decidual immune cells (DICs) between $40\%$ and $60\%$ Percoll solution (densities of 1.056 to 1.077 g/mL) were collected and used for subsequent endometrial LDG (E-LDG) and decidual LDG (D-LDG) isolation.
## Phenotypic analysis of PBMCs, PB-NDG, and EICs/DICs by flow cytometry
The isolated PBMCs and E/DICs were washed with stain buffer and subsequently stained with the antibody-conjugates, including APC-Cy7 CD45, PerCP-Cy5.5 CD15, BV510 CD14, BV711 CD62L, BV605 CD11b/MAC-1, FITC CD16, and Alexa 647 CD66b (BD, USA), for 30 minutes at 4°C in the dark. PB-NDG were stained with the above antibodies except for APC-Cy7 CD45, PerCP-Cy5.5 CD15, and BV510 CD14 because the concentration of PB-DNG with CD45+CD15+CD14- we separated was more than $97\%$ (Figure S1A). Then, the stained cells were washed with stain buffer and acquired via an FCM with CellQuest software. More than 1 × 104 cells in each sample were detected. The results were analyzed using Flowjo software and expressed as a percentage of positive cells.
## LDG isolation and identification
LDG were isolated from the PBMCs and E/DICs by magnetic bead selection and identified by FCM. Briefly, the PBMCs and E/DICs were incubated with anti-CD14 mAbs (Milteny, Germany) for 15 mins, and the CD14- cells were isolated by negative selection using anti-CD14 magnetic beads and MACS dissociator (Milteny). The CD14- cells were further incubated with anti-CD15 mAbs (Milteny) for 15 mins, and the CD15+/CD14- cells (LDG) were isolated by positive selection using anti-CD15 magnetic beads and MACS dissociator. The purity of the isolated LDG was identified by staining the cells for 30 min at 4°C with monoclonal antibodies specific for CD14 and CD15 and evaluating them by FCM. The percentage of CD14-/CD15+ cells was identified as more than $95\%$.
## Quantitation of NTEs via dsDNA detection
dsDNA in NETs of plasma was quantified by Picogreen dsDNA Assay kit (Invitrogen, USA) according to the manufacturer’s instructions.
## Immunofluorescent and immunohistochemical staining
For immunofluorescent staining, paraffin slides of endometrial/decidual tissues were processed and incubated with primary antibodies, including anti-CD15 (Abcam, USA; 1:1000) and anti-H3Cit (ab5103, USA; 1:1000), before being stained with fluorescent secondary antibodies and DAPI. The images were acquired with a fluorescence microscope (Olympus, Japan), and the number of endometrial/decidual samples with Nets positive was counted. Meanwhile, the paraffin slides were processed and stained with PAD4 antibody (Affinity, USA; 1:200) before being stained with secondary antibodies and chromogen substrate for immunohistochemical staining.
## Phorbol-12-myristate-13-acetate stimulation and Nets formation detection
The isolated PB-LDG, PB-NDG, and E/D-LDG were seeded on a poly-lysine-pretreated glass cover in a 48-well plate and stimulated with PMA (100 ng/mL, Sigma) for 4 hours. Then, the cells were fixed with $4\%$ paraformaldehyde (Sigma) and incubated with primary antibodies, including anti-MPO (Abcam, USA; 1:1000) and anti-H3cit (Abcam; 1:1000), followed by staining with fluorescent secondary antibodies and DAPI. Images were obtained using a FluoView FV1000 confocal microscope (Olympus, Japan) and analyzed with Olympus FV10-ASW software (Olympus). The percentage of positively stained cells was calculated, and all sections were assessed independently by three blinded investigators (Ye Hx, Lan Li, and Jia Y), and the quantitative results were determined via consensus.
## Statistical analysis
Statistical analyses and data graphs were generated with GraphPad Prism software (version 9.4, GraphPad Inc., USA). Continuous data were expressed as median with interquartile range (IQR) and assessed for normality using the Kolmogorov-Smirnov and D’Agostino & Pearson tests. Student’s t-test was used to analyze the differences between the two groups. When the variances of the two groups differed in the F test, the Mann-Whitney U-test was used to compare the two groups. One-way analysis of variance with Tukey’s post hoc analysis (for normally distributed data) or Kruskal-Wallis H with Student-Newman-*Keuls analysis* (for non-normally distributed data) was performed for comparisons among three or more groups. A $P \leq 0.05$ was considered statistically significant.
## General characteristics of the enrolled participants
Forty-three participants were enrolled in this study, including 10 unpregnant (UP), 15 normally pregnant (NP), and 18 SA women (Table S1 and S2). There was no significant difference in age and body mass index (BMI) among the three groups and in days of pregnancy (DOP) between the NP and SA groups. Meanwhile, all participants in the SA group experienced at least one SA before this pregnancy (at least two SA if this SA was included) (Table S1). Meanwhile, there was no significant difference in the peripheral blood neutrophils/lymphocytes ratio (PB-NLR), platelet/lymphocytes ratio (PB-PLR), and D-dimer levels among the three groups (Table S3).
## LDG percentage in PBMCs increases after eight weeks of normal pregnancy, while the loss of this increase is associated with SA
In PBMCs, the LDG percentage was quantified via FCM analysis (Figure 1A), and the difference among the groups was investigated. As shown in Figure 1B, the LDG percentage was significantly higher in the NP group than in the UP and SA groups, indicating that the LDG percentage in PBMCs increases in normal pregnancy, and the loss of this increase may be associated with SA. Meanwhile, no significant difference in the MFI of CD16 and CD62L was observed among the three groups, but the MFI of CD11b was significantly higher in the NP group than in the SA group, suggesting an insufficient activation of PB-LDG in the SA group (Figure 1C).
**Figure 1:** *Loss of increase and insufficient activation in PB-LDG is associated with SA. PBMCs were isolated from the peripheral blood of UP, NP, and SA women by density gradient centrifugation. (A) PB-LDG was identified as SSChiCD45+CD15+CD14- singlets according to the gating strategy shown in the five panels. (B) Comparison of the LDG percentage in PBMCs of 10 UP, 12 NP, and 17 SA women. (C) Comparison of the fluorescence intensity (MFI) of CD16, CD11b, and CD62L in PB-LDG among UP, NP, and SA groups. (D) Correlation analysis of the LDG percentage in PBMCs and DOP in pregnant women (NP + SA). Data are presented as dots and median with interquartile range, and each symbol represents an individual donor. (E) Comparison of the LDG percentage in PBMCs between the subgroup with DOP > 56d or ≤ 56d in NP and SA groups. (F) Comparison of the LDG percentage in PBMCs among UP, NP, and SA groups with DOP ≤ 56d or > 56d. T-test or Mann-Whitney U test was used to identify the differences between the two groups, and one-way analysis of variance (ANOVA) with Tukey’s post hoc analysis or Kruskal-Wallis H with Student-Newman-Keuls post hoc analysis was performed for comparisons among the three groups. * p < 0.05, *** p < 0.001, **** p < 0.0001. ns, not significant.*
Interestingly, correlation analysis revealed that the LDG percentage in PBMCs was positively correlated with DOP in the NP group but was not in the SA group (Figure 1D). Then, subgroup analysis was performed according to whether the DOP was more than 56d (when the placenta began to form and take over the function of the corpus luteum at about eight weeks). The LDG percentage in the NP subgroup with DOP > 56d (57-70 days) was significantly higher than that in the subgroup with DOP ≤ 56d (42-56 days). However, subgroup difference was not observed in the SA group (Figure 1E). Meanwhile, in women with DOP ≤ 56d, the LDG percentage showed no significant difference among the three groups. However, in women with DOP > 56d, the LDG percentage in the NP group was significantly higher than in the UP and SA groups (Figure 1F). These findings indicate that the LDG percentage in PBMCs increases mainly after eight weeks of normal pregnancy, and the loss of this increase may also be related to SA.
## Increased CD16int/− PB-LDG correlates with pregnancy, and insufficient activation of the CD16hi PB-LDG may be associated with SA
According to the CD16 expression, peripheral blood NDG (PB-NDG) and PB-LDG can be classified as CD16int/- and CD16hi subpopulations and representative CD16 plots in FCM analysis are shown in Figure 2A. In PB-NDG, no significant difference was observed in the percentage of CD16hi and CD16int/- cells and in the mean fluorescence intensity (MFI) of CD62L and CD11b among the three groups (Table S4 and Figure S1). The percentage of CD16int/- cells was significantly higher, while the percentage of CD16hi cells was significantly lower in LDG than in NDG (Figure 2B). In PB-LDG, the percentage of CD16int/− cells in the NP and SA groups was significantly higher, while CD16hi cells in the SA group were significantly lower than those in the UP group. However, there was no significant difference in the CD16int/− and CD16hi cell percentage between the NP and SA groups (Figures 2C, D). These results suggest that increased CD16int/− PB-LDG is related to pregnancy, while a decreased CD16hi PB-LDG may be associated with SA.
**Figure 2:** *Insufficient activation of decreased CD16hi PB-LDG correlates with SA. (A) PB-LDG and PB-NDG were classified as CD16int/- and CD16hi subpopulations and data were shown from representative CD16 plots in FCM analysis. (B) Comparison of the percentage of CD16int/- and CD16hi cells between PB-LDG and PB-NDG. (C) Representative FCM analysis of the percentage of CD16int/− and CD16hi cells in PB-LDG. (D) Comparison of the percentage of CD16int/− and CD16hi cells in PB-LDG. (E) Comparison of the MFI of CD11b and CD62L between CD16int/- and CD16hi LDG. (F, G) Comparison of the MFI of CD11b and CD62L in CD16hi LDG (F) and CD16 int/- LDG (G). Data are presented as dots and median with interquartile range, and each symbol represents an individual donor. T-test or Mann-Whitney U test was used to identify the differences between the two groups, and one-way ANOVA with Tukey’s post hoc analysis or Kruskal-Wallis H with Student-Newman-Keuls post hoc analysis was performed for comparisons among the three groups. * p < 0.05, ** p < 0.01, **** p < 0.0001. ns, not significant.*
The immune cell surface markers were further analyzed to explore the potential link between PB-LDG and SA. As shown in Figure 2E, the MFI of CD11b in CD16hi LDG was significantly higher than that in CD16int/- LDG, suggesting that CD16hi LDG is more easily activated than CD16int/- subsets. In CD16hi LDG, the MFI of CD11b in the SA group was significantly lower than that in the NP group (Figure 2F), while this difference was not observed in CD16int/- LDG (Figure 2G). These results suggest an insufficient activation of CD16hi LDG in the SA group, and this was further supported by the findings that the MFI of CD11b of CD16hi LDG in UP and NP groups was more than twice that of CD16int/- LDG (mean: 8762.18 vs. 3555.96 and 12942.11 vs. 6196.477, respectively) but less than $50\%$ higher in the SA group (mean: 5051.374 vs. 7005.47) (Table S5). Therefore, the above results indicate that the insufficient activation of the CD16hi PB-LDG may be related to SA.
## LDG percentage in DICs increases in normal pregnancy, and the loss of this increase is associated with SA
EICs/DICs with a density of less than 1.077 mg/ml were isolated, and the LDG percentage in EICs/DICs was quantified. The results were consistent with that in PBMCs, i.e., the NP group was significantly higher than SA and UP groups (Figure 3A), indicating the LDG percentage in DICs increases in normal pregnancy, and the loss of this increase is associated with SA. Meanwhile, the expressions of CD16, CD11b, and CD62L in E/D-LDG were also analyzed. No significant difference in the MFI of CD16 and CD62L among the three groups was observed, but the MFI of CD11b in the NP and SA groups was significantly higher than that in the UP group, suggesting increased activation of D-LDG in early pregnancy (Figure 3B). Although the MFI of CD11b in the SA group was higher than in the NP group, the difference was not statistically significant.
**Figure 3:** *Loss of increase in decidual LDG (D-LDG) is associated with SA. Endometrial/decidual immune cells (EICs/DICs) were isolated, and the LDG percentage in EICs/DICs was quantified and identified. (A) Comparison of the LDG percentage in EICs/DICs of 4 UP, 10 NP, and 11 SA women. (B) Comparison of the MFI of CD16, CD11b, and CD62L in E/D-LDG among UP, NP, and SA groups. Data are presented as dots and median with interquartile range, and each symbol represents an individual donor. One-way ANOVA with Tukey’s post hoc analysis or Kruskal-Wallis H with Student-Newman-Keuls post hoc analysis was performed for comparisons among the three groups. * p < 0.05, ** p < 0.01. ns, not significant.*
## LDG percentage is higher in DICs than in PBMCs, and D-LDG is more easily activated than PB-LDG in early pregnancy
The LDG percentage in PBMCs and EICs/DICs and the surface markers in PB-LDG and E/D-LDG were compared. There was no statistical difference in the LDG percentage between PBMCs and EICs in the UP group (Figure 4A), but a significantly higher LDG percentage in DICs than in PBMCs was observed in the NP and SA groups (Figures 4B, C). Compared with PB-LDG, the MFI of CD16 in UP and NP groups and CD11b in the UP group was significantly lower, while the MFI of CD11b in the SA group was significantly higher in E/D-LDG (Figures 4A-C). Moreover, in the pregnant women (NP + SA), there were significantly higher LDG percentage in DICs and MFI of CD11b in D-LDG and significantly lower MFI of CD16 and CD62L in D-LDG than in PBMCs and PB-LDG, respectively (Figure 4D). These results indicate that LDG percentage is higher in DICs than in PBMCs, and D-LDG is more easily activated than PB-LDG in early pregnancy.
**Figure 4:** *LDG percentage is higher in DICs than in PBMCs, and D-LDG is more easily activated than PB-LDG in early pregnancy. (A-D) The LDG percentage in PBMCs and EICs/DICs and the MFI of CD16, CD11b, and CD62L between PB-LDG and E/D-LDG in the UP (A), NP (B), and SA (C), and pregnant (NP +SA) (D) women were quantified and compared. Data are presented as dots, and each symbol represents an individual donor. T-test or Mann-Whitney U test was used to identify the differences between the two groups. * p < 0.05, ** p < 0.01, *** p < 0.001. ns, not significant.*
## Increased decidual Nets formation is associated with SA
Peripheral blood plasma was collected, and the dsDNA concentration was detected to determine the formation of Nets. The results showed that the dsDNA concentration in UP, NP, and SA groups increased gradually, and the highest value was observed in the SA group and was significantly higher than in the UP group (Figure 5A). Meanwhile, the Nets formation in the endometrial/decidual tissues was investigated with double-label immunofluorescence, in which neutrophils were labeled with CD15 (red) and Nets were labeled with H3cit (green). As shown in Figure 5B, CD15-positive neutrophils in the UP group were morphologically intact with little H3cit expression. The CD15-positive neutrophils were also intact in the NP group, but H3cit increased without prominent reticular structure, suggesting a restrained Nets formation. In the SA group, the CD15-positive neutrophils were fragmentary, and H3cit appeared in a reticular pattern, indicating a large amount of depolymerized chromatin and NETs formation. Quantitatively, the number of Nets-positive decidual/endometrial samples (women) was significantly higher in the SA group (7, $58.33\%$) than in the NP (2, $20\%$) and UP [0] groups, while there was no significant difference between the NP and UP groups (Figure 5C), indicating an increased decidual Nets formation is associated with SA. Because Nets formation was associated with PAD4, immunohistochemical staining also revealed that the PAD4-positive cells in the SA group were overwhelmingly higher than those in the NP and UP groups (Figure 5D).
**Figure 5:** *Increased decidual Nets formation is associated with SA. (A) Comparison of the dsDNA concentration in peripheral blood plasma among UP, NP, and SA groups. (B, C) The Nets formation in the endometrial/decidual tissues was double-labeled, in which neutrophils were labeled with CD15 (red) and Nets were labeled with H3cit (green) (B), and the number of Nets-positive decidual/endometrial samples was counted and compared among UP, NP, and SA groups (C). The white arrows indicated typical neutrophil changes in different groups. (D) Representative image of the immunohistochemical staining of PAD4 (black arrows), which was associated with Nets formation, in endometrial/decidual tissues. Data are presented as dots and median with interquartile range (A, each symbol represents an individual donor) or case number (C). One-way ANOVA with Tukey’s post hoc analysis or Kruskal-Wallis H with Student-Newman-Keuls post hoc analysis was performed for comparisons among the three groups. * p < 0.05, ** p < 0.01. ns, not significant.*
## Increased Nets formation in D-LDG is correlated with early pregnancy
To further clarify whether the Nets formation in PB-LDG and D-LDG was related to pregnancy or SA, the PB-NDG, PB-LDG, and E/D-LDG were isolated and stimulated by PMA, and the Nets formation was quantified. As a result, Nets formation was detected in all cells following PMA stimulation. There was no statistical difference in the percentage of Nets-positive cells in PB-LDG and PB-NDG among the three groups (Figures 6A, B and S2A, S2B) and in the percentage of Nets-positive cells between PB-LDG and PB-NDG (Figure S2C). However, the Nets formation in D-LDG of the SA and NP groups was significantly higher than that in E-LDG of the UP group (Figures 6C, D). Meanwhile, the percentage of Nets-positive cells in the SA and NP groups was significantly higher in D-LDG than in PB-LDG, while no significant difference in the UP group (Figure 6E). These results indicate that both PB-NDG, PB-LDG, and E/D-LDG can form Nets, and increased Nets formation in D-LDG is correlated with early pregnancy.
**Figure 6:** *Increased Nets formation in D-LDG is correlated with early pregnancy. The PB-LDG and E/D-LDG were isolated and stimulated by PMA, and the Nets formation was detected and quantified. (A) Representative image of Nets formation labeled with H3cit (green) and MPO expression (red) in PB-LDG. (B) Comparison of the percentage of Nets-positive cells in PB-LDG. (C) Representative image of Nets formation labeled with H3cit (green) and MPO expression (red) in E/D-LDG. (D) Comparison of the percentage of Nets-positive cells in E/D-LDG. (E) Comparison of the percentage of Nets-positive cells between PB-LDG and E/D-LDG. Data are presented as median with interquartile range. T-test or Mann-Whitney U test was used to identify the differences between the two groups, and one-way ANOVA with Tukey’s post hoc analysis or Kruskal-Wallis H with Student-Newman-Keuls post hoc analysis was performed for comparisons among the three groups. * p < 0.05, ** p < 0.01. ns, not significant.*
## Discussion
The excessive maternal immune responses must be strictly controlled to ensure a successful pregnancy [14]. Although many studies reported the roles of immune cells in maternal-fetal immunity [15], the contribution of neutrophils is often overlooked. In recent years, the involvement of neutrophils and their subpopulations, especially the LDG, in pregnancy is being discovered, but it is still rarely reported in SA (16–18). In this study, we first observed that LDG percentage in PBMCs and DICs increases in normal pregnancy, while the loss of this increase is associated with SA. Meanwhile, we found that the increased CD16int/− cell percentage in PB-LDG correlates with pregnancy, and insufficient activation of the CD16hi PB-LDG may be related to SA. Moreover, the LDG percentage was higher in DICs than in PBMCs, and D-LDG was more easily activated than PB-LDG in early pregnancy. Finally, the histological analysis discovered that increased decidual Nets formation is associated with SA, and the PMA-stimulative assay verified that increased Nets formation in D-LDG correlates with early pregnancy. These findings demonstrated the serial characteristics of LDG and their subpopulations in peripheral blood and endometrium/decidua of unpregnant, normally pregnant, and SA women and highlighted the involvement of LDG in early pregnancy and SA, providing promising targets for further exploration of SA pathogenesis and therapeutics.
The neutrophil is a homogeneous and terminally differentiated cell population with higher density than mononuclear cells because of the enrichment of granular proteins [19]. Recent studies identified several neutrophil subpopulations, of which LDG attracts increasing attention (20–22). However, the definition of granulocyte with lower density is not unified. Except for LDG, many other terms are also commonly used, such as low-density neutrophils (LDN) [6, 23], granulocytic myeloid-derived suppressor cells (G-MDSC) [24, 25], polymorphonuclear myeloid-derived suppressor cells (PMN-MDSC) (26–28). Of note, the definitions and functions are reported differently for these cells. For example, PMN-MDSC and LDN are enriched in the low-density PBMCs fraction following density centrifugation and express similar surface markers, including CD11b+CD14−CD15+CD66b+ [23]. Unlike these phenotypes, Li et al. defined the PMN-MDSC as HLA-DR−/lowCD11b+CD33+CD15+CD14− [26]. Meanwhile, the ability of PMN-MDSC to suppress T cell function was contrary to that of LDN in SLE [23, 29]. Moreover, these cells have been reported in other autoimmune, cancer, and infectious diseases with either pro-inflammatory (LDG) or suppressive effects (PMN-MDSC) [22]. Overall, these cells may be at least partially identical according to the isolation protocol and phenotype identification. Therefore, it is necessary to conduct more studies on this specific subpopulation of neutrophils to achieve unification of definition and identification.
Compared with the UP women, we found that the LDG percentage in the NP group increased in both peripheral blood and decidua. However, this increase was not observed in SA women. These results are similar to those reported by Li et al. on the changes in PMN-MDSC during early pregnancy [26]. In addition, they reported that PMN-MDSC was the main subset of MDSC in human decidua and exhibited an immunosuppressive effect. Our study also confirmed that the LDG percentage in decidua was higher than in PBMCs during early pregnancy. Similar changes in PMN-MDSC were also reported previously [30]. Meanwhile, we found that the LDG percentage in PBMCs is associated with gestational age and significantly increased after eight weeks in a normal pregnancy but exhibited inconspicuous changes in SA. However, verifying whether a similar phenomenon exists in D-LDG requires a more extensive sample size. Moreover, the placenta begins to form and take over the function of the corpus luteum in a process termed luteoplacental shift at about eight weeks of pregnancy [2, 31], whether the increase of LDG percentage in PBMCs is a prerequisite for the normal placental formation and function and the maintenance of normal pregnancy is interesting and worth further exploration.
CD16 expresses on many immune cell surfaces and appears late during neutrophilic maturation (32–34). LDG is a mixed population of mature and immature neutrophils and can be classified into different subsets according to the CD16 expression. In adult anti-neutrophil cytoplasm autoantibody vasculitis (AAV) patients and healthy controls, Ui Mhaonaigh et al. revealed that the CD16+ cell percentage was significantly lower in LDG than in NDG, and there was a significant increase in CD16int/− cells in the LDG compared to NDG fraction. Our study revealed similar changes in CD16hi and CD16int/- LDG from UP, NP, and SA women compared to NDG. However, the changes of CD16 expression in PB-LDG were like that observed in the LDG in umbilical cord blood and granulocyte of humanized mice treated with granulocyte-colony stimulating factor (G-CSF) [33]. Considering that the increase of CD16int/− in LDG may be the result of the acute granulopoiesis-induced increase in the number of immature neutrophils [35], whether the changes of LDG and their subpopulations were a non-specific feature of acute illness need to be verified in more disease models. Nonetheless, we revealed that the percentage of CD16hi LDG in SA women was significantly lower than in UP women, but no significant difference was observed between NP and UP women. Therefore, whether a decreased CD16hi PB-LDG is associated with SA needs more clarification.
Integrin CD11b is a receptor expressed on various leukocytes, such as monocytes, neutrophils, dendritic cells, and NK cells [36, 37]. After granulocyte activation, CD11b transfers from an intracellular pool to the external surface of the neutrophil plasma and plays various biological functions, such as host defense, cellular inflammatory responses, and signal transduction [38, 39]. CD11b exhibits anti-inflammatory effects, and recent studies have shown that CD11b activation inhibits TLR-dependent inflammation and autoimmunity, thereby reducing inflammatory damage [40]. Meanwhile, CD11b deficient mice showed susceptibility to inflammatory and autoimmune diseases [41]. In sepsis and SLE models, CD11b deficiency led to increased levels of pro-inflammatory cytokines [42]. Sacks et al. reported that the expression of CD11b on the surface of leukocytes in NP women was higher than that in UP women [43]. Our study revealed similar results in neutrophils, but CD11b expression in SA women was significantly lower than in NP women, indicating that the decreased CD11b expression might affect the immune response in SA pathogenesis.
Nets are reticular structures mainly composed of citrullinated histones, myeloperoxidase, neutrophil elastase, and other components and are involved in multifarious physical and pathological conditions (44–46). This study discovered that the Nets formation during pregnancy increased in peripheral blood and decidua, and this is consistent with the view that human pregnancy is associated with a mild pro-inflammatory state characterized by circulatory neutrophil activation [47]. Nets formation during pregnancy was regulated in a multi-mode manner, such as HCG and CSF could promote NETosis, while progesterone restrained the NETotic process [48, 49]. In the mouse model of early pregnancy, abundant Nets formation at the maternal-fetal interface could lead to fetal death [14]. This study also found that the Nets formation in SA decidua was higher than that in NP decidua, indicating that excessive Nets formation may be responsible for fetal death in early SA. Moreover, PAD4 deficiency could lead to a failure of Nets formation and a significant reduction of pregnancy loss [50]. We also found that the PAD4-positive cells in the SA decidua were overwhelmingly higher than those in the NP and UP decidua, indicating that PAD4 may be a crucial intersection between the Nets formation and SA occurrence and represents a promising therapeutic target.
This study also has several limitations. Firstly, this study was carried out in a small-sized human sample, and studies with a large ample are still needed. Secondly, SA is a highly heterogeneous disease with complex and diverse etiology. Some maternal risk factors, such as chronic endometritis, endocrine abnormalities, and antiphospholipid syndrome, were not considered in the inclusion criteria, and the relevant subgroup analysis was also not conducted due to the small sample size. Thirdly, DOP-based subgroup analysis was not performed in E/D-LDG because not enough decidual tissues with DOP > 56d were collected. Finally, endometrial and decidual NDG was not isolated for study, and CD16-based subgroup analysis in E/D-LDG was not conducted because the subpopulations could not be distinguished via FCM analysis.
In conclusion, LDG in peripheral blood and decidua participates in the maintenance of early pregnancy, while dysregulated LDG, involving the percentage, phenotype and subpopulations, and ability to form Nets, is responsible for early SA. Of course, more studies with larger sample sizes, optimized functional and rescue assays, and animal experiments, even human trials, are still required in the subsequent exploration of LDG-based immunological mechanisms and therapeutic strategies of SA.
## 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.
## Ethics statement
The studies involving human participants were reviewed and approved by Chengdu Xi’nan Gynecology Hospital. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
HY conceived the project and designed experiments. HY, YD, LiL, and YJ screened and collected human tissues. HY, LaL, QZ, YS, and PY performed the experiments and acquired, analyzed, and interpreted the data. HY wrote the manuscript. YJ and JG supervised the project. 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.
## Author disclaimer
The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.
## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1119756/full#supplementary-material
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|
---
title: Prevalence and associated factors of basilar artery dolichosis in patients
with acute cerebral infarction
authors:
- Shugang Cao
- Mingfeng Zhai
- Jun He
- Ping Cui
- Tingting Ge
- Jian Wang
- Wen’an Xu
- Rongfeng Wang
- Mingwu Xia
journal: Frontiers in Medicine
year: 2023
pmcid: PMC9995486
doi: 10.3389/fmed.2023.832878
license: CC BY 4.0
---
# Prevalence and associated factors of basilar artery dolichosis in patients with acute cerebral infarction
## Abstract
### Introduction
Little attention has been given to the factors associated with basilar artery (BA) dolichosis. This study aims to elucidate the prevalence and associated factors of BA dolichosis in patients with acute cerebral infarction (ACI).
### Methods
We collected the clinical and laboratory data of 719 patients with ACI admitted to our department. Magnetic resonance angiography was used to evaluate the geometric parameters of the BA and intracranial vertebral arteries (VAs). A BA curve length > 29.5 mm or bending length (BL) > 10 mm was identified as BA dolichosis. Univariate and multivariate logistic regression were performed to determine the factors associated with BA dolichosis.
### Results
Among 719 patients with ACI, 238 ($33.1\%$) demonstrated BA dolichosis, including 226 ($31.4\%$) with simple BA dolichosis and 12 ($1.7\%$) with basilar artery dolichoectasia (BADE). Pearson correlation analyses showed that BA curve length was positively correlated with BL ($r = 0.605$). Multivariate logistic regression analysis demonstrated that current smoking (OR = 1.50, $95\%$ CI: 1.02–2.21, $$p \leq 0.039$$), diabetes mellitus (OR = 1.66, $95\%$ CI: 1.14–2.41, $$p \leq 0.008$$), BA diameter (OR = 3.04, $95\%$ CI: 2.23–4.13, $p \leq 0.001$), BA bending (OR = 4.24, $95\%$ CI: 2.91–6.17, $p \leq 0.001$) and BL (OR = 1.45, $95\%$ CI: 1.36–1.55, $p \leq 0.001$) were significantly associated with BA dolichosis.
### Conclusion
This study suggests that BA dolichosis was common in patients with ACI, and the morphological parameters of the vertebrobasilar artery and acquired risk factors (including smoking and diabetes) were risk factors for BA dolichosis.
## Introduction
Basilar artery dolichoectasia (BADE), a typical type of vertebrobasilar dolichoectasia (VBD), is characterized by elongation and dilatation of the BA. Some studies have argued that BADE may result from the combination of congenital developmental defects in the BA and the combined action of multiple risk factors for atherosclerosis (1–3) and that it may evolve dynamically [1, 4]. Passero et al. [ 1]. performed imaging follow-ups for 156 VBD patients with an average disease duration of 11.7 years and showed that $43\%$ of patients had morphological developments in the BA, including increases in BA length (BAL), BA diameter, and bending length (BL). A large BA diameter, high bifurcation of the BA, and elongation and dilatation of the anterior cerebral artery were factors correlated with morphological developments in the BA. In addition, the morphological progression of BA may further influence the prognosis of these patients (5–7).
BADE is an uncommon vasculopathy in the Chinese Han population, and many patients with simple BA dolichosis cannot be classified as having BADE. Our previous study found that among 101 patients with acute pontine infarction, 33 patients ($32.7\%$) presented with simple BA dolichosis, and only one patient ($1.0\%$) developed BADE [7]. Another investigation showed that in 346 community-dwelling older adults, 11 individuals ($3.2\%$) had BA ectasia, 36 individuals ($11.6\%$) had BA dolichosis, and only 4 individuals ($1.2\%$) had BADE [2]. However, little attention has been given to the prevalence of BA dolichosis and its associated risk factors, which is commonly observed in Chinese patients with acute cerebral infarction (ACI). The length of the BA does not remain constant [8], and its morphological remodeling may also be influenced by genetic factors (such as abnormalities in the structure and function of vascular muscle fibers and variations in different vertebrobasilar morphological indices) and acquired environmental factors (1–3). Therefore, based on this hypothesis, this study aimed to elucidate the prevalence and associated factors of BA dolichosis in the Chinese population by analyzing the clinical and imaging data of patients with ACI in our stroke unit.
## Study design and patients
This was a cross-sectional study that purposely selected patients with ACI. All patients consecutively admitted between July 2015 and June 2019 to our department were selected according to the following inclusion criteria: age 18–80 years old, admission within 7 days after onset, and diagnosis of ACI by diffusion-weighted imaging. Patients with infarct foci involving both the anterior and posterior circulation, segmental thickening of the BA or BA aneurysms, evidence of hemodynamically severe BA stenosis (≥$70\%$) or occlusion affecting data measurements, or incomplete clinical or imaging information were excluded. This study was approved by the Institutional Review Board of Hefei Hospital Affiliated to Anhui Medical University. Written informed consent was obtained from all patients or their guardians. All patients were registered in the Anhui Stroke Network Registry.
## Clinical and laboratory data
Detailed clinical data were acquired from the patients, including age, sex, current smoking status, the presence of hypertension, diabetes mellitus, and dyslipidemia, as well as admission systolic and diastolic blood pressure. Hypertension was defined as a resting systolic/diastolic blood pressure of ≥140/≥90 mm Hg on repeated measurements or if the patient was taking anti-hypertension drugs. Diabetes mellitus was diagnosed when the patient had a fasting blood glucose level of ≥7.0 mmol/L, was taking oral hypoglycemic agents, or had been treated with insulin. Dyslipidemia was diagnosed if the patient had a total cholesterol level of ≥5.60 mmol/L, a triglyceride level of ≥1.81 mmol/L, or a low-density lipoprotein level of ≥3.57 mmol/L, or if the patient had taken lipid-lowering medications for these conditions. Laboratory information, including blood glucose, total cholesterol, triglycerides, low-density lipoprotein, C-reactive protein, and homocysteine levels, was systematically recorded.
## Imaging protocol and analysis
Magnetic resonance imaging (MRI) and magnetic resonance angiography (MRA) were performed using a 1.5 Tesla MRI scanner (Siemens Healthineers, Model: Avanto I class, Germany). The geometrical parameters of the BA were analyzed by syngo 3-D VesselView. The scanning parameters and method for evaluating the BA characteristics, including BA diameter, BA curve length, BAL, BL, and BADE, were described in a previously published study [7]. Among them, BAL is defined as the linear distance from the confluence point of the bilateral VAs to the initial point of the BA division into the bilateral posterior cerebral arteries, and BL is defined as the vertical distance from the bending point of the BA to the standard line of the BAL [9, 10]. A BA curve length > 29.5 mm or BL > 10 mm was diagnosed as BA dolichosis, and BA ectasia was defined as a BA diameter > 4.5 mm at any location along its course. Patients meeting the above two criteria simultaneously were considered to have BADE [11]. The severity of the BA bending was classified as moderate (0 < BL ≤ 10 mm) and prominent (BL > 10 mm). We further evaluated the maximum bend of the BA, which was divided into the proximal, middle, and distal portions of the BA. Image analysis was performed by two experienced neurologists, and the mean values of the above parameters were recorded as the results for further analysis. The line of the BAL was used to determine the location of BA bending (toward the right or left side or straight) [10]. When there was any disagreement, a radiologist with 10 years of experience was consulted to resolve the issue. The diameters of the V4 segment of the bilateral vertebral arteries (VAs) were measured. From the vertebrobasilar artery junction, a series of three measurements with 3-mm intervals at each side was taken, and the mean diameter served as the VA diameter [12]. VA dominance was defined as a difference in the diameter of both vertebral arteries of at least 0.3 mm or as an existing asymmetry in the merging of both VAs at the vertebrobasilar junction [10, 12]. A Bland–Altman plot was used to analyze the agreement between the two readers.
## Statistical analysis
All statistical analyses were performed using SPSS version 22.0 for Windows (SPSS Inc., Chicago, IL). Normally distributed variables are expressed as the mean ± standard deviation (mean ± SD), while nonnormally distributed variables are shown as the median (M) and interquartile range (IQR). Categorical variables are expressed as absolute numbers and percentages (%). Differences in continuous variables between groups were assessed by Student’s t-test (normally distributed) or the Mann–Whitney U-test (nonnormally distributed). Differences in categorical variable distributions between groups were assessed by the χ2 test or Fisher’s exact test. Univariate and multivariate logistic regression were performed to determine the factors associated with BA dolichosis. Odds ratios (ORs) and $95\%$ confidence intervals (CIs) were subsequently calculated. Potential relationships between variables were assessed by Pearson correlation analysis, and the correlation coefficient was expressed as r. All tests used a two-sided p-value of 0.05 as a threshold for significance. All plots were drawn using GraphPad Prism software (version 8.0).
## Baseline characteristics
A total of 719 patients with ACI were included in the study, of whom 452 had anterior circulation infarction and 267 had posterior circulation infarction. The mean age was 63.7 ± 10.4 years, and $69.5\%$ were male. Among them, 238 patients ($33.1\%$) demonstrated BA dolichosis, including 226 patients ($31.4\%$) with simple BA dolichosis and 12 patients ($1.7\%$) with BADE, while other 481 patients ($66.9\%$) had no BA dolichosis. The proportion of BA dolichosis in patients with anterior and posterior circulation infarction was $32.5\%$ ($\frac{147}{452}$) and $34.1\%$ ($\frac{91}{267}$), respectively, with no statistically significant difference between the two groups. Further analysis revealed that in patients aged ≤64 years (median age), the proportion of BA dolichosis in patients with anterior and posterior circulation infarction were $35.9\%$ and $34.1\%$, respectively, while in patients aged >64 years, the proportion of BA dolichosis were $29.5\%$ and $34.1\%$, respectively.
A total of 403 patients had BA bending (including 380 patients with moderate bending and 23 patients with prominent bending), and 316 patients did not have BA bending (Figure 1). BA diameter ($p \leq 0.001$) and BL ($p \leq 0.001$) were significantly higher in patients with BA dolichosis than in those with non-BA dolichosis (Figures 2A,B). The BA curve length ($p \leq 0.001$) and BAL ($p \leq 0.001$) in patients with BA bending were significantly higher than those in patients with non-BA bending (Figures 2C,D). The proportion of BA bending was significantly higher in the BA dolichosis group than in the non-BA dolichosis group ($77.7\%$ vs. $45.3\%$, $p \leq 0.001$). No patient was diagnosed with simple BA ectasia. In 378 patients ($94.7\%$) with BA bending, the maximum bend of the BA was located at the middle of the BA, with only 9 patients bending in the proximal region of the BA and 16 patients bending in the distal portion of the BA.
**Figure 1:** *BA bending distribution in the entire study population (A); BA bending distribution in patients with BA dolichosis and non-BA dolichosis (B).* **Figure 2:** *Comparison of BA diameter and BL between patients with BA dolichosis and non-BA dolichosis (A,B); Comparison of BA curve length and BAL between patients with BA bending and non-BA bending (C,D).*
## Univariate and multivariate logistic regression analysis of factors associated with BA dolichosis
Male sex ($p \leq 0.001$), current smoking ($$p \leq 0.001$$), diabetes mellitus ($$p \leq 0.019$$), diastolic blood pressure ($$p \leq 0.013$$), BA diameter ($p \leq 0.001$), left VA diameter ($$p \leq 0.001$$), right VA diameter ($$p \leq 0.002$$), and BA bending ($p \leq 0.001$) were significantly greater in patients with BA dolichosis than in patients without BA dolichosis in univariate analysis (Table 1). After adjusting for variables with a potential association (variables with a p-value <0.1 in univariate analysis), current smoking (OR = 1.50, $95\%$ CI: 1.02–2.21, $$p \leq 0.039$$), diabetes mellitus (OR = 1.66, $95\%$ CI: 1.14–2.41, $$p \leq 0.008$$), BA diameter (OR = 3.04, $95\%$ CI: 2.23–4.13, $p \leq 0.001$), and BA bending (OR = 4.24, $95\%$ CI: 2.91–6.17, $p \leq 0.001$) were significantly associated with BA dolichosis (Table 2). When BA bending was replaced by BL, a quantitative indicator indicating the degree of BA bending, in the above logistic regression model, BL (OR = 1.45, $95\%$ CI: 1.36–1.55, $p \leq 0.001$) was also significantly associated with BA dolichosis (Table 2).
## Correlation analysis between dependent variables
Pearson correlation analyses showed a positive correlation between BA curve length and BL ($r = 0.605$, $p \leq 0.001$; Figure 3). In addition, BA diameter, left VA diameter, and right VA diameter were positively correlated with BA curve length, BAL, and BL, respectively, and the VA diameter difference was also significantly positively correlated with BL (Supplementary Figure).
**Figure 3:** *Pearson correlation analyses showed a positive correlation between BA curve length and BL (r = 0.605, p < 0.001).*
## Discussion
In the present study, we hypothesized that the length of the BA may be related to the innate geometric patterns of the vertebrobasilar artery and acquired risk factors. We found that BA dolichosis was highly associated with smoking, diabetes mellitus, BA diameter, and BA bending in patients with ACI. In addition, the BA curve length was also positively correlated with the BL, BA diameter, and VA diameter.
BADE is uncommon in stroke patients, but we found BA dolichosis to be relatively common in patients with ACI, and that there was little difference in the proportion of BA dolichosis between patients with anterior and posterior circulation infarction ($32.5\%$ vs. $34.1\%$). This may be related to the fact that simple BA dolichosis has a less pronounced effect on stroke than BADE, especially in young and middle-aged patients, which does not usually cause a higher proportion of posterior circulation infarction. However, this proportional difference may widen with increasing age, as suggested by the subgroup analysis of this study, which was more pronounced in patients older than 64 years. This also suggests that BA dolichosis may be influenced by congenital factors and develops when the patient reaches adulthood [1], yielding little difference in the proportion of BA dolichosis between younger patients with anterior and posterior circulation infarction, but may lead to an increased risk of posterior circulation infarction with advancing age and increasing atherosclerotic factors [13].
Although we briefly compared the BA geometrical properties between patients with and without BA dolichosis in our previous study [7], we did not evaluate the factors associated with BA dolichosis and the sample size was small. The present study had a sample size more than 7 times larger than the previous one and analyzed the factors associated with BA dolichosis more systematically. Previous studies have concluded that BADE diagnoses in healthy young people and children suggest congenital susceptibility as a potential cause of congenital developmental defects in the BA [14, 15]. Pathological studies have confirmed that degeneration of the internal elastic lamina and smooth muscular atrophy are the main features of BADE in adults [16]. However, it is challenging to obtain pathological data in a clinical context, but the geometric patterns of the vertebrobasilar artery that are influenced by congenital factors can be directly visualized by vascular imaging. From another perspective, this study revealed that BA curve length was positively related to BA diameter and BL; that is, a greater diameter and curvature of the BA might be related to a longer BA. The vessel radius is the most essential determinant of blood flow; a larger BA diameter leads to more blood flow and greater pulling force, thereby acting as a potential stimulus for morphological changes in the BA (e.g., elongation, ectasia, and/or curvature), especially when BA bending already exists. Multivariate analysis also demonstrated that BA diameter and BA bending were closely related to BA dolichosis, further supporting the above viewpoint. Additionally, we found that BA curve length was positively correlated with BL and that BL, a quantitative indicator representing the degree of BA bending, was another risk factor associated with BA dolichosis. However, the underlying mechanism is not clear. Presumably, in addition to being associated with congenital vascular development, the uneven blood flow within a curved BA, with the greatest blood pressure at the bend, also exacerbates the progression of BA curvature and elongation with increasing age [8].
In addition, the study by Hong et al. showed that the difference in the diameter of the VAs was the only independent predictor of moderate to severe BA dolichosis [12]. Unlike their study, we only demonstrated a positive correlation between the VA diameter difference and BL. Even so, it is still generally believed that the BA usually curves in the opposite direction of the larger VA [12]. The asymmetrical blood flow from the bilateral VAs might be an important hemodynamic contributor to BA mechanical changes, such as BA curvature and elongation [9, 11].
Risk factors for atherosclerosis may also play an important role in the development of BA dolichosis. The BA and VA morphological variants or structural deformation mentioned above can cause atherosclerosis, which in turn further aggravates BA dolichosis, and they can generate a vicious circle thereby increasing the risk of posterior circulation infarction [7, 13, 17, 18]. Known influencing factors of BADE include aging, hypertension, diabetes mellitus, and smoking, among others, which are also targets that merit particular attention and intervention [3, 9]. This study found that smoking and diabetes mellitus were also closely associated with BA dolichosis, supporting the hypothesis that atherosclerosis may be involved in the development of BA dolichosis. Previous studies have suggested that hypertension is critical in the development of VBD, especially the increase in BA diameter due to the influence of high blood pressure and subsequent hemodynamic changes [4]. However, it remains unclear whether blood pressure directly affects BA length. In this study, univariate analysis revealed that the BA dolichosis group had a high proportion of hypertension and significantly elevated diastolic blood pressure, but multivariate analysis did not confirm that they were independent influencing factors of BA dolichosis. The results may need to be further verified with larger sample studies. Additionally, there was a higher proportion of males in the BA dolichosis group. Deng et al. suggested that compared with females, males had a larger BA diameter, which is associated with BA length, while the overall height of males was greater than that of females and therefore could explain the longer BA in male individuals [19].
The present study also has some limitations. First, this study only included patients with ACI. Many patients showed a high prevalence of risk factors for atherosclerosis. Therefore, a separate or comparative analysis of influencing factors of BA dolichosis in healthy people is also warranted. Second, this study did not provide a long-term follow-up of the dynamic evolution of BA curve length. Third, this study did not evaluate the flow dynamics of the vertebrobasilar artery, which may help to elucidate the mechanism of the formation and development of BA dolichosis.
## Conclusion
In this study, we found that BA dolichosis was common in patients with ACI, and the morphological parameters of the vertebrobasilar artery and acquired risk factors (including smoking and diabetes) were risk factors for BA dolichosis.
## 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 the Institutional Review Board of Hefei Hospital Affiliated to Anhui Medical University. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
This work was conceptualized by MX, SC, and XW and all approved the protocol. Data collection was done by MZ, JH, PC, TG, SC, and JW. Statistical analysis was undertaken by MZ and SC. SC, MZ, and JH prepared the manuscript. MX and WX were recipients of the obtained funding and were involved in the interpretation of the data and the manuscript revision work was conceptualized by MX, SC, and WX and all approved the protocol. All authors contributed to the article and approved the submitted version.
## Funding
This study was supported by grants from Anhui Provincial Key Research and Development Plan (1804h08020233).
## 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/articless/10.3389/fmed.2023.832878/full#supplementary-material
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---
title: Metabolic reprogramming by Acly inhibition using SB-204990 alters glucoregulation
and modulates molecular mechanisms associated with aging
authors:
- Alejandro Sola-García
- María Ángeles Cáliz-Molina
- Isabel Espadas
- Michael Petr
- Concepción Panadero-Morón
- Daniel González-Morán
- María Eugenia Martín-Vázquez
- Álvaro Jesús Narbona-Pérez
- Livia López-Noriega
- Guillermo Martínez-Corrales
- Raúl López-Fernández-Sobrino
- Alejandro Castillo-Peña
- Lina M. Carmona-Marin
- Enrique Martínez-Force
- Oscar Yanes
- Maria Vinaixa
- Daniel López-López
- José Carlos Reyes
- Joaquín Dopazo
- Franz Martín
- Benoit R. Gauthier
- Morten Scheibye-Knudsen
- Vivian Capilla-González
- Alejandro Martín-Montalvo
journal: Communications Biology
year: 2023
pmcid: PMC9995519
doi: 10.1038/s42003-023-04625-4
license: CC BY 4.0
---
# Metabolic reprogramming by Acly inhibition using SB-204990 alters glucoregulation and modulates molecular mechanisms associated with aging
## Abstract
ATP-citrate lyase is a central integrator of cellular metabolism in the interface of protein, carbohydrate, and lipid metabolism. The physiological consequences as well as the molecular mechanisms orchestrating the response to long-term pharmacologically induced Acly inhibition are unknown. We report here that the Acly inhibitor SB-204990 improves metabolic health and physical strength in wild-type mice when fed with a high-fat diet, while in mice fed with healthy diet results in metabolic imbalance and moderated insulin resistance. By applying a multiomic approach using untargeted metabolomics, transcriptomics, and proteomics, we determined that, in vivo, SB-204990 plays a role in the regulation of molecular mechanisms associated with aging, such as energy metabolism, mitochondrial function, mTOR signaling, and folate cycle, while global alterations on histone acetylation are absent. Our findings indicate a mechanism for regulating molecular pathways of aging that prevents the development of metabolic abnormalities associated with unhealthy dieting. This strategy might be explored for devising therapeutic approaches to prevent metabolic diseases.
A multiomic approach shows that long-term exposure to the Acly inhibitor SB-204990 modulates molecular mechanisms associated with aging.
## Introduction
During the last century, humans have reached the longest life expectancy in history. However, it is remarkable that the increase in life expectancy is associated with a plethora of age-related pathologies, and it is estimated that only ~$45\%$ of humans reaching 75 years of age describe a good quality of life1. These data indicate the need to develop novel effective therapies to prevent and treat age-related complications in order to promote healthy aging. From a biological point of view, aging is the consequence of the accumulation of a great variety of molecular and cellular modifications over time, which leads to a gradual decrease in physical, metabolic, and mental capacities, as well as an increased risk of disease and death. In mammals, most of the main age-related pathologies that shorten health and life expectancy, such as atherosclerosis, diabetes mellitus, sarcopenia, and hepatic steatosis, can be derived from improper metabolic control2–4.
Acetyl-coenzyme A (Ac-CoA) is a central metabolite produced from all energy sources, such as amino acids, fatty acids, and carbohydrates5. This small molecule plays a key role in a great number of essential cellular processes, including the endogenous synthesis of fatty acids, cholesterol, and coenzyme Q5,6. Moreover, Ac-CoA is the universal acetyl group donor for protein acetylation, a post-translational modification that controls protein stability, localization, and function. ATP-citrate lyase (Acly) is the enzyme catalyzing the generation of nuclear-cytosolic Ac-CoA and oxaloacetate from citrate in the presence of ATP and coenzyme A. It is expressed ubiquitously, although greater expression is found in lipogenic tissues7–9. Moreover, several cancers have been shown to exhibit aberrantly increased Acly activity10–12. Acly is an essential enzyme, as demonstrated by the lack of viability of homozygous Acly knockout mice8. However, heterozygous Acly knockout mice are healthy and fertile and exhibit normal lipid metabolism. These results have supported that a partial loss of Acly activity is compatible with an optimal quality of life.
The central role of Acly in de novo lipogenesis has fostered a need to generate therapeutic strategies based on the use of pharmacological inhibitors as a hypolipidemic strategy for metabolic syndrome and cancer treatment13. Studies using short-term administrations of Acly inhibitors have reported promising results in refraining tumor growth or ameliorating metabolic parameters in mammals14–17. Remarkably, research using bempedoic acid, a dual Ampk activator/Acly inhibitor developed for the treatment of dyslipidemia and cardio-metabolic disease17, has provided positive results in lowering low-density lipoprotein cholesterol in clinical trials, and it is currently in the market18–20. However, the mechanisms that govern the handling of sustained Acly inhibition, and particularly how such mechanisms orchestrate long-term cellular reprogramming, remain to be elucidated.
Here we assessed the consequences of long-term exposure to the Acly inhibitor SB-204990 in mice15. We performed an unbiased multiomic approach integrating transcriptomics, proteomics, and untargeted metabolomics in murine hepatic tissue, given its central role in the maintenance of metabolic homeostasis. Analyses uncovered effects on energy metabolism, mitochondrial function, lipid metabolism, mTOR activity, as well as in the control of the folate cycle. Epigenetic studies indicated that these effects are not associated with global modulations in histone and non-histone protein acetylation. SB-204990 recapitulates certain effects of mTOR inhibitors in standard (STD)-fed mice and produces favorable effects in mice fed with a high-fat diet (HFD), which might provide therapeutic benefits against the current pandemic proportions of obesity-related metabolic disorders that predispose to unhealthy aging.
## Hepatic Acly expression is increased in aging mice
To define age-dependent metabolic changes that contribute to promote aging processes as well as to cause premature death, we evaluated different parameters of glucose homeostasis at different ages in healthy STD-fed wild-type mice. Body weight and the weight of several tissues were greater in adult and old mice (Fig. 1a, b). The assessment of oral glucose tolerance indicated the absence of major age-dependent alterations in glucose or insulin levels during the tests among young and old mice. However, significant differences, specifically in adult vs. old mice, in circulating insulin levels were observed (Fig. 1c–f). The analysis of pyruvate tolerance and insulin sensitivity indicated a reduced ability to promote hepatic gluconeogenesis as well as severe insulin resistance in aged mice (Fig. 1g–j). In fasting conditions, old mice exhibited hyperinsulinemia while maintaining normoglycemia, producing a greater index of the homeostatic model assessment of insulin resistance (HOMA-IR) (Fig. 1k–m). These data indicated that the most prominent effects of aging involve a deterioration of functionality in insulin-target tissues in the control of glucose metabolism, suggesting that these effects could contribute to the aging phenotype. We then focused on determining whether the expression levels of Acly, a gene that occupies a central role in the carbohydrate-lipid metabolism interface, are altered in an age-dependent manner in metabolic tissues. Remarkably, old mice exhibited higher levels of Acly expression and enzyme activity in the liver (Fig. 1n and Supplementary Fig. S1a). These data suggest that hepatic Acly could play an important role in the development of metabolic alterations that occur during the aging process. Fig. 1Hepatic Acly expression is increased in aging mice.a Body weight. $$n = 4$.$ One-way ANOVA. b Tissue weight. $$n = 4$.$ One-way ANOVA. c OGTT. $$n = 8$.$ Two-way ANOVA repeated measures. d Area under the curve (AUC) of the OGTT. $$n = 8$.$ One-way ANOVA. e Insulin levels during an OGTT. $$n = 8$.$ Two-way ANOVA repeated measures. f AUC of insulin levels during an OGTT. $$n = 8$.$ One-way ANOVA. g IPPTT. $$n = 8$$ for young, $$n = 8$$ for adult, $$n = 7$$ for old. Two-way ANOVA repeated measures. h AUC of the IPPTT. $$n = 8$$ for young, $$n = 8$$ for adult, $$n = 7$$ for old. One-way ANOVA. i Insulin tolerance test (ITT). $$n = 7$$ for young, $$n = 7$$ for adult, $$n = 6$$ for old. Two-way ANOVA repeated measures. j AUC of the ITT. $$n = 7$$ for young, $$n = 7$$ for adult, $$n = 6$$ for old. One-way ANOVA. k Circulating glucose levels at 16 h of fasting. $$n = 8$.$ One-way ANOVA. l Circulating insulin levels at 16 h of fasting. $$n = 8$$ for young, $$n = 6$$ for adult, $$n = 8$$ for old. One-way ANOVA. m HOMA-IR index. $$n = 8$$ for young, $$n = 6$$ for adult, $$n = 8$$ for old. One-way ANOVA. n *Acly* gene expression. $$n = 3$$–4. a.u.: arbitrary units. r.u.: relative units. BAT: Brown adipose tissue. Gastroc: Gastrocnemius. Ud: Under the threshold of detection. Data shown are the means ± SEM. * $p \leq 0.05$ Old vs. Young; #$p \leq 0.05$ Adult vs. Old; &$p \leq 0.05$ Young vs. Adult.
## SB-204990 improves metabolic and physical function in HFD-fed mice
In physiological conditions, Acly has been proposed as the main producer of cytosolic Ac-CoA5. Cytosolic Ac-CoA is used for endogenous lipid production and for the production of malonyl-coenzyme A, an inhibitor of the carnitine palmitoyltransferase I, required for fatty acid uptake into the mitochondria21. Radiolabeled [H3]-glucose incorporation into lipids was measured to indirectly assess liponeogenesis in primary hepatocytes isolated from male wild-type mice. Results indicated that SB-204990 elicits a dose-dependent inhibition of glucose-dependent de novo lipogenesis (Supplementary Fig. S2a). Cell death measured via ELISA and urea secretion indicated that toxicity occurs at concentrations greater than 10 µM, with marked toxicity at 100 µM (Supplementary Fig. S2b, c). Experiments in primary pancreatic islets isolated from wild-type mice indicated lower susceptibility to alterations promoted by SB-204990 in functional and viability tests when compared to hepatocytes (Supplementary Fig. S2d–f). Altogether, these results show that the in vitro inhibition of fatty acid biosynthesis mediated by SB-204990 inhibition of *Acly is* achievable at nontoxic concentrations (i.e., ~10 µM).
Pharmacokinetic studies were performed to determine circulating SB-204990 levels upon oral administration in mice. Results indicated that a single dose of 30 mg/kg of body weight of SB-204990 rendered a ~4 µM plasma concentration 2 h post-ingestion in the range of nontoxic concentrations used in in vitro studies (Fig. 2a). These data led us to estimate the optimal dose of SB-204990 for long-term in vivo studies at 0.25 mg compound/g of food (~4 g of food intake/day) in mice. A first cohort of young wild-type mice (5-week-old) was used to evaluate early metabolic responses and spontaneous activity (5-week treatment) by indirect calorimetry, indicating the lack of significant effects of SB-204990 (Supplementary Fig. S2g–p). A second cohort of wild-type mice was fed with a healthy STD or cholesterol-free diabetogenic HFD and were treated or not with SB-204990 for 15 weeks starting at 26 weeks of age. As expected, in vivo treatment with the Acly inhibitor SB-204990 did not alter the enzymatic activity of Acly in liver tissue (Supplementary Fig. S2q)15. The body weight of STD-fed mice was lower than mice fed with HFD (Fig. 2b). HFD-SB-fed mice exhibited lower body weight starting at week 9 of treatment when compared to HFD-fed mice. Daily energy intake and lipid content in feces were not altered by SB-204990 supplementation (Supplementary Fig. S2r, s). However, fasting-induced energy intake was higher in STD-SB when compared to their untreated counterparts (Supplementary Fig. S2t). We next investigated the effects of SB-204990 on glucose homeostasis. An oral glucose tolerance test (OGTT) and an intraperitoneal pyruvate tolerance test (IPPTT) indicated that, although animals fed with the same diets started with similar circulating glucose values, SB-204990 produced a mild impairment in glucose and pyruvate tolerance in STD-SB mice, while the administration of the compound to the HFD-fed mice had the opposite effect (i.e., improvement) when compared to untreated HFD, indicating a pivotal role of Acly in hepatic tissue (Fig. 2c–f). When challenged with an intraperitoneal insulin injection, all groups displayed a deficit in their responses when compared to the STD group (Fig. 2g, h). The determination of insulin levels during an OGTT indicated that STD-SB mice responded with a potent insulin secretion upon glucose challenge, suggesting that insulin resistance is the main underlying cause of deregulated glucose homeostasis in STD-SB mice (Fig. 2i, j). HFD-SB mice exhibited similar insulin levels during the OGTT. The evaluation of glucose levels during a 24-hour fasting period indicated that STD-SB mice exhibited higher glucose levels when compared to STD mice, while HFD-fed mice exhibited similar levels irrespective of SB-204990 supplementation (Fig. 2k, l). Glucose levels and insulin levels in fasting conditions were greater in STD-SB mice when compared to STD-fed mice, which produced a greater HOMA-IR index (Fig. 3a–c). In contrast, HFD-fed mice treated with SB-204990 exhibited lower fasting glucose, fasting insulin, and HOMA-IR values when compared to their HFD-fed counterparts (Fig. 3a–c). The percentage of glycated hemoglobin indicated a trend towards reduced levels in HFD-SB-fed mice when compared to HFD-fed mice ($$p \leq 0.07$$ in two-way ANOVA), with no differences in mice fed with a healthy diet (Fig. 3d).Fig. 2Long-term SB-204990 treatment improves metabolic health in mice fed with HFD and produces insulin resistance in STD-fed mice.a Pharmacokinetic evaluation of circulating SB-204990 levels in mice exposed to oral administration (30 mg/kg of body weight) of SB-204990. $$n = 3$.$ One-way ANOVA on Ranks. b Body weight over the course of the study. $$n = 11$$ for STD and STD-SB. $$n = 10$$ for HFD. $$n = 11$$ for HFD-SB weeks 0–13 and $$n = 9$$ for weeks 13–15. Two-way ANOVA. c OGTT at week 13 of treatment. $$n = 11$$ for STD. $$n = 9$$ for STD-SB. $$n = 10$$ for HFD. $$n = 8$$ for HFD-SB. Two-way ANOVA repeated measures. d AUC of the OGTT. $$n = 11$$ for STD. $$n = 9$$ for STD-SB. $$n = 10$$ for HFD. $$n = 8$$ for HFD-SB. Two-way ANOVA. e IPPTT at week 9 of treatment. $$n = 10$.$ Two-way ANOVA repeated measures. f AUC of the IPPTT. $$n = 10$.$ Two-way ANOVA. g ITT at week 11 of treatment. $$n = 11$$ for STD. $$n = 10$$ for STD-SB. $$n = 10$$ for HFD. $$n = 11$$ for HFD-SB. Two-way ANOVA repeated measures. h AUC of the ITT. $$n = 11$$ for STD. $$n = 10$$ for STD-SB. $$n = 10$$ for HFD. $$n = 11$$ for HFD-SB. Two-way ANOVA. i Insulin levels during an OGTT at week 10 of treatment. $$n = 10$$ for STD. $$n = 9$$ for STD-SB. $$n = 9$$ for HFD. $$n = 8$$ for HFD-SB. Two-way ANOVA repeated measures. j AUC of insulin levels during an OGTT. $$n = 10$$ for STD. $$n = 9$$ for STD-SB. $$n = 9$$ for HFD. $$n = 8$$ for HFD-SB. Two-way ANOVA. k Glucose levels during 24 h fasting at week 14 of treatment. $$n = 11$$ for STD. $$n = 9$$ for STD-SB. $$n = 9$$ for HFD. $$n = 9$$ for HFD-SB. Two-way ANOVA repeated measures. l AUC of glucose levels during 24 h fasting. $$n = 11$$ for STD. $$n = 9$$ for STD-SB. $$n = 9$$ for HFD. $$n = 9$$ for HFD-SB. Two-way ANOVA. SB: SB-204990. STD: standard diet. HFD: high-fat diet. a.u.: arbitrary units. Data shown are the means ± SEM. * $p \leq 0.05$ different times vs. time 0 or STD-SB vs. STD. # $p \leq 0.05$ HFD-SB vs. HFD.Fig. 3SB-204990 improves physical health in mice fed with HFD.a Circulating glucose levels in fasting conditions at week 14 of treatment. $$n = 10$$ for STD. $$n = 9$$ for STD-SB. $$n = 9$$ for HFD. $$n = 9$$ for HFD-SB. Two-way ANOVA. b Circulating insulin levels in fasting conditions at week 14 of treatment. $$n = 10$$ for STD. $$n = 9$$ for STD-SB. $$n = 9$$ for HFD. $$n = 9$$ for HFD-SB. Two-way ANOVA. c HOMA-IR index at week 14 of treatment. $$n = 10$$ for STD. $$n = 9$$ for STD-SB. $$n = 9$$ for HFD. $$n = 9$$ for HFD-SB. Two-way ANOVA. d Percentage of glycated hemoglobin (HbA1c) in blood at week 15 of treatment. $$n = 8$$ for STD. $$n = 8$$ for STD-SB. $$n = 10$$ for HFD. $$n = 8$$ for HFD-SB. Two-way ANOVA. e LDL/VLDL levels in serum at week 15 of treatment. $$n = 7$$ for STD. $$n = 8$$ for STD-SB. $$n = 8$$ for HFD. $$n = 8$$ for HFD-SB. Two-way ANOVA. f Total cholesterol levels in serum at week 15 of treatment. $$n = 6$$ for STD. $$n = 6$$ for STD-SB. $$n = 6$$ for HFD. $$n = 8$$ for HFD-SB. Two-way ANOVA. g Time to fall in an accelerating rotarod at week 8 of treatment. $$n = 11$$ for STD. $$n = 11$$ for STD-SB. $$n = 10$$ for HFD. $$n = 11$$ for HFD-SB. Two-way ANOVA. h Time to fall in wire hang test at week 10 of treatment. $$n = 11$$ for STD. $$n = 11$$ for STD-SB. $$n = 9$$ for HFD. $$n = 10$$ for HFD-SB. Two-way ANOVA. i Tissue weights. $$n = 10$$ for STD. $$n = 9$$ for STD-SB. $$n = 10$$ for HFD. $$n = 9$$ for HFD-SB. Two-way ANOVA. j Representative images of hematoxylin and eosin staining on several tissues. Scale 200 µm. $$n = 6$$ for STD. $$n = 7$$ for STD-SB. $$n = 5$$ for HFD. $$n = 6$$ for HFD-SB. k Adipocyte mean area. $$n = 6$$ for STD. $$n = 7$$ for STD-SB. $$n = 5$$ for HFD. $$n = 6$$ for HFD-SB. Two-way ANOVA. l Serum ALT levels in fasting conditions at week 14 of treatment. $$n = 10$$ for STD. $$n = 9$$ for STD-SB. $$n = 10$$ for HFD. $$n = 9$$ for HFD-SB. Two-way ANOVA. m Serum AST levels in fasting conditions at week 14 of treatment. $$n = 10$$ for STD. $$n = 9$$ for STD-SB. $$n = 10$$ for HFD. $$n = 9$$ for HFD-SB. Two-way ANOVA. SB: SB-204990. STD: standard diet. HFD: high-fat diet. BAT: Brown adipose tissue. Gastroc: gastrocnemius. SKM: skeletal muscle. a.u.: arbitrary units. Data shown are the means ± SEM. Unless otherwise highlighted, *$p \leq 0.05$ STD-SB vs. STD. # $p \leq 0.05$ HFD-SB vs. HFD.
In order to elucidate whether SB-204990-mediated Acly inhibition influences cholesterol homeostasis, circulating levels of low-density/very low-density lipoproteins (LDL/VLDL) and total cholesterol levels were measured (Fig. 3e, f). The administration of SB-204990 in HFD reduced circulating levels of LDL/VLDL and total cholesterol, while no changes were detected in the SB-204990-treated mice under STD when compared to their untreated counterparts. Remarkably, in both cases, values in the HFD-SB group were comparable to healthy-fed STD mice, demonstrating that SB-204990 prevents the deleterious effects of HFD on cholesterol homeostasis.
We next focused on determining the effects on motor function outcomes. Rotarod and wire hang physical performance were improved by SB-204990 in HFD, indicating the overall healthier status of these animals when compared to HFD-fed mice (Fig. 3g, h). Spatial memory was not altered by SB-204990 supplementation, irrespective of the diet (Supplementary Fig. S3a–f). Noticeably, the expression levels of cytokine Ccl2, but not those of other inflammatory or apoptotic markers (e.g., Caspase 3, Tnfα, Nfκb, and Gfap), were already upregulated specifically in the brains of untreated HFD-fed mice at the time of sacrifice (i.e., 15 weeks of treatment) (Supplementary Fig. S3g). At this time, STD-fed mice treated with SB-204990 exhibited increased white adipose tissue (WAT) mass, while mice fed with HFD supplemented with SB-204990 displayed reduced WAT mass when compared to their respective untreated diet (Fig. 3i). Histological examination of tissues indicated a massive reduction in lipid content in hepatic tissue and lower adipocyte size in HFD-SB-treated mice, while no major effects were observed in skeletal muscle and pancreatic sections (Fig. 3j, k). Similar to mice lacking WAT Acly, healthy-fed mice treated with SB-204990 exhibited greater mean adipocyte size in the WAT22 (Fig. 3j, k). Serum levels of ALT (also known as Gpt) were increased in mice fed with HFD when compared to STD-fed mice, while serum AST (also known as Got) levels were not altered in HFD-fed mice. These makers of hepatic damage remained unaltered in mice treated with SB-2049990 in either STD or HFD (Fig. 3l, m). Altogether, these results show that SB-204990 has opposite effects depending on the diet provided. In healthy STD-fed mice, SB-204990 produces an impairment in glucose homeostasis due to insulin resistance, suggesting that a certain level of Acly activity is required to sustain metabolic health. However, under metabolically challenging conditions such as HFD feeding, the inhibition of Acly promoted by SB-204990 improves metabolic and physical health.
## Metabolic reprogramming induced by SB-204990 is independent of global changes in histone acetylation
Intrigued by the contribution of Acly in cellular reprogramming in different metabolic conditions, we evaluated whether restricted Acly activity promoted by SB-204990 could alter histone acetylation, a global mechanism controlling transcription23. Incubation of AML12 hepatocyte cells with acetate, a substrate of the cytoplasmic Ac-CoA synthetase (also known as Acs, AceCS1, or Acss2) to produce cytosolic Ac-CoA, in the order of portal vein concentrations24,25, produced maximal levels of histone acetylation on all core histones assessed (Fig. 4a and Supplementary Fig. S4a). Remarkably, an equimolar abundance of citrate, the substrate of the Acly, did not enhance histone acetylation, suggesting that citrate availability is not limiting acetylation events. Supraphysiologic levels of glucose, which could promote Ac-CoA generation via glycolysis-mitochondrial metabolism, did not result in consistent increases in histone acetylation, substantiating that net increases of histone acetylation might be more dependent on cytoplasmic Ac-CoA synthetase activity rather than Acly activity. Under these cellular conditions, SB-204990 treatment did not decrease histone acetylation levels (Fig. 4a and Supplementary Fig. S4a). Differences between the effects of acetate, citrate, and glucose and the lack of effects of SB-204990 support the notion that cytoplasmic Ac-CoA synthetase is a major source of Ac-CoA in the promotion of histone acetylation events that regulate chromatin structure in AML12 hepatocytes. In mouse liver, the analysis of histone acetylation, global levels of protein acetylation, and total Ac-CoA levels confirmed the lack of differences in SB-204990-treated mice fed with a healthy diet or with HFD (Fig. 4b–f). These data suggest that compensatory mechanisms potentiate Ac-CoA generation upon SB-204990-mediated Acly inhibition. Thus, modulation of global protein (including histone) acetylation is not the root cause of the metabolic effects of SB-204990.Fig. 4Hepatic levels of histone acetylation are not altered by SB-204990.a AML12 cells were cultured under standard conditions and were treated with indicated doses of acetate, citrate, and SB-204990 for 16 h. Glucose concentration in basal conditions is 17 mM, and supplemented is 42 mM. Acid histone isolations were performed, and histone acetylation levels were analyzed by western blot. Representative images of western blots and Ponceau staining are shown. $$n = 3$.$ b Acid histone isolations were performed, and histone acetylation levels were analyzed by western blot in the livers of mice treated or not with SB-204990 for 15 weeks. Representative images of western blots and Ponceau staining are shown. $$n = 3$$ for STD. $$n = 7$$ for STD-SB. $$n = 6$$ for HFD. $$n = 6$$ for HFD-SB. c Quantification of histone acetylation levels of several residues is depicted. $$n = 3$$ for STD. $$n = 7$$ for STD-SB. $$n = 6$$ for HFD. $$n = 6$$ for HFD-SB. Student’s t-test. d Representative images of western blots of protein lysine acetylation are shown. Molecular weight markers are depicted on the left. $$n = 6$$ for STD. $$n = 7$$ for STD-SB. $$n = 5$$ for HFD. $$n = 5$$ for HFD-SB. e Densitometric quantification of hepatic protein lysine acetylation by western blot. $$n = 6$$ for STD. $$n = 7$$ for STD-SB. $$n = 5$$ for HFD. $$n = 5$$ for HFD-SB. Student’s t-test. f Total Ac-CoA levels in hepatic tissue. $$n = 5$$ for STD. $$n = 6$$ for STD-SB. $$n = 4$$ for HFD. $$n = 5$$ for HFD-SB. Two-way ANOVA. SB: SB-204990. STD: standard diet. HFD: high-fat diet. Ac-Lys: Acetylated lysine. r.u.: relative units. Ut: untreated. Data shown are the means ± SEM.*$p \leq 0.05$ STD-SB vs. STD. # $p \leq 0.05$ HFD-SB vs. HFD.
## Multiomic analysis of diet-induced and SB-204990-induced hepatic reprogramming
To gain insight into the underlying biology, we conducted a multiomic molecular profiling of the livers of mice treated with SB-204990 for 15 weeks. The transcriptional profile was evaluated in total RNA samples using cDNA microarray analyses. Untargeted GC/MS-based metabolomics was performed using the same groups of mice. Additionally, isobaric tag for absolute and relative quantification (iTRAQ) proteomics was conducted using samples from the same cohort of mice. We next developed a multiomic analysis to determine the major pathways that could explain the responses to the different diets and the alterations promoted by SB-204990 treatment. As shown in the “butterfly” Venn diagrams, shared transcripts, metabolites, and proteins modulated by the different diets and SB-204990 treatment were identified (Fig. 5a). At the transcriptional level, we observed that a high proportion of significantly modulated transcripts (p ≤ 0.05) was specific to the different experimental conditions ($\frac{425}{891}$ HFD-SB vs. HFD; $\frac{502}{948}$ HFD vs. STD; $\frac{539}{1024}$ HFD-SB vs. STD; $\frac{557}{814}$ STD-SB vs. STD). The analysis of GC/MS-based metabolomic changes revealed similar results ($\frac{1}{3}$ HFD-SB vs. HFD; $\frac{1}{3}$ HFD vs. STD; $\frac{3}{3}$ HFD-SB vs. STD; $\frac{7}{10}$ STD-SB vs. STD). The analysis of iTRAQ (p ≤ 0.05) proteomics revealed a lower degree of specificity ($\frac{141}{999}$ HFD-SB vs. HFD; $\frac{13}{751}$ HFD vs. STD; $\frac{50}{277}$ HFD-SB vs. STD; $\frac{5}{144}$ STD-SB vs. STD). Remarkably, the vast majority of modulations shared between the HFD-SB vs. HFD and HFD vs. STD were found to be reciprocal ($\frac{205}{206}$ transcripts; $\frac{1}{1}$ metabolite; $\frac{731}{731}$ proteins), indicating that changes promoted by HFD are partially reverted by SB-204990 in mice fed with HFD. We next performed a multiomic analysis to determine modulations promoted by SB-204990 treatments independent of the diet (HFD or STD) using the subsets (drop shape in butterfly Venn diagrams) of transcripts, metabolites, and proteins (Fig. 5b). Interestingly, among the common upregulated transcripts Srebf2, a master regulator of cholesterol biosynthesis, was included (Supplementary Data 1, Supplementary Table S1, and Supplementary Data 2). Using the joint pathway analysis (JPA) module from Metaboanalyst 5.026, we found that the TCA cycle, oxidative phosphorylation, branched amino acid degradation, and Ppar signaling were among the most significant ranked with the highest impact modulated pathways (Fig. 5c). Effects on mitochondrial function and fatty acid metabolism were also highlighted using the ToxList module of the Ingenuity Pathway Analysis (IPA) platform (Supplementary Fig. S5a). The analysis of Upstream regulators using IPA revealed Rictor and PPARα among the most significant effectors of SB-204990-induced modulations irrespective of the diet (Fig. 5d). We next took a different approach to understand the biology behind the responses to STD/HFD feeding and SB-204990 treatment in liver tissue. We evaluated the HFD vs. STD-responsive transcriptomic, metabolomic, and proteomic responses in the liver of mice treated or not with SB-204990. Then, we looked for signatures that were specific and common to untreated or SB-204990-treated mice27. Remarkably, only 56 transcripts, 1 metabolite, and 11 proteins were significantly altered in the same direction, indicating that SB-204990 elicits differential responses in STD and HFD diets (Fig. 5e, f). JPA analysis revealed that HFD specifically harnessed responses related to inflammatory pathways (bacterial invasion and phagosome), Ampk signaling, and retinol metabolism, whereas mice treated with SB-204990 reflected specific effects in atherosclerosis, insulin resistance, and metabolism of certain amino acids (Phenylalanine, Arginine, and Proline) (Fig. 5g, h and Supplementary Data 3). The analysis of upstream regulators using IPA highlighted Rictor as the most significantly altered regulator in untreated mice (Fig. 5i). Interestingly, Rictor was not found among the top regulators in mice treated with SB-204990 (Fig. 5j). These results indicate that the Acly inhibitor SB-204990 modulates central pathways of cellular metabolism. Fig. 5Multiomic analysis of the effects of SB-204990 in the liver; a major role in longevity pathways.a *Multiomics analysis* in liver samples using transcriptomics, proteomics, and GC/MS-based metabolomics. Significantly altered transcripts, proteins, and metabolites are shown in Venn diagrams. Upregulation (red), downregulation (blue), and reciprocal regulation (black). b Drop shape analysis indicating diet-independent significant modulation in transcripts, proteins, and metabolites altered by SB-204990 in STD and HFD. c The JPA from MetaboAnalyst 5.0 was used to generate plots indicating the p-value and impact of the top 20 pathways with the lowest p-value according to the analytical scheme shown in Fig. 7b. d The Upstream Regulators module of IPA was used to generate plots depicting the top 25 upstream regulators with the lowest p-value according to the analytical scheme shown in Fig. 7b. e Significantly altered transcripts, proteins, and metabolites are shown in Venn diagrams depicting the effects the HFD vs. STD-responsive transcriptomic, metabolomic, and proteomic responses in the liver of mice treated or not with SB-204990. Upregulation (red), downregulation (blue), and reciprocal regulation (black). f Diagram indicating significant transcriptomic, metabolomic, and proteomic responses promoted by HFD in the liver of mice treated or not with SB-204990. g JPA plots indicating the p-value and impact of the top 20 pathways with the lowest p-value of HFD vs. STD according to the analytical scheme shown in Fig. 7f. Specific pathways are highlighted in yellow. h JPA plots indicating the p-value and impact of the top 20 pathways with the lowest p-value of HFD-SB vs. STD-SB according to the analytical scheme shown in Fig. 7f. Specific pathways are highlighted in blue. i Upstream Regulators plots depicting the top 25 upstream regulators with the lowest p-value according to the analytical scheme shown in Fig. 7f. Specific regulators are highlighted in yellow. j Upstream Regulators plots depicting the top 25 upstream regulators with the lowest p-value according to the analytical scheme shown in Fig. 7f. Specific regulators are highlighted in blue. Transcriptomic analysis; $$n = 4$$ for STD. $$n = 4$$ for STD-SB. $$n = 3$$ for HFD. $$n = 3$$ for HFD-SB. iTRAQ proteomic analysis $$n = 4$.$ Metabolomic GC/MS analysis; $$n = 5$$ for STD. $$n = 7$$ for STD-SB. $$n = 6$$ for HFD. $$n = 6$$ for HFD-SB. SB: SB-204990. STD: standard diet. HFD: high-fat diet.
## Cholesterogenesis and one-carbon metabolism/folate cycle are major effectors of SB-204990 action
In order to investigate the impact of the Acly inhibitor SB-204990 in each diet, we next investigated modulation in healthy-fed (STD diet) mice as well as in unhealthy-fed (HFD) mice treated or not with SB-204990 (Fig. 6a, b and Supplementary Fig. S6a–f). Multiomic analyses using transcriptomics, metabolomics (LC/MS- and GC/MS-based), and proteomics indicated that pyruvate metabolism, cholesterol metabolism, and oxidative phosphorylation were among the most significantly altered pathways in STD mice treated with SB-204990 (Fig. 6a and Supplementary Table S2). A similar analysis in mice fed HFD exposed to SB-204990 indicated that mitochondria-related alterations/diseases exhibited the highest significance (Fig. 6b and Supplementary Data 4). Citrate/TCA cycle and one-carbon pool by folate, which have been identified as key metabolic nodes to enhance healthspan and lifespan, were highlighted with the highest impact by Metaboanalysist JPA in both diets (Fig. 6a, b)27,28. ToxList using the IPA platform, as well as wikipathways analysis using the Transcriptional Analysis Console (TAC) platform, supported alterations in fatty acid metabolism, mitochondrial function, one-carbon metabolism, and methylation (Supplementary Fig. S6a–d). Upstream IPA analysis indicated that Insulin receptor (Insr) and Ppar-α were among the most significant regulators altered in STD-SB mice (Supplementary Fig. S6e), while Rictor was found in HFD-SB mice (Supplementary Fig. S6f). Alterations in pathways controlling the synthesis and utilization of different kinds of fats drove us to determine hepatic changes in lipid species promoted by SB-204990. The analysis of hepatic cholesterol esters indicated a robust reduction in both SB-treated STD and SB-treated HFD-fed mice (Fig. 6c). Taking advantage of our unbiased multiomic approach, we observed that, similar to effects of statins, reduced hepatic cholesterol was associated with increased levels of cholesterogenic machinery, indicating effective inhibition of downstream pathways of Acly activity (Fig. 6d–f, Supplementary Data 1)29,30. The global analysis of hepatic fatty acids indicated minor differences in fatty acid species, revealing a slight reduction in palmitic acid in STD-SB mice and a modest increase in vaccenic acid in HFD-SB mice (Fig. 6g). Consequently, the analysis of triglyceride species revealed a specific decrease in triglycerides containing two palmitic acids (POP and PLP) in STD-SB mice (Fig. 6h). In HFD-SB mice we found greater hepatic levels of several triglyceride species containing two saturated fatty acids (POP, PLS) and lower levels of triglycerides containing polyunsaturated fatty acids (POL, PLL, and OOL), an effect that has been observed in Acly depleted LNCaP cells (Fig. 6h)31. Analysis of polar lipid species indicated reductions of global phosphatidylserine levels in STD-SB mice (Supplementary Fig. S6g). The analysis of modulated hydrophobic species in our untargeted LC/MS metabolomics indicated increased levels of several long-chain phosphatidylcholines species, while short-chain phosphatidylcholines were decreased (Fig. 6i). Moreover, alterations in diglyceride and phosphatidylinositol species were also detected, indicating a profound modulation in the lipidome (Fig. 6i). Remarkably, an overall agreement with these trends was also found in SB-204990 treated mice fed with HFD (Fig. 6i). In addition, we observed a robust reduction of pteridine and betaine precursors related to folate cycle and one-carbon metabolism and a concomitant increased in transmethylated carnitine in STD-fed mice treated with SB-204990 (Fig. 6j). Of note, glycerol 3-phosphate, a precursor in the synthesis of glycerolipids, was increased in the liver of STD-SB mice (Fig. 6j). Interestingly, SB-204990 failed to affect these metabolite levels in mice fed with HFD. These data suggest that SB-204990-induced modulations in the folate cycle are more prominent in healthy-fed conditions, revealing differences in how this pathway is engaged under different feeding regimens. Overall, results shown revealed that SB-204990 produces modulations in pathways related to improved survival and healthspan, which include the metabolism of several amino acids and lipids27,28.Fig. 6SB-204990 modulates fatty acid, cholesterol, and phospholipid metabolism.a, b *Multiomics analysis* in liver samples using transcriptomics, proteomics, and GC/MS- and LC/MS-based metabolomics. The JPA from MetaboAnalyst 5.0 was used to generate plots indicating the p-value and impact of significantly modulated pathways. a STD-SB vs. STD. Transcriptomic analysis; $$n = 4$.$ iTRAQ proteomic analysis $$n = 4$.$ Metabolomic GC/MS analysis; $$n = 5$$ for STD. $$n = 7$$ for STD-SB. Metabolomic LC/MS analysis; $$n = 6$$ for STD. $$n = 7$$ for STD-SB. b HFD-SB vs. HFD. Transcriptomic analysis; $$n = 3$.$ iTRAQ proteomic analysis $$n = 4$.$ Metabolomic GC/MS $$n = 6$.$ Metabolomic LC/MS analysis $$n = 6$.$ c Hepatic cholesterol esters. Chol-P: Cholesteryl palmitate. Chol-Po: Cholesteryl palmitoleate $$n = 8$$ for STD. $$n = 9$$ for STD-SB. $$n = 9$$ for HFD. $$n = 8$$ for HFD-SB. Two-way ANOVA. d Integration of multiomic analysis in cholesterol biosynthetic pathway in HFD-SB mice. Red indicates upregulation. Black indicates no significant differences. e Immunoblots for Hmgcs and Gapdh from liver homogenates. f Densitometric analyses of immunoblots shown in panel E. $$n = 6$$ for STD. $$n = 7$$ for STD-SB. $$n = 5$$ for HFD. $$n = 5$$ for HFD-SB. Student’s t-test. g Hepatic fatty acids. $$n = 9$$ for STD. $$n = 9$$ for STD-SB. $$n = 9$$ for HFD. $$n = 8$$ for HFD-SB. Two-way ANOVA. h Hepatic triglyceride species. P: Palmitic acid. O: Oleic acid. L: Linoleic acid. S: Stearic acid. $$n = 8$$ for STD. $$n = 9$$ for STD-SB. $$n = 9$$ for HFD. $$n = 8$$ for HFD-SB. Two-way ANOVA. i Analysis of significantly altered hydrophobic metabolites by LC/MS. $$n = 6$$ for STD. $$n = 7$$ for STD-SB. $$n = 6$$ for HFD. $$n = 6$$ for HFD-SB. Two-way ANOVA. j Analysis of significantly altered hydrophilic metabolites by LC/MS. $$n = 6$$ for STD. $$n = 7$$ for STD-SB. $$n = 6$$ for HFD. $$n = 6$$ for HFD-SB. Two-way ANOVA. SB: SB-204990. STD: standard diet. HFD: high-fat diet. r.u.: relative units. Ut: untreated. Data shown are the means ± SEM. * $p \leq 0.05$ STD-SB vs. STD. # $p \leq 0.05$ HFD-SB vs. HFD.
## SB-204990 targets hepatic mitochondrial function
Multiomic analysis revealed a marked effect of SB-204990 in pathways related to cellular energetics and mitochondrial pathways in STD and HFD feeding regimens (e.g., TCA cycle, pyruvate metabolism, and oxidative phosphorylation) (Fig. 6a, b, Supplementary Table S2, Supplementary Data 4, and Supplementary Fig. S7a, b). Contrary to the putative role of Acly in the production of Ac-CoA for de novo fatty acid synthesis and despite the fact that glucose incorporation into lipids was reduced by SB-204990, we found that primary hepatocytes exhibited a rapid increase in lipid content upon treatment with SB-204990 (Fig. 7a, b and Supplementary Fig. S2a). Interestingly, similar effects (e.g., larger lipid droplets/adipocyte size) have been observed in healthy-fed mice and cell cultures depleted of Acly activity (Fig. 3j, k)22,31–33. Incubation of primary hepatocytes with acetate or citrate did not increase lipid content (Fig. 7c and Supplementary Fig. S7c). Culture conditions mimicking hyperglycemia showed a trend towards increased lipid accumulation that was statistically significant only when supplemented with acetate. Under basal glucose conditions, SB-204990 produced net increases in lipid content even when culture media was supplemented with citrate and acetate. Lipid accumulation under conditions of SB-204990-mediated Acly inhibition might be explained by restricted metabolic activity. MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) test indicated that, under basal conditions, SB-204990 reduces cellular metabolism (Fig. 7d). Remarkably, acetate supplementation did not restore the metabolic activity of primary hepatocytes treated with SB-204990, supporting previous observations indicating that restricted Acly activity cannot be fully compensated with acetate supplementation22. Similar to treatments using SB-204990, experiments using bempedoic acid, a Food and Drug Administration (FDA)-approved Acly inhibitor, showed increased hepatocyte lipid content and reduced MTT activity, supporting that these effects rely on Acly inhibition (Fig. 7e, f). Intrigued by the potential effect of SB-204990 on mitochondrial performance, which could potentiate lipid accumulation, we evaluated mitochondrial functional dynamics in primary hepatocytes treated with SB-204990. SB-204990 reduced basal and maximal oxygen consumption rates (OCR) under standard culture conditions (e.g., low glucose; LG), while specifically maximal OCR was diminished in cells cultured on high glucose (25 mM) treated with SB-204990 (Fig. 7g). In standard and high glucose media, SB-204990 promoted reduction of extracellular acidification rate (ECAR) in cells challenged with oligomycin and FCCP, suggesting reduced glycolysis (Fig. 7h). Similarly, experiments using bempedoic acid also showed a reduction on maximal OCR in hepatocytes cultured with standard (5 mM) and high glucose (25 mM) concentration and restricted oligomycin-induced and FCCP-induced ECAR (Supplementary Fig. S7d, e). Direct evaluation of glycolysis in primary hepatocytes indicated that SB-204990 produced net decreases in ECAR in both basal and high glucose conditions (Fig. 7i). These results support the allosteric inhibition of glycolysis by intracellular accumulation of citrate34. We next determined whether SB-204990 could elicit effects in the mitochondria in vivo. Freshly isolated liver mitochondria of mice treated with SB-204990 (30 mg/kg of body weight; oral gavage; 3 h of treatment) exhibited a blockade and restricted increase of ADP-induced and FCCP-induced OCR, respectively (Fig. 7j). These data indicate a rapid effect of SB-204990 in mitochondrial uncoupling and suggest that factors involved in energy storage and utilization could be modulated under conditions of Acly inhibition mediated by SB-204990.Fig. 7Reduced Ampk signaling is required for SB-204990-mediated effects in mitochondrial function and lipid accumulation.a–j Primary hepatocytes were treated with SB-204990 or bempedoic acid under different experimental conditions. Unless otherwise stated, hepatocytes were treated for 16 h with 10 µM SB-204990 or 30 µM bempedoic acid. a Representative images of primary hepatocytes stained with Oil red O treated with 10 µM SB-204990. Scale bar 50 µm. $$n = 3$.$ b Quantification of Oil red O staining of primary hepatocytes treated with 10 µM SB-204990. $$n = 3$.$ Two-way ANOVA. c–d Quantification of Oil red O staining and determination of metabolic activity by MTT test in primary hepatocytes treated with 10 µM SB-204990 in the presence of indicated doses of acetate or citrate. Glucose concentration in basal conditions (low glucose: LG) is 5 mM, and supplemented (high glucose: HG) is 25 mM. c Oil Red O staining. $$n = 7$.$ Two-way ANOVA. d MTT test. $$n = 10$.$ Two-way ANOVA. e, f Quantification of Oil red O staining and determination of metabolic activity by MTT test in primary hepatocytes treated with 30 µM bempedoic acid. Glucose concentration in basal conditions is 5 mM, and supplemented is 25 mM. e Oil Red O staining. $$n = 8$.$ Two-way ANOVA. f MTT test. $$n = 9$.$ Two-way ANOVA. g Oxygen consumption rate on primary hepatocytes. $$n = 4$$ for LG. $$n = 5$$ for LG-SB. $$n = 5$$ for HG. $$n = 5$$ for HG-SB. Two-way ANOVA. h Extracellular acidification rate on primary hepatocytes. $$n = 4$$ for LG. $$n = 5$$ for LG-SB. $$n = 5$$ for HG. $$n = 5$$ for HG-SB. Two-way ANOVA. i Extracellular acidification rate on primary hepatocytes on glycolysis stress test. $$n = 5$$ for LG. $$n = 6$$ for LG-SB. $$n = 5$$ for HG. $$n = 4$$ for HG-SB. Two-way ANOVA. j Oxygen consumption rate of freshly isolated liver mitochondria of mice gavaged with SB-204990. Liver samples were collected 3 h post-gavage. $$n = 3$.$ Two-way ANOVA. k Immunoblots for hepatic proteins of mice treated with SB-204990 for 15 weeks. Densitometric quantification is shown in Supplementary Fig. S7f. $$n = 6$$ for STD. $$n = 7$$ for STD-SB. $$n = 5$$ for HFD. $$n = 5$$ for HFD-SB. l Immunoblots of primary hepatocytes treated or not with SB-204990 for 16 h. Densitometric quantification is shown in Supplementary Fig. S7i. $$n = 4$.$ m–o Primary hepatocytes were treated with SB-204990 or not in the presence of metformin (500 µM) for 16 h. m Representative images of primary hepatocytes stained with Oil red O. Scale bar 50 µm. $$n = 10$.$ n Quantification of Oil red O staining. $$n = 10$.$ Two-way ANOVA. o Quantification of metabolic activity by MTT. $$n = 10$.$ Two-way ANOVA. STD: standard diet. HFD: high-fat diet. SB: SB-204990. Bemp: Bempedoic acid. Glu: glucose. Oligo: Oligomycin. FCCP: Carbonyl cyanide-p-trifluoromethoxyphenylhydrazone. Rot: Rotenone. Ant A: Antimycin A. ADP: Adenosine diphosphate. 2-DG: 2-deoxyglucose. NS: not significant. r.u.: relative units. Ut: untreated. Data shown are the means ± SEM. Unless otherwise stated *$p \leq 0.05$ SB vs. Ut or LG-SB vs. LG. # $p \leq 0.05$ HFD-SB vs. HFD. & $p \leq 0.05$ HG-acetate vs. acetate.
We next focused on determining modulations promoted by SB-204990 in the liver of mice fed with a healthy STD or an obesogenic HFD. We found that pThr 172 Ampk phosphorylation was reduced in the liver of mice treated for 15 weeks with SB-204990, irrespective of the diet, indicating a rich energetic status. Moreover, we found markers of restricted mTOR signaling and increased Sirt1 expression in STD-SB-204990 mice, whereas unaltered mTOR signaling and increased Sirt1 and Nmnat expression were found in HFD-SB-204990 mice (Fig. 7k and Supplementary Fig. S7f). These results suggest that enhanced Sirt1 expression or activity via greater NAD+ availability and restricted mTOR activity (specifically in STD-SB mice) could be concomitant to reduced Ampk activity27,35. Importantly, markers of mitochondrial content and biogenesis were reduced in the liver of mice treated with SB-204990 fed with a healthy diet, while expression levels of Acaa2, involved in mitochondrial β-oxidation, were increased in both STD-fed and HFD-fed mice treated with SB-204990 (Fig. 7k and Supplementary Fig. S7f). Modulations of mitochondrial functionality could alter stress resistance and oxidative damage accumulation36. Therefore, we next sought to determine expression levels of markers of the DNA damage response and lipid peroxidation. Interestingly, liver lysates of STD-SB mice exhibited reduced levels of Gadd153, while the livers of HFD-SB exhibited reduced lipid peroxidation (lysine 4-hydroxynonenal). Remarkably, the expression of the antioxidant protein Sod1 was increased in the livers of STD-SB and HFD-SB mice, while markers of autophagy exhibited divergent effects (Supplementary Fig. S7g, h). Under healthy feeding conditions, Fas levels were reduced, supporting the notion that reduced mitochondrial metabolism might have a greater specific weight promoting lipid accumulation in vivo than alterations on de novo lipogenesis (Fig. 7k and Supplementary Fig. S7f). In order to determine effects occurring early upon exposure to the Acly inhibitor SB-204990, we determined modulations on pathways altered in the liver of mice using cell cultures of primary hepatocytes. Similar to hepatic tissue of healthy-fed mice, we found that pThr 172 Ampk phosphorylation and pSer 79 Acc phosphorylation were reduced in primary hepatocytes treated with SB-204990 for 16 h, whereas alterations on markers of Sirt1 or mTOR activity were absent (Fig. 7l and Supplementary Fig. S7i). Primary hepatocytes were treated simultaneously with SB-204990 and the Ampk activator metformin (Fig. 7m–o and Supplementary Fig. S7j–l). Metformin normalized metabolic activity and lipid levels in SB-204990-treated hepatocytes, indicating that these effects require the blockade of Ampk activity. Moreover, metformin further exaggerated mitochondrial effects when cells were treated with SB-204990. Similar results were obtained using the Ampk activator AICAR (Supplementary Fig. S7m–o). Importantly, pharmacological inhibition and activation of other pathways altered by SB-204990, such as mTOR and folate cycle, did not revert alterations on MTT activity and lipid content promoted by SB-204990, indicating a major role of Ampk inhibition on these effects (Supplementary Fig. S7o). Together, these results indicate that SB-204990 modulates molecular mechanisms of aging, such as liponeogenesis and mitochondrial metabolic hubs, playing an important role in the control of energy metabolism via Ampk signaling.
## Discussion
The identification of efficient compounds able to reprogram cellular metabolism to prevent metabolic diseases is critical in the current aging society. In humans, nutritional interventions promoting improved metabolic control, such as caloric restriction or exercise, are challenging or even not feasible given the medical condition of the individuals. Advances in the knowledge of molecular mechanisms of metabolic control have been obtained through studies using genetically modified organisms, limiting the translational potential of the results generated. In contrast, pharmacological interventions might, in principle, be easily applied in clinical practice. In this study, we show that SB-204990 improves metabolic and physical health in diet-induced obese mice, which are known to suffer premature death37. It is important to mention that SB-204990 elicits different physiological effects depending on the feeding diet. In mice fed with a healthy standard diet, SB-204990 produced a metabolic imbalance with moderate insulin resistance, which is reminiscent of the effects of the geroprotector mTOR inhibitor rapamycin as well as the lack of adipose Rictor38–40, while SB-204990 produced improvements in glucoregulation in mice fed with HFD. In this regard, mice treated with SB-204990 fed with a healthy diet exhibited restricted phosphorylation of 4E-bp1, a well-known marker of mTOR activation, while another marker of mTOR activation, the phosphorylation of S6, was not significantly affected. These alterations in mTOR signaling were not detected in mice treated with SB-204990 fed with HFD. These results suggest that alterations in specific branches of mTOR signaling, which could be mediated by differential activation of phosphatases or other kinases that regulate targets of mTOR, might contribute to the development of compromised glucoregulation specifically in SB-204990-treated STD-fed mice. Whether compromised glucoregulation produced by SB-204990, similar to rapamycin, could produce unhealthy aging should be the focus of further research.
A potential molecular mechanism of cellular reprogramming that could occur under conditions of SB-204990-mediated Acly inhibition is the epigenetic modulation of histones via acetylation. Several reports have shown that restriction of Acly expression produces reduced levels of Ac-CoA and limited histone acetylation in cell cultures23,41. However, separate reports have also shown that upon Acly deficiency, acetylation levels of several histone residues are not altered in cell cultures and tissues22,42. Our results indicate that the acetylation of several histones can be enhanced by acetate but not by citrate, suggesting that cytoplasmic Ac-CoA synthetase could play a greater role in providing Ac-CoA than Acly to produce global increases in histone acetylation. Under these conditions, the Acly inhibitor SB-204990 did not affect global protein acetylation levels, further suggesting that the role of Acly in this important cellular process is limited. The analysis of different tissues has shown that compensatory mechanisms might be involved in maintaining unaffected histone acetylation levels in vivo in mice fed with challenging diets or mice lacking Acly22,43. Among those, alterations in the expression or activity of Ac-CoA-generating enzymes, such as cytoplasmic Ac-CoA synthetase or malonyl-CoA decarboxylase, could participate in this process. In mouse liver, SB-204990 failed to modulate Ac-CoA levels and histone acetylation and non-histone protein acetylation levels, indicating that the effects of SB-204990 on hepatic cholesterol levels and overall physiology are separated from global changes in histone and non-histone protein acetylation. Remarkably, these data suggest that different availability of Ac-CoA, subcellular compartmentation of Ac-CoA, which has not been evaluated herein, or regulatory processes exist in the modulation of cholesterol synthesis and histone acetylation upon Acly inhibition22. Despite these results, it is not possible to rule out that transcriptional changes produced by SB-204990 are due to changes in histone acetylation levels in specific transcriptional regulatory regions of target genes. These potential changes could orchestrate metabolic arrangements that could contribute to the physiological effects of SB-204990.
Our multiomic analysis has identified novel mechanisms and confirmed the enrichment of well-established pathways that depend on Acly activity. Among expected pathways modulated under restricted Acly activity, cholesterogenesis appears to be greatly restricted. These data support the effectiveness of Acly inhibition as a therapeutic target to reduce cholesterol levels. In this regard, another Acly inhibitor that also has an Ampk activatory effect, bempedoic acid, has successfully undergone clinical trials as a cholesterol-lowering agent, and it is now approved by the FDA and the European Medicine Agency (EMA)20. Fatty acid metabolism is another anticipated pathway modulated in mice treated with SB-204990. Changes in fatty acid profiles have been observed in conditions of restricted Acly activity31, and significant changes in phosphatidylserine levels and modulations on several phosphatidylcholines, diglycerides, and triglycerides species were observed in our study. These changes, indicating a modulation towards longer phosphatidylcholine species in mice treated with SB-204990, might affect membrane fluidity, limiting the activity of proteins in lipid rafts that play an important role in cellular signaling, including insulin sensitivity44. New pathways altered by the Acly inhibitor SB-204990 include changes in amino acid metabolism, mTOR, folate cycle, and one-carbon metabolism. These pathways have been highlighted in genetic and nutritional models of extended longevity27,45–47. Remarkably, the control of mTOR on Acly activity and stability has been demonstrated48, and previous observations indicated that the lack of Acly produces reduced mTOR signaling in differentiating adipocytes49. Our data indicate that targeting Acly in healthy feeding conditions produces reduced mTOR signaling in vivo, indicating the existence of effective crosstalk among master regulators of cellular metabolism and their targets. These central pathways play a role in controlling effectors of cellular energetics50,51. Multiomic analyses also highlighted effects on cellular energetics and mitochondria. Interestingly, SB-204990, as well as bempedoic acid, promoted lipid storage despite the fact that glucose incorporation into lipids was reduced in primary hepatocytes. Although similar results (e.g., larger lipid droplets/adipocyte size) have been observed in other experimental models depleted of Acly activity22,31–33, this is particularly interesting since Acly participates in de novo lipogenesis. Our results showing that SB-204990 and bempedoic acid produce net decreases in mitochondrial oxygen consumption suggest that reduced mitochondrial metabolism could contribute to accumulate lipids in hepatocytes treated with SB-204990 or bempedoic acid. Experiments performed in primary hepatocytes indicated that neither acetate nor citrate supplementation promoted lipid accumulation. These data suggest that restricted mitochondrial metabolism or alternative Ac-CoA sources, such as malonyl-CoA via malonyl-CoA decarboxylase activity, but not acetate or citrate, could contribute to accumulate intracellular lipids in hepatocytes treated with SB-204990 or bempedoic acid. Efficient mitochondrial performance is central to achieve longer and healthy life expectancy52. Effects of SB-204990 and bempedoic acid on mitochondrial function (i.e., producing restricted oxygen consumption) are similar to other pharmacological interventions that extend lifespan, such as rapamycin and metformin, which are considered mild mitochondrial poisons36,51,53. Despite lower oxygen consumption, mice exhibited lower activation of the master regulator of cellular energetics Ampk, which reflects a rich energetic status. The Ampk activators metformin and AICAR blunted the effects of SB-204990 in the alteration of metabolic activity and promotion of lipid storage while producing a synergistic effect in mitochondrial functional dynamics, suggesting a relevant role of Acly activity in cellular energetics. These effects were associated in vivo with restricted mTOR signaling and increased levels of Sirt1 (STD-SB mice) or the NAD+ generator Nmnat (HFD-SB). Sirt1, Ampk, and mTOR have complex crosstalk but are undoubtedly intertwined in the control of Acly stability and play a central role in lifespan and healthspan38,54,55.
Our study was carried out in a rather limited number of male C57BL/6 mice. Given the different responses to diets of male and female mice and sex-specific differences in metabolic control, sex differences are plausible and may be relevant56–58. Our work provides evidence of the effects in the hepatic tissue of the Acly inhibitor SB-204990. It remains to be investigated whether similar metabolic signatures occur in other organs. Several findings described herein must be taken into consideration in any potential further work using SB-204990 and await further investigation. In particular, the effects of Acly inhibitors on different feeding regimens, Acly-independent effects of SB-204990 and/or bempedoic acid, effects of acetate metabolism on longevity pathways, and the possibility of targeting monocarbonated metabolism to achieve healthy aging in mammals represent central questions that deserve further research. Although further work is required, our study posits that pharmacological inhibitors of the Acly produce profound modulations on lipid and mitochondrial metabolism that depend on the diet consumed, which might have important consequences for physical health, metabolic control, and life expectancy.
## Animals, diets, and in vivo treatments
Mice experimentations were approved by the CABIMER Animal Committee and performed in accordance with the Spanish law on animal use RD $\frac{53}{2013}$ and the EU Directive ($\frac{2010}{63}$/EU) for animal research. Eight-week old wild-type male C57BL/6 mice were purchased from Janvier Labs (Le Genest‐Saint‐Isle, France). Mice were housed in individually ventilated cages (Tecniplast, Buguggiate, Italy). Souralit plus $\frac{29}{12}$ bedding (Souralit, Gerona, Spain) was sterilized by autoclave and added to each cage. Mice were group‐housed at 3–4 mice per cage. Mice were maintained on a 12-h light/dark cycle and had ad libitum access to rodent chow TD2914 (Envigo, Barcelona, Spain) and water until the initiation of the treatments. One cohort of mice was treated or not with SB-204990, mice were distributed in four different groups at 26 weeks of age, and treatments were initiated: [1] standard TD2914 diet (STD) with the following percentages of calories distribution: $67\%$ from carbohydrates, $20\%$ from proteins, and $13\%$ from fats. [ 2] STD + 250 mg/kg of SB-204990 (STD-SB). [ 3] a cholesterol-free high-fat diet (HFD) TD.06414 (Envigo, Barcelona, Spain) with the following percentages of calories distribution: $21.4\%$ from carbohydrates, $18.3\%$ from proteins, and $60.3\%$ from fats ($37\%$ saturated, $47\%$ monounsaturated, and $16\%$ polyunsaturated). [ 4] HFD + 250 mg/kg of SB-204990 (HFD-SB). At week 41 of life, these mice were euthanized at 16 h of fasting for the ex vivo analyses. Another cohort of mice was treated or not with SB-204990 starting at 5 weeks of age using the STD TD2914 diet. This cohort was subjected to indirect calorimetry analyses and was sacrificed at 10 weeks of life.
## Chemicals
SB-204990 was purchased from Tocris and Sreeni Labs Private Limited, and bempedoic acid was purchased from Cayman Chemical.
## Metabolic tests
For OGTTs, mice were fasted for 6 h at 10 a.m. and received a dose of glucose (3 g/kg) by gavage. For IPPTT, mice were fasted for 6 h from 10 a.m. and received an intraperitoneal injection of sodium pyruvate (2 g/kg). For the ITT, mice were fasted for 3 h from 10 a.m. and received an intraperitoneal injection of insulin (1.5 IU/kg). For the HOMA-IR, mice were fasted from 8 p.m., and determinations were performed at 16 h of fasting. To determine glucose levels, blood samples were taken by venipuncture using a Precision Xceed glucometer (Abbott, Madrid, Spain). Insulin was measured in plasma using ELISA kits (Crystal Chem, Downers Grove, IL, USA).
## RNA isolation and semi-quantitative RT–PCR
Total RNA was isolated from frozen tissues using the easy-blue total RNA Extraction kit (#17061, iNtRON Biotechnology, Inc., Seongnam, Korea). RNA concentration and quality were determined with the NanoDrop® Spectrophotometer ND-100. Total RNA (0.5–2 µg) was used to synthesize cDNA with the iScript™ cDNA Synthesis Kit (Bio-Rad Laboratories). Primer sequences are presented in Supplementary Data 5. The mRNA expression was calculated by the 2−ΔΔCT method and normalized to the expression of Rps29.
## Acly activity
Acly activity was determined as described in ref. 59. In brief, liver tissue was lysed in ice-cold 220 mM mannitol, 70 mM sucrose, 5 mM potassium HEPES buffer, pH 7.5 containing 1 mM dithiothreitol. Lysates were centrifuged at 600 × g for 10 min to precipitate the nuclei and debris. Then, the supernatant was centrifuged at 5500 × g for 10 min to precipitate the mitochondrial fraction, and the supernatant was then centrifuged at 20,000 × g for 20 min to generate a cytosolic fraction used to measure enzyme activity. Enzymatic activity was measured in 5 mM citrate, 0.3 mM coenzyme A, 3 mM ATP, 0.15 mM NADH, 10 mM MgCl2, 10 mM dithiothreitol, and 6 units/ml of malate dehydrogenase in 100 mM Tris chloride buffer, pH 8.5 at 37 °C. The NADH disappearance was monitored at 340 nm for 1 min for background, after which 5 mM citrate was added to determine Acly activity.
## Primary hepatocyte isolation
Primary hepatocytes were isolated from the livers of wild-type male mice as previously described with minor modifications60. In brief, livers were perfused with Hank’s Balanced Salt Solution (5.33 mM KCl, 0.44 mM KH2PO4, 0.34 mM Na2HPO4, 138 mM NaCl, 4.17 mM NaHCO3, and 25 mM HEPES) containing 5 mM glucose supplemented with 0.5 mM EGTA and 25 mM HEPES (pH 7.4 at 37 °C) using a CTP100 peristaltic pump (Thermo Fisher). Once the liver was perfused, the perfusion solution was changed to DMEM (Sigma-Aldrich D5546) supplemented with 100 U/ml Penicillin and 0.1 mg/ml Streptomycin (pen-strep), 10 mM HEPES, and 100 U/ml of collagenase (Type IV, Worthington). Cells were released from the liver, and the cell suspension was filtered using a 70-μM cell strainer. Then, cells were centrifuged at 50 × g for 2 min three times. Cell pellets were resuspended in DMEM (Sigma-Aldrich D5796) supplemented with pen-strep, 10 mM HEPES, 10 nM dexamethasone, 2 mM L-glutamine, 1 mM sodium pyruvate, and $10\%$ fetal bovine serum (FBS) and seeded into plates precoated with collagen type I (Sigma-Aldrich St. Louis, MO, USA). Then, 60 min post-seeding the media was changed to DMEM (Sigma-Aldrich D5546) supplemented with pen-strep, 10 mM HEPES, 100 nM dexamethasone, and $10\%$ FBS. Media was changed 3 h later to serum-free DMEM (Sigma-Aldrich D5546) supplemented with pen-strep, 5 mM HEPES, 10 nM dexamethasone, 2 mM L-glutamine, 1 mM sodium pyruvate, and cells were cultured for up to 16 h for various experiments.
## Pancreatic islet procuration and culture
Pancreatic islets were isolated from wild-type mice by intraductal collagenase perfusion as previously described61. Islets were cultured for 16 h in RPMI 1640 supplemented with $10\%$ FBS, pen-strep, 2 mM glutamine, 1 mM sodium pyruvate, 50 µM β-mercaptoethanol, and 10 mM HEPES.
## Glucose incorporation into lipids
Glucose incorporation into lipids was determined in primary hepatocytes, as previously described, with minor modifications44. Primary hepatocytes were seeded in 12-well culture dishes at 2.5 × 105 cells per well and were incubated for 16 h under different experimental conditions. After a 60-min preincubation in 20 mM HEPES, pH 7.4, 114 mM NaCl, 4.7 mM KCl, 1.2 mM KH2PO4, 1.16 mM MgSO4, 2.5 mM CaCl2, 3 mM glucose, and $1\%$ fatty acid-free bovine serum albumin at 37 °C, cells were treated with 30,000 cpm of [3-3H] glucose (0.5 Ci/mol, Perkin Elmer NET331C005MC) and plates were sealed with an optical adhesive film. The incubation was terminated 2 h later by adding 1 ml of methanol:phosphate buffered saline (PBS) (2:3) to the cells. Cells were collected with gentle pipetting, centrifuged at 700×g, and washed twice with PBS. Then, 200 μl of 0.2 M NaCl was added to the cell pellet, and the mixture was immediately frozen in liquid N2. Samples were thawed, and a mixture composed of 750 μl of CHCl3:methanol (2:1) and 50 μl of 0.1 N KOH per sample was added. Then, vigorous vortexing was applied, and the lipid and aqueous fractions were separated by centrifugation at 2000×g for 20 min. The top aqueous layer was discarded, and the bottom lipid-soluble layer was washed with 200 μl of methanol:water:CHCl3 (48:47:3). Aliquots (200 μl) of the lipid-soluble phase were transferred into scintillation vials, and radiolabeled lipids were quantified using liquid scintillation cocktail (Perkin Elmer).
## Cell death
Cell death was assessed by ELISA according to the instructions of the manufacturer (Sigma-Aldrich 115446775001). Optical density at 405 nm against 490 nm reference was determined with a Varioskan Flash spectrophotometer (Thermo Scientific). In the case of primary hepatocytes, cells were seeded in 12-well plates. In the case of pancreatic islets, 10 islets per well were used.
## MTT test
MTT activity was determined using the Cell Proliferation Kit I according to the recommendations of the manufacturer (Roche, Spain). Optical density was determined at 575 nm with a reference wavelength of 690 nm using a Varioskan Flash spectrophotometer (Thermo Scientific, Spain). In the case of primary hepatocytes, cells were seeded in 12-well or 24-well plates. In the case of pancreatic islets, 35 islets per well were used.
## Urea test
Urea production was determined in primary hepatocytes, as previously described, with minor modifications62. In brief, primary hepatocytes from wild-type mice were seeded in 12-well plates at a density of 3 × 105 cells per well and were incubated for 16 h under different experimental conditions. Then, media was replaced, and urea production was measured after 2 h using the MAK006 kit according to the manufacturer’s instructions (Sigma-Aldrich St. Louis, MO, USA) using a Varioskan Flash spectrophotometer (Thermo Scientific).
## Glucose-stimulated insulin secretion
Groups of 10 pancreatic islets were washed in 500 μl of Krebs-Ringer bicarbonate-HEPES buffer (KRBH) (140 mM NaCl, 3.6 mM KCl, 0.5 mM NaH2PO4, 0.5 mM MgSO4, 1.5 mM CaCl2, 2 mM NaHCO3, 10 mM HEPES, and $0.1\%$ BSA) and pre-incubated at 37 °C for 45 min in 300 μl of KRBH. Islets were then centrifuged, and the KRBH buffer was discarded. Subsequently, fresh KRBH supplemented with 2.2 mM glucose was added, and islets were incubated for 30 min. Next, the buffer was harvested (basal insulin secretion), and 500 μl of KRBH supplemented with 22 mM glucose was added. Islets were incubated for 30 min at 37 °C, and then the buffer was harvested (stimulated insulin secretion). Insulin levels were measured using a mouse insulin ELISA kit according to the manufacturer’s instructions (Crystal Chem).
## Pharmacokinetic studies
Wild-type mice received an oral gavage of SB-204990 (30 mg/kg of body weight), and serum samples were collected at different time points. SB-204990 in serum samples was quantified by a chromatography and mass spectrometry method. Briefly, 150 µl of acetonitrile containing 2,4,dichlorohydrocinnamic acid (internal standard) were added to 50 µl of serum samples, which were vortex-mixed and centrifuged at 20,000 × g at 4 °C for 15 min. Then, 110 µl of supernatant was transferred to the autosampler vial for analysis. The chromatographic experiments were carried out on an Agilent 1290 HPLC system (Agilent, CA). The separation of SB-204990 was accomplished using a C18 Supelco 50 × 2.1 mm, 3 µm column set at 4 °C. The mobile phase consisted of water/acetonitrile $\frac{90}{10}$ with $0.1\%$ formic acid as component A and acetonitrile/water $\frac{90}{10}$ with $0.1\%$ formic acid as component B using a linear gradient. Mass spectrometry analysis was performed using the mass spectrometer model API 4000 system from Applied Biosystems/AB Sciex. The data were acquired and analyzed using Multiquant 2.1.1. ( Sciex). Negative electrospray ionization data were acquired using multiple reaction monitoring (MRM). Standards were characterized using the MRM transitions ($\frac{389}{329}$) for SB-204990 and (218,$\frac{9}{174.8}$) 2,4,dichlorohydrocinnamic. The instrumental settings were 500 °C and 4500 V, and compound parameter settings for collision energy were −24eV for SB-204990 and −16eV for 2,4,dichlorohydrocinnamic.
## Indirect calorimetry
Mouse metabolic rate was assessed by indirect calorimetry in an OxyletPro system (PanLab Harvard Apparatus). Mice were housed singly with water and food available ad libitum and maintained at ~22 °C under a 12:12-h light:dark cycle (light period 08:00–20:00). The concentrations of oxygen and carbon dioxide were monitored at the inlet and outlet of the sealed chambers to calculate oxygen consumption. Each chamber was measured for 60 s at 10-min intervals, and data were recorded for ~48 h total. Locomotor activity was monitored using an infrared photocell beam (rearing) and a sensor platform (activity). Food and water intake were automatically monitored using sensors in each cage.
## Barnes Maze
The method was performed according to a previously published protocol63. In brief, on the pre-training trial, mice were pre-trained to enter the escape box, guiding them to the escape box. Mice remained in the box for 2 min. Then, training trials were initiated (4 days). Mice were trained four trials per day, and trials were separated by 15 min. Mice were allowed to explore the maze for up to 3 min and were guided to the escape box. Once the mice entered the box, the buzzer was turned off, and the mice remained in the box for 1 min. The following day, subjects received a probe trial for 90 s to determine short-retention memory. During the probe trial, the escape box was removed. Primary latency and total attempts were recorded. Without further training, 7 days after, mice were tested for another probe trial for 90 s to determine long-term retention memory.
## Fecal lipid content
Lipid extraction from 1 g of dried feces was performed by chloroform-methanol as previously described in ref. 64. In brief, feces were lysed, and $0.9\%$ NaCl solution was added and vortexed. Then, a similar volume of chloroform:methanol (2:1) was added, and samples were vortexed. Samples were centrifuged at 1000 × g for 10 min, and the lipid-containing nonpolar phase was extracted. Then, samples were air-dried, and lipids were quantified.
## Glycated hemoglobin
HbA1c levels were determined in blood samples according to the manufacturer’s protocol (Crystal Chem).
## Cholesterol and LDL/VLDL determinations
Cholesterol and low-density/very low-density lipoprotein (LDL/VLDL) in serum were determined using the EnzyChrom Assay kit (BioAssay Systems, E2HL-100), according to the manufacturer’s instructions.
## Rotarod
Results from rotarod tests are presented as the time to fall from an accelerating rotarod (4–40 rpm over 5 min). Mice were given a 1-minute habituation trial at 4 rpm on the day before the experiment. The results shown are the averages of three trials per mouse65.
## Wire hang
For the wire hang test, mice were allowed to grip a horizontal 1 mm wire with four paws for up to 60 s, and the latency to fall from the wire was determined. Three different trials were performed with each mouse, and the results shown are the averages of the three trials65.
## Determination of AST and ALT
Serum concentrations of AST and ALT were measured using a Cobas Integra 400 plus automated analyzer (Roche Diagnostics).
## Histochemistry and cytochemistry
Dissected tissues and cell cultures were fixed in $4\%$ paraformaldehyde. Tissue sections (5 μm thick) were deparaffinized and rehydrated as previously described in ref. 60. For hematoxylin and eosin staining, sections were immersed in hematoxylin (Merck) and eosin (Merck) for 4 and 2 min, respectively. Evaluation of lipid content was performed using Oil red O staining. Quantification of lipid content was performed after isopropanol solubilization at 510 nm. The histological study of the stained sections was carried out using a Leica DM6000B microscope equipped with a DFC390 camera (Leica, Barcelona, Spain) and an Olympus IX71 with an Olympus DP70 camera (Olympus). Adipocyte size was quantified with ImageJ software (National Institutes of Health).
## AML12 cell culture
AML12 cells were obtained from ATCC and were maintained and propagated in DMEM/nutrient mixture F-12 Ham with $10\%$ fetal bovine serum, pen-strep, ITS Liquid Media Supplement (Sigma-Aldrich), and 0.1 μm dexamethasone. For experiments, cells were exposed to $10\%$ dialyzed FBS in DMEM/nutrient mixture F-12 Ham containing 17 mM glucose and were treated with additional glucose (up to 42 mM), citrate, acetate, and SB-204990 for 16 h. Samples were snap frozen and maintained at −80 °C until processed.
## Acid histone extraction
AML12 cell pellets (2 × 106 cells) and liver samples were cut into small pieces (60–70 mg) and transferred to an Eppendorf tube. Thereafter, 1 ml of Triton Extraction Buffer (TEB; PBS containing $0.5\%$ Triton X 100 (v/v), $0.02\%$ (w/v) NaN3) + inhibitors (Trichostatin 10 μM, nicotinamide 10 mM, sodium butyrate 50 mM, with protease, and phosphatase inhibitors P0044, P5725, and P8340) were added per 200 mg of tissue/1 × 107 cells, and samples were homogenized. Then, lysates were incubated on rotation for 10 min at 4 °C, and the mixture was centrifuged at 2000 rpm for 10 min at 4 °C. The supernatant was removed, and the pellet was resuspended in 100 μl of 0.2 N HCl and incubated on rotation overnight at 4 °C. Afterwards, samples were centrifuged at 2000 rpm for 10 min at 4 °C, and the supernatant was transferred to a new tube. Next, HCl was neutralized with NaOH 0.2 N, and samples were stored at −80 °C until used.
## Western blot
For total protein isolation, samples were lysed in radioimmunoprecipitation assay buffer (20 mM Tris–HCl, 150 mM NaCl, 1 mM Na2EDTA, 1 mM EGTA, $1\%$ NP-40, $1\%$ sodium deoxycholate, pH 7.5) with protease, phosphatase, and deacetylase inhibitors P0044, P5725, P8340, and SC-362323. Western blots were performed according to standard methods, which involved, in certain cases, membrane stripping and incubation with a primary antibody of interest, followed by incubation with a horseradish peroxidase-conjugated secondary antibody and enhanced chemiluminescence. Antibodies used are presented in Supplementary Data 5. Blots were quantified with ImageJ, and the bands of interest were normalized to Ponceau S, β-actin, and/or Gapdh staining, as previously validated in refs. 66,67.
## Ac-CoA determination
Ac-CoA was determined using the MAK039 kit following the instructions of the manufacturer (Sigma-Aldrich).
## Transcriptome profiling
cRNA preparations were hybridized to mouse Clariom™ S Assay Array chips using the standard protocols of the Genomic Core Facility of CABIMER (Affymetrix, Santa Clara, CA, USA). Briefly, 50 ng of total RNA from each biological sample was used as a template to prepare biotinylated fragmented cRNA using a two-cycle amplification step. Image analysis, quality control, and quantification of data were performed using the Affymetrix GeneChip Command Console Software 2.0. Statistic data analysis was performed using the Transcriptome Analysis Console (TAC), which included the processing of fluorescence data (raw data), data normalization, the exchange value of expression of a condition regarding control, and statistical parameters appropriate to establish a degree of credibility (p-value) (Affymetrix). Transcripts that showed p-value <0.05 were selected as differentially expressed genes. In the TAC analyses module, Wikipathways was used. Raw data are accessible at GSE196853.
## Untargeted metabolomics
Metabolite extraction was performed in frozen tissues using 400 µl of MeOH:water ($\frac{8}{1}$, v-v). After vortexing, samples underwent five repeated freeze–thaw cycles and were kept on ice for 1 h. Then, samples were centrifuged (10 min, 15200 rpm at 4 °C), and 100 μl and 250 μl of supernatant were transferred to autosampler vials for respective liquid chromatography/mass spectrometry (LC/MS) and gas chromatography/mass spectrometry (GC/MS) analysis. For LC/MS and MS/MS analysis, samples were injected into a UHPLC system (1290 Agilent) coupled to a quadrupole time of flight (QTOF) mass spectrometer (6550 Agilent Technologies) operated in positive electrospray (ESI+) and negative electrospray (ESI−) ionization mode. Tissue extracts were separated using a Waters Acquity UPLC BEH HILIC (1.7 µm, 2,1 × 150 mm). The solvent system was $A = 50$ mM ammonium acetate in water and B = acetonitrile/water with 50 mM ammonium acetate. The linear gradient elution used started at $95\%$ A (time 0–2 min) and finished at $95\%$ B (9.5 min). The injection volume was 2 μl. ESI conditions were gas temperature 150 °C, drying gas 11 L/min, nebulizer 35 psig, fragmentor 120 V, and skimmer 65 V. The instrument was set to acquire over the m/z range of 50–1200 with an acquisition rate of 3 spectra/s. MS/MS was performed in targeted mode, with a default iso width (the width half-maximum of the quadrupole mass bandpass used during MS/MS precursor isolation) of 1,3 m/z (narrow). The collision energy was fixed at 10, 20, 30, and 40 V. For GC/MS analysis, samples were dried under a stream of N2 gas and lyophilized before chemical derivatization with 40 µl of methoxyamine in pyridine (30 µg/ml) for 45 min at 60 °C. Subsequently, samples were silylated using 25 µl of N-methyl-N-trimethylsilyltrifluoroacetamide with $1\%$ trimethylchorosilane (Thermo Fisher Scientific) for 30 min at 60 °C to increase the volatility of metabolites. A 7890B GC system coupled to a 7250 QTOF mass spectrometer (Agilent Technologies) was used for GC-MS analysis. Derivatized samples (1 µl) were injected in the gas chromatograph system equipped with an Agilent 19091S-433UI HP5-ms Ultra Inert 86 stationary phase column (30 m × 0.25 mm × 0.25 μm). Helium was used as a carrier gas at a flow rate of 1.5 ml/minute in constant flow mode. The temperature gradient used was from 70 to 190 °C at a heating rate of 11 °C/m and from 190 to 325 °C at 21 °C/minute, finally holding for 4 min. Electron impact ionization was conducted at 70 eV, and the source temperature was set to 230 °C. Mass spectra were recorded after a solvent delay of 3 min, with the analyzer acquiring in full-scan MS mode at a rate of 5 scan/sec within a mass range of 35–700 m/z. Upon acquisition, raw LC/MS proprietary vendor files were converted to the open standard format mzML using Proteowizard MS-convert68 and subsequently processed by XCMS software (version 3.10.2)69. XCMS analysis of these data provided a matrix containing the retention time, m/z value, and integrated peak area for aligned features across samples (a feature being defined as molecular entity with a unique m/z and a specific retention time). QC samples were used to account for analytical variation. In LC/MS experiments, features significantly modified by SB-204990 in STD and HFD were prioritized for MS/MS identification in targeted mode. Metabolites were identified as conforming to Level 2, as specified by Schymanski et al., since their accurate mass and experimental MS/MS spectra coincide with the fragmentation pattern of chemical standards from the METLIN, MassBank, and/or NIST17 databases70. For GC/MS analyses, raw GC/MS data were converted to mzXML format using Proteowizard MS-convert. Subsequent data deconvolution and alignment were performed using eRah, and metabolite identification was conducted by matching fragment spectra with compound spectra present in the Golm database and NIST17 libraries71. Data processing was conducted in R versions 3.6.1 and 4.0.3 (R-Foundation for statistical computing, www.Rproject.org). Raw data are accessible at 10.5281/zenodo.6222260.
## Proteomic analysis
iTRAQ labeling with two 8-plex iTRAQ kits was performed in the Proteomic facility of the Institute of Biomedicine of Seville using their standard protocols. Individual liver samples were isolated in urea lysis buffer (8 M urea, 25 mM Tris, 100 mM NaCl, 25 mM NaF, 10 mM Na4P2O7, 50 mM β-glycerophospate, 1 mM Na3VO4, 1:100 protease inhibitors, and 1:100 deacetylase inhibitors, pH 8). Samples were sonicated for 10 s and centrifuged at 20,000 × g for 15 min at 4 °C. The supernatant was harvested and stored at −80 °C. Then, samples were reduced with 50 mM tris-(2-carboxyethyl)phosphine (AB Sciex) at 60 °C for 1 h with shaking and were subsequently alkylated using 200 mM S-methyl methanethiosulfonate (AB Sciex) for 30 min at room temperature. Samples were then trypsinized at 37 °C in a 10:1 ratio (w/w) of substrate/enzyme in a water bath overnight (Promega). Then, samples were speed-vac dried. The iTRAQ-labeling assay was conducted according to the manufacturer’s instructions (iTRAQ 8-plex, AB Sciex). Briefly, peptides were reconstituted in 1 M triethylammonium bicarbonate (Sigma-Aldrich St. Louis, MO, USA), $0.05\%$ SDS, 1:100 phosphatase inhibitor cocktail, 1:100 protease inhibitor cocktail, and $0.002\%$ benzonase (Novagen, Argentina) and labeled with one isobaric amine-reactive tag. After 2 h of incubation, labeled samples were pooled, dried at 45 °C, and stored overnight at 4 °C. iTRAQ-labeled samples were desalted using Oasis HLB C18 cartridges (Waters, Milford, MA, USA) and dried using a vacuum centrifuge. Peptides were then prefractionated using MCX Oasis columns (Waters) and increasing concentrations (50–2000 mM) of ammonium formate. Fractions were collected, individually washed using C18 cartridges, and dried. Peptides from each fraction were separated using nano-liquid chromatography (nano LC 1000, Thermo Scientific) and analyzed by means of nano-electrospray ionization (Proxeon Biosystems, Odense, Denmark) connected to a Q Exactive Plus Orbitrap mass spectrometer (Thermo Scientific). Briefly, 13 µl of each fraction was loaded, preconcentrated, and washed in an Acclaim PepMap (75 µm × 2 cm, nanoViper, C18, 3 µm, 100 Å) precolumn (Thermo Scientific). Peptides were separated in an analytical column (75 µm × 15 cm, nanoViper, C18, 2 µm, 100 Å, Acclaim PepMap RSLC) for 240 min at 200 nL/minute (Thermo Scientific). Peptides were eluted with a gradient of buffer A ($0.1\%$ formic acid, $100\%$ H2O) to buffer B ($0.1\%$ formic acid, $100\%$ acetonitrile). The Q Exactive system was used for MS/MS analysis in the positive ion and information-dependent acquisition mode. Proteins were identified and quantified using Proteome Discoverer (v2.1, Thermo Fisher Scientific), using three embedded search nodes; Mascot72 (v2.5.1), Sequest HT (Thermo Fisher Scientific), and MS Amanda73 (v2.1.5) search algorithms. The Percolator algorithm was used to calculate the false discovery rate (FDR) of peptide spectrum matches, set to a q-value of 0.0574. Entrez labels and gene names were retrieved with the R interface to Uniprot web services R package version 2.20.0. Raw counts were normalized using the weighted trimmed mean of M-values method75, and the batch effect for the two iTRAQ experiments was removed using ComBat76. The contrast between different conditions was carried out with the quasi-likelihood test implemented in edgeR77. Raw data are accessible at 10.5281/zenodo.6140992.
## Multiomic analyses
Transcriptomic, proteomic, and metabolite profiles were analyzed using MetaboAnalyst version 5.026 and Ingenuity pathway analysis (IPA) (Qiagen). Significantly modulated transcripts, proteins, and metabolites were included in the analyses, and protein levels were prioritized when protein and transcript levels were available. Multiomics analyses were performed utilizing the Joint Pathway Analysis (JPA) module from MetaboAnalyst 5.0. including the settings “All pathways; hypergeometric test, closeness centrality, combine queries”. IPA analyses used the modules Upstream Regulators, ToxList, and Canonical Pathways.
## Hepatic lipid analyses
Hepatic lipids were extracted from ~20 mg of tissue using hexane-isopropanol, as previously described in ref. 64. Samples were analyzed by GLC as previously described in ref. 78. In brief, fatty acid methyl esters (FAMES) were obtained from isolated lipids by heating the samples at 80 °C for 1 h in 3 ml of methanol/toluene/H2SO4 (88:10:2 v/v). Heptadecanoic acid ($\frac{1}{10}$ w/w) was added to each sample as an internal standard to allow quantification. After cooling, 1 ml of heptane was added, and the samples were mixed. FAMES were recovered from the upper phase and then separated and quantified using a Hewlett–Packard 5890A gas chromatograph (Palo Alto, CA, USA) with a Supelco SP-2380 capillary column of fused silica (30 m length, 0.25 mm i.d., 0.20 μm film thickness) (Bellefonte, PA, USA). Hydrogen was used as the carrier gas, with a linear gas rate of 28 cm/s. The detector and injector temperatures were set at 220 °C, and the oven was set at 170 °C, with a split ratio of 1:50. Fatty acids were identified using standards (Sigma-Aldrich, St. Louis, MO, USA). Triacylglycerides and cholesterol esters were separated and quantified by GLC as previously described79 with an Agilent 6890 gas chromatograph (Palo Alto, CA, USA). Triheptadecanoic acid was added to the samples as the internal standard for quantification. The injector and detector temperatures both were 380 °C, the oven temperature was 345 °C, and a head pressure gradient from 70 to 120 kPa was applied, changing this last parameter depending on the column. The gas chromatography capillary column was a J & W Scientific DB-17HT (15 m length, 0.25 mm i.d., 0.15 μm film thickness) (Folsom, CA, USA), with a linear gas rate of 50 cm/s, the split ratio was 1:80, and the detector was a flame ionization detector (FID). The different triacylglycerides and cholesterol esters were identified with respect to known samples, and the FID response was corrected. Polar lipids were analyzed and quantified by HPLC80. Separation by HPLC was carried out in a Waters 2695 Module (Milford, MA) equipped with a Waters 2420 ELSD. The column used was a Lichrospher 100 Diol 254-4 (5 μm; Merck) applying a method based on a linear binary gradient of solvent mixtures containing different proportions of hexane, 2-propanol, acetic acid, water, and trimethylamine. The flow was 1 ml/min, data were processed using Empower software, and the ELSD was regularly calibrated using commercial high-purity standards for each lipid.
## Oxygen consumption and ECAR
Mitochondrial bioenergetics on primary hepatocytes were measured using an XF24 Extracellular Flux Analyzer (Agilent)81. After 16 h of treatment, cells were washed with Seahorse assay media (Seahorse Bioscience), which was supplemented with glucose at 10 mM, pyruvate at 1 mM, and glutamine at 2 mM, pH 7.2. Cells were incubated in a CO2-free incubator at 37 °C for 1 h. Then, OCR and ECAR were measured. OCR and ECAR were determined in basal conditions and through consecutive injections of oligomycin (4 μM) at minute 27, carbonyl cyanide 4-(trifluoromethoxy) phenylhydrazone (FCCP; 2 μM) at minute 52, rotenone (1 μM) at minute 78, and antimycin A (5 μM) at minute 104. For the glycolysis stress test, after 16 h of treatment, cells were washed with Seahorse assay media (Seahorse Bioscience), which was supplemented with L-glutamine at 2 mM, pH 7.2. Cells were incubated in a CO2-free incubator at 37 °C for 1 h. ECAR was determined in basal conditions and through consecutive injections of glucose (10 mM) at minute 27, oligomycin (1 μM) at minute 52, and 2-deoxyglucose (50 mM) at minute 78. OCR rates in fresh liver mitochondria were determined in samples of mice exposed to an oral gavage of SB-204990 (30 mg/kg of body weight) following the isolated mitochondria protocol of Agilent. In brief, 3 h after gavage, the liver was extracted, and the quadrate lobule was transferred to a gentleMACSTM dissociation tube with 5 ml of ice-cold MSHE buffer (70 mM sucrose, 210 mM mannitol, 5.0 mM HEPES, 1.0 mM EGTA, $0.5\%$ (w/v) fatty acid-free BSA, pH 7.2). Subsequently, the tissue was homogenized on a gentleMACS tissue dissociator under the mouse-mito tissue isolation cycle protocol. Fresh samples were filtered twice through a 40-µm mesh filter and finally through a 10-µm mesh filter. The filtrate was centrifuged at 9000 × g for 10 min at 4 °C, and the pellet was resuspended in 200 µl of ice-cold BSA-free MSHE to quantify protein content. Then, fatty acid-free BSA $0.5\%$ (w/v) was added, and samples were diluted in warm (37 °C) MAS buffer (70 mM sucrose, 220 mM mannitol, 10 mM KH2PO4, 5 mM MgCl2, 2 mM HEPES, 1 mM EGTA, 10 mM succinate, 2 µM rotenone, $0.5\%$ (w/v) fatty acid-free BSA, pH 7.2). Then, 5 µg of mitochondria per well were plated in Seahorse plates, and plates were centrifuged at 2000 × g for 20 min at 4 °C using a swinging bucket microplate adapter. OCR was determined through consecutive injections of ADP (4 mM) at minute 13, oligomycin (3.16 µM) at minute 20, FCCP (4 µM) at minute 26, and antimycin A (4 µM) at minute 31.
## Statistics and reproducibility
The statistical analysis was performed using SigmaPlot 12.0 (SigmaPlot, Barcelona, Spain) and GraphPad Prism 7 (GraphPad Software Inc, San Diego, CA). Different statistical tests based on the number of groups and recommendations of the statistical SigmaPlot 12.0 and GraphPad Prism 7 were used. Statistical tests are reported in figure legends. Normality was assumed in statistics. Dunn’s post hoc test was used in ANOVA on ranks. Dunnett’s post hoc test was used in Supplementary Fig. S2c, and Bonferroni post hoc test was used in Fig. 7b and Supplementary Fig. S2g, i, m, o. The remaining analyses were performed using Tukey’s post hoc test. Data are shown as means ± SEM. Significance is reported at p ≤ 0.05.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Peer Review File Supplementary Information Description of Additional Supplementary Files Supplementary Data 1 Supplementary Data 2 Supplementary Data 3 Supplementary Data 4 Supplementary Data 5 Reporting Summary The online version contains supplementary material available at 10.1038/s42003-023-04625-4.
## Peer review information
Communications Biology thanks Suowen Xu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Eve Rogers. Peer reviewer reports are available.
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|
---
title: Prophylactic and curative effects of Carica papaya Linn. pulp extract against
carbon tetrachloride-induced hepatotoxicity in male rats
authors:
- Nadia Zaki Shaban
- Olfat M. Awad
- Ghada M. Fouad
- Afaf M. Hafez
- Ahmed Alaa Abdul-Aziz
- Sarah M. El-Kot
journal: Environmental Science and Pollution Research International
year: 2022
pmcid: PMC9995559
doi: 10.1007/s11356-022-24083-5
license: CC BY 4.0
---
# Prophylactic and curative effects of Carica papaya Linn. pulp extract against carbon tetrachloride-induced hepatotoxicity in male rats
## Abstract
Several chemicals and medications induce cellular damage in various organs of the body by activating reactive substances’ metabolism leading to various pathological conditions including liver disease. In this study, we evaluated the prophylactic and curative effects of *Carica papaya* Linn. pulp water extract (PE) against CCl4-induced rat hepatotoxicity. Five groups of rats were created, control, PE, CCl4, (PE-CCl4): The rats were administered with PE pre and during CCl4 injection, and (PE-CCl4-PE): The rats were administered with PE pre, during, and after CCl4. The markers of oxidative stress (“OS”: oxidant and antioxidants), inflammation [nuclear factor-κB, tumor necrosis factor-α, and interleukin-6], fibrosis [transforming growth factor-β], and apoptosis [tumor suppressor gene (p53)] were evaluated. Additionally, liver functions, liver histology, and kidney functions were measured. Also, PE characterization was studied. The results showed that PE, in vitro, has a high antioxidant capacity because of the existence of phenolics, flavonoids, tannins, terpenoids, and minerals. Otherwise, the PE administration [groups (PE-CCl4) and (PE-CCl4-PE)] exhibited its prophylactic and therapeutic role versus the hepatotoxicity induced by CCl4 where PE treatment improved liver functions, liver histopathology, and renal functions by decreasing oxidative stress, inflammation, fibrosis, and apoptosis induced by CCl4. Our study elucidated that PE contains high amounts of phenolics, flavonoids, tannins, terpenoids, and ascorbic acid. So, PE exerted significant prophylactic and curative effects against hepatotoxicity induced by CCl4. These were done by enhancing the markers of antioxidants and drug-metabolizing enzymes with reductions in lipid peroxidation, inflammation, fibrosis, and apoptosis. PE administration for healthful rats for 12 weeks had no negative impacts. Consequently, PE is a promising agent for the prohibition and therapy of the toxicity caused by xenobiotics.
## Introduction
People are constantly exposed exogenously to different amounts of chemicals. These chemicals have been revealed to have mutagenic or carcinogenic properties in experimental frameworks. Exposure can happen exogenously when these chemicals are present in air, food, or water, and endogenously when they are metabolized and yield pathophysiological states such as inflammation. Toxicants are artificial toxic chemicals, and they could be created by humans or occur naturally (Manahan 2009). Alternatively, toxins are poisons produced in the living cells or organs of animals, insects, plants, and bacteria (Manahan 2009; Hodgson 2011). Toxicants (xenobiotics) are characterized by vast production and distribution processes, and increasing ubiquity in the environments, homes, and bodies. Toxicants can be present in various forms in the air, water, food, and soil (Manahan 2009; Hodgson 2011). Toxicants are processed in the human body through enzyme-catalyzed phase I and phase II processes. Lipophilic xenobiotic substances are prone to phase I reactions, which make them more water-soluble and interact through polar functional group correlation. The cytochrome P-450 enzymatic system catalyzes the majority of phase I operations, which are microsomal mixed-function oxidase reactions. Conjugated reactions are referred to as phase II reactions. It happens when an endogenous type is related to the activity of an enzyme on a polar functional group, which is usually the result of phase I xenobiotic reactions. The product of the conjugation of the phase II reactions is usually less soluble in lipids, more soluble in water, less toxic than the original xenobiotic compound, and easier to eliminate from the body (Chen 2012). In addition to the cellular response to cytokines, bacterial invasion, and xenobiotics, reactive oxygen species (ROS) are produced by mitochondrial oxidative metabolism. The imbalance caused by overflowing ROS or oxidants over the cell’s ability to develop an effective antioxidant response is referred to as oxidative stress (OS) (Shaban et al. 2021c). Various disease conditions, such as diabetes, atherosclerosis, neurodegeneration, and cancer, are linked to the OS, which causes macromolecular damage (Diao et al. 2011; Lixin et al. 2019). The oxidation of cysteine residues on proteins by ROS changes protein structure and/or function. When cysteine residues are oxidized, reactive sulfenic acid is formed, which can form disulfide bonds with nearby cysteines or be further oxidized to sulfinic or sulfonic acid. Sulfenic acid can also be transformed into sulfenamide in the existence of nitrogen. These redox alterations can be reversed by reducing systems like glutathione (GSH), and thioredoxin, except sulfonic acid, and to a lesser extent sulfinic acid (Roos and Messens 2011). The antioxidants play a remarkable function in the antagonizing and quenching of free radicals to obtain an equilibrium among free radicals and the antioxidants for normal physiological function. If the equilibrium is skewed towards free radicals, a variety of pathological diseases develop (Shaban et al. 2021a, b, c). Antioxidants have anti-inflammatory, anti-allergic, antithrombotic, antiviral, and anti-carcinogenic properties in addition to their ability to eliminate free radicals.
CCl4 is a synthetic chemical and does not occur naturally in the environment. It is a powerful hepatotoxic chemical that is commonly used to cause hepatic fibrosis/cirrhosis, hepatocellular cancer, and liver damage in experimental animals (Reyes-Gordillo et al. 2017; Shaban et al. 2021a, b, c, 2022a). Nevertheless, it has many industrial applications. It was primarily used to make chlorofluorocarbons used in refrigeration. In addition, it was employed as a cleaning agent and a component in fire extinguishers (Reyes-Gordillo et al. 2017; Abu-Serie et al. 2021; Shaban et al. 2021a, b, c, 2022a). Because of the health risks and the substantial environmental harm caused by chlorofluorocarbons, its usage has been phased out by advanced various nations. But till now, CCl4 is used to show watermarks on stamps, and it is employed as a chlorine source according to the Appel reaction. CCl4 has been utilized in proton NMR spectroscopy. Also, CCl4 is utilized in the production of lava lamps. The oxidative damage caused by CCl4 in tissues can be explained as lipid peroxidation where lipid peroxidation starts after activation of CCl4 by cytochrome (CYP) 2E1, CYP2B1, or CYP2B2, and possibly CYP3A, forming the trichloromethyl radical CCl3*. Oxygen reacts with CCl3* to form the trichloromethyl peroxyl radical, CCl3OO*, a highly reactive. CCl3OO* starts the lipid peroxidation chain reaction, which targets and degrades polyunsaturated fatty acids found in phospholipids (Reyes-Gordillo et al. 2017; Shaban et al. 2021a, b, c, 2022a).
According to recent studies, antioxidants derived from natural sources are an effective strategy to prevent or eliminate the detrimental effects caused by hazardous substances or medications (Shaban et al. 2013, 2022b; Nisar et al. 2017). In comparison to manufactured medications, antioxidants include a lot of phenol chemicals and have fewer negative effects (Muhammad et al. 2019; Abu-Serie and Habashy 2020; Shaban et al. 2020, 2021a, b, c). Carica papaya Linn. ( C. papaya), a tropical fruit, is widespread around the world and present in yellow-green, yellow-orange, and orange-red colors (Malacrida et al. 2011; Shaban et al. 2021a, b, c). C. papaya pulp has a high nutritive value. The ripened papaya pulp is commonly eaten fresh like a melon, just peeled and seedless. It is used in the food industry such as marmalade, puree, jelly, jam, ice cream, juice, chunks, mixed beverages, and papaya powder (Saran and Choudhary 2013).
The papaya pulp is rich in minerals and vitamins, especially A, B, C, and K (Hassan et al. 2013). Also, papaya pulp contains flavonoids and alkaloids such as carpasemine and carpain (Hassan et al. 2013). The quantities of flavonoids in papaya pulp are impacted by the fruit’s ripeness (Addai et al. 2013). Danielone, a phytoalexin substance, is specific to papaya fruit and is responsible for the antifungal activity of the plant against several fungal types (Colletotriclum and Gloesporioides) that affect papaya. Additionally, it has been shown that the papaya pulp prevents heart attacks and strokes. The unripe papaya pulp contains various types of digestive enzymes such as papain and chymopapain (i.e., vegetable pepsin) which help in the digestion of food proteins. Otherwise, the unripe papaya fruits contain latex content, so it is never eaten. Consequently, the current study was designed to investigate the prophylactic and therapeutic effects of C. papaya pulp extract (PE) against CCl4-induced hepatotoxicity where we predestined the antioxidant and anti-inflammatory, antiapoptotic, and antifibrotic impacts of PE via the determination of their indicators. Also, the liver functions, lipid profile, kidney functions, and histological examination of the liver were determined. The phytochemical constituents and characterization of PE were evaluated.
## Chemicals and reagents
Rutin (RU), gallic acid (GA), catechin, ursolic acid (UA), Folin–Ciocalteau reagent, 2,2 diphenyl-1-picrylhydrazyl (DPPH), 2,4 dinitrophenyl hydrazine (DNPH), 5, 5′, dithiobis-2-nitrobenzoic acid (DTNB), butylated hydroxytoluene (BHT), CCl4 (reagent grade, $99.9\%$), 2,2-azinobis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), NADPH, and GSH were acquired from Sigma-Aldrich, St Louis, MO, USA. Thiobarbituric acid (TBA) was gained from El-Nasr Pharmaceutical Chemicals Co. (Alex., Egypt). Ascorbic acid [(Asc): vitamin C] and Trolox were bought from Riedel-de Haën, Germany. Biozol reagent was purchased from Invitrogen, CA, USA. SYBER Green 1-step qRT-PCR Kit was purchased from Thermo Scientific, USA. Primers for tumor necrosis factor (TNF)-α, nuclear factor-kappa B (NF-κB), transforming growth factor (TGF)-β1, interleukin (IL)-6, and the tumor suppressor gene p53 were acquired from Bioneer, Korea. Kits for alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), total protein (TP), albumin, creatinine, urea, triglycerides (TG), low-density lipoprotein cholesterol (LDL-c), and high-density lipoprotein cholesterol (HDL-c) were gained from Biodiagnostic, Cairo, Egypt.
## Plant
The Caricaceae family’s C. papaya fruit was obtained from Nubaria, Behera, Egypt. Fruits were chosen based on shape, regularity, color, size, and the absence of fungal disease.
## Preparation of PE aqueous extract
The peel and seeds from the unripe fresh fruit pulp were removed. About 500 g of the fruit flesh was cut into pieces and homogenized with 0.5 L of distal H2O using a blender (Moulinex, France). The homogenate was filtered using gauze and the filtrate was lyophilized using Lyophilizer (Virtis 248625 Freeze Dryer; USA), where the residue was stored in a sealed bottle at 4 °C for further studies (Josiah et al. 2011).
## Resolve the total phenolics, flavonoids, tannin, triterpenoid, and Asc
The total phenolic content was established as GA equivalents (eq) in mg/g PE, employing the Folin–Ciocalteau reagent (Taga et al. 1984). The total flavonoid content was measured as mg rutin eq/g PE, applying $5\%$ sodium nitrite solutions and $10\%$ aluminum chloride (Zhishen et al. 1999). Also, total tannin content was assessed calorimetrically like mg catechin eq/g PE, employing $2\%$ vanillin in methanol (Price et al. 1978). The content of triterpenoid was defined as mg ursolic acid eq/g PE by utilizing $5\%$ vanillin in glacial acetic acid (Bai et al. 2007). Moreover, the concentration of Asc in PE was assessed by applying 2,4 dinitrophenyl hydrazine (Omaye and Reddy 1962).
## Phenolics and flavonoids assessment
High-performance liquid chromatography (HPLC) was utilized for the separation of PE (100 µL) by employing a chromatographic column 5 μm, 4.6 mm × 150 mm Eclipse XDB–C18 (Agilent Technologies, Palo Alto, CA, USA), where the divorce flow rate was put at 0.75 mL/min, wavelength 320 nm, and the mobile phase employed was acetonitrile: $1\%$ formic acid: 2-propanol (22:70:8), pH 2.5 Zhu et al. [ 2004].
## Minerals assessment
One gram of fresh papaya pulp was put in a porcelain crucible and ignited in a muffle furnace at 500 °C for 12 h to obtain ash. The ash was cooled, dissolved in 5 mL nitric acid (6 M), warmed, and filtrated using acid-washed filter paper. The filtrate was diluted using deionized H2O to 25 mL and then the mineral contents, including Ca, Co, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, S, Se, and Zn, were determined employing an inductively 5100 coupled plasma optical emission spectrometer (ICP-OES, AGILENT, USA) (Alzahrani et al. 2017).
## Total antioxidant capacities (TAC) of PE
TAC of PE as well as Asc (as standard) was determined according to the method of Tyagi et al. [ 2010]. The scavenging activity of PE and Asc against ABTS + was detected using Trolox as standard (Re et al. 1999). The ferric-reducing power of the PE and Asc was measured as claimed by Tyagi et al. [ 2010]. Also, the DPPH scavenging capacity of PE and Asc was evaluated according to the method of Blois [1958] with some improvement. Briefly, 100 µL of DPPH was added to 500 µL of successive concentrations (0–1 mg/mL) of PE, ethanol (as control), and Asc (as standard), mixed well, and incubated in the dark for 20 min and at 25 °C, and then the absorbances were recorded at 490 nm. The scavenging activity of PE and Asc against DPPH was measured consistent with the following equation:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Inhibition \left(\%\right)=[1-({Absorbance}_{\mathrm{extract}}/{Absorbance}_{\mathrm{control}})\times 100]$$\end{document}Inhibition%=[1-(Absorbanceextract/Absorbancecontrol)×100] The relationship between the inhibition (%) and different concentrations of PE and Asc was plotted to estimate their IC50 ($50\%$ inhibitory concentration) (Blois 1958).
## Animals
Forty mature male Sprague–Dawley rats (8–10 weeks old and 150 ± 20 g) were purchased from the Faculty of Agriculture, University of Alexandria, Egypt. The rats were verified for normal health condition and were maintained for 2 weeks for laboratory environment adaptation in well-ventilated cages, an ambient temperature: 25 ± 0.5 °C, and a 12-h light/dark cycle. The rats were given free access to standard commercial rat food and tap water (Shaban et al. 2021a, b, c).
## Experimental strategy
All animals were distributed into 5 groups, 8 rats per each. Group 1 (C): in the control rats without any treatment ($$n = 8$$); group 2 (PE): rats were orally administered (utilizing oral gavage) with 500 mg of PE/kg body weight (BW) daily for 12 weeks (Josiah et al. 2011); group 3 (CCl4): in the intoxicated group ($$n = 8$$), the animals were subcutaneously (s.c.) injected with 2 mL of CCl4/kg BW for 8 weeks (3 times/week) (Karabulut et al. 2014); group 4 (PE-CCl4): in this group, rats were treated with PE (the same as in group 2) and at the start of the 3rd week, they were injected (s.c.) with CCl4; and group 5 (PE-CCl4-PE): rats were treated with PE and CCl4 like in group 4 with continual treatment with PE for 2 weeks following the end of CCl4 injection. The experimental design is shown in Fig. 1.Fig. 1The experimental design At the completion of the experiment, feeding was clogged 12 h prior to scarification. Carbon dioxide gas was used to anesthetize rats for dissection. The blood was gathered from the portal veins in clear test tubes, allowing clotting by standing for 15 min at room temperature, and centrifuged for 10 min and at 1000 × g and the serum was kept at − 20 °C till used for the determination of liver functions, lipid profile, and kidney functions. The liver’s specimens were isolated, soaked in cold saline solution ($0.9\%$ NaCl), and split immediately into three parts. The first one was placed in formalin ($10\%$) for the histological examination. The second portion was saved at − 80 °C in RNA later solution till used for the determination of markers of inflammation, apoptosis, and fibrosis. The third sample was homogenized in cold sodium phosphate buffer (0.1 M; pH 7.4) comprising saline solution, centrifuged for 20 min at 10,000 × g and the supernatant was maintained at − 80 °C till used for the determination OS parameters (Shaban et al. 2021a, b, c).
## Lipid profile and liver and kidney function tests
For assessment of liver function, the TP and albumin levels and the activities of ALT, AST, and ALP were determined using kits (Gornall et al. 1949; Doumas et al. 1971; Reitman and Frankel 1957; Belfield and Goldberg 1971). Also, the lipid profile (TG, LDL-c, and HDL-c) was evaluated in the serum using kits (Richmond 1973; Fassati and Prencipe 1982). Kidney functions including creatinine and urea levels were assayed using kits (Patton and Croush 1977; Jaffé 1986).
## Determinations of OS markers
The oxidants [malondialdehyde (MDA) and nitric acid (NO)] were determined in liver homogenates for the estimation of lipid peroxidation and protein oxidation, respectively. MDA was analyzed using TBA (Ohkawa et al. 1979). The NO level was evaluated using the Griess reagent (Montgomery and Dymock 1961). The antioxidant parameters were estimated in the liver homogenates. The GSH level was evaluated according to Ellman [1959] by the reaction of GSH with DTNB providing a yellow product measured at 412 nm and expressed as mg/mg protein. Glutathion reductase (GSR) activity was determined according to Goldberg and Spooner [1983] by the oxidation of NADPH in the presence of GSSG (oxidized form of GSH) and the product was measured at 340 nm, expressed as µmol/min/mg protein. The glutathione-S-transferase (GST) activity was evaluated by the reaction of GSH with GST substrate (p-nitrobenzyl chloride) forming a product which measured at 310 nm (Habig et al. 1974). The superoxide dismutase (SOD) activity was established by an indirect process (Marklund and Marklund 1974). The activity of SOD is characterized as the enzyme quantity that inhibits the pyrogallol autoxidation rate throughout standard conditions, and the variation in the absorbance at 420 nm was determined in 2 min. SOD is expressed as U/mg protein. The activity of total glutathione peroxidase (t-GPx) was measured by establishing the oxidation of NADPH in the sample in the existence of cumene hydroperoxide and GSH at 412 nm (Paglia and Valentine 1967).
## Determination of markers of inflammation, fibrosis, and apoptosis
Assessment of the expression of NF-κB, IL-6, TNF-α, TGF-β, and p53.
The hepatic RNA of each rat was extracted in accordance with the kit’s instructions. The frozen liver tissues were cut into small slices, transferred to an Eppendorf tube containing 1 mL Biozol reagent (Mou et al. 2013) and then the tissues were homogenized using a glass homogenizer. The homogenate was incubated at 4 °C for 15 min, then 1 mL glycogen was added and combined well, next chloroform was added, and the mixture was left for 15 min and at 4 °C. For the precipitation of the RNA content, the mixture was centrifuged and the aqueous layer was assigned into nuclease-free Eppendorf tube and the same volume of cold isopropyl alcohol was added. The precipitate (RNAs) was then washed, processed with DNAase to remove any remaining DNA, and held at − 80 °C until utilized. The absorbance of RNA samples at 260 and 280 nm ratio (A260/A280) was used to assess the quality of the extracted RNA samples. A spectrophotometer (BioDrop Lite, Australia) was used to determine the amount of RNA, and gel electrophoresis on $2\%$ agarose gel stained with ethidium bromide was used to confirm the quality of the RNA.
Quantitative reverse transcriptase PCR was used to measure the levels of NF-κB, IL-6, TNF-α, TGF-β, and p53 expressions in the extracted RNA samples using a SYBR green PCR master mix one-step kit (Todorova et al. 2006; Shimojo et al. 2006; Chiu and Yang 2007; Yar et al. 2011; Róka et al. 2019). In brief, in a 10 μL reaction volume, the following ingredients were added in the following order: 0.5–3.4 μL RNA template (RNA sample), 5 μL 1-step QPCR SYBER mix (1 ×), 0.5 μL of each forward and reverse primers, 0.5 μL RT-enhancer, 0.1 μL verso enzyme mix, and 0–2.9 μL water (PCR grade) and at 95 °C for denaturation. The reactions included one cycle 10-min reverse transcription at 45 °C, one cycle 2 min of polymerase activation at 95 °C, and tracked by 40 cycles for 15 s and at 95 °C for denaturation, then annealing for 1 min and at 60 °C and the extension for 30 s and at 72 °C. Based on the number of PCR cycles where the increasing fluorescence curve crosses a threshold cycle, the expression levels of all groups under study were determined (CT). The relative expressions of NF-κB, IL-6, TNF-α, TGF-β, and p53 genes were achieved applying comparative CT (ΔΔCT) method, and β-actin (reference gene) was used as internal control. ΔCT and ΔΔCT were calculated by the following equations: ΔCT = CT (any marker) − CT (β-actin) and ΔΔCT = ΔCT (Sample) − ΔCT (β-actin control). The expression fold changes were calculated from this formula: Expression fold change = 2 − ΔΔCT (Shaban et al. 2022a, b). All primers were used are recorded in (Table 1).Table 1Quantitative reverse transcriptase polymerase chain reaction (RT-PCR) techniqueNameSequenceProduct sizeAccession numberBeta-actinForward: AGC CAT GTA CGT AGC CAT CC189NM_031144Reverse: CTC TCA GCT GTG GTG GTG AAP53Forward: GTC GGC TCC GAC TAT ACC ACT ATC246NM_030989Reverse: CTC TCT TTG CAC TCC CTG GGGNF-κBForward: ACG ATC TGT TTC CCC TCA TCT154AF079314.2Reverse: TGC TTC TCT CCC CAG GAA TAIL-6Forward: AGT TGC CTT CTT GGG ACT GA217M26744Reverse: ACA GTG CAT CAT CGC TGT TCTGF-βForward: CTT TGC TCA TGG CAG TAC ATC TG152NM_013174Reverse: CCT TTA ACA ACA TCC CGA TTC CTNF-αForward: AGA TGT GGA ACT GGC AGA GG178X66539Reverse: CCC ATT TGG GAA CTT CTC CT
## Histological probation of liver tissues
For liver histological investigations, the liver tissues were cleaned, mended, and encased in paraffin wax (Suzuki and Suzuki 1998). Hematoxylin and eosin (H&E) stain was used to stain sections of 5 μm thickness.
## Statistical analysis
Comparative analyses matching between the means of the two groups were performed using SPSS software to examine the antioxidant and anti-inflammatory properties of PE against CCl4-induced liver damage (Version 25). The data, which was provided as a mean standard deviation, were analyzed using one-way ANOVA analysis (SD). The significance threshold was set at $p \leq 0.05.$
## PE mineral and phytochemical compositions
The PE contains substantial amounts of phenolics, flavonoids, triterpenoids, tannins, and Asc, according to the phytochemical components (Table 2). HPLC analysis revealed that PE includes a variety of phenolic and flavonoid components (Fig. 2). Table 2 also shows that PE contains a variety of minerals, which were arranged according to their concentration gradients: K ˃ Ca ˃ Na ˃ Mg ˃ S ˃ Fe ˃ Zn ˃ Se ˃ Cu ˃ Mn ˃ Ni ˃ Mo ˃ Co.Table 2Phytochemicals and minerals ingredients of C. papaya pulp extract (PE). All values are presented as mean ± SD ($$n = 3$$)Phytochemicals ingredientsCompoundConcentration (mg eq/g extract)CompoundConcentration (mg eq/g extract)Total phenolics38.79 ± 0.00Triterpenoids0.571 ± 0.00Total flavinoids7.06 ± 0.00Ascorbic acid0.241 ± 0.01Tannins content72.84 ± 0.01Elements compositionsElements nameConcentration (mg/100 g tissue)Elements nameConcentration (mg/100 g tissue)K1157 ± 0.02Se1.1 ± 0.00Ca557 ± 0.00Cu0.94 ± 0.00Na269.35 ± 0.00Mn0.895 ± 0.00Mg248.75 ± 0.00Ni0.06 ± 0.00S99.15 ± 0.00Mo0.04 ± 0.00Fe3.545 ± 0.00Co0.005 ± 0.00Zn1.815 ± 0.00Fig. 2Characterization of the C. papaya pulp extract (PE). a HPLC chromatogram of PE; b 2,2-azino-bis (3-ethylbenzthiazoline-6-sulfonic acid) (ABTS) scavenging activity of PE; c ferric-reducing antioxidant power (FRAP) of PE; d α, α-diphenyl-β-picrylhydrazyl (DPPH) scavenging activity of PE. Asc and Trolox are standard. Results are shown as mean ± SD ($$n = 3$$)
## Antioxidant capacities
The TAC of PE was found to be 552.7 mg Asc eq/g. The IC50 values for ABTs + and DPPH scavenging activities were calculated and presented in mg/mL. PE’s scavenging activity was indirectly proportional to the IC50 values. PE had IC50 values of 0.697, 9.262, and 0.601 mg eq/mL for DPPH radical scavenging ability, ferric-reducing power, and anti-ABTs + potential, respectively (Fig. 2). PE and Asc’s antioxidant potentials versus DPPH and ABTs + revealed that PE inhibited ROS in a concentration-dependent style.
## PE diminished CCl4-induced hepatotoxicity
The administration of rats with CCl4 (CCl4 group) triggered significantly ($p \leq 0.05$) declines in TP and albumin levels with significant elevations ($p \leq 0.05$) in ALT, AST, and ALP activities matched to the C group (Table 3). In contrast, PE treatment pre, during, and/or after CCl4 administration enhanced liver functions as shown from the increases of the levels of albumin and TP significantly ($p \leq 0.05$) compared with the CCl4 group, while the activities of ALT, AST, and the ALP were significantly ($p \leq 0.05$) reduced. Otherwise, the healthy rats which were given PE alone, the liver functions were not significantly altered ($p \leq 0.05$) when compared to healthy untreated rats (Table 3).Table 3Effect of PE on liver functions, lipid profile, and kidney functions ParametersGroupsCPECCl4PE-CCl4PE-CCl4-PETP (g/dl)54.3 ± 0.02*54.5 ± 01*5.08 ± 0.00#26.9 ± 0.0039.7 ± 0.00Albumin (g/dl)36.1 ± 0.68*35.5 ± 0.57*5.84 ± 1.60#13.4 ± 0.7724.9 ± 2.70ALT (U/L)16.0 ± 2.13*16.0 ± 2.05*91.0 ± 3.49#46.7 ± 3.9227.5 ± 4.09AST (U/L)44.6 ± 4.15*40.8 ± 3.33*226.9 ± 4.57#113.9 ± 11.4780.7 ± 2.06ALP (U/L)67.2 ± 2.12*72.32 ± 2.86*171.0 ± 4.24#125.9 ± 3.21115.2 ± 7.01TG (mg/dl)112.8 ± 6.08*111.0 ± 9.67*412.2 ± 5.83#312.2 ± 8.65267.7 ± 5.49LDL-c (mg/dl)52.4 ± 0.22*56.0 ± 4.12*831.9 ± 1.82#388.2 ± 3.58275.9 ± 3.03HDL-c (mg/dl)40.6 ± 1.77*38.8 ± 2.31*13.8 ± 2.31#23.1 ± 2.5930 ± 02.67S. urea (mg/dl)22.5 ± 1.73*22.4 ± 1.57*98.9 ± 3.50#54.5 ± 3.4338.2 ± 0.79Creatinine (mg/dl)0.38 ± 0.05*0.37 ± 0.05*1.83 ± 0.08#1.18 ± 0.050.96 ± 0.12All values are presented as mean ± SD ($$n = 8$$), (*) indicates significance when compared to the control at $p \leq 0.05$, and (#) indicates significance when compared to the CCl4 group at $p \leq 0.05.$ C group: control rats; PE group: rats received only papaya pulp extract; CCl4 group: rats administrated with CCl4; PE-CCl4 group: rats received papaya pulp extract before and during CCl4 injection; PE-CCl4-PE group: rats received papaya pulp extract before, during, and after CCl4 injection
## Levels of lipid profile
The administration of rats with CCl4 caused significant changes in the lipid profile, where HDL-c level was decreased significantly ($p \leq 0.05$), while LDL-c and TG levels were elevated significantly ($p \leq 0.05$), compared with the C group (Table 3). Otherwise, treatment of rats with PE pre, during, and/or following CCl4 administration improved the lipid profile where there was a significant ($p \leq 0.05$) increase in HDL-c level compared to the CCl4 group with significant ($p \leq 0.05$) declines in TG and LDL-c levels. Administration of PE alone caused nonsignificant fluctuations in the lipid profile as compared to the C group (Table 3).
## PE treatment diminished OS in the liver caused by CCl4
The levels of MDA and NO and GSR activity in rats administered with CCl4 were elevated significantly ($p \leq 0.05$) related to the C group (Fig. 3). However, the GSH level and GST, SOD, and GPx activities were decreased significantly ($p \leq 0.05$). Treatment with PE pre, during, and/or after CCl4 injection significantly ($p \leq 0.05$) reduced MDA and NO levels and GSR activity as assimilated with the CCl4 group. Also, these treatments improved significantly ($p \leq 0.05$) the GSH level and the activities of GST, t-GPx, and SOD. PE administration to healthy rats exhibited nonsignificant ($p \leq 0.05$) differences in MDA, NO, and GSH levels and GSR, GST, GPx, and SOD activities equated with the control rats (Fig. 3).Fig. 3Effect of PE on CCl4-induced oxidative stress in the liver. a MDA levels; b NO levels; c GSR activities; d GSH levels; e GST activities; f SOD activities; and g GPx activities. Where all values are presented as mean ± SD ($$n = 8$$), (*) = significance as compared with control at $p \leq 0.05$, (#) = significance as compared with the CCl4 group at $p \leq 0.05.$ C group: control rats; PE group: rats receive only papaya pulp extract; CCl4 group: rats administrated with CCl4; PE-CCl4 group: rats received papaya pulp extract before and during CCl4 injection; PE-CCl4-PE group: rats received papaya pulp extract before, during, and after CCl4 injection
## PE treatment diminished liver inflammation caused by CCl4
The relative gene expressions of NF-κB, IL-6, and TNF-α were up-regulated significantly ($p \leq 0.05$) in rats after CCl4 administration when compared with the C group (Fig. 4a–c). Conversely, their expressions were down-regulated significantly ($p \leq 0.05$) in rats treated with PE pre, during, and/or after administration of CCl4 associated with the CCl4 group. Also, administration with PE caused nonsignificant ($p \leq 0.05$) alterations in the levels of inflammatory as related to the C group (Fig. 4a–c).Fig. 4 Effect of PE on the hepatic inflammation, fibrosis, and apoptosis stimulated by CCl4 administration. The relative gene expression of a NF-κB, b TNF-α, c IL-6, d TGF-β, and e p53. Where all values are presented as mean ± SD ($$n = 8$$), (*) = significance as compared with control at $p \leq 0.05$, (#) = significance as compared with the CCl4 group at $p \leq 0.05.$ C group: control rats; PE group: rats receive only papaya pulp extract; CCl4 group: rats administrated with CCl4; PE-CCl4 group: rats received papaya pulp extract before and during CCl4 injection; PE-CCl4-PE group: rats received papaya pulp extract before, during, and after CCl4 injection
## PE treatment diminished liver fibrosis and apoptosis caused by CCl4
The expressions of p53 and TGF-β gene levels were significantly ($p \leq 0.05$) up-regulated in rats injected with CCl4, associated with the C group (Fig. 4d and e), while their expressions were down-regulated significantly ($p \leq 0.05$) in rats treated with PE pre, during, and/or after CCl4 administration as compared with the CCl4 group. Further, the levels of TGF-β and p53 gene expressions changed nonsignificantly ($p \leq 0.05$) in the healthy rats after PE administration compared with the C group (Fig. 4).
## PE treatment improved renal dysfunction induced by CCl4
The CCl4 administration caused nephrotoxicity where serum creatinine and urea levels were significantly ($p \leq 0.05$) increased compared to the C group. Conversely, PE treatment pre, during, and/or after CCl4 administration reduced nephrotoxicity as exposed from the significant ($p \leq 0.05$) reduction of urea and creatinine levels. The renal functions changed nonsignificantly ($p \leq 0.05$) in healthy rats after PE treatment (Table 3).
## Liver histopathology of different studied groups
Histopathological examination of the C group showed normal histological structure of the liver with no remarkable pathological changes (Fig. 5, C). The results of the healthy rats after PE administration (Fig. 5, PE) displayed that there are no variations in the liver histology when equated with the C group indicating that the natural phyto-antioxidants of PE did not induce any apparent alterations neither in the hepatic parenchyma (liver cells) nor in the stroma (connective tissue content). CCl4 administration induced dispersed focal degenerative changes in the liver parenchyma appeared as focal pale areas with hepatocyte vacuolation (steatosis) or cell degeneration (empty cells with dark pyknotic nuclei) alternating with foci with intact eosinophilic hepatocytes (Fig. 5, CCl4 1). A histopathological feature described as piece meal degeneration. On the level of individual cells (Fig. 5, CCl4 2), groups of pale degenerated hepatocytes were seen with individual intact eosinophilc hepatocytes in between. CCl4 affected also the liver stroma as it enhanced the deposition of abundant bundles of collagen fibers in the portal tract, around the central veins and along the boundaries separating between the liver lobules resulting in distortion of general architecture of lobules. It was also associated with hemorrhages in the micro-vasculature of the liver (central vein, hepatic sinusoids, and portal tract vessels) (Fig. 5, CCl4 3 and 4). On the contrary, PE treatment before and during CCl4 injection (Fig. 5, PE-CCl4, A and B) and PE treatment before, during, and after CCl4 injection (Fig. 5, PE-CCl4-PE, A and B) caused a relative improvement in the histopathology of the hepatocyte lesion caused by CCl4, since the last treatment gave the best results. Fig. 5Microscopic examination of rat liver tissues from different groups. Where (C) represents the control group (H & E stain, Mic. Mag. × 100). This shape shows that the hepatic lobules (arrows) were arranged in a normal organization, the cords of hepatocytes (h) which radiate from the central veins (CV) are separated by narrow slit-like sinusoids (s), and a normal portal tract (PT) is demonstrated at the upper left corner of a hepatic lobule. ( PE) signifies the PE group (H & E stain, Mic. Mag. × 100): normal liver architecture was demonstrated, and the central veins (CV) occupied the center of the hepatic lobule (arrows) with cords of hepatocytes (H) radiating from them, and the portal tract (PT) was located at the angle between the adjacent hepatic lobules. [ CCl4 [1] and [2]] represent the CCl4 group stained with H & E. [1] [Mic. Mag. × 100] shows focal areas of eosinophilic hepatocytes (*) alternate with pale areas of vacuolated hepatocytes (♦), a preserved organization of the cords of hepatocytes (H) in many hepatic lobules was noticed, and some portal tracts (PT) show dense cellular infiltration (arrow). In [2] (Mic. Mag. × 400), diffused vacuolation of hepatocytes (h) and groups of degenerated hepatocytes without nuclei (arrow) have appeared. [ CCl4 [3] and [4]] signifies the CCl4 group stained with Gomori’s trichrome stain and Mic. Mag. × 100. In [3], thick green bands of fibers were noticed between the liver lobules (arrows), and the vacuolated hepatocytes also appeared. In [4], dilatation of blood sinusoids (s) between hepatocytes (h) and the abnormal inspissation of red blood cells (arrows) in the hepatic sinusoids as in the portal tract tributaries (PT) were presented. The (PE-CCl4) represents the microscopic examination of rat liver tissue of the PE-CCl4 group. In A (H & E stain, and Mic. Mag. × 100), the histological changes persisted as foci of intact eosinophilic hepatocytes (♦) adjacent to pale vacuolated hepatocytes (*). CV, central vein; PT, portal tract. However, in B (Gomori’s Trichrome stain and Mic. Mag. × 400), excessive collagen (star) depositions appeared in the portal tract (PT), and a wide dispersion of degenerated cells (arrow) was noticed among the hepatocytes (H). CV, central vein with inspissated blood. The (PE-CCl4-PE) denotes the PE-CCl4-PE group. In A (H & E stain, and Mic. Mag. × 100), partial recovery varied between apparently normal hepatocytes (H1), vacuolated hepatocytes (H2), and thinned-out hepatic cords formed of degenerated dense cells with dark nuclei (H3). The distorted structure of the portal tract (PT) with persistent excessive stromal cellular infiltrates (arrow) and dilated, blood-engorged portal vein tributaries (*) was illustrated. In B (Gomori’s Trichrome stain, Mic. Mag. × 400), the abundant deposits of collagen fibers were illustrated (arrows pointing to green bands). “ H,” hepatocytes
## Discussion
The CCl4 continues to serve as a valuable pattern chemical for understanding the mechanisms of hepatotoxic consequences such as fatty degeneration, fibrosis, cirrhosis, and carcinogenicity (Saile and Ramadori 2007; Diao et al. 2011). Otherwise, liver disease’s incidence is increasing worldwide due to the uses of drugs, chemical poisons, viral infections, and alcohol intake. Previous studies showed that the antioxidants from plant origin perform a crucial role in the detoxification ensued from CCl4. Therefore, in this study, we illuminated the mechanism of hepatotoxicity induced by CCl4 via generating of active metabolites and other free radicals. In addition, we evaluated the role of PE components in the liver protection and detoxification of hepatotoxicity and improving liver functions and liver architecture. The results of this study indicated that the administration of CCl4 resulted in a significant increase in the levels of MDA (a lipid peroxidation product) and NO (a highly reactive molecule, and the main product of RNS) and GSR activity relative to the C group. However, there were a reduction in GSH level and the activities of SOD, t-GPx, and GST in comparison with the C group. This suggests that CCl4 increased the OS, which lead to the elevation of lipid peroxidation of polyunsaturated fatty acids and oxidation of protein and other macromolecules and this led to liver damage.
Mechanistic studies demonstrated that the metabolism of CCl4 via CYP2E1, to hugely reactive free radical (CCl3• and CCl3OO•), performs a crucial role in the presumed style of action. As CCl3• and CCl3OO• can covalently bind locally to cell macromolecules, priority is given to the polyunsaturated fatty acids of the membranes where the free radicals trigger lipid peroxidation by assaulting polyunsaturated fatty acids causing generation of chain of free radicals (Zhao et al. 2017; Shaban et al. 2021c). The peroxidation of lipid membrane causes its disruption, and this interrupts the permeabilities of mitochondrial, endoplasmic reticulum, and plasma membranes, leading to the harm of membrane probity, loss of calcium cell detention, and homeostasis. All these changes can make a significant contribution to subsequent cell damage and leakage of microsomal enzymes (Shaban et al. 2013; Zhao et al. 2017). Reactive aldehydes, particularly 4-hydroxynonenal, are fatty acid breakdown products that bind readily to protein functional groups and impede the activity of key enzymes. Additionally, the elevation of the NO level after CCl4 administration indicating that CCl4, CCl3•, and CCl3OO• activated the inducible nitric oxide synthase (Germoush et al. 2018; Munakarmi et al. 2020) where the elevation in the NO level suppressed the growth of the lymphocytes and injures the encirclement cells (Munakarmi et al. 2020; Shaban et al. 2021a, b, c). Previous research has also shown that CCl4 poisoning causes hypomethylation of cellular components, which inhibits protein synthesis in RNA and lipoprotein secretion in phospholipids (Dalle-Donne et al. 2009).
In addition, nonenzymatic antioxidant GSH protects cells from the damaging effects of reactive oxygen species. The GSH can neutralize and scavenge the free radicals, where the GSH is oxidized to GSSG (Shaban et al. 2013, 2014, 2022a, b, c; Habashy et al. 2018). Also, in the OS state and via protein S-glutathionylation, GSH participates in the preservation of the thiol and organization of the thiol proteins redox in the cells (Shaban et al. 2013, 2014) according to the reaction:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{RSH}+\mathrm{GSH}+\left[\mathrm{O}\right]\to \mathrm{GSSR}+{\mathrm{H}}_{2}\mathrm{O}$$\end{document}RSH+GSH+O→GSSR+H2O Furthermore, GSH is utilized to detoxify hazardous compounds such as methylglyoxal and formaldehyde, which are produced as a result of the OS, via the glyoxalase system (glyoxalase I and II) (Dringen et al. 2015). Likewise, GSH is important as a cofactor for the GPx, a selenoprotein enzyme, which reduces inorganic and organic hydroperoxides (Shaban et al. 2014; Germoush et al. 2018). Also, GSH is used as a substrate for GST, a drug-metabolizing enzyme. When this enzyme reacts with numerous dangerous chemical species like halides, epoxides, and free radicals, it helps create inactive products (Shaban et al. 2014). Additionally, GSH plays an important role in the reduction of methemoglobin (MetHbFe+3) into hemoglobin (HbFe+2), but in proteins, it performs in the creation and maintenance of disulfide bonds (Dorman et al. 2002). Therefore, all these reactions of GSH led to reduction its level in rats after CCl4 administration. Moreover, the decline in GSH level may be owed to its reaction with NO or ONOO − to produce S-nitroso GSH (Van der Vliet et al. 1997). Wherever, GSH reduction could also contribute to the stimulation of lipid peroxidation (Shaban et al. 2013). In contrast, GSR is responsible for the conversion of GSSG to GSH to maintain the redox state in the cells. The elevation of GSR activity after CCl4 administration perhaps suggests an alteration to oxidative condition (Korhonen et al. 2005). The inhibition of GPx and GST activities in rats after CCl4 administration may be owed to the reduction of GSH level. Also, their inhibition could be associated with the direct communication of the free radicals such as CCl3•, CCl3OO•, and reactive aldehydes with the functional groups of these enzymes. Otherwise, SOD catalyzes the diversion of the superoxide radical (O2•¯) into hydrogen peroxide (H2O2) (Shaban et al. 2014, 2013). The inhibition of SOD in rats after CCl4 administration, in this study, may be due to the interaction of the free radicals with its active site or with the enzyme gene expression (Shaban et al. 2013, 2022c).
At the molecular level, our findings revealed that CCl4 administration triggered up-regulation of the gene expressions of NF-κB, TNF-α, TGF-β, IL-6, and p53 indicating that CCl4 activated these markers. The NF-κB, a transcription factor, performs a main function in the progressions of inflammation and apoptosis. NF-κB is stimulated by a diversity of inducers, involving inflammatory cytokines among cells, and pathogen-originated materials (Abdel-Rahman et al. 2016; Chen et al. 2018). Normally, NF-κB exists in the cytoplasm in the deactivated form since it is combined with IκBα, an inhibitory subunit. While throughout the OS state, the ROS induces phosphorylation leading to the separation of the IκBα subunit resulting in activation of NF-κB. The activated NF-κB leakages into the nucleus and induces the expression of inflammatory mediators (Girard et al. 2009; Chen et al. 2018). So, the stimulation of NF-κB gene expression in this study revealed that CCl4 provoked liver inflammation. Otherwise, the stimulation of gene expression of IL-6, TNF-α, and TGF-β, beside elevation of the NO level, indicates that CCl4 induced fibrosis and apoptosis. TNF-α is a pro-inflammatory cytokine that interferes with liver damage through a variety of biological functions (Dinarello 2000). Also, IL-6, a pro-inflammatory mediator released by Kupffer cells (KCs), stimulates the biosynthesis of the cytokines which participates in the inflammatory response of the induced liver damage (Dinarello 2000). Also, TGF-β is a key mediator for the progression of inflammatory response and fibrosis. TGF-β regulates the inflammation and fibrosis through the interaction with a NF-κB pathway. TGF signaling and hepatic stellate cells (HSCs) are both activated by active NF-κB, and the activated HSCs are changed into myofibroblasts, which promote collagen deposition in the extracellular matrix (Meyer et al. 1990; Eltahir et al. 2020). Moreover, p53 is a well-known tumor suppressor protein that manages DNA repair systems and the cell cycle seizure in cases of prolonged OS exposure, mitogenic oncogenes, apoptosis, etc. ( Han et al. 2019). Our data showed up-regulation of p53 gene expression in hepatocytes of rats administered with CCl4 and this indicates that CCl4 induced apoptosis that increased with increasing of the OS. Also, the stimulation of a NF-κB pathway leads to up-regulation of p53 gene expression (Lee et al. 2019). Otherwise, the elevation of NO in rat hepatocytes after CCl4 administration suggests that CCl4 caused apoptosis since NO may react with O· − 2 radicals producing the peroxynitrite anion (ONOO −) resulting in DNA damage and stimulation of the nuclear poly-ADP-ribose polymerase (PARP-1). PARP-1 motivates NAD + hydrolysis, which results in the cellular energy depletion and necrotic cell death (Saada et al. 2010). Moreover, accumulation of NO· in mitochondrial leads to the depolarization of mitochondrial and leaks the cytochrome c from mitochondria to the cytosol causes apoptosis (López et al. 2010). Moreover, the elevation of OS promotes apoptosis via up-regulation of gene expression of Baxand p53 and down-regulation of Bcl-2 and Bcl-xL gene expression (Han et al. 2019).
Otherwise, the histopathological examination confirmed the biochemical and molecular results since histopathology of the rat liver after CCl4 administration showed extensive histological modifications in the hepatic tissues; characterized by severe hepatocellular deteriorations, necrosis, fatty alterations, and existence of inflammatory cells. Therefore, our data showed the levels of serum AST, ALT, and ALP were elevated after CCl4 administration as matched with the C group, while albumin and TP levels were declined. This established that CCl4 induced liver injury which leads to decline the protein biosynthesis and leakage of the liver enzymes into the blood circulation. Also, CCl4 poisoning causes hypomethylation of cellular modules, which suppresses protein synthesis in the case of RNA (Unsal et al. 2021). Additionally, the data exposed that CCl4 intoxication changed the lipid profile, where LDL-c and TG levels were raised but HDL-c level was dropped. This may be related to the liver damage and the failure of liver cells to metabolize lipid, besides impairing the transformation of cholesterol to bile acids. Moreover, CCl4 administration caused significant elevations in creatinine and urea levels, as matched to the C group indicating that CCl4 induced nephrotoxicity. Our conclusions concur with the previous findings, which described that CCl4 caused hepatotoxicity and nephrotoxicity (Shaban et al. 2021a, b, c, 2022a, b).
On the other hand, in this study, the liver pathohistological outcomes confirmed that therapy of rats with PE before, throughout, and/or following CCl4 administration diminished the hepatic injury created by CCl4 and improved the liver architecture. Consequently, the liver functions and lipid profile were improved significantly as shown from the reduction of AST, ALT, ALP, TG, and LDL-c with elevations in the total protein and HDL-c levels when contrasted with the CCl4 group. Also, the attenuation of the liver injury induced by CCl4 was proven by the diminution of the OS, inflammation, fibrosis, and apoptosis as revealed from the results which we discussed as follows. The current data revealed that PE treatment reduced the OS as NO and MDA levels and the activity of GSR were decreased as matched by the CCl4 group, while the activities of GST, SOD, and t-GPx and GSH level were increased. The reduction in OS designates that PE has antioxidant activity against CCl4 intoxication and has capable scavenging activities against ROS and RNS. Our data revealed that PE is rich in phenolic compounds, flavonoids, tannins, triterpenoids, and Asc. Moreover, PE analysis using HPLC revealed that it contains quinol, caffeine, chlorgenic acid, caffeic acid, vanillic acid, ellagic acid, myricetin, and rosmarinic acid. Additionally, previous studies elucidated that PE contains phytosterols and tocopherols (Rodrigues et al. 2019). All these compounds as well as some minerals in PE, especially Zn, Se, Cu, Mn, and Ni (Table 2), have antioxidant activities against ROS and RNS (Alotaibi et al. 2017; Shaban et al. 2013). In the cells, these compounds exhibit the protection and therapeutic effects against oxidative damage, but with different mechanisms, some of them were discussed later. The antioxidant activity of PE was proved with the current results which revealed that the TAC of PE in vitro is extremely high. Also, PE has scavenging activities against ABTS + and DPPH and ferric-reducing power. As a result, the reduction of NO and MDA in rats treated with PE could be linked to the polyphenolic compounds in PE where polyphenolics are excellent inhibitors for the nitrosation process and can prevent oxidative damage due to their ability to scavenge ROS and RNS. Also, polyphenolic substances boost GSH levels as well as the activities of t-GPx and SOD, but they limit GSR activity (Moskaug et al. 2005; Shaban et al. 2013). The antioxidant abilities of plant polyphenols have been linked to their reactivity as electron or hydrogen donors, ability to stabilize unpaired electrons, and ability to terminate Fenton processes (Shaban et al. 2013; Eltahir et al. 2020). The mechanism of the phenolics action as an antioxidant differs according to their structures where chlorogenic reacts with free radicals producing new radicals, which are stabilized by the action of electron resonance of the aromatic nucleus in its structure (Jung et al. 1999; Shaban et al. 2013), while vanillin reacts with the free radicals via self-dimerization (Tai et al. 2012). In contrast, vanillic acid has moderate antioxidant and anti-inflammatory activities because its carboxyl group acts as an electron donor subunit or self-dimerization with the free radicals (Vinoth and Kowsalya 2018). GA interferes with ROS generation (Bello and Idris 2018). However, in the case of quinols, the quinol group (QH2) interacts with the peroxyl radical (ROO•) forming semiquinone radical (QH•) which can reduce another ROO• since it has been shown that the interaction between ROO• radical with quinols is faster than its interaction with lipid molecules. This process leads to quench ROO• resulting in the prevention of the formation of more radicals as lipid peroxyl (LOO•) and terminates the lipid peroxidation process (Lokhmatikov et al. 2014; Shaban et al. 2021a, b, c). Catechol molecule and catechol-containing flavonoids such as quercetin have a nonenzymatic antiradical scavenging activity. During the OS, catechol moiety changes to semiquinone radicals and quinones by oxidation where the oxidized products can arylate the critical free SH group of GSR and inhibit its enzyme activity (Boots et al. 2003). Furthermore, ellagic acid reduces CCl4 metabolism via the reduction of total hepatic CYP2E1 and CYP-450 (Ahn et al. 1996; Shaban et al. 2014), while caffeine and its metabolite (theophylline) are potent inhibitors against HO•¯via its trapping (Vieira et al. 2020). Rutin, myricetin, and ellagic acid inhibit xanthine oxidase activity leading to the suppression of the O2•¯ formation resulting in the increase in SOD activity (Zhang et al. 2017). However, kaempferol activates the production of antioxidant enzymes like catalase, GPx, and GST (Yang et al. 2014). *In* general, polyphenolics boost GSH levels and t-GPx and SOD activities, but they suppress the GSR activity (Moskaug et al. 2005; Shaban et al. 2013). Because of their ability to scavenge ROS and RNS, polyphenolics, notably chlorogenic acid and caffeic acid, are excellent nitrosation inhibitors and can prevent oxidative damage. Polyphenols may thus be effective not only in reducing oxidative damage but also in inhibiting the formation of mutagenic and carcinogenic n-nitroso compounds in the body (Shaban et al. 2014). Moreover, hydroxycinnamic acids, including coumaric and caffeic, are effective antioxidants through the donation of electrons or hydrogen atoms, and this attached to the presence of a phenolic nucleus and the side chains (Liu et al. 2020). Otherwise, rutin and tannins exhibit antioxidant properties by chelating metal ions, for example, Fe (II), stopping the Fenton process, and thereby ending OS (Ahn et al. 1996; Karamać 2009; Saravanan et al. 2006; Shaban et al. 2013 and 2014). As well, the incidence of Asc and triterpenoids in PE increases its antioxidant power, whereas triterpenoids can chelate Fe (II) (Shaban et al. 2014).
Otherwise, our data revealed that PE contains considerable amounts of minerals with different concentrations. The existence of S, Cu, Zn, Mn, and Se in PE stimulates the antioxidant system via the activation of SOD and t-GPX. SOD is found in three isoforms, including (Cu/Zn)-SOD, (Mn)-SOD, and extracellular-SOD; therefore, Cu, Zn, and Mn are essential elements in its activity (Abu-Serie et al. 2018). Also, *Se is* also involved in the production of t-GPX protein and its function (Bermingham et al. 2014).
Furthermore, the present findings showed that there was a decline in the hepatic TNF-α, NF-κB, IL-6, p53, and TGF-β gene expression, with a significant decline in the NO level in rats injected with CCl4 and treated with PE (PE-CCl4) and (PE-CCl4-PE), when compared with the CCl4 group. This signifies that PE has anti-inflammatory, antifibrotic, and antiapoptotic influences and this is because of its valuable components mentioned above, especially phenolics and flavonoids. Myricetin and kaempferol in PE besides their actions as antioxidants inhibit the effect of several cytokines like TNF-α, IFN-γ, IL-2, and IL-6, demonstrating their anti-inflammatory and antifibrotic roles (Cao et al. 2014; Sekiguchi et al. 2019). Also, previous investigations revealed that kaempferol has antiapoptotic activity (Sekiguchi et al. 2019). Additionally, myricetin reduces NF-κB gene expression and prevents its activation and inhibits the inflammatory markers, especially inducible nitric oxide synthase (iNOS) and cyclooxygenase‐2 (Li et al. 2019; Afroze et al. 2020). Moreover, treatment with PE reduced the nephrotoxicity induced by CCl4 as creatinine and urea levels were lower than the CCl4 group. This implies that PE has prophylactic and therapeutic purposes towards the kidney toxicity induced by CCl4. Generally, our results showed that phenolic and flavonoid compounds, terpenoids, Asc, some minerals, etc., in PE performed a critical role in reducing the free radicals generated from CCl4 metabolism. And this led to the reduction of lipid peroxidation and safeguarding membrane lipids from the oxidative destruction, inflammation, fibrosis, and apoptosis. Commonly, the results showed that PE treatment (pre, during, and after CCl4 administration) gives better results than the treatment with PE pre and during CCl4 administration.
Otherwise, the present outcomes confirmed that administration of healthy rats with PE alone for 14 weeks triggered nonsignificant alterations in markers of the OS, inflammation, and apoptosis as compared to the C group, while there were no variations in liver histology.
## Conclusion
PE demonstrated its protective and therapeutic effect in opposition to hepatotoxicity caused by CCl4 by reducing the oxidative stress, infammation, fibrosis, and apoptosis. Additionally, PE treatment diminished the nephrotoxicity induced by CCl4 administration. PE treatment reduced PE treatment (pre, during, and after CCl4 administration) gave better results than the treatment with PE pre and during CCl4 administration. The beneficial effect of PE may be due to its content, as it contains high quantities of phenolics, flavonoids, tannins, terpenoids, and ascorbic acid. For 12 weeks, healthy rats were given PE; there were no adverse effects. The prevention and treatment of the toxicity brought on by xenobiotics can thus be accomplished with PE, which is a promising drug. Figure 6 shows the diagrammatic representation of the protective and therapeutic roles of PE against CCl4-induced rat hepatotoxicity. Fig. 6Diagrammatic representation of the protective and therapeutic roles of PE against CCl4-induced rat hepatotoxicity. PE reduced the OS, inflammation, fibrosis, and apoptosis caused by CCl4, leading to improved liver architecture and liver functions
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|
---
title: 'Analysis of gene expression profiles in Alzheimer’s disease patients with
different lifespan: A bioinformatics study focusing on the disease heterogeneity'
authors:
- Ji Zhang
- Xiaojia Li
- Jun Xiao
- Yang Xiang
- Fang Ye
journal: Frontiers in Aging Neuroscience
year: 2023
pmcid: PMC9995587
doi: 10.3389/fnagi.2023.1072184
license: CC BY 4.0
---
# Analysis of gene expression profiles in Alzheimer’s disease patients with different lifespan: A bioinformatics study focusing on the disease heterogeneity
## Abstract
### Objective
Alzheimer’s disease (AD) as the most frequent neurodegenerative disease is featured by gradual decline of cognition and social function in the elderly. However, there have been few studies focusing on AD heterogeneity which exists both genetically and clinically, leading to the difficulties of AD researches. As one major kind of clinical heterogeneity, the lifespan of AD patients varies significantly. Aiming to investigate the potential driving factors, the current research identified the differentially expressed genes (DEGs) between longer-lived AD patients and shorter-lived ones via bioinformatics analyses.
### Methods
Qualified datasets of gene expression profiles were identified in National Center of Biotechnology Information Gene Expression Omnibus (NCBI-GEO). The data of the temporal lobes of patients above 60 years old were used. Two groups were divided according to the lifespan: the group ≥85 years old and the group <85 years old. Then GEO2R online software and R package of Robust Rank Aggregation (RRA) were used to screen DEGs. Bioinformatic tools were adopted to identify possible pathways and construct protein–protein interaction network.
### Result
Sixty-seven AD cases from four qualified datasets (GSE28146, GSE5281, GSE48350, and GSE36980) were included in this study. 740 DEGs were identified with 361 upregulated and 379 downregulated when compared longer-lived AD patients with shorter-lived ones. These DEGs were primarily involved in the pathways directly or indirectly associated with the regulation of neuroinflammation and cancer pathogenesis, as shown by pathway enrichment analysis. Among the DEGs, the top 15 hub genes were identified from the PPI network. Notably, the same bioinformatic procedures were conducted in 62 non-AD individuals (serving as controls of AD patients in the four included studies) with distinctly different findings from AD patients, indicating different regulatory mechanisms of lifespan between non-AD controls and AD, reconfirming the necessity of the present study.
### Conclusion
These results shed some lights on lifespan-related regulatory mechanisms in AD patients, which also indicated that AD heterogeneity should be more taken into account in future investigations.
## Introduction
Alzheimer’s disease (AD), featured by progressive decline of cognition and individual social functioning, is the most prevalent neurodegenerative disease in older people (Scheltens et al., 2016). AD accounts for more than half of all dementia cases, leading to serious burdens on the patients, the families and the society as a whole (Jia et al., 2018). The typical pathological characteristics of AD were recognized to be hyper-phosphorylated tau aggregations and amyloid-β (Aβ) plaques in the brain (Bakota and Brandt, 2016). However, it has been well aware that Aβ pathology and tau pathology could not represent the whole picture of the pathogenesis of AD. Thus, researchers have developed more hypotheses hoping to clarify its pathogenesis, such as neuroinflammation, oxidative stress and mitochondrial dysfunction, protein oxidation, lipid peroxidation, etc. ( Serrano-Pozo et al., 2011). However, the exact mechanisms leading to the beginning and development of AD still need to be further clarified.
One major reason might be the huge heterogeneity of AD, both genetically and clinically (Devi and Scheltens, 2018). It has long been acknowledged that the clinical manifestations of AD patients vary significantly in many aspects including but not limited to the onset age, progressive rate, the lifespan, the affected cognitive domains, and so on(Lam et al., 2013). Thanks to the uncovering of many AD risk genes using high-throughput biochips in recent decades, AD has been recognized to be the dysregulation of a substantial number of genes resulting in the alteration of their complex interactions, which finally leads to the varieties of disease manifestations (Zhu et al., 2017). Some previous studies have focused on the link between its genetic and clinical heterogeneity with results suggesting that using more genetically or clinically homogeneous patients may be helpful to identify additional risk genes. Lo et al. ’s [2019] study performed stratified gene-based genome-wide association studies (GWAS) and polygenic variation analyses in the younger and older age-at-onset groups in order to explore genetic heterogeneity of AD related to age and locate risk genes showing different effects across age. Belloy et al. [ 2020] probe the link between longevity gene KLOTHO and the APOE4-AD risk and found that KL-VS (a functional variant of KLOTHO) heterozygosity was significantly associated with decreased risk for AD and conversion to AD, and also reduced Aβ biomarkers in individuals who carry APOE4 but not in those who do not carry APOE4. These results suggest that there might be different regulatory mechanisms in different AD subgroups, which are of great significance to be further investigated. However, AD was studied as a monolithic disease in most studies and compared with non-AD controls, which might cause considerable confounding when exploring its pathogenesis.
Notably, the lifespan of AD patients also exhibits considerable heterogeneity. Some AD patients present with later onset and/or slower progression leading to longer lifespan, while some others might have significantly shorter lifespan. Although one of the major targets of AD intervention is to prolong patients’ lifespan, the heterogeneity in AD lifespan has not been much explored. Aoyagi et al. ’s [2019] study quantically measures the intracellular self-propagating conformers in postmortem brain samples from AD patients and shows that the longevity-dependent reduction in self-propagating tau conformers were identified in spite of increasing levels of total insoluble tau, demonstrating an inverse correlation between longevity and the amounts of pathological tau conformers in AD patients. The underlying mechanisms have not been clarified so far. In this case, analyzing lifespan-related gene expression profiles in AD patients might be a promising strategy to provide information about the genetic regulatory mechanisms underlying the phenotype of different lifespans. To date, there has been no such study published before.
This study acquired qualified gene profiles of AD patients from GEO database and the differentially expressed genes (DEGs) between AD patients with longer lifespan and shorter lifespan were meta-analyzed using the R package of Robust Rank Aggregation (RRA). Then, the functional pathway annotations and protein–protein interaction (PPI) networks of DEGs were performed via bioinformatics approaches. We investigate the lifespan-related regulatory mechanisms in AD patients at a molecular level and help uncovering potential candidate genes for AD intervention.
## Dataset selection and data preprocessing
The Gene Expression Omnibus (GEO)1 is a public repository for researchers worldwide to submit high-throughput microarray and next-generation sequence functional genomic datasets. All data are available for download without charge (Barrett et al., 2013). The datasets of gene expression profiles used in the present study were obtained from GEO with the search strategy as follows: (((Expression profiling by high throughput sequencing [DataSet Type]) OR Expression profiling by array [DataSet Type]) AND homo sapiens[Organism]) AND Alzheimer’s disease[Title] (Figure 1). The inclusion criteria of qualified datasets were as follows: investigating the expression profiles by arrays or high throughput sequencing in GEO; using brain samples of AD cases and non-AD controls; containing complete information of age at death. Since the brain samples were donated by volunteers and collected postmortem, the ages displayed in these studies were in fact the ages at death, serving as a qualified indicator of lifespan.
**Figure 1:** *Data set selection flowchart.*
Through literature reviewing, it was found that several datasets (for example GSE48350, GSE5281, GSE36980) are designed to obtain samples from multiple brain regions of one donor. However, one previous study(Moradifard et al., 2018) has proved that the gene expression profiles vary across different brain regions. Thus, it might cause substantial confounding if all the samples were included in the meta-analysis. Thus, only the samples of temporal lobe were chosen for the analysis in order to minimize the heterogenicity of samples. If one dataset included samples of different regions in the temporal lobe, the region with the largest sample size was chosen. With regard to the cut-off age, it was firstly set to be above 80 years old which has been reported to be the average life expectancy of Chinese elderly (Huang et al., 2021). After several grouping attempts, the cut-off age of 85 years old was selected which would include more datasets and make the grouping more balanced. Then, according to the lifespan: the group with longer lifespan (> = 85 years old) and the group with shorter lifespan (<85 years old), the samples of each dataset were divided into two groups. In addition, the samples with age over 60 years old were chosen to lower the possible influences of unnatural deaths.
## DEGs identification
The R tools GEOquery and limma from the Bioconductor project were used to export and analyze the gene expression data of the comparisons between AD patients (AD patients with longer lifespan vs. those with shorter lifespan). Bioconductor, an open-source software project built on the R programming language, offers tools for the study of high-throughput genetic data. The R package GEOquery transforms GEO data into R data structures for usage by other R tools (Davis and Meltzer, 2007). Differentially expressed genes (DEGs) between the two groups of each dataset with p values <0.05 were selected to be further analyzed. Then the values of fold changes (FC) were log2 transformed and represented as log FC in short. Log FCs which were below zero indicated the DEGs were down regulated, and vice versa. The meta p values of the DEGs were calculated using the R package of Robust Rank Aggregation (RRA) and the results were represented as meta-analysis scores (Kolde et al., 2012). The RRA technique, which can manage fluctuating gene content from various microarray platforms in the presence of noise or with partial rankings, is based on a comparison of real data with a null model that assumes random order of input lists. Besides, the mean values of log FCs were also calculated. Genes with meta p values less than 0.05 and average |log FC| ≥ 1 were considered as final DEGs. Data processing was performed using Python Jupyter Notebook (Edition 5.0.0).
Notably, data of non-AD controls were also analyzed using the same methodology to serve as comparisons. The non-AD data came from the included datasets and were used to be controls of AD patients in the original studies.
## Gene functional enrichment analysis
The DEGs were uploaded to Metascape2 (Zhou et al., 2019). Pathway and process enrichment analyses were carried out with ontology sources of KEGG pathway, GO Biological Processes, Reactome Gene Sets, Canonical Pathways, and WikiPathways. Genes of the whole genome were adopted as the enrichment background. Terms with p value <0.01, count of genes ≥3, and an enrichment factor > 1.5 were collected and grouped into clusters based on their membership similarities. The top 20 clusters were collected using the most statistically significant term in each cluster as the representative.
Protein–protein interaction (PPI) enrichment analysis was conducted based on the following databases: STRING, BioGrid, OmniPath, InWeb_IM. If the network contains 3 to 500 proteins, the Molecular Complex Detection (MCODE) algorithm would be applied to identify densely connected network components. Pathway and process enrichment analysis was applied to each MCODE component independently, and the three best-scoring terms by value of p were retained as the functional description of the corresponding components.
## Hub genes identification and association enrichment analysis
To screen hub genes, CytoHubba plug-in of Cytoscape was utilized to analyze PPI networks exported from the corresponding Metascape results in the present study (Jeong et al., 2001). The top 15 hub genes ranked by the method of Maximal Clique Centrality (MCC) were calculated. Enrichment analysis were also performed in ontology categories of DisGeNET via Metascape (Piñero et al., 2017). DisGeNET integrates data from expert curated repositories, GWAS catalogs, animal models and the scientific literature to provide information about the genetic basis of human diseases. Genes of the whole genome were adopted as the enrichment background. Terms with p value <0.01, count of genes ≥3, and an enrichment factor > 1.5 were collected and grouped into clusters based on their membership similarities.
## Analysis of immune infiltration and hub genes
*The* gene sets of 28 immune cells and four classes of immune factors were downloaded from TISIDB database.3 The following 28 types of immune cells were obtained: central memory CD4+ T cells (CD4+ Tcm), central memory CD8+ T cells (CD8+ Tcm), type-2 T helper cells (Th2), CD56dim natural killer cells (CD56− NK), activated CD8+ T cells (CD8+ Ta), activated CD4+ T cells (CD4+ Ta), activated B cells (Ba), effector memory CD8+ T cells (CD8+ Tem), effector memory CD4+ T cells (CD4+ Tem), macrophages, eosinophils, memory B cells (Bm), immature dendritic cells (DCi), gamma delta T cells (γδT), CD56bright natural killer cells (CD56+ NK), monocytes, mast cells, natural killer cells (NK), immature B cells (Bi), type-1 T helper cells (Th1), neutrophils, plasmacytoid dendritic cells (DCp), natural killer T cells (NK T), type-17 T helper cells (Th17), follicular helper T cells (Tfh), regulatory T cells (Tregs), myeloid-derived suppressor cells (MDSC), and activated dendritic cells (DCa). The four classes of immune factors include 41 chemokines, 24 immunosuppressive factors, 46 immunostimulatory factors, and 18 immune receptors.
The ssGSEA algorithm, which classifies gene sets with common biological functions, physiological regulation, and chromosomal localization, was employed via R packages (GSVA 1.42.0) to comprehensively assess the immunologic characteristics of each sample included in the analyses (Hänzelmann et al., 2013). Normalized data of gene expression profiles were compared with the gene sets to demonstrate the enrichment of immune cells in each AD brain samples. Then, ANOVA was adopted to identify immune cell types with significant differences between the groups with longer lifespan and shorter lifespan. Pearson correlations between the gene expression level of each hub gene and the concentrations of immune cells were carried out using cor.test in R software (version: 4.0.3). The hub genes were identified in 2.4.
The correlations between the gene expression levels of each hub gene and the gene sets of immune factors were also calculated, respectively. Then, the pairs of hub genes and immune-related molecules with |cor| > 0.6 & p value<0.05 were selected to generate a circos plot via Cytoscape.
*The* gene expression profiles of GSE48350 samples (Table 1) were used to perform immune infiltration analysis. As shown in Figures 5A,B, the fractions for activated B cell, effector memory CD8 T cell, plasmacytoid dendritic cell and type 1 T helper cell in the longer-lived AD group were remarkably higher than in those of shorter-lived ones.
**Figure 5:** *Immune infiltration analysis between longer-lived AD and shorter-lived ones. (A) The column diagram displaying the relative percentage of the 28 immune cells between in AD samples. (B) The difference of immune infiltration between longer-live AD (orange) and shorter-lived ones (gray; * indicates p-values < 0.05). (C) Correlations between hub genes and the infiltration levels of the 28 immune cells. (D) Circos plot of the interactions between the hub genes and immune-related molecules.*
Since most pathways identified in the downregulated DEGs were inflammation related, the top 10 hub genes identified in the downregulated DEGs and the top 3 hub genes identified in the upregulated DEGs were selected for the association analysis with immune cells and immune factors. As shown in Figure 5C, STAT1 was positively correlated with gamma delta T cell, activated CD4 T cell, immature dendritic cell and activated CD8 T cell. MX1 was positively correlated with immature dendritic cell and gamma delta T cell. IFIT3 was negatively correlated with immature B cell, activated B cell and mast cell. IFIT1 was positively correlated with effector memory CD4 T cell and negatively correlated with neutrophil, type 17 T helper cell, effector memory CD8 T cell, natural killer cell, type 1 T helper cell and central memory CD8 T cell. IRF4 was positively correlated with activated CD4 T cell, eosinophil and type 2 T helper cell. DDX58 was positively correlated with gamma delta T cell. HDAC6 was positively correlated with monocyte. SRC was positively correlated with CD56 bright natural killer cell and negatively correlated with effector memory CD4 T cell. RPL24 was negatively correlated with T follicular helper cell, immature dendritic cell, mast cell and activated CD4 T cell. BRD4 was positively correlated with CD56 bright natural killer cell and negatively correlated with effector memory CD4 T cell, gamma delta T cell and central memory CD8 T cell. There were no significant findings when analyzing the associations between immune cells and the remaining hub genes (OAS3, XAF1, and IFI6). Protein–protein interaction plot of hub genes and immune-related molecules was shown as Figure 5D.
## Identification of DEGs
The flowchart of dataset selection was shown in Figure 1. Four qualified microarray datasets (GSE48350, GSE5281, GSE28146, GSE36980) and one dataset of high throughput sequencing (GSE173955) were identified according to the inclusion and grouping criteria. Thereinto, the samples used in GSE173955 were also used in GSE36980 as stated in the abstract of the article (Mizuno et al., 2021). In order to include more samples and reduce batch effect and other confounding, GSE36980 were included in the analysis rather than including both or GSE173955 alone.
In total, 129 samples (62 non-AD controls and 67 AD cases) were analyzed in this study; the grouping and baseline information were shown in Table 1. After comparing longer-lived AD patients with shorter-lived ones in each dataset, genes with $p \leq 0.05$ were selected and formed a list, respectively. The Venn diagram showing the overlap of the four gene lists was displayed as Figure 2A. After meta-analysis, a list of 740 DEGs with 361 upregulated and 379 downregulated was identified in the AD group with longer lifespan compared to that with shorter lifespan. The top 15 most significantly upregulated and downregulated genes when comparing longer-lived individuals with shorter-lived ones in AD patients were shown in Table 2.
In addition, the data of non-AD controls were also analyzed using the same methodology to serve as comparison and 888 DEGs were identified with 459 up-regulated and 429 down-regulated. Volcano plots showing DEGs from both comparisons (the groups of AD and non-AD controls) were as Figure 2B. The Venn diagrams showing the overlap of AD and non-AD DEGs were exhibited in Figure 2C.
## Gene functional enrichment analysis of DEGs and hub genes identification
The top 20 clusters with their representative enriched terms (one per cluster) of the up-and downregulated DEGs in the AD and non-AD comparisons were displayed in Figure 3. More details of the top five clusters were shown in Tables 3, 4. The PPI networks and MCODE components identified in the DEGs of the AD comparison were shown in Figures 4A,B. The top clusters (one term per cluster) of enrichment analysis in DisGeNET were shown in Figure 4C.
When comparing AD patients with longer lifespan to those with shorter lifespan, the three best-scoring terms identified via pathway and process enrichment analysis to each MCODE component were as follows: cellular response to nitrogen compound (GO: 1901699, Log10(P) = −7.9), cellular response to organonitrogen compound (GO: 0071417, Log10(P) = −7.9) and regulation of intracellular transport (GO:0032386, Log10(P) = −7.3) in the upregulated DEGs; Interferon Signaling (R-HSA-913531, Log10(P) = −7.6), regulation of viral process (GO:0050792, Log10(P) = −7.6), Interferon alpha/beta signaling (R-HSA-909733, Log10(P) = −7.3) in the downregulated DEGs.
The top 15 hub genes identified in the PPI network of the up-regulated DEGs were SRC (MCC score = 44), RPL24 (MCC score = 33), BRD4 (MCC score = 32), RPL10L (MCC score = 30), CSK (MCC score = 22), JAK2 (MCC score = 20), MRPL4 (MCC score = 20), UBD (MCC score = 19), EIF5A (MCC score = 18), WDR61 (MCC score = 16), CLUH (MCC score = 16), EZH2 (MCC score = 15), CAPN1 (MCC score = 13), ACTN2 (MCC score = 13), CLIC2 (MCC score = 12), in order of ranks. The top 15 hub genes identified in the PPI network of the downregulated DEGs were STAT1 (MCC score = 5,079), MX1 (MCC score = 5,066), IFIT3 (MCC score = 5,064), IFIT1 (MCC score = 5,064), OAS3 (MCC score = 5,043), IRF4 (MCC score = 5,043), XAF1 (MCC score = 5,043), IFI6 (MCC score = 5,040), DDX58 (MCC score = 65), HDAC6 (MCC score = 25), RSL1D1 (MCC score = 24), BIRC3 (MCC score = 22), RPS6 (MCC score = 21), BRD7 (MCC score = 14), RRP12 (MCC score = 14), in order of ranks.
## Discussion
In the present study, 740 DEGs with 361 upregulated and 379 downregulated were identified comparing AD patients with longer lifespan to those with shorter lifespan. Bioinformatic analyses were performed based on these DEGs, and the significant findings would be discussed as below. Notably, the same bioinformatic procedures and analyses were conducted basing on the data of non-AD controls (Table 1), with distinctly different findings from those identified in the AD comparison (Figures 2C, 3). These results indicated that the underlying regulatory mechanisms of AD lifespan might be quite different from those of non-AD controls, reconfirming the necessity of the present study. Investigating lifespan-related gene expression profiles in AD patients would help to understand the genetic background possibly impacting its clinical course, which has not been published before.
In the lifespan-related pathways identified in the present study, multiple clusters of pathways were directly or indirectly associated to neuroinflammation. The directly associated clusters included those represented by the pathways of interferon Signaling (R-HSA-913531) and regulation of response to cytokine stimulus (GO:0060759) in the downregulated DEGs. The indirectly associated clusters included those about antiviral responses represented by the pathway of regulation of viral process in the downregulated DEGs; those about metabolism processes represented by the pathways of Adipogenesis, glucose metabolic process in the downregulated DEGs; Diseases of metabolism in the upregulated DEGs; and those about autophagy represented by the pathways of apoptotic cell clearance, Phagosome in the upregulated DEGs. These results indicated that neuroinflammation might be closely related to the regulation of AD lifespan.
Amounts of evidence, involving increasing numbers of activated microglial and astroglia in the brains of AD patients, elevated pro-inflammatory cytokine in AD brains, and epidemiological proof that chronic non-steroidal anti-inflammatory drug used before AD associates to a lower incidence, have suggested that neuroinflammation, an early-emerging and continuously existing feature of AD, plays a significant part in the pathogenesis of the disorder (Calsolaro and Edison, 2016). Interferons (IFNs) are a superfamily of cytokine proteins that play a significant part in host immune response to pathogens, infections, and various diseases (de Weerd and Nguyen, 2012). It has been proved that they are critical in the exacerbation of neuroinflammation and actively contribute to AD progression (Taylor et al., 2018). Also, studies have shown that active virus infections in brain may not only accelerate amyloid deposition and the progression of AD (Eimer et al., 2018; Mangold and Szpara, 2019), but also, by inhibiting autophagy, disrupt clearance of the aberrant proteins, resulting in their accumulation and deposition, and finally to AD onset and progression (Itzhaki, 2017). dysregulation of metabolism processes would lead to metabolic changes, induction of obesity and adipose tissue inflammation, resulting in the acceleration of systemic low-grade inflammation and then accumulation of toxic amyloid, eventually the onset of AD (Więckowska-Gacek et al., 2021). Regulation of these pathways might result in the mitigation of excessive neuroinflammation in AD brains and thus leading to longer lifespan. In addition, the results of immune infiltration analysis also supported this conclusion, which showed that four kinds of immune cells increased significantly in longer-lifespan AD patients and the hub genes corelated with multiple immune cells and immune factors, indicating that the regulation of AD lifespan might be intertwined with the complex networks of neuroinflammation.
Thus, identifying key mediators regulating the neuroinflammation process might be helpful to develop anti-inflammatory therapies for AD (Taylor et al., 2018). Among the identified hub genes, STAT1, which ranked the first in the hub gene list identified in the downregulated DEGs and corelated with multiple immune cells and immune factors, has already come into notice of researchers. The protein encoded by STAT1 is activated by varieties of ligands including IFN-α, EGF, IFN-γ, PDGF, and IL6. Zhang et al. ’s [2021] study shows that STAT1 knockout suppresses AD typical pathologies. Another study identifies that STAT1 activation abolishes expression of N-methyl-D-aspartate receptors (NMDARs), while the downregulation of STAT1 efficiently mitigates Tau-induced suppression of NMDAR expression and improves the function of synapses and performances in memory tests (Li et al., 2019). He et al. ’s [2021] study shows that the overexpression of STAT1 inhibitor represses several AD markers expressions and accelerate the proliferation of mouse hippocampal neuronal cells. These findings might offer some explanations why the downregulated expression of STAT1 is associated with longer lifespan of AD patients in the present study. In addition, the recent study of Zhang et al. [ 2022] shows that pharmacological degradation and inhibition of BRD4, which affects transcriptional regulation of autophagy and lysosome genes, significantly increase Aβ levels that are related to AD neuropathology in cell models, indicating that the upregulation of BRD4 might be beneficial for AD, consistent with the findings of the present study that BRD4 was upregulated in longer-lived AD patients and corelated with multiple immune cells and factors (Figure 5).
Interestingly, the enrichment analysis via DisGeNET (Figures 4C,D) revealed noteworthy overlaps with neoplastic diseases in both up-and downregulated DEGs of AD comparison. Several AD-lifespan-related pathways identified in the present study were also related to cancer, such as positive regulation of cell death, Malignant pleural mesothelioma, Hippo signaling pathway in the downregulated DEGs and apoptotic cell clearance, Signaling by Receptor Tyrosine Kinases in the upregulated DEGs. These results indicated that the regulation of AD Lifespan and cancer might share common pathways. Nudelman et al. have reviewed about ten hallmark biological alterations which overlap in the pathogenesis of cancer and AD (Nudelman et al., 2019), and proposed that pathways related to inflammation might exhibit similar roles and parallel directions of regulation in the pathogenesis of cancer and AD (Nudelman et al., 2019). It has been assumed that inflammation might accelerate the earliest development of neoplastic progression, especially a chronic state of systemic inflammation. To survive, tumors need to shift the subclasses of immune cells attacking the tumor toward those promoting inflammation and tumor growth (Singh and Singh, 2015; Goswami et al., 2017). As for AD, increasing pro-inflammatory cytokine burden has been proved in AD patients’ brains. Epidemiological studies have also shown that long-term use of chronic non-steroidal anti-inflammatory drugs prior to AD onset relates to a lower incidence (Taylor et al., 2018). Thus, regulating the overlapping pathways or genes related to inflammation might be beneficial for the interventions of both cancer and LOAD.
Recent studies have shown that HDAC6 might be of dual function in the regulation of both AD and cancer. Ruzic et al. ’s [2022] study discovered two HDAC6 inhibitors with anti-breast cancer activity. As for AD, HDAC6, has shown elevated levels in AD with direct interaction with the tau protein (Qureshi and Chinnathambi, 2022) while Sreenivasmurthy et al. ’s [2022] study shows that inhibiting HDAC6 leads to activation of chaperone-mediated autophagy and alleviation of tau pathology in AD models. In the present study, HDAC6 was among the top 10 hub genes identified in the downregulated DEGs of longer-lived AD and corelated with multiple immune cells and factors, indicating that HDAC6 was closely associated with neuroinflammation and its downregulation might be helpful to prolong AD lifespan, concurring with previous studies.
Also, IL6 (meta $$p \leq 0.002$$, log FC = −1.01) and CD36 (meta $$p \leq 0.012$$, log FC = 1.96) might be potential therapeutic targets, both of which were involved in the pathway related to neuroinflammation in the present study. Escrig et al. study shows that the inhibition of IL-6 trans-signaling partially rescues the AD-induced mortality and reverses AD-induced cognitive and emotional changes in AD animal models, presenting strong potentials as a powerful therapeutic target in AD (Escrig et al., 2019). Interestingly, blocking IL-6 or inhibiting its associated signaling has been proposed to be a potential therapeutic strategy for the treatment of cancers with IL-6-dominated signaling (Kumari et al., 2016). As for CD36, Wang et al. ’s study found that upregulating CD36 expression ameliorated hypoxia-induced neuroinflammation, diminished Aβ deposition, and improved spatial memory defects in APP/PS1 mice (Wang et al., 2014). Meanwhile, Fang et al. [ 2019] report about the tumor-suppressive effects of CD36 and that CD36 inhibits growth and metastasis of colorectal cancer cells in vivo. These findings indicate that IL6 and CD36 might exert parallel function in the regulation of both AD and cancer, serving as promising targets for the two.
To sum up, neuroinflammation might take the center stage in the regulation of AD lifespan and it might be of particular importance to uncover the pathways or genes related to inflammation, especially those exhibiting parallel directions of regulation in the pathogenesis of cancer and AD, which might be promising targets for both diseases.
## Limitations
The findings of the present study must be interpreted in the light of certain limitations. Firstly, the data used in the present study were obtained from multiple studies, increasing the risk of confounding effects, such as sample size, sample sources and processing, quality and amount of RNA, microarray platform and so on. However, we tried to minimize these effects by selecting samples from the temporal lobe only and including datasets using similar techniques; we also adopted RRA for gene list integration and meta-analysis to reduce batch effects. Secondly, due to the limited number of genes exported from GEO2R when using the standard of adjust value of $p \leq 0.05$, p value < 0.05 was adopted for the first screening of DEGs, which might cause false positive results. However, after the first screening, we used RRA for value of p meta-analysis, which is designed to integratively select DEGs appearing in multiple datasets with high ranking. RRA has been reported to be robust and accurate in detecting DEGs across datasets. Then the results were screened for the second time using the standards of meta p values less than 0.05 and average |log FC|s ≥ 1 in order to further reduce false positive rate. Thirdly, since RNA-Seq technique is more powerful than microarray in evaluating gene expression profiles, thorough search and data digging were performed to locate suitable RNA-seq datasets for the present study. One such dataset was located but not included as previously mentioned. Continuous attention will be paid to newly-published studies or datasets in order to incorporate more data timely.
## Conclusion
The results of the present study showed that neuroinflammation might take the center stage in the regulation of AD lifespan and it might be of particular importance to uncover the pathways or genes related to inflammation, especially those exhibiting parallel directions of regulation in the pathogenesis of cancer and AD, which might be promising targets for both diseases. The involved pathways and genes identified in the present study might provide information about lifespan-related genetic mechanisms in AD patients and help developing promising strategies in further investigation.
## Data availability statement
The data presented in the study are deposited in the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) repository, accession numbers: GSE48350, GSE5281, GSE28146, GSE36980.
## Author contributions
FY was responsible for the rationale and the design of the study and edited and approved the final manuscript. JZ and YX conducted the series of dataset search, data processing, and relevant bioinformatics analyses. JZ wrote the manuscript. XL and JX assisted with the data analyses and manuscript editing, respectively. All authors contributed to the article and approved the submitted version.
## Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was carried out in Chengdu, China, and funded by Clinical Research and Translational Foundation of Sichuan Provincial People’s Hospital (2021LY11).
## 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.
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|
---
title: The abdominal aortic aneurysm-related disease model based on machine learning
predicts immunity and m1A/m5C/m6A/m7G epigenetic regulation
authors:
- Yu Tian
- Shengjie Fu
- Nan Zhang
- Hao Zhang
- Lei Li
journal: Frontiers in Genetics
year: 2023
pmcid: PMC9995589
doi: 10.3389/fgene.2023.1131957
license: CC BY 4.0
---
# The abdominal aortic aneurysm-related disease model based on machine learning predicts immunity and m1A/m5C/m6A/m7G epigenetic regulation
## Abstract
Introduction: Abdominal aortic aneurysms (AAA) are among the most lethal non-cancerous diseases. A comprehensive analysis of the AAA-related disease model has yet to be conducted.
Methods: Weighted correlation network analysis (WGCNA) was performed for the AAA-related genes. Machine learning random forest and LASSO regression analysis were performed to develop the AAA-related score. Immune characteristics and epigenetic characteristics of the AAA-related score were explored.
Results: Our study developed a reliable AAA-related disease model for predicting immunity and m1A/m5C/m6A/m7G epigenetic regulation.
Discussion: The pathogenic roles of four model genes, UBE2K, TMEM230, VAMP7, and PUM2, in AAA, need further validation by in vitro and in vivo experiments.
## Introduction
The heart pumps arterial blood through the aorta, then into its branches to supply the body in the abdominal section called the abdominal aorta. An abdominal aortic aneurysm is diagnosed when the diameter of the abdominal aorta enlarges by more than $50\%$ of its diameter for various reasons (Chaikof et al., 2018). It is commonly understood that the abdominal aorta, which was originally straight, has an abnormal expansion, like blowing up a balloon. When the development reaches a certain extent, it may burst suddenly. Therefore, an abdominal aortic aneurysm is sometimes compared to a “bomb with no timing” (Sakalihasan et al., 2005).
Most patients with abdominal aortic aneurysms are asymptomatic. Occasionally, patients find a “pulsing lump” on their stomach by accident or during a visit for another medical condition. If an abdominal aortic aneurysm suddenly causes severe abdominal pain, it is often a sign that it has ruptured or has ruptured. In addition, enlarged aneurysms can compress other organs in the abdominal cavity, such as intestinal compression, causing nausea, vomiting, abdominal distension, and discomfort. Compression of the ureter can cause hydronephrosis and so on.
Abdominal aortic aneurysms (AAA) cause three main harms: 1) The enlarged abdominal aorta compresses the surrounding vital organs and tissues, affecting their physiological functions. 2) *Thrombus is* easy to form in the lumen, which blocks the lower limb blood vessels after the thrombus falls off, leading to acute ischemic necrosis of the limb, just like the sudden water cut or power cut in daily life. 3) Under the continuous impact of blood flow, the abdominal aorta gradually enlarges, and when it exceeds the maximum tolerance limit, the aneurysm will burst and cause sudden death (Sakalihasan et al., 2005). Although aneurysms and solid tumors are entirely different concepts, the risk of death is more significant once ruptured than any solid tumor (Wanhainen et al., 2019).
Vascular inflammation is the first occurrence of AAA (Marquez-Sanchez and Koltsova, 2022). In the early stages of AAA, immune cells infiltrate and aggregate in the blood vessels, leading to inflammatory responses in the vessel walls. Inflammatory cells stimulate smooth muscle cells to secrete matrix metalloproteinase, which degrades elastin and collagen, reduces the stability of the artery wall, and induces apoptosis of vascular smooth muscle cells, thus playing an essential role in the occurrence and development of AAA (Marquez-Sanchez and Koltsova, 2022). Further understanding the mechanism of regulating immune cell activation in AAA will provide important targets for therapeutic intervention.
As an important research direction of post-transcriptional gene regulation, N1 methyladenosine (m1A), 5-methylcytosine (m5C), N6-methyladenosine (m6A), and 7-methylguanosine (m7G) modified RNA is widely found in eukaryotic cells and plays a vital role in a variety of biological processes (Wu et al., 2021). They also function as a novel mechanism in cardiovascular diseases, including heart failure, coronary heart disease, and hypertension (He et al., 2019b; Wu et al., 2021). However, the specific roles of m1A/m5C/m6A/m7G regulation in AAA have not been fully elucidated.
In this regard, we developed an AAA-related model and provided new mechanism insights for immunity and m1A/m5C/m6A/m7G regulation in AAA using large-scale bioinformatics analysis.
## Data collection
GSE98278 (48 AAA samples) and GSE47472 (14 AAA samples and eight normal samples) from the Gene Expression Omnibus (GEO) database were collected and used for the follow-up studies. GSE98278 and GSE47472 were RMA normalized and conformed to a normal distribution.
## WGCNA for the AAA-related genes
Weighted correlation network analysis (WGCNA) was performed on the top 5,000 genes from GSE47472 using the R package WGCNA. Gene significance was used to quantify the relationship between specific genes and macrophage densities, while module membership was used to illustrate the relationship between module eigengenes and gene expression profiles. To guarantee a scale-free topology network and create a TOM matrix, a power of β = 16 was automatically generated by the pickSoftThreshold function, and a scale-free R2 = 0.86 automatically derived by the softConectivity function were used as soft-threshold parameters. Using the plotEigengeneNetworks function, the module dendrogram showing the link between the eigengenes and the macrophages was plotted after the module eigengenes had been recalculated. Enrichment analysis for Gene Ontology-Biological Process (GO-BP), Gene Ontology-Cell Component (GO-CC), Gene Ontology-Molecular Function (GO-MF), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to the genes in the turquoise module was performed in GSE47472 by the R package clusterProfiler.
5,000 genes were used as the input for generating the cluster dendrogram in GSE47472 (Figure 1A). The scale-free topology model’s scale independence and mean connectivity in GSE47472 are calculated (Figure 1B). Module-type relationships in AAA and normal samples in GSE47472 are shown in Figure 1C, in which the turquoise module showed the highest positive correlation with AAA and the highest negative correlation with normal. Module membership and gene significance in the turquoise module in GSE47472 are shown in Figure 1D, in which the correlation coefficient reached 0.74. The module genes in the turquoise module were extracted for enrichment analysis for GO-BP, GO-CC, GO-MF, and KEGG pathways (Figure 2). Notably, Ubiquitin mediated proteolysis, phosphoric ester hydrolase activity, nuclear ubiquitin ligase complex, and interleukin-1 beta secretion were highly enriched.
**FIGURE 1:** *WGCNA for the AAA-related genes. (A) Cluster dendrogram of the input genes in GSE47472. (B) Scale independence and mean connectivity of the scale-free topology model in GSE47472. (C) Module-type relationships in AAA and normal samples in GSE47472. (D) Module membership and gene significance in the turquoise module in GSE47472.* **FIGURE 2:** *Enrichment analysis for GO-BP, GO-CC, GO-MF, and KEGG pathways related to the genes in the turquoise module in GSE47472.*
## Identification of the AAA-related genes
The R package limma identified the differentially expressed genes (DEGs) between AAA and normal samples. The R package pheatmap was used to visualize the DEGs between AAA and normal samples. The R package venn was used to visualize the intersected genes between the limma-based DEGs and the WGCNA-based genes in GSE47472. The R package Pi performed the GSEA for GO pathways related to the AAA-related genes in GSE47472.
The volcano plot displayed the limma-based DEGs between AAA and normal samples in GSE47472 (Figure 3A). Heatmap further showed the DEGs between AAA and normal samples in GSE47472 (Figure 3B). Venn plot displayed the 26 intersected AAA-related genes between the limma-based DEGs and the WGCNA-based genes in GSE47472 (Figure 3C). The expression pattern of the 26 intersected AAA-related genes in AAA and normal samples in GSE47472 revealed that the expression of most of the genes was significantly higher in AAA (Figure 3D). GSEA for GO pathways related to the AAA-related genes was performed in GSE47472 (Figure 4). Notably, the G2/M transition of the mitotic cell cycle and inflammatory response were positively enriched. In contrast, regulation of autophagy, RNA metabolic process, histone methylation, glycosaminoglycan metabolic process, histone H2A monoubiquitination, interleukin-27-mediated signaling pathway, and regulation of response to DNA damage stimulus were negatively enriched.
**FIGURE 3:** *Identification of the AAA-related genes. (A) Volcano plot for the limma-based DEGs between AAA and normal samples in GSE47472. (B) Heatmap for the DEGs between AAA and normal samples in GSE47472. (C) Venn plot for the intersected genes between the limma-based DEGs and the WGCNA-based genes in GSE47472. (D) Box plot for the intersected genes between the limma-based DEGs and the WGCNA-based genes in GSE47472.* **FIGURE 4:** *GSEA for GO pathways related to the AAA-related genes in GSE47472.*
## Identification of the AAA-related clusters
The R package ConsensusClusterPlus was used to identify the AAA-related clusters based on the AAA-related genes in GSE98278 using the kmdist method. The R package pheatmap was used to visualize the AAA-related genes in the AAA-related clusters in GSE98278.
A consensus cluster of AAA samples in GSE98278 was performed based on the AAA-related genes. Consensus CDF curves in GSE98278 revealed the most smooth curve with the $k = 2$ Figure 5A. The delta area in GSE98278 reflected the relative changes in the area under the CDF curves (Figure 5B). The clustering results were believed to be the most robust with $k = 2.$ *The consensus* matrix with $k = 2$ in GSE98278 showed consistency clustering among samples (Figure 5C). The PCA of the AAA-related clusters in GSE98278 revealed that the samples in the two clusters were separated (Figure 5D). Heatmap further displayed the AAA-related genes in the AAA-related clusters in GSE98278 (Figure 5E).
**FIGURE 5:** *Identification of the AAA-related clusters. (A) Consensus CDF curves in GSE98278. (B) Delta area in GSE98278. (C) Consensus matrix with k = 2 in GSE98278. (D) PCA of the AAA-related clusters in GSE98278. (E) Heatmap for the AAA-related genes in the AAA-related clusters in GSE98278.*
## Development of the AAA-related score
The R package limma identified the DEGs between the AAA-related clusters in GSE98278. Random forest (machine learning) was performed for dimension reduction of the limma-based DEGs between the AAA-related clusters in GSE98278. LASSO regression analysis (machine learning) was performed to develop the AAA-related score, which lambda.min = 0.0063. The AAA-related score was calculated as the sum of gene*coefficient. The R package pROC was used to generate the ROC curves of four model genes in predicting the AAA-related clusters in GSE98278.
The volcano plot displayed the limma-based DEGs between the AAA-related clusters in GSE98278 (Figure 6A). Random forest was performed for dimension reduction of the limma-based DEGs between the AAA-related clusters in GSE98278 (Figure 6B), which came to eight limma-based DEGs between the AAA-related clusters in GSE98278 (Figure 6C). Heatmap further displayed the eight limma-based DEGs between the AAA-related clusters in GSE98278 (Figure 6D). Coefficients in LASSO regression analysis in GSE98278 are shown in Figure 6E. Partial likelihood deviance in LASSO regression analysis in GSE98278 is shown in Figure 6F. The AAA-related score was calculated as follows: −1.7542*UBE2K + −0.1469*TMEM230 + −1.5033*VAMP7 + −1.3816*PUM2 + 27.7793832. The ROC curves of four model genes (UBE2K, TMEM230, VAMP7, and PUM2) predict the AAA-related clusters in GSE98278 (Figure 7A). The expression of PUM2, TMEM230, and VAMP7 was relatively lower in AAA compared with normal samples in GSE47472 (Figure 7B). The expression of four model genes in AAA and normal samples was further validated by qRT-PCR assay. In accordance with the previous finding, the expression of PUM2, TMEM230, and VAMP7 was relatively lower in AAA compared with normal samples in our mice model (Figure 7C).
**FIGURE 6:** *Development of the AAA-related score. (A) Volcano plot for the limma-based DEGs between the AAA-related clusters in GSE98278. (B) Random forest for dimension reduction of the limma-based DEGs between the AAA-related clusters in GSE98278. (C) Box plot for the eight limma-based DEGs between the AAA-related clusters in GSE98278. (D) Heatmap for the eight limma-based DEGs between the AAA-related clusters in GSE98278. (E) Coefficients in LASSO regression analysis in GSE98278. (F) Partial likelihood deviance in LASSO regression analysis in GSE98278.* **FIGURE 7:** *Validation of four model genes. (A) ROC curves of four model genes in predicting the AAA-related clusters in GSE98278. (B) The expression of four model genes in AAA and normal samples in GSE47472. (C) The expression of four model genes in AAA and normal samples by qRT-PCR assay.*
## Immune characteristics of AAA-related score
The R package pheatmap was used to visualize the association between AAA-related score and CIBERSORT-based immune cells in GSE98278. The R package Pi performed the GSEA for GO pathways related to AAA-related score in GSE98278. m1A-related genes, m5C-related genes, m6A-related genes, and m7G-related genes were collected from the previous study (Li et al., 2022; Shao et al., 2022).
Heatmap displayed the association between AAA-related score and 22 CIBERSORT-based immune cells in GSE98278 (Figure 8A), in which AAA-related score positively correlated with plasma cells, activated dendritic cells, memory B cells, naïve B cells, activated CD4 memory T cells, and CD8 T cells while negatively correlated with macrophage M0, macrophage M1, macrophage M2, and monocytes. GSEA was performed for GO pathways related to AAA-related scores in GSE98278 (Figure 8B). Double-strand break repair via homologous recombination, protein phosphorylation, interferon-gamma-mediated signaling pathway, immune response, cellular response to DNA damage stimulus, T cell costimulation, T cell activation, innate immune response, and T cell receptor signaling pathway were positively enriched. In sum, AAA-related scores were associated with an immune hot microenvironment.
**FIGURE 8:** *Immune characteristics of AAA-related score. (A) Heatmap for the association between AAA-related score and CIBERSORT-based immune cells in GSE98278. (B) GSEA for GO pathways related to AAA-related score in GSE98278.*
## Induction of AAA mice model
Alzet osmotic minipumps (Model 2004; ALZA Scientific Products, Mountain View, California, United States) were implanted into APOE−/− mice (C57BL/6J) at 8 weeks of age. Pumps were filled either with saline vehicle or solutions of Ang II (Sigma Chemical Co., St. Louis, Missouri, United States) that delivered (subcutaneously) 1,000 ng/min/kg of Ang II for 28 days. Pumps were placed into the subcutaneous space of mice through a small incision in the back of the neck. All procedures involving animals were approved by the Animal Care and Use Committee at Dalian Medical University.
## RT-qPCR assay for validation of four model genes
Six AAA and six normal samples were collected from our mice model. Total RNA was extracted by Trizol. The absorbance was measured at 260 and 280 nm, and the concentration and purity were calculated. The RT-qPCR assay was performed with SYBR method. All the primers were designed with primer 5.0. H-actin (https://www.ncbi.nlm.nih.gov/gene/60; F ACCCTGAAGTACCCCATCGAG R AGCACAGCCTGGATAGCAAC; Product length 224bp). H-UBE2K (https://www.ncbi.nlm.nih.gov/gene/3093; F AGCGAGGAGACGAGCAAAAA R ACAAATAGCCCCTGTGACGG; Product length 222bp). H-TMEM230 (https://www.ncbi.nlm.nih.gov/gene/29058; F GCTGTCAGGCTACATCAGCA R ACCACGGTAGCCTTTGGATG; Product length 230bp) H-VAMP7 (https://www.ncbi.nlm.nih.gov/gene/6845; F ACTTCCTGGAGGATTTTGAACG R TGTCTGTGCTCTTGAACCGT; Product length 95bp). H-PUM2 (https://www.ncbi.nlm.nih.gov/gene/23369; F GGAATGGGAGAGACCATTCAA R CTTTCTGATCGCGGAGACAGT; Product length 112bp).
## Statistical analyses
The Wilcoxon or Kruskal–Wallis test was used to compare non-parametric variables, while the t-test or one-way ANOVA was used to compare parametric variables. Spearman’s correlation analysis was used to calculate correlation coefficients. All statistical analyses were two-sided, and $p \leq 0.05$ was considered statistically significant.
## Epigenetic characteristics of AAA-related score
The association between AAA-related score and m1A-related genes in GSE98278 is shown in Figure 9A, in which the association was insignificant. The association between AAA-related score and m5C-related genes in GSE98278 is shown in Figure 9B, in which AAA-related score was significantly associated with DNMT1, NSUN2, NSUN5, and YBX1. The association between AAA-related score and m6A-related genes in GSE98278 is shown in Figure 9C, in which AAA-related score was significantly associated with FTO, HNRNPC, METTL3, and RBM15. The association between AAA-related score and m7G-related genes in GSE98278 is shown in Figure 9D, in which AAA-related score was significantly associated with CYFIP1, EIF3D, EIF4E3, NSUN2, and NUDT11. AAA-related scores positively correlated with most m1A/m5C/m6A/m7G-related genes.
**FIGURE 9:** *Epigenetic characteristics of AAA-related score. (A) The association between AAA-related score and m1A-related genes in GSE98278. (B) The association between AAA-related score and m5C-related genes in GSE98278. (C) The association between AAA-related score and m6A-related genes in GSE98278. (D) The association between AAA-related score and m7G-related genes in GSE98278.*
## Discussion
In the era of big data, excavating novel biomarkers or models in predicting the pathogenic mechanisms of AAA using large-scale bioinformatics analysis has been increasingly attractive. A previous study revealed differential expression of transcripts in the peripheral blood of AAA, indicating functional roles in proteolysis, inflammation, and apoptotic processes based on microarray-based gene expression profiling (Butt et al., 2016). A miRNA signature in AAA suggested that miRNAs play a role in AAA pathogenesis (Pahl et al., 2012). Besides, a lncRNA signature in AAA demonstrated that lncRNA candidates are related to the pathogenesis of AAA (Yang et al., 2016). The characteristic of inflammatory infiltration in the perivascular adipose tissue (PAT) surrounding AAA may be represented by a cell network dominated by FOS made up of activated mast cells, plasma cells, and Tfh cells (Ding et al., 2022). However, these models were not validated by in vitro validation. Besides, most of the model applications and universality were not so good.
In our study, WGCNA, as a generally used algorithm for determining feature-related genes, was used to determine the AAA-related genes for a comprehensive exploration of the pathogenic characteristics of AAA. Ubiquitin-mediated proteolysis, phosphoric ester hydrolase activity, nuclear ubiquitin ligase complex, and interleukin-1 beta secretion were highly enriched based on the WGCNA-derived module genes. Notably, a previous study has proven that potential therapeutic usefulness exists in altering how the ubiquitin-proteasome system function in vascular disorders (Wang et al., 2020). So, the AAA-related genes by WGCNA were believed to be reliable.
G2/M transition of mitotic cell cycle and inflammatory response were positively enriched based on limma-based DEGs between AAA and normal samples. In contrast, regulation of autophagy, RNA metabolic process, histone methylation, glycosaminoglycan metabolic process, histone H2A monoubiquitination, interleukin-27-mediated signaling pathway, and regulation of response to DNA damage stimulus were negatively enriched based on limma-based DEGs between AAA and normal samples. It is thought that one of the primary molecular pathways underpinning AAA development is inflammation in the AAA wall (Stepien et al., 2022). Therefore, the limma-based DEGs were also believed to be reliable.
By intersecting WGCNA-derived module genes and limma-based DEGs, the intersected genes had a solid potential to represent AAA. In this study, based on the WGCNA-derived module genes and limma-based DEGs between AAA and normal samples, the AAA-related score was developed using the random forest and LASSO machine learning algorithms. Artificial neural networks are a machine learning method that deep learning, a branch of artificial intelligence, employs to mine patterns and forecast outcomes from massive data sets. Research into the utility of deep learning in understanding the complicated biology of the disease has been boosted by the growing usage of deep learning across healthcare domains and the availability of extensively defined disease datasets (Tran et al., 2021). Although preliminary findings seem encouraging, this is a rapidly developing field with fresh insights into disease biology and deep learning. As machine learning has been widely proven with robust ability in model development, the AAA-related disease model was thought to be reliable.
Among the four model genes, UBE2K, TMEM230, VAMP7, and PUM2 were widely reported in human diseases. UBE2K could promote the progression of hepatocellular carcinoma via the regulation of c-Myc (Lei et al., 2022). TMEM230 was revealed to be a marker in Parkinson’s disease (Wang et al., 2021). VAMP7-dependent secretion of RTN3 was proven to regulate neurite growth (Wojnacki et al., 2020). PUM2 was a hazardous marker for mitochondrial dynamics and mitophagy during aging (D'Amico et al., 2019). Our study showed that UBE2K had significantly higher expression in AAA than in normal samples. In contrast, TMEM230, VAMP7, and PUM2 had significantly lower expression in AAA compared with normal samples. In other words, UBE2K was a hazardous marker in AAA, while TMEM230, VAMP7, and PUM were favorable markers in AAA. The clinical meaning and predictive value of UBE2K, TMEM230, VAMP7, and PUM2 were promising.
AAA-related score positively correlated with regulatory T cells (Tregs), plasma cells, activated dendritic cells, naïve B cells, activated CD4 memory T cells, and CD8 T cells. Double-strand break repair via homologous recombination, protein phosphorylation, interferon-gamma-mediated signaling pathway, immune response, cellular response to DNA damage stimulus, T cell costimulation, T cell activation, innate immune response, and T cell receptor signaling pathway were positively enriched in high AAA-related score AAA samples. Studies have shown the presence of multiple inflammatory cell types in AAA, such as macrophages, CD4+ T cells, and B cells, which play an essential role in the diseased aortic wall through phenotypic regulation (Okrzeja et al., 2022). In addition, recent evidence suggests that toll-like receptors, chemokine receptors, and complements in the innate immune system are involved in the progression of AAA (Li et al., 2018).
In our study, AAA-related scores positively correlated with most m1A/m5C/m6A/m7G-related genes. The AAA-related score was significantly associated with m5C-related genes DNMT1, NSUN2, NSUN5, and YBX1. The AAA-related score was significantly associated with m6A-related genes FTO, HNRNPC, METTL3, and RBM15. The AAA-related score was significantly associated with m7G-related genes CYFIP1, EIF3D, EIF4E3, NSUN2, and NUDT11. M1A, m5C, m6A, and m7G are new types of RNA methylation. M1A, m5C, m6A, and m7G are widely involved in regulating cardiovascular diseases, including heart failure, cardiac hypertrophy, aneurysm, and vascular calcification. Studies have also shown the expression pattern and functional significance of m6A-related genes in AAA (Zhang et al., 2021). m6A-related genes were found to play non-negligible roles in the occurrence of AAA ruptured (rAAA) (Fu et al., 2022). Besides, increased methylation levels of YTHDF3, FTO, and METTL14 were revealed to be associated with the progression of AAA (He et al., 2019b). METTL3 modulates m6A-dependent primary miR34a processing to induce AAA development and progression (Zhong et al., 2020). M6A could influence the circRNA-miRNA-mRNA network in hypoxia-mediated pulmonary hypertension. Moreover, the METTL3/YTHDF2/PTEN axis was proven to promote hypoxia-induced pulmonary artery hypertension (Zhou et al., 2021). YTHDF1 was reported to regulate pulmonary hypertension through the control of MAGED1 (Hu et al., 2021). NSUN2 influences autotaxin expression and T cell recruitment to control aneurysm development (Miao et al., 2021). In AAA, RBM15 knockdown decreased CASP3 expression in an m6A-dependent way (Fu et al., 2022). In AAA, YBX1 was discovered to be a direct target of GAS5, and it also collaborated with GAS5 to control the downstream target p21 through a positive feedback loop (He et al., 2019a). However, the specific mechanisms of other m1A/m5C/m6A/m7G epigenetic regulation (DNMT1, NSUN5, FTO, HNRNPC, CYFIP1, EIF3D, EIF4E3, NUDT11) in AAA have not been fully explored.
Our study developed a reliable AAA-related disease model for predicting immunity and m1A/m5C/m6A/m7G epigenetic regulation. The pathogenic roles of four model genes, UBE2K (hazardous), TMEM230 (favorable), VAMP7 (favorable), and PUM2 (favorable), in AAA, need further validation by in vitro and in vivo experiments.
## 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.
## Ethics statement
The animal study was reviewed and approved by the Animal Care and Use Committee at the Dalian Medical University.
## Author contributions
YT, SF, NZ, HZ, and LL designed and drafted the manuscript. YT, SF, NZ, HZ, and LL wrote figure legends and revised the manuscript. YT, SF, NZ, HZ, and LL conducted data analysis. All authors have read and approved the final manuscript.
## 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.
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---
title: What are the key drivers to promote continuance intention of undergraduates
in online learning? A multi-perspective framework
authors:
- Jintao Zhang
- Mingbo Zhang
- Yanming Liu
- Liqin Zhang
journal: Frontiers in Psychology
year: 2023
pmcid: PMC9995597
doi: 10.3389/fpsyg.2023.1121614
license: CC BY 4.0
---
# What are the key drivers to promote continuance intention of undergraduates in online learning? A multi-perspective framework
## Abstract
### Introduction/Aim
The purpose of this study is to investigate the key predictors of online learning system continuance intention using expectation-confirmation theory and information system success model as the theoretical framework.
### Methods
A total of 537 respondents participated in the questionnaire to measure their self-reported responses to eight constructs (perceived usefulness, interaction, confirmation, satisfaction, continuance intention, information quality, system quality, service quality). Convenience sampling was used to obtain participants in this study. Partial least square structural equation model is used for data analysis.
### Results
The results showed that all the hypotheses were validated except that there was no significant positive relationship between online learning interaction and student satisfaction. Meanwhile, the variance of the continuance intention of the online learning system reached $74.0\%$, falling within the moderate to substantial. In addition, the multi-group analysis of perceived usefulness, satisfaction and continuance intention showed that there was no significant gender difference in the above two relationships.
### Discussion
Finally, this study also puts forward the theoretical and practical implications of college students’ continuance intention of online learning system.
## Introduction
Online learning system can be regarded as one of the most prominent web-based educational advancements (Cheng and Yuen, 2018). According to Supriyadi et al. [ 2020], an online learning system, often known as “digital learning,” or “electronic learning (e-learning),” was a digital-based learning technology device such as desktop computers, laptops, tablets, and smartphones. Previous research on online learning system had mostly concentrated on issues of user satisfaction (USAT) and implementation effectiveness (Kovačević et al., 2021; Tsang et al., 2021) during COVID-19. Within the last few decades, large number of researchers argued that academics should place greater emphasis on students’ continuous use of online learning system than on their initial acceptance and subsequent success with such systems (Soria-Barreto et al., 2021; Al-Adwan et al., 2022a,b). The research on the continuous use of college students’ online learning system is divided into two parts: one is concerned with the continuous use behavior of college students’ online learning system (Al-Adwan et al., 2021, 2022a), and the other is concerned with college students’ intention to continue using their online learning system (Soria-Barreto et al., 2021; Al-Adwan et al., 2022b). According to Limayem et al. [ 2007], continuance intention (CI) of college students to use the online learning system is a key predictor of their continuous use behavior. Therefore, it is critical to focus on the CI of online learning systems by college students during the COVID-19 pandemic and in the period to come.
Researchers have investigated college students’ CI of online learning systems based on different theoretical frameworks, such as Expectation Confirmation Theory (ECT; Gupta et al., 2021; Wang et al., 2021), Technology Acceptance Model (TAM; Ahmad et al., 2020; Ashrafi et al., 2020; Alyoussef, 2021), The Unified Theory of Acceptance and Use of Technology (UTAUT; Liu and Pu, 2020; Wang et al., 2021), Theory of Planned Behavior (TPB; Dalvi-Esfahani et al., 2020; Baranova et al., 2022), etc. For example, based on ECT and TBP, Baranova et al. [ 2022] selected 301 undergraduate and postgraduate students and investigated the factors influencing CI in blended environment. The results indicated that $82\%$ of the variance of the CI could be explained by integrating the two models. Liu and Pu [2020] took UTAUT and TAM as the theoretical framework to propose an explanatory model of learners’ CI in online learning systems. The research results showed that this model could explain $71.5\%$ of the variance of the CI in online learning systems. The above results indicate that ECT, TAM,UTAUT,TPB, and other models are very suitable to explain the CI of college students in online learning system. In addition, a number of researchers have examined the use of online learning by students in different countries during the COVID-19 outbreak. For example, Alismaiel et al. [ 2022] studied the influence factors of COVID-19 on online learning and academic performance of 480 Saudi Arabian higher education students, and found that teacher-student interaction had a significant positive impact on online learning during the COVID-19 pandemic. Faura-Martínez et al. [ 2022] studied how 3,080 Spanish college students adapt to online learning and attend training during the COVID-19 pandemic, and the results showed that college students were not prepared for online learning, and their academic performance still declined despite spending a lot of time studying every day. Jin et al. [ 2021] studied 854 college students’ intention to use online learning platforms in China during the COVID-19 pandemic, and the results showed that perceived security risk, learning convenience and service quality, usability, usability, teachers’ teaching attitude, task and technology fit, and habits all significantly affected their willingness to switch from offline learning platforms to online learning platforms. Cifuentes-Faura et al. [ 2021] investgated the impact of COVID-19 on university students in Cambodia, Nigeria, Oman and Spain, and the results showed that COVID-19 had a negative impact on the well-being of students in these four countries, with students not receiving adequate social support and safety protection from others or teachers when they needed it. Thus, there are five major challenges facing higher education institutions during the COVID-19 pandemic: synchronous/asynchronous learning tool integration, access to technology, faculty and student online capabilities, academic dishonesty, and privacy and confidentiality (Turnbull et al., 2021).
Although previous research on CI had built on the TAM, ECT, TPB, UTAUT (Bhattacherjee, 2001; Venkatesh et al., 2003), it was found that using only constructs from these theories was inadequate to deal with the important issues of continuation intention of using technology in an online learning context. In light of this, we deemed it crucial to identify the elements of the online learning environment that motivate users to stick with it over time. According to Yan et al. [ 2021] ECT and information system success model (ISSM) are the two most important models for investigating the CI of college students in online learning systems. Therefore, we offered a unified model that took into account not only ECT but also the ISSM and other aspects, such as interaction (INT), that could have a distinct impact on students’ tendency to continue their online education (Lee, 2010).
The goal of this research is to better understand the factors that motivate people to keep using online learning systems. Therefore, the current study is an attempt to fill this gap by providing a comprehensive model that takes into account several antecedents of CI and putting the hypotheses to the test in the context of online education. In this study, we conduct a survey administered to undergraduate students in Chinese institution that offers courses using a hybrid model of online and traditional instruction. In addition, Partial Least Squares (PLS) is used for our data analysis needs. Therefore, the research questions of this study are as follows: What follows is a brief overview of how the rest of this paper is structured. The literature on ECT, ISSM, and INT will be discussed in the next section. In the second section, we describe our theoretical model built on the foundation of the literature review. Methodology is addressed in the following section, and then findings and analysis of the data are presented in Section 4. We finish with a discussion of the study’s findings and their theoretical and practical implications.
## ECT
As a model for foreseeing and understanding SAT and continuance behavior, ECT has gained widespread acceptance in recent years (Bhattacherjee, 2001). Key predictors of SAT in ECT were user confirmation (CON) and expectations. The user’s expectations are expressed through CON, and the absence of CON indicates that those expectations were not realized. To conclude, there is a good relationship between CON and SAT (Halilovic and Cicic, 2013). Many educational research have also made use of the ECT framework to investigate into students’ CI (Wang et al., 2021).
Researchers have used ECT as a theoretical framework to study the continuance behavior of numerous types of information systems in recent years (Yang and Jiang, 2020; Wang et al., 2021). To determine if online learning technology can assist students accomplish course learning tasks during the epidemic, Yang and Jiang [2020] developed a task-technology fit (TTF) model that extends ECT. They employed a partial least squares structural equation model to test the hypotheses based on data from 854 valid responses. The findings confirmed that the theoretical framework developed for this study adequately described students’ CI in online learning context (Fu et al., 2022). In order to explain learners’ CI in a massive open online course (MOOC), Dai et al. [ 2020] proposed a research model that modified and extended the Expectation Confirmation Model (ECM) by including cognitive and affective variables, capturing reflections on the past and anticipations of the future, and taking into account both intrinsic and extrinsic motivations. Chinese university student data was used to test the proposed model. According to the findings, $48\%$ of continuation intention may be explained by the proposed model. In addition, according to previous empirical findings and the theory of expectation confirmation, Lu et al. [ 2019] investigated the factors that contributed to user SAT and how that SAT affects their actions in MOOC. Seven factors were considered in the research: CON, usefulness, interest, flow, SAT, CI to use, and intention to recommend (ITR). For this study, a total of 300 respondents were polled. The results indicated that flow and interest were significant variables that increased MOOC SAT as measured by ECT. Recent research using ECT to study online learning is summarized in Table 1.
**Table 1**
| Authors | Research contexts | Constructs | Fundamental theories | Key findings |
| --- | --- | --- | --- | --- |
| Yang and Jiang (2020) | Online education platform | COM, Exception (EXP), PU, SAT, CI | ECM | CON → SAT; EXP → PU; CON → PU; PU → SAT; PU → CI; SAT → CI |
| Wang et al. (2021) | Online learning | CON, PU, TTF, SAT, CI | ECT + TTF | CON → PU; CON → SAT;PU → SAT; SAT → CI; PU → CI; TTF → PU |
| Jiang and Li (2020) | Virtual Community | Expected Confirmation (EC), SAT, PU, Perceived Service Quality (PSQ), User Experience (UE) | ECT | CON → SAT; PU → CON; PU → PSQ; PSQ → UE |
| Dai et al. (2020) | MOOCs | CON, usefulness (USE), SAT, CI, curiosity (CUR), attitude (ATT) | ECT | CON → ATT; CON → SAT; ATT → CI; SAT → ATT; CUR → CI |
| Lu et al. (2019) | MOOCs | CON, PU, SAT, CI, ITR, flow (FLO), perceived interested (PI) | ECT+ Flow theory | CON → SAT; CON → PU; CON → PI; CON → FLO; PU → SAT; PI → SAT; FLO → SAT; SAT → CI; SAT → ITR |
According to the research, ECT can be utilized to describe how a student’s exposure to an online learning system impacted their real educational experience. Therefore, we adopted ECT as the framework to investigate what aspects impact learners’ SAT and CI with their online learning system.
## ISSM
ISSM was created by DeLone and McLean [1992]. Organizational impact, individual impact, use, SAT, information quality (INQ), and system quality (SYQ) are the six components of this model that evaluate the efficacy of an information system. According to the model, the constructs of system quanlity and INQ directly affected user SAT and information system usage (SU). The SAT with and use of the system affects the impact on the individual, which in turn affects the impact on the organization. To forecast information SU and user SAT, however, William and Ephraim [2003] improved and refined the original model by include another quality factor—service quality (SEQ).
A number of studies using the D&M model in an online education setting have shown varying estimates of the total variance explained by quality parameters. Recent research using D&M model to study online learning is summarized in Table 2. Empirically extending the DeLone and McLean information systems success model, Alksasbeh et al. [ 2019] created a model that captured the most essential quality attributes of MSN apps. It was also shown that there was a significant correlation between overall quality characteristics and student SAT and behavioral intent to use. Using the ISSM, Efiloğlu Kurt [2019] examined how students felt about a certain online learning system. The empirical findings, which were derived from the students’ self-reported perceptional evaluations of the e-learning system, confirmed that while SYQ had a significant impact on both SU and user SAT, INQ had a significant impact on user SAT but not on SU. Hsu [2021] uncovered the factors that affected the success of MOOCs for English as a Foreign Language (EFL) students (gender, age, and opened to experience). It was shown that the extent to which EFL students were willing to try new things had a substantial effect on their evaluations of the service, system, and INQ provided by MOOCs. In addition, the quality of the system was recognized as the most important factor in determining whether or not EFL students will use and be satisfied with MOOCs. Higher levels of use intention, contentment, and perceived value would result in more frequent and deeper engagement with MOOCs.
**Table 2**
| Authors | Research contexts | Constructs | Fundamental theories | Key findings |
| --- | --- | --- | --- | --- |
| Efiloğlu Kurt (2019) | E-learning | SYQ, INQ, SU, USAT, system success (SS) | ISSM | USAT → SS; SU → SS; SYQ → SU; SYQ → USAT; INQ → USAT |
| Iqbal et al. (2022) | Digital library | INQ, SYQ, SEQ, use (USE), USAT | ISSM | SYQ → USE;INQ → SAT; SEQ → USE; SAT → USE |
| Xu and Du (2021) | Digital library | SYQ, INQ, SEQ, DLs’ affinity (DAF), USAT, intention to use (ITU) | ISSM + Media affinity theory | SYQ → SAT; SAT → INT |
| Hsu (2021) | LMOOCs | INQ, SEQ, SYQ, use intention (UINT), SAT, use of system (UOS), benefit (BEN) | ISSM | SYQ → INT; SYQ → SAT; INT → UOS; INT → SAT; UOS → BEN; SAT → BEN |
| Alksasbeh et al. (2019) | Mobile social networks apps | INQ, SYQ, SEQ, networking quality (NEQ), SAT, behavioral intention to use (BIU) | ISSM | INQ → SAT; SYQ → SAT; SEQ → SAT; NEQ → SAT; SAT → BIU |
## INT
It’s common knowledge that INT is crucial to effective online learning. Learner-learner INTs, learner-instructor INTs, and learner-content INTs are the three types of INTs proposed by Moore [1989], which were most commonly used to describe how people communicate in online learning or technology-based learning settings (Garrison et al., 2003; Yildiz Durak, 2018). While learner-learner INTs occur between students for the goal of sharing knowledge or ideas on course topics, learner-instructor INTs are bidirectional and help to increase or maintain students’ engagement with teaching materials. It’s useful for both mental and social appearances. Learner-content INT occurs each time a student engages with content in a way that leads to growth in that learner’s knowledge, outlook, and/or mental framework (Moore, 1989). By viewing education as a social and cognitive process, Abou-Khalil et al. [ 2021] argued that three types of INTs (learner-learner, learner-instructor, and learner-content INTs) were recognized as a fundamental framework to provide the minimal connections necessary for effective online learning in a crisis situation. Previous research had indicated the positive influence of these INTs on student SAT in distance education (Pham et al., 2019). Student-teacher INTs, learner-learner INTs, and learner-content INTs were all found to promote online learning performance and boost online students’ SAT with their courses by Lu et al. [ 2013]. High-interactivity online courses are associated with higher levels of student motivation, achievement, and SAT than their less-interactive counterparts (Croxton, 2014). As a result, we hypothesize that the three forms of INTs mentioned above will have an impact on the success and SAT of students engaged in online education. According to Moore and Kearsley [1996], education, whether in-person or virtual, should focus and investigate INT. It’s a way for students to interact with their teachers, peers, and course materials to learn new things and deepen their understanding (Moore, 1989).
## PU and CI
Davis [1989] first used the PU to describe an individual’s expectation that adopting a new system will improve his or her performance on the job. According to Bhattacherjee [2001], people do not care about the passage of time if they believe they would profit from a particular behavior. PU was found to significantly affect continuation intention, and its explanatory power was further supported by later studies (Wang et al., 2021). Furthermore, ECT-related investigations have revealed that PU influences continuation intention positively. According to the current study, when students utilize an online learning system and evaluate its utility, they are more inclined to continue using it. Therefore, Hypothesis 1 is proposed:
## PU and SAT
According to Zeithaml [1988], SAT is the result of a school’s success. PU is correlated strongly with SAT, according to studies of ECT (Yang and Jiang, 2020; Wang et al., 2021). In addition, The PU of online learning systems is an important predictor of students’ learning SAT (Wilson et al., 2021; Kerman et al., 2022). That is to say, the more agreeable and practical the online learning system is seen to be by the students utilizing it during the pandemic, the greater their level of SAT will be. Therefore, we put forward Hypothesis 2:
## PU and INT
Many research support the idea that students’ PU makes a direct or indirect contribution to their INT in online learning (Chang and Chen, 2020; Widjaja and Widjaja, 2022). Chang and Chen [2020] research also proved one of their hypotheses, namely that the PU of an e-learning system has a positive and significant effect on INT. Sher [2009] and Ramadiani [2019] discovered a positive and statistically significant association between learner-instructor INTs and the PU of e-learning system. In addition, Ramadiani [2019] found that learner-content and learner-instructor INTs have a substantial impact on the PU of e-learning system. In this study, once users find that the online learning system can effectively improve learning efficiency or performance, it can effectively improve the frequency and quality of INT. Therefore, we put forward Hypothesis 3:
## CON and PU
Cognitive dissonance theory was used by Bhattacherjee [2001] to back up the claim that the degree of user CON has a positive effect on PU; a first-time user of an information system cannot confirm whether using the system allows for improved performance because they lack relevant experience. So the user would have low PU to the new system and it is easy to confirm. The user begins operations, and the system’s ability to provide advantages is confirmed in increments as time progresses. Consequently, people gradually adapt their initial thinking to post-anticipation (PU). In subsequent investigations, the effect of CON on PU has also been validated (Bhattacherjee, 2001). In the context of the current study, the degree of CON (the comparison between expectation and experience) generated by students using online learning during the pandemic will modify the previous expectation and change it to post expectation (PU). Thus, post expectation (PU) increases with the degree of CON (Wu and Wang, 2020). Therefore, Hypothesis 4 is proposed:
## CON and SAT
Expectation and CON play a role in shaping levels of SAT. The extent of CON is derived from the gap between user expectations and actual use, whereas user SAT is derived from the level of CON (Bhattacherjee, 2001). In accordance with the ECT, user CON is a crucial requirement for SAT (Lee, 2010). CON is the evaluation of whether or not users have met their expectations in the context of the usage of information systems, and it is relevant to SAT (Halilovic and Cicic, 2013). In ECT-related literature, the effect of CON on SAT has been validated (Wang et al., 2021). The greater the degree of CON (comparison between anticipation and post-experience) of students utilizing an online learning system, the greater their feelings following the experience. Thus, the greater the CON, the more likely students are to have high SAT. Therefore, Hypothesis 5 is proposed:
## INQ and SAT
INQ, which has been extensively studied in previous research, is defined as users’ perceptions of the quality of information (such as information obtained from a system, accuracy of the information, relevance of the information, timeliness, and completeness of the information) presented on a website (McKinney et al., 2002). For instance, research by Klobas and McGill [2010] and Eom et al. [ 2012] indicated a strong correlation between INQ and learning management system use and SAT. In addition, Nuryanti et al. [ 2021] indicated that INQ is a crucial predictors of learning management SU and SAT. Therefore, we may assume that improved quality of information in the online learning system will positively lead to an increase in PU, perceived SAT, and SU. Thus, we hypothesize that:
## SYQ and SAT
SYQ is the degree of usability and task completion (Isaac et al., 2019). An online learning system’s SYQ is crucial for a positive UE (Ahn et al., 2004). In addition, it is noted as having an effect on performance attributes, function, and accessibility (McKinney et al., 2002). Urbach [2010] further demonstrated the significance of e-learning systems’ navigability, accessibility, structure, visual logic, and stability in ensuring a positive user learning experience. In addition, SYQ has a direct and significant impact on a person’s performance proposed by DeLone and McLean [2002]. Therefore, the current research hypothesizes that:
## INT and SAT
Numerous research support the notion that INT contributes either directly or indirectly to student SAT in a online learning context (Niu et al., 2022b). In addition to the variables of motivation, course structure, and teacher facilitation/knowledge, Baber [2020] shown that INT is the most influential factor in determining students’ perceived learning outcomes, which in turn impacts student SAT. Chen et al. [ 2020] were also successful in proving one of their hypotheses, namely that INT quality of an e-learning system has a positive and statistically significant impact on student SAT. In the meantime, Ares Albirru [2021] characterized the interactive learning environment in terms of communication and exploration of activities, and discovered that the INT aspect influences perceived SAT. From several previous studies already carried out, a hypothesis can be developed that INT will influence e-learning perceived SAT and PU that can be detailed as follows:
## SEQ and SAT
The SEQ of e-learning systems requires the responsiveness, empathy, trust, and safety of the supporting personnel. According to previous research, SEQ is vital to SAT and use (Tan et al., 2021), and in the context of online learning, SEQ has a significantly positive effect on online learning SU and students’ SAT (Pham et al., 2019). Teddy and Martha [2018] used structural equation modeling (SEM) to examine the link between SEQ and student SAT using a survey of 1,000 students from 13 universities and colleges in Riau Province. The outcome revealed a substantial positive relationship between SEQ and student SAT. Our argument is that the SEQ has an effect on both individual SAT and performance. Therefore, the current research hypothesizes that:
## SAT and CI
According to the literature on ECT, user SAT is a primary factor influencing their intention to reuse the online learning system (Oliver, 1980; Oliver and Bearden, 1985). In the discussion of research on CI and information systems, Bhattacherjee [2001] claimed that CI is mostly driven by the level of SAT given by actual use. Relevant research had demonstrated that SAT had a substantial influence on the CI (Bhattacherjee, 2001; Cheng, 2019; Lu et al., 2019). In conclusion, ECT provides adequate predictive power for the relationship between SAT and intention to continue (Wu and Wang, 2020). In the context of the current study, user likelihood to change the system decreases as SAT with the platform increases, and online learning CI increases with SAT. Therefore, Hypothesis 10 is proposed: Based on the above hypothesis, the hypothesized model of this study is proposed (Figure 1).
**Figure 1:** *Research model. PU, perceived usefulness; INT, interaction; CON, confirmation; SAT, satisfaction; CI, continuance intention; INQ, information quality; SYQ, system quality; SEQ, service quality.*
## Participants
This study collected a sample of three colleges (Jiangxi Science and Technology Normal University, Jiangxi University of Applied Science, and Nanchang University) in China that used Super Star, the target online learning system. Students who had utilized Super Star for at least one semester were the units of analysis in this study. In this study, we used an online survey questionnaire to gather information. Over all, 562 students participated in the survey. In total, 537 respondents were applied to validate and evaluate the research model after incomplete and invalid responses ($$n = 25$$) were eliminated. Adequate sampling, as described by Gravetter and Forzano [2018], increases the likelihood that participants will complete the questionnaire depending on their availability and motivation to do so. Convenience sampling was used for this study since it is one of the quickest and easiest survey methods to implement and manage. Chi-square tests for gender (male = $45.23\%$, female = $52.54\%$; $$p \leq 0.500$$) were used to assess the sample’s representativeness. No statistically significant discrepancy between the sample and population distribution was found, which indicated that the sample waw highly representative. Data were collected between May 2022 and August 2022.
The valid sample included 247 male and 290 female, 117 were freshmen, 90 were sophomores, 180 were juniors, and 150 were seniors. Among them, 67 students use the online learning system for <1 h a day, 157 students use the online learning system for 1–2 h a day, 201 students use the online learning system for 2–3 h a day, 112 students use the online learning system for 3–4 h a day.
## Measurements
As was previously noted, the information was gathered through a web-based survey questionnaire split into two primary parts. In the first section, we asked for demographic information about each respondent. In the second part, eight constructs from the theoretical framework were evaluated. More specifically, there were 32 indicators in Part 2. The 7-point Likert scale ranges from 1 (completely disagree) to 7 (completely agree), and all questions were adopted directly from relevant literature (Appendix A).
Two methods were used to check the reliability and validity of the questionnaire before the actual data gathering. The first step involved a panel of four academic experts with extensive experience in online education evaluating the measurement instruments. Among the four assessors, there was a $90.5\%$ level of consensus. Furthermore, the panel’s suggestions to further strengthen the study’s reliability and validity were taken into account. Secondly, a pilot research involving 60 students was conducted to assess the validity of the eight components. The results show that all constructs are reliable because their respective Cronbach’s alpha values are >0.7 (Hair et al., 2019).
Four items created by Mohammadi [2015] were adopted to assess students’ views on PU of online learning system [2015]. Pursuant to the aims of the present investigation, the term “Moodle” was substituted with “online learning system” in the original scale (e.g., “Using online learning enables me to accomplish my tasks more quickly”). Three items created by Bhattacherjee [2001] were used to measure the students’ CON of online learning system. The initial scale was modified by switching “OBD” for “online learningn system” (e.g., “My experience with using online learning system was better than what I expected.”). Chung and Chen [2020]‘s six-item scale was employed to gauge participants’ INT in online learning. It mainly includes three types of INT, namely student-teacher INT (e.g., “The instructor is supportive when a student had difficulties or questions”), student–student INT (e.g., “The course foster student-to-student INT for supporting productive learning”), and student-content INT (e.g., “The course content provides mutual INT to facilitate student learning”). Three items created by Gefen et al. [ 2003] were adopted as a means of gauging online student SAT with their educational experiences. The original scale was modified by substituting “online learning” for “Travelocity.com” for the purposes of this research. We used a scale created by Cheng et al. [ 2019] to assess students’ commitment to continuing their online education. The scale developed by Cheng [2019] was what we used to determine whether or not students intended to continue their online education. The SYQ can be evaluated by how simple it is to use the system. The value and trustworthiness of the data provide metrics by which the INQ construct may be evaluated. When students have issues with the online learning system and the responsible staff responds to them quickly, with the appropriate level of expertise, and within the expected time frame, the SEQ becomes better. Urbach [2010] developed and field-tested scales to assess constructs including information, system, and SEQ.
## Statistical analyses
This study employed a two-stage procedure for performing partial least squares (PLS) analysis. Reliability and validity analyses were performed in the first stage, while the structural model’s path coefficients and explanatory power were derived and validated in the second stage. The aforementioned two stages were conducted to ensure the constructs’ validity and reliability, as well as to check the inter-construct relationships (Anderson and Gerbing, 1988). PLS was considered because it is capable of dealing with both the model constructs and the measurement items at the same time, making it ideal for examining the causal relationships between constructs (Petter et al., 2007). Also, PLS is well-suited for dealing with the association between variables in abnormal data distribution because of its flexible criteria for the normality and random of the variables. Additionally, it is useful for evaluating complicated predictive model (Chin, 1998). This study investigated the causal relationship between PU, CON, INT, SAT, CI, INQ, SYQ, and SEQ; meanwhile, each construct contained a number of measurement items. Consequently, PLS was preferable to other SEM analysis methods for this study in order to examine the causal relationship between variables, decrease measurement errors, and prevent collinearity. In addition, according to Majchrzak et al. [ 2005], the minimum number of samples should be 5–10 times the number of model paths. According to the proposed criteria, the 537 samples and 6 maximal paths in this investigation are adequate for PLS analysis. The SmartPLS (Version 3.2.7) created by Ringle et al. [ 2015] was used in this research.
## Results
Prior to actually progressing on to the measurement model, the common method variance (CMV) was assessed. Harman’s one factor test is conducted to determine the presence of CMV (Hair et al., 2019). Consequently, exploratory factor analysis (EFA) was performed. As shown by the outcome, none of these variables explained more than $50\%$ of the variation in the measurement items. With a variance explained of $30.2\%$, the construct of CON exhibited the greatest variance. Consequently, it was clear that CMV was not present.
## Outer model
At this section, the constructs and the accompanying measuring items were evaluated for validity and reliability. In accordance with Hair et al. [ 2019], several tests were carried out, including internal consistency reliability (Cronbach’s alpha), discriminant validity, and convergent validity. Convergent validity can be established if the average variance extracted (AVE) is >0.5 (Fornell and Larcker, 1981) and the items loadings on the intended theoretical constructs are in excess of 0.700 (Hair et al., 2021). Cronbach’s alpha and other composite reliability estimates of constructs must be >0.700 (Hair et al., 2019). Table 3 shows all items with loadings larger than the recommended value of 0.708; AVE of each construct is more than 0.5 and Cronbach’s alpha and composite reliability values >0.7. These results demonstrated that the dataset possesses good validity and reliability.
**Table 3**
| Construct | Item | Loading | Cronbach’s α | CR | AVE |
| --- | --- | --- | --- | --- | --- |
| PU | PU1 | 0.872 | 0.824 | 0.914 | 0.727 |
| PU | PU2 | 0.822 | 0.824 | 0.914 | 0.727 |
| PU | PU3 | 0.834 | 0.824 | 0.914 | 0.727 |
| PU | PU4 | 0.883 | 0.824 | 0.914 | 0.727 |
| CON | CON1 | 0.824 | 0.816 | 0.899 | 0.749 |
| CON | CON2 | 0.838 | 0.816 | 0.899 | 0.749 |
| CON | CON3 | 0.931 | 0.816 | 0.899 | 0.749 |
| INT | INT1 | 0.815 | 0.851 | 0.936 | 0.712 |
| INT | INT2 | 0.878 | 0.851 | 0.936 | 0.712 |
| INT | INT3 | 0.815 | 0.851 | 0.936 | 0.712 |
| INT | INT4 | 0.831 | 0.851 | 0.936 | 0.712 |
| INT | INT5 | 0.811 | 0.851 | 0.936 | 0.712 |
| INT | INT6 | 0.908 | 0.851 | 0.936 | 0.712 |
| SAT | SAT1 | 0.845 | 0.847 | 0.882 | 0.714 |
| SAT | SAT2 | 0.852 | 0.847 | 0.882 | 0.714 |
| SAT | SAT3 | 0.839 | 0.847 | 0.882 | 0.714 |
| CI | CI1 | 0.834 | 0.807 | 0.915 | 0.73 |
| CI | CI2 | 0.837 | 0.807 | 0.915 | 0.73 |
| CI | CI3 | 0.845 | 0.807 | 0.915 | 0.73 |
| CI | CI4 | 0.9 | 0.807 | 0.915 | 0.73 |
| SEQ | SEQ1 | 0.809 | 0.844 | 0.895 | 0.681 |
| SEQ | SEQ2 | 0.815 | 0.844 | 0.895 | 0.681 |
| SEQ | SEQ3 | 0.842 | 0.844 | 0.895 | 0.681 |
| SEQ | SEQ4 | 0.836 | 0.844 | 0.895 | 0.681 |
| INQ | INQ1 | 0.831 | 0.843 | 0.894 | 0.678 |
| INQ | INQ2 | 0.834 | 0.843 | 0.894 | 0.678 |
| INQ | INQ3 | 0.818 | 0.843 | 0.894 | 0.678 |
| INQ | INQ4 | 0.811 | 0.843 | 0.894 | 0.678 |
| SYQ | SYQ1 | 0.906 | 0.789 | 0.907 | 0.709 |
| SYQ | SYQ2 | 0.822 | 0.789 | 0.907 | 0.709 |
| SYQ | SYQ3 | 0.826 | 0.789 | 0.907 | 0.709 |
| SYQ | SYQ4 | 0.812 | 0.789 | 0.907 | 0.709 |
In this study, two criteria were used to examine discriminant validity. Firstly, the criterion developed by Fornell and Larcker [1981] was used. According to the findings, each construct’s AVE square root should be higher than its correlation with any other construct in the model. Table 4 reveals this criterion was met, hence discriminant validity exists.
**Table 4**
| Unnamed: 0 | CI | CON | INQ | INT | PU | SAT | SEQ | SYQ |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| CI | *0.854 | | | | | | | |
| CON | **0.748 | 0.865 | | | | | | |
| INQ | 0.757 | 0.709 | 0.823 | | | | | |
| INT | 0.714 | 0.751 | 0.77 | 0.844 | | | | |
| PU | 0.736 | 0.844 | 0.652 | 0.682 | 0.853 | | | |
| SAT | 0.830 | 0.822 | 0.751 | 0.772 | 0.79 | 0.845 | | |
| SEQ | 0.767 | 0.68 | 0.811 | 0.763 | 0.633 | 0.734 | 0.825 | |
| SYQ | 0.749 | 0.709 | 0.815 | 0.738 | 0.658 | 0.746 | 0.812 | 0.842 |
Secondly, this study conducted the heterotrait–monotrait ratio (HTMT) approach to test discriminant validity (Henseler et al., 2015). As shown in Table 5, all values are ≤0.85. The existence of discriminant validity and the findings of the Fornell-Larcker criterion are therefore corroborated. In conclusion, the validity and reliability of each instrument employed in this investigation were found to be adequate.
**Table 5**
| Unnamed: 0 | CI | CON | INQ | INT | PU | SAT | SEQ |
| --- | --- | --- | --- | --- | --- | --- | --- |
| CI | | | | | | | |
| CON | 0.799 | | | | | | |
| INQ | 0.801 | 0.759 | | | | | |
| INT | 0.745 | 0.798 | 0.811 | | | | |
| PU | 0.786 | 0.812 | 0.697 | 0.722 | | | |
| SAT | 0.805 | 0.822 | 0.797 | 0.808 | 0.847 | | |
| SEQ | 0.811 | 0.729 | 0.823 | 0.803 | 0.677 | 0.779 | |
| SYQ | 0.795 | 0.762 | 0.803 | 0.779 | 0.706 | 0.795 | 0.764 |
## Inner model
Predictive relevance (Q2), explanatory power (R2), and proposed hypothesis were all evaluated using structural model analysis (Hair et al., 2019). Prior to analyzing the proposed structural relations, it is essential to examine collinearity to ensure that the regression results are unbiased. Accordingly, we assessed collinearity by calculating the variance inflation factor (VIF) values of the inner model, as suggested by Hair et al. [ 2019]. It is recommended that VIF estimates be around or below 5 when checking for the lack of collinearity. According to Table 6, no collinearity issues were present because all constructs got a VIF of <5.
**Table 6**
| Unnamed: 0 | CI | INT | PU | SAT |
| --- | --- | --- | --- | --- |
| CI | | | | |
| CON | | | 1.0 | 4.531 |
| INQ | | | | 3.521 |
| INT | | | | 3.311 |
| PU | 2.66 | 1.0 | | 3.595 |
| SAT | 2.66 | | | |
| SEQ | | | | 4.612 |
| SYQ | | | | 4.44 |
The proposed model’s predictive accuracy was measured using R2 and Q2. Following the advice of Hair et al. [ 2013], a 5,000 bootstrap re-samples technique was carried out to examine all proposed paths. Furthermore, estimates for Q2 were calculated using the blindfolding process. Table 7 shows that the proposed model has sufficient predictive accuracy because all dependent variables have Q2 estimates more than 0.
**Table 7**
| Construct | R 2 | Q 2 |
| --- | --- | --- |
| PU | 0.713 | 0.572 |
| INT | 0.466 | 0.364 |
| SAT | 0.774 | 0.682 |
| CI | 0.74 | 0.631 |
According to Table 7, seven different constructs account for 74.0 percent of the CI variance (R2 = 0.740). Meanwhile, Six constructs were involved in the explanation of $74.4\%$ of the SAT variance (R2 = 0.744). According to Chin [1998], such explanation power falls within the moderate to substantial category. To assess the predictive power, the PLS predict technique was also used (Hair et al., 2019). These outcomes, coupled with the fact that all of these variables had positive Q2 values, show the proposed model’s medium to high predictive accuracy.
Effect size (f2) measures if an independent latent variable has a substantial impact on a dependent latent variable. Values of 0.02, 0.15, 0.35 indicates the predictor variable’s low, medium, or large effect in the structural model (Cohen, 1988). This study focuses on the effect of exogenous latent variables on endogenous latent variables (CI and SAT). According to Table 8, the effect size of PU and SAT on the CI was 0.038 and 0.764, respectively, the effect size of CON, INQ, INT, PU, SEQ, SYQ on the SAT was 0.08, 0.2, 0.046, 0.076, 0.1, 0.1. Therefore, Therefore, the effect size of PU on CI is between low and medium, the effect size of SAT on CI is large. In addition, the effect size of CON, INT, PU, SEQ, SYQ on SAT is between low and medium, the effect size of INQ on SAT is between medium and large.
**Table 8**
| Unnamed: 0 | CI | SAT |
| --- | --- | --- |
| CON | | 0.08 |
| INQ | | 0.2 |
| INT | | 0.046 |
| PU | 0.038 | 0.076 |
| SAT | 0.764 | |
| SEQ | | 0.1 |
| SYQ | | 0.1 |
Based on the results of the path analysis presented in Figure 1 and Table 9, we can infer that H1–H5 and H7–H10 are supported, but hypothesis 6 is not. Compared with PU (β = 0.162, $$p \leq 0.000$$), SAT has a greater impact on CI. Compared to other constructs (PU: β = 0.248, $$p \leq 0.000$$; SYQ: β = 0.108, $$p \leq 0.000$$; INT: β = 0.186, $$p \leq 0.000$$; SEQ: β = 0.103, $$p \leq 0.000$$), CON (β = 0.286, $$p \leq 0.000$$) is the most important predictor of SAT. In addition, PU was positively correlated with INT (β = 0.682, $$p \leq 0.000$$). CON was positively correlated with PU (β = 0.844, $$p \leq 0.000$$).
**Table 9**
| Unnamed: 0 | Path | β | SD | T Statistics | Value of p | Result |
| --- | --- | --- | --- | --- | --- | --- |
| H1 | PU → CI | 0.162 | 0.023 | 6.924 | 0.0 | Supported |
| H2 | PU → SAT | 0.248 | 0.025 | 9.857 | 0.0 | Supported |
| H3 | PU → INT | 0.682 | 0.014 | 47.819 | 0.0 | Supported |
| H4 | CON → PU | 0.844 | 0.008 | 109.35 | 0.0 | Supported |
| H5 | CON → SAT | 0.286 | 0.031 | 9.177 | 0.0 | Supported |
| H6 | INQ → SAT | 0.056 | 0.037 | 1.515 | 0.13 | Unsupported |
| H7 | SYQ → SAT | 0.108 | 0.03 | 3.579 | 0.0 | Supported |
| H8 | INT → SAT | 0.186 | 0.028 | 6.719 | 0.0 | Supported |
| H9 | SEQ → SAT | 0.103 | 0.029 | 3.613 | 0.0 | Supported |
| H10 | SAT → CI | 0.727 | 0.023 | 31.785 | 0.0 | Supported |
## Multi-group analysis
According to Matthews [2017], before performing a multi-group analysis, it is necessary to determine that the sample size of each subgroup must be large enough to meet statistical power guidelines. In this study, the sample sizes were 537. Following the more rigorous recommendations from a power analysis, 75 observations per group are needed to detect R2 values of around 0.25 at a significance level of $5\%$ and a power level of $80\%$. Therefore, the group-specific sample sizes can be considered sufficiently large (Cohen, 1992).
The next procedure is to conduct a test of measurement invariance. In PLS-SEM, the measurement invariance of composite models (MICOM) process is used to check for measurement invariance (Henseler et al., 2016). During this procedure, three steps should be included: configural invariance, compositional invariance, and equality of composite mean values and variances (Henseler et al., 2016). Firstly, configural invariance must be conducted in MICOM (Henseler et al., 2016). Its involved three criteria: (a) identical indicators per measurement model, (b) identical data treatment, and (c) identical algorithm settings or optimization criteria (Henseler et al., 2016). Since all of the aforementioned requirements have been satisfied, configural invariance has been proven.
Step two of MICOM is to investigate at compositional invariance, which occurs when composite scores are established uniformly across groups (Dijkstra and Henseler, 2011). According to Table 10, compositional invariance is obtained when the original correlations are equal to or higher than the $5.00\%$ quantile correlation (given in the $5.00\%$ column).
**Table 10**
| Unnamed: 0 | Original correlation | Correlation permutation mean | 5.00% | Permutation p-Values |
| --- | --- | --- | --- | --- |
| PU | 1.0 | 0.999 | 0.997 | 0.392 |
| SAT | 0.999 | 0.997 | 0.992 | 0.832 |
In the third phase of MICOM, composite equality of mean and variance in different groups should be checked. The mean original difference and variance original difference both need to be within the $95\%$ confidence interval respectively, as illustrated in Table 11. All constructs that passed the measurement invariance test are further supported by the Permutation p-values (M) and Permutation p-values (V) >0.05 shown in Table 11.
**Table 11**
| Unnamed: 0 | Mean-Original difference (male–female) | Mean-Permutation mean difference (male–female) | 2.50% | 97.50% | Permutation p-values (M) | Variance-original difference (male–female) | Variance-permutation mean difference (male–female) | 2.50%.1 | 97.50%.1 | Permutation p-values (V) |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| PU | −0.132 | 0 | −0.086 | 0.088 | 0.221 | 0.303 | −0.003 | −0.127 | 0.422 | 0.089 |
| SAT | −0.138 | 0 | −0.087 | 0.083 | 0.311 | 0.29 | −0.003 | −0.136 | 0.328 | 0.077 |
Once the invariance is confirmed, the next step is to check if there are appreciable differences between the path coefficients of the theoretical models for the two sets of data. According to the Table 12, there is no gender gap in any of the relationships. Furthermore, the permutation p-values (>0.05) support this conclusion.
**Table 12**
| Unnamed: 0 | Path coefficients original (male) | Path coefficients original (female) | Path coefficients original difference (male–female) | Path coefficients permutation mean difference (male–female) | 2.50% | 97.50% | Permutation p-values |
| --- | --- | --- | --- | --- | --- | --- | --- |
| PU → CI | 0.186 | 0.147 | 0.039 | −0.002 | −0.093 | 0.095 | 0.429 |
| SAT → CI | 0.727 | 0.726 | 0.001 | 0.002 | −0.093 | 0.092 | 0.981 |
## Discussion
The purpose of this study is to investigate the key predictors of online learning system CI. In the literature review, we introduce expectation-confirmation theory and the ISSM as the theoretical framework, and INT as the important predictor of the CI of online learning system. In addition, we hypothesized CI of online learning system is predicted by several constructs: PU, INT, CON, SAT, INQ, SYQ, SEQ. The model was validated by partial least square structural equation model technique. All hypotheses were supported except for the relationship between INT and SAT, accounting for $74.0\%$ of the total variance in CI of online learning systems. After multi-group analysis, it was found that there was no significant gender difference between PU, SAT and CI of online learning system. In the following paragraphs, we will discuss the important findings of this study. In addition, Hypotheses H1, H2, and H3 gained empirical support. This confirms that PU is a determinant of CI, SAT and INT. That is to say, when students believe that online learning systems can effectively improve academic performance, their SAT will be increased, their CI will be increased. In addition, in online learning environments, valuable knowledge and ideas often stimulate the INT of student–student, student-teacher and student-content. These results confirm the findings of Wang et al. [ 2021], who found that PU is critical to the success of information systems (such as SAT, intention to continue use, etc.). In this study, to improve the CI, SAT and INT of college students’ online learning system, on the one hand, the learning content should be changed, so that students can get valuable information, so as to improve their academic performance and the ability to use knowledge to solve problems. On the other hand, improve the efficiency of online learning system, so that students can easily operate, save time.
Hypotheses H4 and H5 were accepted. The results showed that CON significantly influences PU and SAT. The findings demonstrated that PU and SAT are highly influenced by CON. Such results are consistent with earlier ECM-based investigations (Yang and Jiang, 2020). Additionally, it was discovered that CON was a stronger indicator of students’ SAT than PU (Niu and Wu, 2022a). This shows that satisfying students’ expectations for how online learning activities are performed is significantly more crucial to their SAT and may indirectly affect their intention to continue to use. In this study, in order to improve the SAT of college students’ online learning system, on the one hand, students’ demands for the online learning system should be extensively collected to meet their expectations. On the other hand, the functions of online learning system should be publicized to college students so that they can have a correct and comprehensive understanding of the learning system and form reasonable expectations.
The hypotheses H7 and H9 were supported by data. That is to say, the level of SAT with an online learning system is strongly related to the SYQ and the SEQ provided by the system. This demonstrates that people who utilize online learning systems are more content with their jobs and more productive when they have a positive perception of their utilization. It is predicted that the higher the quality of the used online learning system, the more satisfied the system’s end users will be with it. The results of this study support and extend Ajzen [1991] Theory of Reasoned Action (TRA), according to which a person uses an information system if using it would result in benefits for himself. Additionally, user SAT increases in direct proportion to the quality of services offered by the online learning system (Sasono and Novitasari, 2020). The research undertaken by Wang [2008] to examine the success of e-commerce in Taiwan and Wang and Liao [2008] to examine the success of e-government in *Taiwan is* confirmed and expanded by the findings of this study. Between SEQ and customer SAT, both studies demonstrate a significant and positive association.
INQ is not a very important predictor of student SAT in an online learning context, as evidenced by the lack of support for hypothesis H6. The findings of Eom et al. [ 2012] are at odds with this outcome, which found that user SAT was significantly correlated with the quality of the information provided to them based on the D&M information system success model for e-learning. The reason for the inconsistent results may be that, in the context of COVID-19, students passively accept online learning, while the frequency and quality of INT (student–student INT, student-teacher INT, and student-content INT) decrease, leading to the shift of factors affecting students’ SAT to hardware (computer, network, etc.) and learning system characteristics (SEQ, SYQ, PU).
H8 was supported by the data. That is to say, INT (student–student INT, student-teacher INT, student-content INT) is an important predictor of online learning SAT. This result is consistent with Ares Albirru [2021], which detailed the interactive learning context and discovered that the INT has an impact on the degree of students’ SAT. Based on the results of this research, the level of user SAT with an online learning system increases as both the quality and frequency of INTs increase inside that system. In this study, one of the most important ways to improve college students’ online learning SAT is to strengthen INT. On the one hand, student–student INT can realize knowledge sharing, information collision, and generate the meaning construction of knowledge; On the other hand, instructor-student INT can realize knowledge transmission, student feedback, and change the way of knowledge transmission.
Since H10 was supported, and students’ SAT is a crucial indicator of the CI. That is to say, the higher the students’ SAT with the online learning system, the stronger their CI, and the more likely they are to have continuous use behavior. The findings of Cheng [2019] are confirmed by these findings, which indicated that students’ SAT is a significant predictor of their CI based on ECM and TTF. In this study, the more satisfied users are with an online learning system, the more likely they are to continue using it. In this study, to improve college students’ SAT with online learning system, they can start from the following aspects, such as learning materials, learning system and teacher support, so as to enhance their CI.
In addition, there was no significant gender difference in the relationship between PU and CI and in the relationship between SAT and CI. The above results are consistent with Tam [2020], which studied the intention of continuous use of mobile banking in Vietnam based on the TAM, the TTF and the ECM, and concluded that there was no gender difference in the relationship between SAT and CI, and there was no gender difference in the impact of PU on the CI (Wu and Wang, 2020). In other words, gender does not need to be considered in the process of enhancing students’ CI in online learning systems usage.
## Implications
In the context of online learning, this study creates a research model that explains $77.4\%$ and $74.0\%$ of the variance in users’ SAT and CI using the system, respectively. As a result, the research model for this study has greater predictive power in relationship to the gathered information. The next section includes specific theoretical and practical implications.
## Theoretical implications
Firstly, the proposed model makes an effort to establish an unified framework based on the three fundamental viewpoints (namely, ECM, ISSM, and INT) that may affect the CI of online learning. The findings demonstrated conclusively that the research model adequately supported the primary aim of the study, namely, that the integrated model provides a sufficient theoretical framework to explain online learners’ CI of the online learning system. Secondly, the findings of this study demonstrate the importance of ECM in determining students’ SAT with their intention to continue using the online learning system. In the meantime, the findings indicate that students place a disproportionate amount of emphasis on their CON of expectations toward the system when establishing their SAT with the system, which is the most important direct driver of their intention to continue using the system, and that the salience of SAT is significantly greater than PU. The results also suggest that user SAT is the most important factor in determining the intention to continue using an information system, as users lay a greater focus on the CON of their expectations than on their post-adoption beliefs when determining their levels of SAT. As a result, it is essential to consider students’ level of contentment while analyzing their propensity to engage in further online education. This result recommends that in order to increase students’ intention to continue using the online learning system, academic institutions should first focus on identifying sources of students’ disconfirmation and then rethink how to strive to optimize their SAT by reassuring their expectations and supporting their efficient system use.
## Practical implications
This study will benefit online learning systems by increasing student engagement and efficacy. Firstly, an online learning system should meet the needs of its users by giving them useful and interesting information and encouraging INT between learners. To better meet students’ demands, online learning systems should understand their users. Additionally, the intention of learners to continue using and advocating online learning is also strongly influenced by their level of SAT. Secondly, CON also accurately predicts how satisfied students are with online education. Because of this, it is necessary to educate students on how to utilize the online learning system effectively in order to enhance their CON of expectations and further maximize their SAT. Finally, Online instructors should employ vivid examples, appealing instructional techniques, and valuable and engaging content to encourage participant engagement. With the help of these methods, teachers may be able to meet their students’ needs and expectations.
## Conclusion
The purpose of this study is to investigate the key predictors of online learning system CI. The results show that CON, PU, INT, SYQ, and SEQ are effective predictors of the CI of online learning system, while there is no significant positive correlation between the INQ and the SAT of an online learning system. In addition, there were no significant gender differences in PU, SAT and CI of online learning system. This study extends the field of CI of online learning system. On the one hand, the proposed model makes an effort to establish an unified framework based on the three fundamental viewpoints (namely, ECM, ISSM, and INT) that may affect the CI of online learning. On the other hand, the findings of this study demonstrate the importance of ECM in determining students’ SAT with their intention to continue using the online learning system. Additionally, this study also provide some suggestions for instructors of online learning and decision-makers.
On the basis of the current study’s limitations, potential directions for further research are recommended. The first recommendation concerns the design of the survey. Without a doubt, the process should be set up to protect personal information and keep people from having to fill out the same questionnaires over and over again. Future studies using qualitative interviews or case studies may be able to confirm these empirical findings, as the survey results do not allow for the inference of causality. Secondly, Our sample consists of learners who engaged in online education throughout the COVID-19 epidemic. This indicates that these students’ perspective toward online learning is wholly passive. When learning is transferred from passive students to active students, entirely new outcomes may be attained. The variables affecting students’ SAT and intent to continue learning online would be entirely different for those who actively engage in this mode of instruction. As a comparison to the existing sample, their perspectives are worthwhile researching. Additionally, attitudes among students in various disciplines and courses may vary. Thirdly, this study only explores that there is no significant gender difference in the relationship between college students’ PU, SAT, and CI of online learning system. But why there is no significant gender difference in these two relationships has not been explored. Future studies can be designed to continue to specifically examine the role of gender in these relationships. Fourthly, In view of the impact of the novel coronavirus epidemic, convenience sampling method was adopted in this study, which on the one hand may lead to higher sampling error, and on the other hand may cause the results of the study cannot be generalized to a larger research field. In the future, the COVID-19 epidemic will be over. It is suggested that researchers adopt random sampling or other more scientific sampling methods to obtain results with lower sampling error and more generalization validity. Finally, The effectiveness of students’ learning was not examined in this study, despite the fact that our qualitative findings suggest that it is a major concern. As a result, we anticipate further study on measuring students’ learning as a result of extensive online instruction. The creation of a wide range of reliable evaluation tools is another area deserving of study.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Author contributions
JZ and LZ: conceptualization and writing original draft. MZ and YL: data curation. JZ and MZ: writing–review and editing. All authors contributed to the article and approved the submitted version.
## Funding
This study is funded by Key Project of Ministry of Education of China in 2018, No: DCA180322.
## 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.
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|
---
title: 'How to Prevent the Drop-Out: Understanding Why Adults Participate in Summative
eHealth Evaluations'
authors:
- Marian Z. M. Hurmuz
- Stephanie M. Jansen-Kosterink
- Lex van Velsen
journal: Journal of Healthcare Informatics Research
year: 2023
pmcid: PMC9995638
doi: 10.1007/s41666-023-00131-8
license: CC BY 4.0
---
# How to Prevent the Drop-Out: Understanding Why Adults Participate in Summative eHealth Evaluations
## Abstract
The aim of this study was to investigate why adults participate in summative eHealth evaluations, and whether their reasons for participating affect their (non-)use of eHealth. A questionnaire was distributed among adults (aged ≥ 18 years) who participated in a summative eHealth evaluation. This questionnaire focused on participants’ reason to enroll, their expectations, and on whether the study met their expectations. Answers to open-ended questions were coded by two researchers independently. With the generalized estimating equations method we tested whether there is a difference between the type of reasons in use of the eHealth service. One hundred and thirty-one adults participated ($64.9\%$ female; mean age 62.5 years (SD = 10.5)). Their reasons for participating were mainly health-related (e.g., being more active). Between two types of motivations there was a difference in the use of the eHealth service: Participants with an intellectual motivation were more likely to drop out, compared to participants with an altruistic motivation. The most prevalent expectations when joining a summative eHealth evaluation were health-related (like expecting to improve one’s health). $38.6\%$ of the participants said their expectation was fulfilled by the study. In conclusion, We encourage eHealth evaluators to learn about adults’ motivation to participate in their summative evaluation, as this motivation is very likely to affect their results. Including altruistically motivated participants biases the results by their tendency to continue participating in a study.
## Introduction
High drop-out among participants in eHealth studies, is a common problem (e.g. [1–3]) which impacts study results. For example, a study loses statistical power [4], and it becomes difficult to determine the effectiveness of eHealth services [5, 6]. When experiencing drop-out, it is important to investigate why this occurs. Maybe it can be prevented by adapting a study, adapting the eHealth service, or by giving better explanations to participants. When looking at summative eHealth evaluation (i.e. evaluation when an eHealth service is already developed and ready to be assessed for its effects and uptake [7, 8]) reports, they do not disclose reasons for drop-out rates (e.g. [9–13]), or provide short explanations, like a loss of contact [14], participants moving house [15, 16], personal/family reasons [14, 17, 18], studies being too time consuming [19], participants being too busy or with a lack of time [14, 16], being reluctant towards using technology [19], technical problems [18], not wanting to be confronted with a medical condition [19], or medical problems [14, 16, 18]. However, these are all merely short explanations and reasons for dropping-out are often not being reported, potentially not even investigated in-depth. A first step in reducing the number of drop-outs in eHealth use in summative eHealth evaluations is to examine participants’ reasons for participating.
A lot of research focusing on motivations of different groups to participate in health studies has been conducted. Soule and colleagues [20] studied, among 164 patients suffering from heart diseases, the importance of four different motivations (intellectual motivation, altruistic motivation, health motivation, and financial motivation) to participate in observational health research. They found that the most important reason to participate was altruistic: Participants wanted to help future patients in the same situation, or to help the researchers. The least important motivation, they found, was financial. In another study conducted in Canada, 39 adults were interviewed about reasons for participating in different kinds of health studies. These adults, it turned out, primarily participated for their own health gain: to have access to drugs, to have access to healthcare, and to have access to technologies for monitoring their health. Also in this study, receiving a financial incentive was not a pre-dominant motivation [21]. Furthermore, Bouida and colleagues [22] investigated among patients, healthy volunteers and doctors in Tunisia reasons for enrolling in clinical trials. This population mentioned two main reasons. The first one was related to altruism, and the second reason was that they thought it is important to contribute to improving the healthcare. All three studies suggest that, in healthcare, adults primarily participate in studies to either help themselves or others.
For the context of eHealth, studies that uncover reasons for participating in summative evaluations among adults are scarce. Coley and colleagues [23] studied reasons for participating in a randomized controlled trial (RCT) involving an eHealth service focusing on prevention of cardiovascular diseases among older adults in Finland, France and the Netherlands. The top three main motivations for participants to take part were contributing to science, improving one’s lifestyle to improve health, and obtaining additional medical monitoring. However, a limitation of this study is that it was about participating in an RCT, which is a highly controlled study [24]. This could have influenced older adults’ willingness to participate. In another study we found, James and colleagues [25] investigated facilitators to participate in eHealth studies among African American women. They found that being interested in the topic, wanting to be more educated about the topic and contributing to the greater good were the three most mentioned facilitators. However, they asked women to complete a survey and think about the facilitators for participating in eHealth studies in general. These women did not actual participate in an eHealth evaluation. Finally, we also found an article focusing on employees’ reasons to participate in an eHealth intervention in their workplace in the United Kingdom (UK) [26]. So, this article is looking at an eHealth intervention which is implemented in different UK worksites, and not at an eHealth evaluation. However, we still looked into those reasons which were mostly related to improving their own health or to liking the intervention. Next to these, recommendations of other colleagues was also a reason to participate. Taking the prior literature into consideration, we still need more studies with a diverse range of eHealth services. Because now it does not focus on summative eHealth evaluations, even though in these type of evaluations researchers struggle with high drop-out rates. By gaining knowledge on participants’ motivation to participate in a summative eHealth evaluation, we can better explain the high drop-out rates in eHealth use in summative evaluations and we can tune their setup towards the participants’ needs and try to reduce the number of drop-outs. So, in this article, we report on a study in which we investigated adults’ motivations to participate in different summative eHealth evaluations, conducted in real-world settings, and tested whether their reasons affect the (non-)use of eHealth.
## Study Design
To answer the following research question “What are adults’ motivations to participate in summative eHealth evaluations, and does these reasons affect the (non-)use of eHealth?”, we conducted a cross-sectional study. By having participants completing one questionnaire at a specific moment, we were able to answer our research question. The specific moment they completed the questionnaire was after they participated in a summative eHealth evaluation. Within three studies in which different eHealth services were evaluated, participants were asked to complete this online questionnaire about their reasons to participate and their expectations of the study. With this information we could answer the research question under investigation. The participants completed the questionnaire directly after they finished the study or directly after they dropped out. All three studies were conducted in the Netherlands.
## eHealth Services
Motivations to participate in the evaluation of three different eHealth services were inventoried. The first service, Stranded (see Fig. 1), is a web-based, gamified eHealth service for (pre-)frail older adults. Stranded [27] consists of two parts: a falls prevention programme based on the OTAGO Programme [28], and cognitive minigames. The falls prevention programme consists of physical exercise videos that older adults can perform at home. These exercises focus on improving muscle strength, balance, and flexibility. The minigames are different kinds of puzzle games. The duration of the study evaluating Stranded was four weeks. The second eHealth service, Council of Coaches (COUCH) [29] (see Fig. 2), is a web-based service designed for adults with Diabetes Mellitus Type 2 or Chronic Pain, and older adults who are dealing with age-related impairments. The goal of COUCH is to encourage a healthy lifestyle via conversations with virtual coaches. Within COUCH six different coaches are available: a physical activity coach, a nutrition coach, a social coach, a cognitive coach, a chronic pain coach (only available for users with chronic pain), and a diabetes coach (only available for users with diabetes). During the summative evaluation of COUCH, participants could use the eHealth service for four weeks. The last eHealth service, the selfBACK app [30–32] (see Fig. 3), is a mobile self-management application for adults with neck and/or low back pain. The selfBACK app provides users with a weekly tailored plan to self-manage this pain. The weekly plain focusses on three aspects: Physical activity (i.e., daily step data), physical exercises to strengthen the muscles and increase flexibility, and educational messages to motivate users and to give them advice. This study with the selfBACK app lasted for six weeks. Fig. 1Screenshot of eHealth service StrandedFig. 2Screenshot of eHealth service Council of Coaches. ( Names of the virtual coaches f.l.t.r.: Carlos (peer), Olivia (physical activity coach), Emma (social coach), Katarzyna (diabetes coach), Helen (cognitive coach), Coda (helpdesk robot), François (nutrition coach))Fig. 3Screenshot of eHealth service selfBACK app (showing weekly self-management plan)
## Study Population
The study population of our study were the participants of different eHealth evaluations. Within the Stranded and COUCH evaluations, the participants were 55 years of age or older and able to speak and read Dutch. Within the selfBACK app evaluation, the participants were 18 years or older with neck and/or low back pain and able to speak and read Dutch. Participants were recruited via mass mailing and advertisements in newspapers and on social media.
## Data Collection
An online questionnaire was distributed, consisting of seven questions (see Appendix A for full questionnaire). First, two questions on demographics (age and gender), and one multiple choice question, inventorying how participants came across the study (e.g., advertisement in local newspaper, social media, friend/family/colleague). Then, there was one open question, asking why participants wanted to participate in the study. Finally, to have more in-depth information about expectations towards summative eHealth studies that participants have, we posed three more questions. These questions elicited participants’ initial expectations of the study (open question), asked whether the study met these expectations (closed question yes/no), and questioned why the study did (not) meet their expectations (open question).
## Data Analyses
We calculated descriptive statistics (frequency, mean, standard deviation, percentages) within SPSS v.19 to describe the demographics, to describe how participants came across the summative evaluation, and to inventory whether the evaluation met their expectations. Two authors (MH and SJK) coded all open-ended questions thematically. Here, we used a deductive approach to code the reasons for participating in a study. The themes by Soule and colleagues [20] were used as the initial codebook: Intellectual motivation (i.e., being interested in the study), altruistic motivation (i.e., helping researchers and/or future patients), health motivation (i.e., wanting to improve one’s health), financial motivation (i.e., receiving compensation (which does not need to be necessarily a monetary compensation)), and other motivations (e.g., fun, gaining knowledge). We used an inductive approach to code the other two open-ended questions (what were the expectations, why the study did (not) meet these expectations). The first and second authors (MH and SJK) coded all answers separately, and then discussed them together until there were no disagreements left.
To test for differences between the different motivation types, we conducted logistic regression analyses according to the generalized estimating equations (GEE) method [33] within SPSS. The dependent variable was whether or not the participants used the eHealth service during the length of the study; predictors were the types of motivations. We opted for the GEE method, as some participants mentioned multiple reasons for participating. To be able to compare all three motivations (altruistic motivation versus intellectual motivation, altruistic motivation versus health motivation, and intellectual motivation versus health motivation), we performed the GEE analysis twice with different reference categories. After these analyses, we corrected the p-values according to the Holm-Bonferroni method [34]. We excluded the category ‘other motivation’ from these analyses, as this was a relatively small, heterogeneous group of reasons that did not make for a sensible collection.
## Ethics
All studies were conducted according to the principles of the Declaration of Helsinki (64th WMA General Assembly, Fortaleza, Brazil, October 2013) and in accordance with the Medical Research Involving Human Subjects Act (Dutch law). The Medical Research Ethics Committee CMO Oost-Nederland stated that these studies do not require formal medical ethical approval (file numbers: 2019–5296, 2019–5555, 2020–6501). All participants signed an informed consent form before participating.
## Results
A total of 131 adults completed the questionnaire. Their mean age was 62.5 years (SD = 10.5); $64.9\%$ was female. Fifty-three participants took part in the Stranded evaluation, 49 evaluated COUCH, and 29 evaluated the selfBACK app. Most participants came across the studies via advertisements in local newspapers ($66.4\%$). From 101 adults of the total study population, we have data whether they continued using the eHealth service during the full length of the study. Of these participants, just over half of the study population used the eHealth service during the full length of the study: 55 out of 101 adults ($54.5\%$). Table 1 shows the distribution of the demographics, data regarding how participants were recruited and data regarding use of the eHealth service of the different groups. Table 1Demographics and descriptive statistics for completed study of the total study population, and divided into the three eHealth groupsTotal group ($$n = 131$$)Stranded group ($$n = 53$$)COUCH group ($$n = 49$$)selfBACK app group ($$n = 29$$)Age (RangeM (SD))23 – 8762.5 (10.5)55 – 8464.4 (6.3)55 – 8765.4 (7.5)23 – 7753.9 (15.7)Gender (N | %)Male46 | $35.1\%$17 | $32.1\%$14 | $28.6\%$15 | $51.7\%$Female85 | $64.9\%$36| $67.9\%$35 | $71.4\%$14 | $48.3\%$*Recruited via* … (N | %)Advertisement newspaper87 | $66.4\%$28 | $52.8\%$35 | $71.4\%$24 | $82.8\%$Advertisement social media9| $6.9\%$4 | $7.5\%$4 | $8.2\%$1 | $3.4\%$Friend/family/ colleague25 | $19.1\%$11| $20.8\%$10 | $20.4\%$4 | $13.8\%$Email research panel8 | $6.1\%$8| $15.1\%$--Other2 | $1.5\%$2| $3.8\%$--Continued using eHealth service for length of study (N | %)Yes55 | $42.0\%$20 | $37.7\%$11 | $22.4\%$24 | $82.8\%$No46 | $35.1\%$6 | $11.3\%$38 | $77.6\%$2 | $6.9\%$Missing30 | $22.9\%$27 | $50.9\%$-3 | $10.3\%$
## Reasons to Participate
In total, 129 participants gave one or more reason(s) for participating in an evaluation, with a total of 157 reasons. Most of these reasons were related to health motivation ($$n = 81$$). Examples of these reasons are that they want to improve/maintain their health, to live a healthy life, to have more energy, to relieve their pain, or to be more physically active. “Because of an often found disease in the family, Type 2 Diabetes, I find it important to take my responsibility regarding my lifestyle.” ( P-100, female, 62 years, COUCH study).“The older you get, the more attention you need to pay to your physical health. This requires discipline and at the same time the ability to keep it together. I saw the exercises you provided as an opportunity to strengthen this.” ( P-32, male, 76 years, Stranded study).
The second most mentioned motivation was intellectual motivation ($$n = 41$$), followed by altruistic motivation ($$n = 22$$), and other motivations ($$n = 13$$). No participant gave a financial motivation to participate in these studies. Reasons related to intellectual motivation were, for example, being interested in the study or being curious about the eHealth service under investigation. “Out of curiosity. I wanted to know what kind of exercises such a programme offers. Whether it is useful for me. Whether it is fun. Why exercises and games are implemented in one programme?” ( P-39, female, 79 years, Stranded study).
Regarding their altruistic motivation, participants said they wanted to help the research(er) or wanted to help improve healthcare for future older adults/patients. “Because I think that if you want to develop new tools, technologies or drugs, you also need people who are willing to act as ‘guinea pigs’.” ( P-27, female, 59 years, Stranded study).
Other motivations participants mentioned for participating in these studies were: just for fun ($$n = 5$$), wanting to be introduced to eHealth ($$n = 5$$), because peers motivated them to participate ($$n = 2$$), and because of the reputation of the research centre ($$n = 1$$).
Table 2 shows the number of participants who used the eHealth service during the full length of the study and those that abandoned using the service, per motivation type. The statistical analyses show a clear difference in the degree of eHealth service use between participants with an altruistic motivation and participants with an intellectual motivation (see Table 3). The risk that participants drop out is 12.2 times higher among those with an intellectual motivation compared to those with an altruistic motivation ($$P \leq 0.042$$, $95\%$-CI = 1.648 – 90.827).Table 2Cross table showing number of times (not) continued use of eHealth service per motivation typeType of motivationNumber of participants who used eHealth service during length of studyNumber of participants who abandoned use of the eHealth serviceTotalsIntellectual motivationN = 17N = 16N = 33Altruistic motivationN = 13N = 1N = 14Health motivationN = 37N = 29N = 66TotalsN = 67N = 46N = 113aa $$n = 113$$, because some participants had two different reasons to participate in the studyTable 3Results logistic regression according to GEE methodComparisonOdds ratio$95\%$ Confidence IntervalP-valueCorrected P-valueAltruistica x Intellectual12.21.65 – 90.80.0140.042Altruistica x Health10.21.28 – 80.90.0280.056Intellectuala x Health0.830.40 – 1.730.6240.624a Motivation category used as reference valueThe underlined entries are the significance values
## Expectations for the eHealth Evaluation
When asking the participants about their initial expectations for the eHealth evaluation, 70 participants mentioned at least one expectation (with a total of 79 expectations), 39 participants indicated they had no expectations, 16 participants did not answer this question properly (i.e., not providing an expectation, but mentioning something else), and the remaining 6 participants only indicated that their expectations were (too) high. Most expectations were health-related ($$n = 41$$), followed by content-related ($$n = 34$$), and technology-related expectations ($$n = 4$$).
The health-related expectations can be divided into four kinds: Expecting to improve one’s health ($$n = 28$$), expecting to perform physical exercises ($$n = 6$$), expecting to become aware of one’s lifestyle ($$n = 5$$), and expecting to maintain one’s health ($$n = 2$$).“I expected to receive some exercises that might relieve my neck pain in some cases.” ( P-110, female, 33 years, selfBACK study).
Content-related expectations were divided into six kinds: Expecting to receive help/tips ($$n = 15$$), expecting to receive a positive prompt or nudge ($$n = 7$$), expecting to receive personalised content ($$n = 6$$), expecting to receive a combination of exercises and games ($$n = 3$$), expecting to receive a lot of content ($$n = 2$$), and expecting to be talking to real coaches ($$n = 1$$).“My expectation was that I would receive a personalised exercise programme […].” ( P-109, male, 34 years, selfBACK study).
Finally, technology-related expectations were either that participants thought the eHealth service was easy to use ($$n = 2$$), or that the eHealth service had a high maturity level ($$n = 2$$).“Beforehand, I thought it would be a simple programme, easy to start and fun to use as a variation.” ( P-48, female, 62 years, Stranded study).
Of the 70 participants who mentioned a specific expectation, 27 indicated that participating within the study fulfilled their expectation(s) ($38.6\%$). Twenty-two participants gave a reason why their expectation(s) was/were fulfilled. This was either content-related ($$n = 13$$) (e.g., the eHealth service had suitable content, users received a positive prompt/nudge from the eHealth service), health-related ($$n = 8$$) (e.g., improved health state), or personal ($$n = 1$$) (enjoyed the eHealth service). The 43 participants whose expectations were not fulfilled, all explained their answer. The most mentioned reason was content-related ($$n = 29$$) (e.g., lack of specific or personalised content), followed by personal reasons ($$n = 9$$) (e.g., no fit with technology, lack of time), health-related ($$n = 7$$) (no improvement in health state), or technology-related ($$n = 7$$) (e.g., experienced problems while using the technology).
## Discussion
In this paper, we investigated the reasons of adults to participate in summative eHealth evaluations in real-world settings, and tested whether their reasons affect the degree to which they used the eHealth service during the study. Finally, we elicited participants’ expectations when joining these evaluations and assessed whether these expectations were met.
With regard to reasons for participating in summative eHealth evaluations, our findings show that most adults participate in order to actively do something for their own health state (e.g., improving their fitness levels, relieving pain). Townsend and Cox [21] also found that health-related reasons to participate in health studies are dominant. However, based on other prior literature (e.g., [20, 22, 23]), we expected that altruism would be (one of) the most prevalent reason to participate in summative eHealth evaluations. In our study, this reason was only a minor driver for participation. Furthermore, in our study, financial motivation was not mentioned by any participant as a reason to participate in a summative eHealth evaluation. It should be noted though, that in none of the studies there was a substantial financial compensation; the participants knew they would receive a small gift to thank them for their participation. Apparently this did not influence their reason to participate in the study. The literature shows a different picture. Here, financial incentives are one of the reasons to participate [25, 35, 36]. Explanations for the differences in the reasons for participating that we identified and those found in other studies, could be attributed to the use of the term ‘small gift’ in our information letters, or the different healthcare systems in the countries in which the studies were performed. After all, whether or not to participate in a health study when being in a healthcare system where every citizen is fully insured for a low fee (like in the Netherlands) might lead to a different incentive than when one lives in a country where being insured is less self-evident (like in the United States). In all, these results imply that during the recruitment process, potential participants should be primarily informed about the role the evaluation or the intervention can play with regard to their own health.
When analysing whether the reason to participate affected use of the eHealth service, we saw a difference in use between altruistically and intellectually motivated participants. Intellectually motivated adults are more likely to discontinue use of an eHealth service before the end of a study compared to altruistically motivated participants. In a time where optimizing adherence is a hot topic (some people even talk about an ‘engagement crisis’), we think this is an important finding. In order to further our understanding of adherence, studying the role of motivation is not new. Other researchers have, for example, studied the role of personal motivation types for complying with persuasive eHealth functionality [37]. We propose that in future evaluations focusing on eHealth use, researchers identify participants’ motivations at the beginning of the study. Later, they can then use this motivational profile to explain drop-outs and eHealth service use. The usefulness of this data would be enhanced by knowing the motivational profile of the addressable market for an eHealth service, so that the generalizability of the evaluation results can be made insightful.
Finally, our findings show that the expectations adults have about summative eHealth evaluations are mostly health-related or content-related. They expect that by participating in these studies, they will improve their health state, and receive helpful, personalised advice. Other studies also found that participants expect to receive this type of personalised content and these health benefits (e.g. [38–40]). When developing eHealth services with involvement of end-users, end-users often mention personalised content as an important factor (e.g. [41, 42]). In order to increase the success of a recruitment strategy, evaluators should therefore stress the health potential of taking part in the study and the eHealth service, and, if applicable, should stress the personalised features of the technology.
## Study Limitations
Our study has some limitations. First of all, in the three included studies, participants were recruited via self-enrolment. As a result, participants may have been motivated to participate in eHealth evaluations more than if we could have picked participants from the population at random. Possible, this has biased our results somewhat. Second, we chose to ask the study population after participation why they chose to participate and which expectations they had before starting the study. There is a possibility that participants were not sure about their initial reasons anymore, or their answers might have been affected by the study and by the eHealth service used. However, we do not think this had a major impact on the results, because of the comprehensive answers participants gave, and because there was no participant that mentioned (s)he was unable to recall his or her reasons. To confirm our findings, we propose that future summative eHealth evaluations identify participants’ reasons and expectations before starting. Third, during the primary eHealth evaluation there were some participants lost to follow-up. These participants did also not complete the questionnaire used in the current paper. We need to take in mind that this could have an affected the findings and their generalizability. Finally, our study was conducted in the Netherlands. We think that the healthcare system of the country participants live, influences the findings. In the Netherlands, residents have relatively good access to healthcare, as everyone has an healthcare insurance, and the general practitioner acts as a gatekeeper [43]. As it is easy to access healthcare for free in the Netherlands, we think that reasons such as ‘participating in study to gain access to healthcare’ do not play a role among our participants, or only marginally. So, the conclusions we can draw with our findings, do not directly apply to other countries with other healthcare systems.
## Conclusions
Drop-outs are a concern in science, in medical studies, and in summative eHealth evaluations. It is in the researchers’ interests to minimize the number of drop-outs in a study and to understand the reasons of the persons who decide to stop in an evaluation. For the case of summative eHealth evaluations, recruitment strategies should be focused on stressing the potential health benefits of participating in an evaluation and using the eHealth service. Offering a small monetary compensation will probably not benefit recruitment in a study where it is based on self-enrolment. Additionally, if the eHealth intervention offers personalised information or advice, this should be stressed in recruitment strategies, as participants appreciate such a feature. Using this strategy could probably result in a higher number of participants among those who expect personalisation, as their expectation will be confirmed. Altogether, researchers need to keep in mind when recruiting study participants, that those participants still represent the target population of the eHealth service under study. The strategies given here should not alter the representativity of the population.
## Appendix A
Questionnaire [English version translated for aim of this paper by first author (MH)] *What is* your gender? [ male / female]*What is* your age?Through which channel did you find out about this study? [ advertisement in local newspaper / advertisement on social media / flyer / friend family colleague / email from research panel / other channel]Why did you want to participate in this study?What were your expectations prior to this study?Have these expectations been met? [ yes / no]Please indicate why this study has fulfilled your expectations / Please indicate why this study has not fulfilled your expectations Questionnaire [Dutch version which was distributed among the participants] *Wat is* uw geslacht? [ man / vrouw]*What is* your age?Via welk kanaal bent u in aanraking gekomen met dit onderzoek? [ krant / social media / flyer / vriend familie collega / e-mail van onderzoekspanel / anders]Waarom wilde u deelnemen aan dit onderzoek?Wat waren uw verwachtingen vooraf aan het onderzoek?Is er voldaan aan deze verwachtingen? [ ja / nee]Geef aan waarom het onderzoek aan uw verwachtingen heeft voldaan / Geef aan waarom het onderzoek niet aan uw verwachtingen heeft voldaan
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|
---
title: A bidirectional Mendelian randomized study of classical blood lipids and venous
thrombosis
authors:
- Liu Lin
- Pan Luo
- Mingyi Yang
- Jiachen Wang
- Weikun Hou
- Peng Xu
journal: Scientific Reports
year: 2023
pmcid: PMC9995644
doi: 10.1038/s41598-023-31067-z
license: CC BY 4.0
---
# A bidirectional Mendelian randomized study of classical blood lipids and venous thrombosis
## Abstract
There is still some controversy about the relationship between lipids and venous thrombosis (VTE). A bidirectional Mendelian randomization (MR) study was conducted to clarify the causal relationship between three classical lipids (low-density lipoprotein (LDL), high-density lipoprotein (HDL) and triglycerides (TGs)) and venous thromboembolism (VTE) (deep venous thrombosis (DVT) and pulmonary embolism (PE)). Three classical lipids and VTE were analysed by bidirectional Mendelian randomization (MR). We used the random effect inverse variance weighted (IVW) model as the main analysis model and the weighted median method, simple mode method, weighted mode method and MR–Egger methods as supplementary methods. The leave-one-out test was used to determine the influence of outliers. The heterogeneity was calculated by using Cochran Q statistics in the MR–Egger and IVW methods. The intercept term in the MR‒Egger regression was used to indicate whether horizontal pleiotropy affected the results of the MR analysis. In addition, MR-PRESSO identified outlier single-nucleotide polymorphisms (SNPs) and obtained a stable result by removing outlier SNPs and then performing MR analysis. When we used three classical lipids (LDL, HDL and TGs) as exposure variables, no causal relationship between them and VTE (DVT and PE) was found. In addition, we did not find significant causal effects of VTE on the three classical lipids in reverse MR analysis. There is no significant causal relationship between three classical lipids (LDL, HDL and TGs) and VTE (DVT and PE) from a genetic point of view.
## Introduction
Venous thromboembolism (VTE) is a complex disease that includes deep venous thrombosis (DVT) and its most dangerous complication, pulmonary embolism (PE)1. There are many risk factors for DVT, such as trauma, cancer or gene mutations, that promote blood hypercoagulability2. The Virchow triad is generally considered to be the main pathogenesis of VTE, including blood flow disturbance, a hypercoagulable state of blood and a procoagulant state of the blood vessel wall3. VTE affects millions of people around the world, and more importantly, severe PE can seriously threaten the lives of patients. VTE is associated with a considerable disease burden, which continues to grow as the global population lives longer4,5.
In fact, many factors affect the occurrence and development of VTE. In addition to trauma, cancer and other factors, researchers have found that lipids, such as low-density lipoprotein (LDL), high-density lipoprotein (HDL) and triglycerides (TGs), may also affect VTE. A meta-analysis of a case‒control study and a cohort study found that HDL and TGs were significantly associated with venous thrombosis6. Another study found that elevated LDL cholesterol levels were only associated with VTE in men7. However, in view of the heterogeneity observed in these studies, the results of the meta-analysis must be carefully interpreted. Other studies have found that LDL, HDL and TGs have no effect on VTE8. Therefore, there is still some controversy about the relationship between lipids and VTE.
Mendelian randomization (MR) can use genetic variation as an instrumental variable (IV) of exposure and strengthen the causal inference of the exposure-outcome relationship by reducing confounding factors9. MR follows the law of random distribution of genetic variation during conception. Because genotypes precede the progression of the disease and are largely independent of postnatal lifestyle or environmental factors, this technique can minimize confounding factors and avoid deviations caused by reverse causality10. MR can avoid some typical biases in observational studies, such as small sample sizes and short follow-up times11. Based on the existing genetic database, gene variants that regulate lipids and VTE can be regarded as IVs to further study the causal relationship between lipid levels and VTE risk. And there is no research to prove whether VTE can cause the increase of circulating lipids. Bidirectional Mendelian randomized study refers to the use of two samples of Mendelian randomization method to test whether there is a causal relationship between the two traits. In this study, we used MR analysis to test whether an increase in classical blood lipids can lead to VTE, and then to test whether VTE can lead to an increase in circulating lipids. Therefore, a bidirectional MR study was conducted to clarify the causal relationship between three lipids (LDL, HDL and TGs) and VTE (DVT and PE).
## Methods
We performed bidirectional MR analysis of classical lipids (LDL, HDL and TGs) and VTE (DVT and PE). First, we used classical lipids (LDL, HDL and TGs) as exposure variables to conduct two-sample MR analysis with VTE (DVT and PE). In addition, to further clarify whether VTE affects lipids, we used VTE as an exposure variable to analyse the causal relationship between VTE and classical lipids. The hypothesis of this study is that there is no causal relationship between VTE and classical blood lipids.
The research design is shown in Fig. 1. Since this study is based on existing publications and public databases, no additional ethical approval or consent is needed. Figure 1Analysis flow chart. We performed bidirectional MR analysis of classical lipids (LDL, HDL and TG) and VTE (DVT and PE). First, we used classical lipids (LDL, HDL and TG) as exposure variables to conduct two-sample MR analysis with VTE (DVT and PE). In addition, to further clarify whether VTE affects lipids, we used VTE as an exposure variable to analyse the causal relationship between VTE and classical lipids.
## Data resources
Summary data for DVT were downloaded from the Neale Lab database (http://www.sussex.ac.uk/lifesci/nealelab/) and the MRC IEU OpenGwas repository (https://gwas.mrcieua.ac.uk/). The data of all individual participants were from the UK Biobank study. The DVT data came from a large meta-analysis of a genome-wide association study (GWAS), which included a total of 337,159 subjects of European origin (6767 DVT cases and 330,392 controls). GWAS data for PE were derived from the UK Biobank (41202#I269), including a total of 463,010 subjects of European origin (1846 PE cases and 461,164 controls). The UK *Biobank is* a large prospective cohort study involving approximately 500,000 people between 37 and 76 years old (99.5 percent between 40 and 69 years old) across the UK12. GWAS data for classic lipids (LDL, HDL and TGs) were derived from the MRC IEU OpenGwas repository, including a total of 337,159 subjects of European origin. Sample processing, determination details, genotyping and quality control of these classical lipid GWAS data can be obtained from previously published studies13.
## Selection of instrumental variables
When screening IVs, we followed the three basic hypotheses of MR: first, genetic variation should be closely related to exposure; second, variation should not be affected by the confounding factors of the relationship between exposure and outcome; and third, exposure should affect only the outcome (that is, pleiotropy should be eliminated, and exclusion limitation should be satisfied). Therefore, we selected exposure-related SNPs at genome-wide significance ($p \leq 5$ × 10–8) as IVs. In addition, none of the instrument SNPs were in a state of linkage disequilibrium (LD). We performed the aggregation process (R2 < 0.001, large window size = 10,000 kb) to eliminate LD between SNPs. A missing SNP in the LD control group was also deleted. Third, SNPs with a minimum allele frequency (MAF) < 0.01 were removed.
If the SNP for a particular request did not exist in the generated GWAS, a search was conducted for an SNP (agent) in LD with the requested SNP (target). In addition, to test whether there was a weak tool deviation in the IV, we used F statistics14 (F = R2 (n − k − 1)/k (1 − R2), where R2 is the exposure variance explained by the selected tool variable (obtained from the MR Steiger directivity test), n is the sample size and k is the total variable). If the F statistic of the IV is much greater than 10, it is very unlikely that the deviation of instrument variables is very small.
## Mendelian randomized analysis
We used the random effect inverse variance weighted (IVW) model (permitting heterogeneity in causal estimates) as the main analysis model and the weighted median method15, simple mode method16,17, weighted mode method16,18 and MR–Egger method19 as supplementary methods20. The random effect IVW method regresses genetic associations with the outcome on associations with exposure and fixes the intercept at zero. It provides robust causal estimates when there is heterogeneity and in the absence of horizontal pleiotropy21,22. Since there is heterogeneity and no horizontal pleiotropy in this analysis, we take the results of random effect IVW as the main results. The weighted median method provides consistent causal estimates when the effective tool has more than $50\%$ of the weight23,16. The weighted mode-based causal estimate consistently estimates the true causal effect when the largest group of instruments with consistent MR estimates is valid18. The MR‒Egger reversion can recognize and adjust for pleiotropy (p for intercept < 0.05). However, this technique produces estimations of low precision24.
## Pleiotropy and sensitivity analysis
We employed MR‒Egger regression to assess the potential pleiotropic effects of IVs. The intercept term in the MR‒Egger regression can be used to indicate whether horizontal pleiotropy affects the results of the MR analysis24. MR-PRESSO is a method for the detection and correction of outliers in IVW linear regression. MR-PRESSO analysis attempts to reduce heterogeneity in causal effect estimation by removing SNPs that cause greater than expected heterogeneity. The heterogeneities were quantified by the Cochran Q statistic in the IVW method and MR‒Egger regression, with a P value of 0.05 indicating considerable heterogeneity. Additionally, to identify potentially influential SNPs, we performed a leave-one-out sensitivity analysis.
The beta value and odds ratio (OR) show the kind of relationship between exposure variables and outcome variables. A correlation of $P \leq 0.016$ for the beta value ($\frac{0.05}{3}$ exposures) was considered to be significant, and a correlation between $p \leq 0.016$ and < 0.05 was considered to be a suggestive correlation. The threshold of $p \leq 0.05$ was used in all sensitivity analyses. Investigations were carried out with the MendelianRandomization, TwoSampleMR and MR-PRESSO packages in R version 4.1.2 [2021-11-01]25.
## Selection of tool variables
The details of all independent SNPs associated with exposure after the SNPs of incompatible alleles were removed are shown in Supplementary File 1. The SNP details of calculating F statistics in all analyses can be found in Supplementary File 1. In our study, the F statistics of IVs related to exposure were all greater than 10, indicating that the possibility of variable deviation of weak tool variables was very small.
## Causal relationship between LDL (exposure) and DVT (outcome)
As shown in Table 1, in the MR analysis of LDL and DVT when LDL was used as an exposure variable, according to the results of IVW, there was a causal relationship between an increase in LDL level and a decrease in DVT risk (beta = − 0.003, $95\%$ CI (− 0.005, − 0.001); OR = 0.996, $95\%$ CI (0.994, 0.998); Pbeta = 9.925e−4) (Fig. 2a). In addition, the P values of the weighted median (beta = − 0.004, $95\%$ CI (− 0.006, − 0.002); Pbeta = 2.136e−5), MR–Egger (beta = − 0.003, $95\%$ CI (− 0.005, − 0.002); Pbeta = 0.0143) and weighted mode (beta = − 0.003, $95\%$ CI (− 0.005, − 0.002); Pbeta = 7.058e−5) methods were all less than 0.05. Only the P value of the simple mode (beta = − 0.001, $95\%$ CI (− 0.007, 0.003); Pbeta = 0.513) method was greater than 0.05. The heterogeneity analysis found that there was heterogeneity in the analysis (the Q-p values of IVW and MR–Egger were both less than 0.05). The MR–Egger intercept showed that there was no horizontal pleiotropy in the analysis (MR–Egger intercept p value > 0.05) (Table 1). The MR-PRESSO results showed that there were some SNPs that affected the stability of the results, and after removing these SNPs, it was found that LDL had no effect on DVT (Table 2). The scatter plots and funnel plots are shown in Supplementary File 2, Figs. S1 and S7. The leave-one-out test plot showed that there were no SNPs affecting the result (Supplementary File 2, Fig. S13).Table 1MR estimates from different methods of assessing the causal effect of classical lipids on VT.Exposure-outcomeNo. of SNPIVWWeighted medianWeighted modeSimple modeMR-EggerBeta ($95\%$CI)OR ($95\%$CI)P valueCochran Q statistics (df)Q-P valueBeta ($95\%$CI)P valueBeta ($95\%$CI)P valueBeta ($95\%$CI)P valueBeta ($95\%$CI)P valueIntercept (Se)P valueCochran Q statistics (df)Q-P valueLDL-DVT70− 0.003 (− 0.005, − 0.001)0.996 (0.994, 0.998)9.925e−4144.31[69]2.98e−7− 0.004 (− 0.006, − 0.002)2.136e−5− 0.003 (− 0.005, − 0.002)7.058e−5− 0.001 (− 0.007, 0.003)0.513− 0.003 (− 0.005, − 7e−4)0.01436.48e−6 (9.86e−5)0.947144.30 [68]2.01e−7HDL-DVT86− 5e−4 (− 0.003, 0.002)0.999 (0.996, 1.002)0.669208.32 [85]2.39e−12− 0.001 (− 0.004, 0.001)0.246− 8e−4 (− 0.003, 0.001)0.4600.002 (− 0.003, 0.008)0.374− 3e−4 (− 0.004, 0.003)0.870− 1.36e−5 (1e−4)0.901208.28 [84]1.52e−12TG-DVT54− 0.003 (− 0.006, − 1e−4)0.996 (0.993, 0.999)0.038125.71[53]7.62e−8− 0.004 (− 0.007, − 8e−4)0.013− 0.003 (− 0.006, − 3e−4)0.034− 0.003 (− 0.009, 0.002)0.305− 0.002 (− 0.008, 0.002)0.335− 4.49e−5 (1e−4)0.740125.44 [52]5.22e−8LDL-PE39− 4e−4 (− 0.001, 5e−4)0.999 (0.998, 1.0005)0.38249.60 [38]0.09− 5e−4 (− 0.001, 8e−4)0.462− 7e−4 (− 0.002, 5e−4)0.275− 3e−4 (− 0.002, 0.002)0.809− 6e−4 (− 0.002, 0.001)0.5001.30e−5 (4.89e−5)0.79149.51 [37]0.08HDL-PE613.641 (− 4e−4, 0.001)1.0003 (0.999, 1.001)0.38277.41 [60]0.06− 1.472 (− 0.001, 9e−4)0.7833.100 (− 6e−4, 0.001)0.5201.009 (− 0.001, 0.003)0.4453.300 (− 0.001, 0.001)0.9952.35e−5 (3.13e−5)0.45476.67 [59]0.06TG-PE444e−4 (− 6.872, 0.001)1.0004 (0.999, 1.001)0.41956.33 [43]0.080.001 (6.561, 0.003)0.0410.001 (− 1.056, 0.002)0.0750.001 (− 1.848, 0.004)0.4220.001 (− 6.662, 0.003)0.173− 5.26e−5 (4.66e−5)0.26554.67 [42]0.09Se standard error, SNP single nucleotide polymorphism, MR Mendelian randomization, IVW inverse variance weighting, VT venous thromboembolism, PE pulmonary embolism, DVT deep venous thrombosis, LDL Low density lipoprotein, HDL High-density lipoprotein, TG triglyceride, CI confidence interval. Figure 2Forest plots of variant specific inverse variance estimates for the causal association between VTE (exposure) and classical lipids (exposure). ( a) LDL-DVT; (b) HDL-DVT; (c) TG-DVT; (d) LDL-PE; (e) HDL-PE, (f) TG-PE.Table 2MR-Presso results (classical lipids—VT).Exposure-outcomeMR analysisCasual estimateSDT-statP-valueGlobal test-p valueOutliers (snp)HDL-DVTRaw− 5e−40.001− 0.4530.650 < 0.001rs10761771rs11065987rs12748152rs174535rs731839Outlier-corrected− 0.0010.001− 1.2600.210LDL-DVTRaw− 0.0020.001− 2.0510.461 < 0.001rs12748152rs3758348rs7412Outlier-corrected− 0.0020.001− 1.9010.061TG-DVTRaw− 0.0030.001− 2.0670.043 < 0.001rs10761762rs731839Outlier-corrected− 0.0020.001− 1.5780.120TG-PERaw4e−45e−40.8070.4230.08NAOutlier-correctedNANANANALDL-PERaw− 3e−45e−4− 0.7640.4480.128NAOutlier-correctedNANANANAHDL-PERaw3e−44e−40.9160.3620.102NAOutlier-correctedNANANANASD standard deviation, SNP single nucleotide polymorphism, MR-PRESSO MR-Pleiotropy Residual Sum and Outlier method, PE pulmonary embolism, DVT deep venous thrombosis, LDL low density lipoprotein, HDL high-density lipoprotein, TG triglyceride.
## Causal relationship between DVT (exposure) and LDL (outcome)
In the MR analysis of LDL and DVT, when DVT was used as an exposure variable, there was no causal relationship between gene-predicted LDL and DVT (the Pbeta values in all analytical models were greater than 0.05) (Table 3, Fig. 3a). In addition, horizontal pleiotropy analysis and heterogeneity analysis found that the results of this MR analysis were not affected by heterogeneity or horizontal pleiotropy (IVW: Q-P value = 0.943, MR–Egger Q-P value = 0.834, intercept P value = 0.831) (Table 3). MR-PRESSO did not find any SNPs that affected the stability of the results (Table 4). The scatter plot is shown in Supplementary File 2, Fig. S19. The leave-one-out test plot showed that the analysis results were very stable (Supplementary File 2, Fig. S25).Table 3MR estimates from different methods of assessing the causal effect of VT on classical lipids. Exposure-outcomeNo. of SNPIVWWeighted medianWeighted modeSimple modeMR-EggerBeta ($95\%$CI)OR ($95\%$CI)P valueCochran Q statistics (df)Q-P valueBeta ($95\%$CI)P valueBeta ($95\%$CI)P valueBeta ($95\%$CI)P valueBeta ($95\%$CI)P valueIntercept (Se)P valueCochran Q statistics (df)Q-P valueDVT-LDL30.872 (− 1.631, 3.376)2.392 (0.195, 29.261)0.4940.116 [2]0.9430.859 (− 1.864, 3.582)0.5360.833 (− 2.404, 4.072)0.6630.741 (− 2.432, 3.915)0.6922.854 (− 11.740, 17.449)0.7660.0230.8310.043 [1]0.834DVT-HDL30.183 (− 2.546, 2.913)1.201 (0.078,18.428)0.8952.754 [2]0.2520.585 (− 2.268, 3.440)0.6871.117 (− 1.855, 4.090)0.5380.407 (− 3.695, 4.510)0.86310.277 (− 3.234, 23.789)0.3760.0210.3770.545 [1]0.460DVT-TG31.482 (− 0.759, 3.724)4.404 (0.467, 41.449)0.1940.807 [2]0.6671.645 (− 0.973, 4.264)0.2181.766 (− 1.323, 4.856)0.3791.913 (− 1.225, 5.052)0.3546.872 (− 6.289, 20.035)0.4920.0210.5640.143 [1]0.704PE-LDL214.365 (− 28.58, 57.32)173 (3.83e−13, 7.83e + 24)0.51240.648[1]1.82e−10NANANANANANANANANANANANAPE-HDL2− 3.516 (− 22.19, 15.16)0.0297 (2.29e−10,384)0.71210.8 [1]0.001NANANANANANANANANANANANAPE-TG24.796 (− 0.656, 10.25)121.1 (0.518, 28,289)0.0840.350 [1]0.554NANANANANANANANANANANANASe standard error, SNP single nucleotide polymorphism, MR Mendelian randomization, IVW inverse variance weighting, VT venous thromboembolism, PE pulmonary embolism, DVT deep venous thrombosis, LDL low density lipoprotein, HDL high-density lipoprotein, TG triglyceride, CI confidence interval. Figure 3Forest plots of variant specific inverse variance estimates for the causal association between classical lipids (exposure) and VTE (exposure). ( a) DVT-LDL; (b) DVT-HDL; (c) DVT-TG; (d) PE-LDL; (e) PE-HDL; (f) PE-TG.Table 4MR-Presso results (VT—classical lipids).Exposure-outcomeMR analysisCasual estimateSDT-statP-valueGlobal test-p valueOutliers (snp)DVT-LDLRaw0.2690.6460.4170.7040.806NAOutlier-correctedNANANANADVT-HDLRaw− 0.1411.034− 1.0360.8990.410Outlier-correctedNANANANADVT-TGRaw0.9360.7431.2580.22970.680Outlier-correctedNANANANASD standard deviation, SNP single nucleotide polymorphism, MR-PRESSO MR-Pleiotropy Residual Sum and Outlier method, DVT deep venous thrombosis, LDL low density lipoprotein, HDL high-density lipoprotein, TG triglyceride.
## Causal relationship between HDL (exposure) and DVT (outcome)
In the MR analysis of HDL and DVT, when HDL was used as an exposure variable, the results of all models showed that there was no causal relationship between the level of HDL and the risk of DVT (Pbeta value > 0.05 in all models) (Table 1) (Fig. 2b). The heterogeneity test found that there was heterogeneity (the Q-p values of IVW and MR–Egger were both less than 0.05) (Table 1). The MR–Egger intercept showed that there was no horizontal pleiotropy in the analysis (MR–Egger intercept p value > 0.05) (Table 1). The MR-PRESSO results showed that there were some SNPs that affected the stability of the results (MR-PRESSO Global test P value < 0.05), and after removing these SNPs, it was found that HDL had no effect on DVT (Table 2). The leave-one-out test plot did not find problematic SNPs (Supplementary File 2, Fig. S14). The scatter plots and funnel plots are shown in Supplementary File 2, Figs. S2 and S8.
## Causal relationship between DVT (exposure) and HDL (outcome)
In the MR analysis of HDL and DVT, when DVT was used as an exposure variable, the results of all analytical models showed that DVT did not affect the level of HDL (the Pbeta values in all models were greater than 0.05) (Table 3, Fig. 3b). The results of the horizontal pleiotropy analysis and heterogeneity analysis showed our MR results were not affected by heterogeneity or horizontal pleiotropic effects (IVW: Q-P value = 0.252, MR–Egger: Q-value = 0.460, intercept P value = 0.377) (Table 3). MR-PRESSO did not find any SNPs that affected the stability of the results (Table 4). The scatter plot is shown in Supplementary File 2, Fig. S20. The analysis chart of the retention method showed no SNPs that affected the results, indicating that the analysis results were stable (Supplementary File 2, Fig. S26).
## Causal relationship between TGs (exposure) and DVT (outcome)
As shown in Table 1, when TGs were used as an exposure variable in the MR analysis, according to the results of IVW, there was a genetic causal relationship between TGs and DVT risk reduction (beta = − 0.003, $95\%$ CI (− 0.006, − 1e−4); OR = 0.996, $95\%$ CI (0.993, 0.999); Pbeta = 0.038) (Fig. 2c). The P values of the weighted median (beta = − 0.004, $95\%$ CI (− 0.007, − 8e−4); Pbeta = 0.013) and weighted mode (beta = − 0.003, $95\%$ CI (− 0.006, − 3e−4); Pbeta = 0.034) methods were all consistent with the results of IVW (Table 1). However, the results of the MR–Egger (beta = − 0.002, $95\%$ CI (− 0.008, 0.002); Pbeta = 0.335) and simple mode (beta = − 0.003, $95\%$ CI (− 0.009, 0.002); Pbeta = 0.305) methods indicated that the level of TGs did not affect the incidence of DVT (Table 1). The heterogeneity test found that there was heterogeneity (the Q-P values of the IVW and MR–Egger were both less than 0.05) (Table 1). For horizontal pleiotropy analysis, the MR–Egger intercept p value was greater than 0.05 (Table 2); these results indicated that there was no horizontal pleiotropy in this analysis. The results of MR-PRESSO showed that there were some SNP outliers in this analysis. After removing these SNP outliers, it was found that there was no causal relationship between TGs and DVT (Table 2). The results of the leave-one-out test showed that there was no SNP that affected the stability of the results (Supplementary File 2, Fig. S15). The scatter and funnel plots are shown in Supplementary File 2, Figs. S3 and S9.
## Causal relationship between DVT (exposure) and TGs (outcome)
As shown in Table 3, when DVT was used as an exposure variable in the MR analysis, there was no genetic causal relationship between TGs and DVT risk (the Pbeta values were greater than 0.05 in all analytical models) (Fig. 3c). The horizontal pleiotropy analysis and heterogeneity analysis indicated that our MR results were not affected by heterogeneity or horizontal pleiotropy (Tables 3, 4). The scatter plot is shown in Supplementary File 2, Fig. S21. The leave-one-out test plot showed that the analysis results were very stable (Supplementary File 2, Fig. S27).
## Causal relationship between LDL (exposure) and PE (outcome)
In the MR analysis of LDL and PE, when LDL was used as an exposure variable, there was no genetic causal relationship between LDL and PE risk (Pbeta > 0.05 in all analytical models) (Table 1, Fig. 2d). The p value of the Q statistic was less than 0.05, indicating heterogeneity (Table 1). However, there was no horizontal pleiotropy in this analysis (the MR–Egger intercept p value was greater than 0.05) (Table 1). There was no SNP affecting the stability of the results according to the leave-one-out test (Supplementary File 2, Fig. S16). The scatter plots and funnel plots are shown in Supplementary File 2, Figs. S4 and S10.
## Causal relationship between PE (exposure) and LDL (outcome)
In the MR analysis of LDL and PE, when PE was used as an exposure variable, because two related IVs were included, only IVW model analysis could be carried out (Table 3). According to the IVW analysis, there was no genetic causal relationship between PE and LDL levels (beta = 14.365, $95\%$ CI (− 28.58, 57.32); Pbeta = 0.512) (Table 3, Fig. 3d). Heterogeneity analysis showed that there may be some heterogeneity in this analysis (Q-P value of the IVW = 1.82e−10). Since there were only 2 IVs included in this analysis, it was impossible to perform the sensitivity and leave-one-out tests. The scatter plot is shown in Supplementary File 2, Fig. S22.
## Causal relationship between HDL (exposure) and PE (outcome)
In the MR analysis of HDL and PE, when HDL was used as an exposure variable, there was no genetic causal relationship between LDL and PE risk (Pbeta > 0.05 in all analytical models) (Table 1, Fig. 2e). The heterogeneity analysis in this study showed that the analysis had heterogeneity (the Q-P values of IVW and MR–Egger were both less than 0.05) (Table 1). However, horizontal pleiotropy analysis showed that there was no horizontal pleiotropy (MR–Egger intercept P value > 0.05) (Table 1). The leave-one-out test showed that no SNP affected the stability of the results (Supplementary File 2, Fig. S17). The scatter plots and funnel plots are shown in Supplementary File 2, Figs. S5 and S11.
## Causal relationship between PE (exposure) and HDL (outcome)
In the MR analysis of HDL and PE, when PE was taken as the exposure variable, because two related IVs were included, only the IVW model analysis could be carried out. According to the IVW analysis, there was no causal relationship between HDL and PE risk (beta = − 3.516, $95\%$ CI (− 22.19, 15.16); Pbeta = 0.712) (Table 3, Fig. 3e). The results of heterogeneity analysis showed that there may be some heterogeneity in this analysis (Q-P value of IVW = 0.001) (Table 3). In addition, because there were few IVs included in this analysis, it was impossible to carry out sensitivity analysis and leave-one-out tests. The scatter plot is shown in Supplementary File 2, Fig. S23.
## Causal relationship between TGs (exposure) and PE (outcome)
In the MR analysis of TGs and PE, when TGs were taken as an exposure variable, there was no genetic causal relationship between TGs and PE risk (Pbeta > 0.05 in all analytical models) (Table 1, Fig. 2f). Heterogeneity analysis showed that there was heterogeneity (the Q-P values of IVW and MR–Egger were both less than 0.05) (Table 1). However, horizontal pleiotropy analysis showed that there was no horizontal pleiotropy (the P values of the MR–Egger intercept was greater than 0.05) (Table 1). The results of the leave-one-out test show that the results of the analysis are robust (Supplementary File 2, Fig. S18). The scatter plot and funnel plot are shown in Supplementary File 2, Figs. S6 and S12.
## Causal relationship between PE (exposure) and TGs (outcome)
In the MR analysis of TGs and PE, when PE was used as an exposure variable, because two related IVs were included, only IVW analysis could be carried out (Table 3). According to the IVW analysis, PE did not affect the level of TGs (beta = 4.796, $95\%$ CI (− 0.656, 10.25); Pbeta = 0.084) (Table 3, Fig. 3f). The results of the heterogeneity analysis showed that there was no heterogeneity (Q-P value of IVW = 0.554). In addition, because there were few IVs included in this analysis, it was impossible to carry out sensitivity analysis and leave-one-out tests. The scatter plot is shown in Supplementary File 2, Fig. S24.
## Discussion
In this study, we used bidirectional MR studies to analyse the causal relationship between VTE (DVT and PE) and classical lipids (LDL, HDL and TGs). Through our MR analysis, we did not find a causal relationship between VTE and classical lipids. Even though the IVW model of some analyses (such as LDL-DVT and TG-DVT) had a P value less than 0.05, after MR-PRESSO removed the outliers, there was no causal relationship between them.
We found that there is no causal relationship between VTE and blood lipids from the point of view of genetics, which provides evidence for clinical research. Of course, the results may vary dependent on population, sample size, genotyping, alternative genetic methods etc. Additionally, there are further issues such as confounding, instrument strength, population stratification that may bias results. At present, there is still some controversy in clinical research on the causal relationship between LDL and VTE. For example, Petter and others have found that there is no correlation between LDL and VTE26. However, Dai et al. believed that a higher LDL value is significantly related to an increased risk of DVT in female patients after TKA27. Our results showed that the level of classical lipids does not affect the incidence of DVT. These results are consistent with some previous clinical results, such as the results of Morelli et al. 's MEGA study showing that classical lipids (LDL, HDL and TGs) were not associated with the risk of venous thrombosis8. In addition, Schouwenburg et al. also found that the level of blood lipids did not affect the risk of VTE28. In patients with recurrent VTE, Morelli et al. did not find a causal relationship between lipids and VTE29. Although observational control studies and two meta-analyses have shown that hypolipidaemic drugs such as statins can significantly reduce the risk of VTE through mechanisms related to multiple drug effects, it is likely to be a process independent of cholesterol reduction30. For example, statins can induce the expression of Krupp-like factor 2, which in turn promotes the expression of thrombomodulin on endothelial cells, thereby enhancing the activity of the protein C anticoagulation pathway31. In addition, statins can reduce the level of inflammatory markers32 and reduce tissue factor expression and thrombin production33.
A previous meta-analysis showed that patients with venous thrombosis had higher average levels of TGs and lower average levels of HDL6. However, most of the studies included in this meta-analysis were small case‒control studies that could not control for confounding factors6. In the observational study, there are several possible reasons to explain the association between lipids and VTE. First, there were some confounding factors in the patients included, such as obesity. Elevated blood lipids tend to occur in obese patients, especially those with abdominal obesity, which is associated with increased thrombin formation and decreased fibrinolysis34. In addition, obesity is associated with inactivity, which is another risk factor for thrombosis35,36. Furthermore, blood lipids usually increase with blood sugar, while diabetes is usually associated with increased levels of procoagulant factors and endogenous fibrinolysis inhibition37,38. Although some studies have shown that elevated LDL levels accelerate the activation of prothrombin, factor X and factor VII, HDL enhances the protein C anticoagulant pathway and reduces thrombin production39. However, this study did not show an association between classical lipids and the risk of VTE. This suggests that the prethrombotic effects of dyslipidaemia may be too mild to truly affect the risk of VTE or that these effects can be offset by other mechanisms. Of course, this needs to be verified by further experiments.
The current bidirectional MR analysis has several advantages. First, this study is the first to infer a causal relationship between classical lipids (LDL, HDL and TGs) and VTE (DVT and PE) from a genetic perspective. Moreover, bidirectional analysis ensured the inference of bidirectional causality between VTE and lipids. However, this study has some limitations. First, the people in the MR analysis were of European descent, so this study may not be applicable to other races. Second, there may be overlapping participants in the exposure and outcome studies, but it is difficult to estimate the extent of sample overlap. In addition, although many cases of VTE were found in the current GWAS analysis, they could not be stratified or adjusted for in the analysis. Of course, larger sample sizes of clinical studies and experiments are needed to confirm our conclusions. We could confirm that an exposure-associated SNP is thus far only reported to be associated with that particular exposure, but we cannot guarantee that the same SNP is not associated with other traits (confounders); the association might remain to be identified, or the SNP might be associated with an underlying risk factor that is unrecognized. Although researchers have proposed many solutions that meet the assumptions of the MR model, these strategies can only detect a violation of the hypothesis but can never confirm whether it is true. Therefore, these shortcomings may lead to biased estimates.
## Conclusion
Through bidirectional MR studies, we found that there was no genetic causal relationship between VTE (DVT and PE) and classical lipids (LDL, HDL and TGs), which laid a foundation for future studies of VTE and classical lipids.
## Supplementary Information
Supplementary Information 1.Supplementary Information 2. The online version contains supplementary material available at 10.1038/s41598-023-31067-z.
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---
title: The Geriatric Nutritional Risk Index predicts sarcopenia in patients with cirrhosis
authors:
- Chisato Saeki
- Akiyoshi Kinoshita
- Tomoya Kanai
- Kaoru Ueda
- Masanori Nakano
- Tsunekazu Oikawa
- Yuichi Torisu
- Masayuki Saruta
- Akihito Tsubota
journal: Scientific Reports
year: 2023
pmcid: PMC9995649
doi: 10.1038/s41598-023-31065-1
license: CC BY 4.0
---
# The Geriatric Nutritional Risk Index predicts sarcopenia in patients with cirrhosis
## Abstract
Patients with cirrhosis are at high risk for sarcopenia and malnutrition, which are associated with reduced quality of life and increased mortality. We investigated the relationship between the Geriatric Nutritional Risk Index (GNRI) and sarcopenia/gait speed and assessed the usefulness of the GNRI for predicting sarcopenia in patients with cirrhosis. We evaluated 202 patients with cirrhosis and divided them into three groups based on baseline GNRI values: low (L)-GNRI (< 94.0, $$n = 49$$), intermediate (I)-GNRI (between 94.0 and 109.5, $$n = 103$$), and high (H)-GNRI groups (> 109.5, $$n = 50$$). Sarcopenia was diagnosed according to the criteria of the Japan Society of Hepatology. The prevalence of sarcopenia and slow gait speed was the lowest in the H-GNRI group ($8.0\%$ and $26.0\%$, respectively) and the highest in the L-GNRI group ($49.0\%$ and $44.9\%$, respectively). They increased stepwise with a decline in the GNRI group ($p \leq 0.001$ and $$p \leq 0.05$$, respectively). The GNRI values were significantly and positively correlated with handgrip strength, skeletal muscle mass index, and gait speed. Multivariate analysis identified lower GNRI as an independent risk factor for sarcopenia. The optimal cutoff value of the GNRI for predicting sarcopenia was 102.1 (sensitivity/specificity, $\frac{0.768}{0.630}$). The GNRI was significantly associated with sarcopenia and physical performance and could be a helpful screening tool for predicting sarcopenia in patients with cirrhosis.
## Introduction
The liver plays a pivotal role in nutrient metabolism, and deterioration of the liver functional reserve leads to malnutrition. Patients with cirrhosis, especially decompensated cirrhosis, frequently have malnutrition, with the prevalence of 50–$90\%$1–4. In such patients, reduced glycogen synthesis and storage and increased glycogenolysis promote gluconeogenesis from muscle-derived amino acids, leading to proteolysis and muscle loss5. Consequently, patients with cirrhosis complicated by malnutrition develop sarcopenia, defined as the progressive loss of skeletal muscle mass and function6,7. Sarcopenia is a critical risk factor for poor quality of life, mortality, and liver-related complications such as hepatic encephalopathy and infections8–10. Therefore, early diagnosis and appropriate therapeutic intervention for sarcopenia, such as nutrition and exercise therapy, are crucial for patients with cirrhosis. The European Working Group on Sarcopenia in Older People (EWGSOP) has adopted the SARC-F questionnaire as an initial screening tool for sarcopenia risk11. The SARC-F has high specificity for predicting sarcopenia without the use of specialized equipment; however, it has extremely low sensitivity in real-world clinical settings12–15. Therefore, it is desirable to establish a more sensitive screening method for sarcopenia.
The Geriatric Nutritional Risk Index (GNRI), which is calculated based on body weight and serum albumin level, was originally developed as a simple nutritional assessment tool to estimate the risk of morbidity and mortality in hospitalized older patients16. The GNRI scoring system classifies individuals into the following four nutrition-related risk groups, with lower GNRI scores indicating a higher risk of morbidity and mortality: major risk, GNRI < 82; moderate risk, 82 to < 92; low risk, 92 to ≤ 98; and no risk, > 9816. Intriguingly, in previous studies on patients undergoing hemodialysis, lower GNRI was a predictor of reduced muscle mass and strength and ability to walk17–19. Therefore, the GNRI may be a helpful screening tool for sarcopenia and physical performance, along with an assessment of malnutrition-related risk in patients with cirrhosis.
However, no studies have elucidated the relationship between the GNRI and sarcopenia/physical performance (gait speed) in patients with cirrhosis. This study aimed to examine this relationship and evaluate whether the GNRI is helpful for predicting sarcopenia in patients with cirrhosis.
## Patient characteristics
The baseline characteristics of the 202 patients enrolled in this study are presented in Table 1. The study cohort included 132 men ($65.3\%$) and the median age was 69.0 (59.0–76.0) years. The frequencies of Child–Pugh class B/C (i.e., decompensated cirrhosis) and mALBI grade ≥ 2 were $31.7\%$ ($\frac{64}{202}$) and $55.4\%$ ($\frac{112}{202}$), respectively. The median GNRI value was significantly lower in patients with Child–Pugh class B/C than in those with Child–Pugh class A (i.e., compensated cirrhosis) (93.9 vs 106.2, $p \leq 0.001$; see Supplementary Fig. S1A online). Similarly, it was significantly lower in patients with mALBI grade ≥ 2 than in those with mALBI grade 1 (96.0 vs 108.2, $p \leq 0.001$; see Supplementary Fig. S1B online). The frequencies of sarcopenia and slow gait speed were $27.7\%$ ($\frac{56}{202}$) and $36.1\%$ ($\frac{73}{202}$), respectively. Table 1Comparison of clinical characteristics among the three groups based on the GNRI values. Variable ALLL-GNRII-GNRIH-GNRIp-valuePatients, n (%)20249 (24.3)103 (51.0)50 (24.8)Men, n (%)132 (65.3)30 (61.2)66 (64.1)36 (72.0)0.492Age (years)69.0 (59.0–76.0)70.0 (59.0–76.5)71.0 (63.0–76.0)64.0 (53.0–73.0)0.021BMI (kg/m2)23.6 (21.2–26.1)20.6 (19.0–22.2)23.2 (21.5–25.1)27.8 (25.4–30.3)< 0.001Etiology HBV/HCV/Alcohol/other, n$\frac{18}{67}$/$\frac{64}{530}$/$\frac{18}{17}$/$\frac{1413}{33}$/$\frac{33}{245}$/$\frac{16}{14}$/150.290 Child–Pugh score6 (5–7)7 (6–8)5 (5–7)5 (5–5)< 0.001 Child–Pugh A/B + C, n$\frac{138}{6416}$/$\frac{3374}{2948}$/2< 0.001 ALBI score − 2.53 (− 2.85– − 2.05) − 1.85 (− 2.14– − 1.65) − 2.56 (− 2.85– − 2.18) − 2.85 (− 3.01– − 2.58)< 0.001 mALBI grade ($\frac{1}{2}$a/2b/3)$\frac{90}{40}$/$\frac{68}{45}$/$\frac{3}{37}$/$\frac{448}{25}$/$\frac{30}{037}$/$\frac{12}{1}$/0< 0.001 GNRI102.6 (94.0–109.5)87.6 (80.8–91.1)102.5 (97.6–106.2)115.7 (111.0–120.7)< 0.001 Total bilirubin (mg/dL)0.9 (0.6–1.3)1.0 (0.5–1.6)0.9 (0.6–1.3)0.9 (0.7–1.1)0.966 Albumin (g/dL)3.9 (3.4–4.2)3.2 (2.8–3.5)3.9 (3.6–4.2)4.4 (4.0–4.5)< 0.001 Prothrombin time (%)82 (68–94)78 (60–93)82 (67–96)85 (74–93)0.163 eGFR (mL/min/1.73 m2)64 (51–77)65 (42–79)65 (51–77)61 (51–77)0.999 M2BPGi (C.O.I)3.06 (1.54–6.16)5.64 (2.83–8.07)2.99 (1.66–5.79)1.66 (1.10–3.41)< 0.001 BCAA (µmol/L)398 (325–477)340 (300–395)402 (326–461)463 (393–505)< 0.001 Zinc (µg/dL)63 (54–74)54 (43–64)64 (55–73)73 (61–85)< 0.001Handgrip strength (kg) All patients25.3 (18.5–34.9)21.5 (16.6–28.8)25.3 (19.1–34.0)31.5 (22.5–41.0)< 0.001 Men31.6 (25.0–37.4)25.6 (20.5–33.0)31.6 (25.2–36.7)37.8 (28.9–43.5)< 0.001 Women17.2 (14.4–22.3)16.8 (14.6–20.0)17.9 (14.5–22.5)21.9 (12.9–23.2)0.456SMI (kg/m2) All patients6.92 (5.95–7.85)6.37 (5.36–6.94)6.81 (5.96–7.63)7.93 (6.95–8.73)< 0.001 Men7.34 (6.78–8.23)6.82 (6.32–7.11)7.33 (6.71–8.02)8.22 (7.39–9.00)< 0.001 Women5.83 (5.20–6.39)5.53 (5.05–6.11)5.84 (5.08–6.34)6.43 (5.80–7.46)0.011Sarcopenia, n (%)56 (27.7)24 (49.0)28 (27.2)4 (8.0)< 0.001Gait speed (m/s)1.06 (0.85–1.25)1.04 (0.75–1.21)1.06 (0.86–1.21)1.12 (0.99–1.35)0.045Slow gait speed, n (%)73 (36.1)22 (44.9)38 (36.9)13 (26.0)0.144Values are shown as median (interquartile range) or number (percentage). Statistical analysis was performed using the chi-squared test or the Kruskal–Wallis test, as appropriate. ALBI albumin-bilirubin, BCAA branched-chain amino acid, BMI body mass index, eGFR estimated glomerular filtration rate, GNRI Geriatric Nutritional Risk Index, HBV hepatitis B virus, HCV hepatitis C virus, M2BPGi Mac-2 binding protein glycosylation isomer, mALBI modified Albumin-Bilirubin, SMI skeletal muscle mass index.
## Clinical characteristics of the GNRI-based patient groups
The proportions of L-GNRI, I-GNRI, and H-GNRI were $24.3\%$ ($\frac{49}{202}$), $51.0\%$ ($\frac{103}{202}$), and $24.8\%$ ($\frac{50}{202}$), respectively (Table 1). There were significant differences among the three groups with regard to age ($$p \leq 0.021$$), Child–Pugh and ALBI scores ($p \leq 0.001$ for both), and M2BPGi, BCAA, and zinc levels ($p \leq 0.001$ for all). Of note, HGS ($p \leq 0.001$), SMI ($p \leq 0.001$), and gait speed ($$p \leq 0.014$$) decreased significantly in a stepwise fashion as the GNRI groups declined (Fig. 1A–C). Accordingly, the L-GNRI group had the highest prevalence of low muscle strength ($59.2\%$), low muscle mass ($65.3\%$), sarcopenia ($49.0\%$), and slow gait speed ($44.9\%$), whereas the H-GNRI group had the lowest prevalence of low muscle strength ($22.0\%$), low muscle mass ($12.0\%$), sarcopenia ($8.0\%$), and slow gait speed ($26.0\%$) (Fig. 1D–G). The prevalence of these sarcopenia-related complications increased in a stepwise fashion as the GNRI groups declined ($$p \leq 0.05$$ for slow gait speed; $p \leq 0.001$ for all the rest).Figure 1Comparison of clinical characteristics among the low-Geriatric Nutritional Risk Index (L-GNRI), intermediate-GNRI (I-GNRI), and high-GNRI (H-GNRI) groups. ( A) Handgrip strength ($p \leq 0.001$), (B) skeletal muscle mass index (SMI; $p \leq 0.001$), and (C) gait speed ($$p \leq 0.014$$) significantly decreased stepwise with decreasing GNRI values. ( D–G) The L-GNRI group had the highest prevalence of low muscle strength ($59.2\%$), low muscle mass ($65.3\%$), sarcopenia ($49.0\%$), and slow gait speed ($44.9\%$), while the H-GNRI group had the lowest prevalence of low muscle strength ($22.0\%$), low muscle mass ($12.0\%$), sarcopenia ($8.0\%$), and slow gait speed ($26.0\%$). A stepwise increase in the prevalence of these complication was observed with decreasing GNRI values ($$p \leq 0.05$$ for slow gait speed; $p \leq 0.001$ for all the rest). J–T Jonckheere–Terpstra test, C–A Cochran–Armitage test.
## Correlations between GNRI and sarcopenia-related factors
The GNRI values were significantly correlated with the following clinical factors: age, Child–Pugh score, ALBI score, PT, M2BPGi, BCAA, and zinc (Table 2). Notably, significant and positive correlations were found between the GNRI values and HGS ($r = 0.339$; $95\%$ confidence interval [CI], 0.207–0.459; $p \leq 0.001$), SMI ($r = 0.453$; $95\%$ CI, 0.332–0.559; $p \leq 0.001$), and gait speed ($r = 0.210$; $95\%$ CI, 0.068–0.344; $$p \leq 0.003$$) (Fig. 2A–C).Table 2Correlations between the GNRI values and baseline characteristics. VariableCorrelation coefficient ($95\%$ CI)p valueAge (years)− 0.148 (− 0.285 to − 0.007)0.035Child–Pugh score− 0.590 (− 0.676 to − 0.489)< 0.001ALBI score− 0.711 (− 0.775 to − 0.633)< 0.001Toral bilirubin− 0.026 (− 0.167 to 0.117)0.718Prothrombin time0.177 (0.036 to 0.311)0.012eGFR (mL/min/1.73 m2)0.020 (− 0.122 to 0.162)0.776M2BPGi (C.O.I)− 0.435 (− 0.545 to − 0.311)< 0.001BCAA (µmol/L)0.407 (0.281 to 0.519)< 0.001Zinc (µg/dL)0.497 (0.379 to 0.600)< 0.001Handgrip strength (kg)0.339 (0.207 to 0.459)< 0.001SMI (kg/m2)0.453 (0.332 to 0.559)< 0.001Gait speed (m/s)0.210 (0.068 to 0.344)0.003ALBI albumin-bilirubin, BCAA branched-chain amino acid, CI confidence interval, eGFR estimated glomerular filtration rate, M2BPGi Mac-2 binding protein glycosylation isomer, SMI skeletal muscle mass index. Figure 2Correlations between Geriatric Nutritional Risk Index (GNRI) and handgrip strength, skeletal muscle mass index (SMI), and gait speed. GNRI values were significantly and positively correlated with (A) handgrip strength ($r = 0.339$, $p \leq 0.001$), (B) SMI ($r = 0.453$, $p \leq 0.001$), and (C) gait speed ($r = 0.210$, $$p \leq 0.003$$).
## Factors associated with sarcopenia
On univariate analysis, the following six variables were significant factors related to sarcopenia: age, etiology, Child–Pugh score, ALBI score, BCAA, and GNRI (see Supplementary Table S1 online). Finally, multivariate analysis revealed that advanced age (odds ratio [OR], 1.109; $95\%$ CI, 1.062–1.157; $p \leq 0.001$), low BCAA (OR, 0.989; $95\%$ CI, 0.984–0.994; $p \leq 0.001$), and low GNRI values (OR, 0.932; $95\%$ CI, 0.895–0.970; $p \leq 0.001$) were significant and independent factors related to sarcopenia in patients with cirrhosis (Table 3).Table 3Significant factors associated with sarcopenia in patients with liver cirrhosis. VariableUnivariateMultivariateOR ($95\%$ CI)p valueOR ($95\%$ CI)p valueAge (years)1.077 (1.040–1.115)< 0.0011.109 (1.062–1.157) < 0.001Etiology0.732 (0.526–1.020)0.065Child–Pugh score1.273 (1.016–1.593)0.036ALBI score1.999 (1.110–3.600)0.021BCAA (µmol/L)0.990 (0.986–0.994)< 0.0010.989 (0.984–0.994)< 0.001GNRI0.920 (0.891–0.950)< 0.0010.932 (0.895–0.970)< 0.001ALBI albumin-bilirubin, CI confidence interval, BCAA branched-chain amino acid, GNRI Geriatric Nutritional Risk Index, OR odds ratio.
## Optimal cutoff values of age, GNRI, and BCAA for predicting sarcopenia
Figure 3 summarizes the cutoff values and diagnostic performances of age, GNRI, and BCAA for predicting sarcopenia. The cutoff values of age, GNRI, and BCAA were 73.5 years [area under the curve (AUC), 0.72; sensitivity/specificity, $\frac{0.679}{0.760}$], 102.1 (0.75; $\frac{0.768}{0.630}$), and 372 µmol/L (0.75; $\frac{0.714}{0.740}$), respectively (Fig. 3A,B). The prevalence of sarcopenia was $52.1\%$ ($\frac{38}{73}$), $43.9\%$ ($\frac{43}{98}$), and $51.3\%$ ($\frac{40}{78}$) in patients with age ≥ 73.5 years, GNRI ≤ 102.1, and BCAA ≤ 372 µmol/L, respectively (Fig. 3C). Some patients had two risk factors, while others had three risk factors (Fig. 4A). Therefore, we investigated changes in the prevalence of sarcopenia according to the number of risk factors (Fig. 4B). The group with all three risk factors had the highest prevalence of sarcopenia among the four groups ($77.8\%$ [$\frac{21}{27}$]; $p \leq 0.001$, adjusted residual = |6.2|), whereas the group with no risk factors had the lowest prevalence of sarcopenia ($1.8\%$ [$\frac{1}{56}$]; $p \leq 0.001$, adjusted residual = |5.1|; Fig. 4B). The prevalence of sarcopenia significantly increased stepwise as the number of risk factors increased ($p \leq 0.001$).Figure 3The receiver operating characteristic curve analysis for age, Geriatric Nutritional Risk Index (GNRI), and branched-chain amino acid (BCAA) in the prediction of sarcopenia. ( A,B) The cutoff value for age was 73.5 years, with an area under the curve (AUC), sensitivity, and specificity of 0.72, 0.679, and 0.760, respectively. The cutoff value for GNRI was 102.1, with AUC, sensitivity, and specificity of 0.75, 0.768, and 0.630, respectively. ( C) The cutoff value for BCAA was 372 µmol/L, with AUC, sensitivity, and specificity of 0.75, 0.714, and 0.740, respectively. ( C) The prevalence of sarcopenia stratified by risk factors (age ≥ 73.5 years, Geriatric Nutritional Risk Index ≤ 102.1, and branched-chain amino acid ≤ 372 µmol/L). The prevalence of sarcopenia in each group was $52.1\%$ ($\frac{38}{73}$), $43.9\%$ ($\frac{43}{98}$), and $51.3\%$ ($\frac{40}{78}$), respectively. Figure 4(A) Proportion of the number of risk factors in each risk group (age ≥ 73.5 years, Geriatric Nutritional Risk Index ≤ 102.1, and branched-chain amino acid ≤ 372 µmol/L). ( B) The prevalence of sarcopenia in each group stratified by the number of risk factors. The prevalence of sarcopenia in the group with all three risk factors was the highest among the four groups ($77.8\%$ [$\frac{21}{27}$]; $p \leq 0.001$, adjusted residual = |6.2|), while the prevalence of sarcopenia in the group with no risk factors was the lowest ($1.8\%$ [$\frac{1}{56}$]; $p \leq 0.001$, adjusted residual = |5.1|), and its prevalence increased stepwise as the number of risk factors increased ($p \leq 0.001$). C–A Cochran–Armitage test; C–S Chi-squared test.
## Discussion
Malnutrition and sarcopenia are frequent complications that aggravate prognosis and quality of life and are serious health concerns in patients with cirrhosis8–10. Early assessment and therapeutic intervention for these complications are crucial. In the current study, we examined the relationship between the GNRI and sarcopenia-related components (muscle strength, muscle mass, and gait speed) and evaluated whether the GNRI is helpful for predicting sarcopenia in patients with cirrhosis. Notably, HGS, SMI, and gait speed significantly decreased stepwise with a decline in the GNRI-based groups and were significantly and positively correlated with the GNRI values. Accordingly, the prevalence of low muscle strength, low muscle mass, sarcopenia, and slow gait speed significantly increased stepwise with a decline in the GNRI-based groups. Multivariate analysis identified lower GNRI as a significant and independent factor related to sarcopenia. This is the first study to focus on the relationship between the GNRI and sarcopenia (muscle strength and muscle mass loss) and gait speed in patients with cirrhosis.
In one study of hospitalized older patients, the GNRI values were positively correlated with HGS and SMI, and could predict sarcopenia, with a GNRI cutoff value of 89.0420. In another study of patients undergoing hemodialysis, the high GNRI group (GNRI ≥ 100.8; mean, 103.1) had higher HGS and lean mass index than the low GNRI group (GNRI < 96.8; mean, 93.4)17. The GNRI was a significant and independent factor associated with independent walking ability, with a cutoff value of 86.717. A recent study on patients with diabetes demonstrated that the low GNRI group (GNRI < 98; mean, 94.1) had a higher prevalence of sarcopenia than the high GNRI group (GNRI > 98; mean, 116.7)21. The former had higher levels of C-reactive protein than the latter, and the duration of diabetes (chronic inflammatory condition) was negatively correlated with the GNRI values. A previous study on patients with chronic kidney disease also revealed that GNRI was negatively correlated with the levels of interleukin-6 (proinflammatory cytokine)22. Increased levels of proinflammatory cytokines, such as interleukin-6 and tumor necrosis factor-α, promote proteolysis and cause sarcopenia through the activation of the ubiquitin–proteasome system5. These results suggest that low GNRI may be associated with chronic inflammatory status (e.g., cirrhosis, chronic kidney disease, and diabetes) and consequent loss of muscle mass and strength, and could be a good predictor of secondary sarcopenia.
The EWGSOP adopts the SARC-F questionnaire comprising the following five items as an initial screening tool for the assessment of sarcopenia: strength (S), assistance with walking (A), rising from a chair (R), climbing stairs (C), and falling (F)11. Every component has a score of 0–2, and a total score of ≥ 4 is suspected to have sarcopenia. Intriguingly, the SARC-F, like the GNRI, is associated with nutritional and inflammatory conditions in patients with gastrointestinal diseases23,24. One study reported that higher SARC-F scores were associated with moderate or severe malnutrition, as categorized using the controlling nutritional status score that is calculated from serum albumin level, total lymphocyte count, and total cholesterol level23. Another study reported that the SARC-F score had a positive correlation with the neutrophil to lymphocyte ratio, which reflects the inflammatory and immune status24.
However, a validation study of older Japanese adults showed that the SARC-F had high specificity ($97.3\%$) but low sensitivity ($8.0\%$) for identifying sarcopenia13. Similarly, a meta-analysis of seven studies, including 12,800 older adults, revealed high specificity ($90\%$) but low sensitivity ($21\%$) of the SARC-F14.
Meanwhile, another study of patients with chronic liver disease showed that modified SARC-F score of ≥ 1 had a higher discriminability for identifying sarcopenia than conventional SARC-F score of ≥ 4, with sensitivity and specificity of $65\%$ and $68\%$, respectively25. Compared with the conventional SARC-F, the present study demonstrated that the GNRI could predict sarcopenia with lower specificity ($63.0\%$) but considerably higher sensitivity ($76.8\%$). Furthermore, the GNRI appears to yield higher sensitivity than the modified SARC-F score25. Given that the GNRI is calculated based on actual/ideal weight and serum albumin level, this index system is simple to apply and can be used even for individuals who have difficulty answering a questionnaire, such as those with dementia or an uncooperative attitude. Therefore, in clinical practice, the GNRI may be a convenient and suitable initial screening tool for sarcopenia.
The GNRI was originally established to estimate the risk of morbidity and mortality in hospitalized older patients (mean age, 83.8 years)16. The cutoff GNRI values for major, moderate, low, and no nutrition-related risks were < 82, 82 to < 92, 92 to ≤ 98, and > 98, respectively. Subsequent studies have demonstrated that the GNRI is useful for estimating the prognosis of patients with cancer, including hepatocellular carcinoma26,27. In elderly patients who underwent hepatectomy for hepatocellular carcinoma, the moderate- and major-risk groups (based on the original classification) were independent risk factors related to postoperative liver failure and severe complications27. It should be noted that the GNRI cutoff value of 102.1 for sarcopenia in this study cohort was higher than that of the original GNRI classification16 and the value of 89.04 for sarcopenia in hospitalized older patients (as described above)20. Given that the liver has multiple functions and plays a crucial role in nutrient metabolism, liver cirrhosis induces malnutrition, hypoalbuminemia, hyperammonemia, and body weight loss, and reduces IGF-1, vitamin D, testosterone, and BCAA levels, which are closely involved in the development of secondary sarcopenia5,28,29. Therefore, the GNRI cutoff value for sarcopenia in patients with cirrhosis appears to be higher than that for individuals without liver dysfunction. The GNRI-based assessment may be useful for introducing earlier nutrition and exercise therapy interventions to prevent the development of sarcopenia in patients with cirrhosis.
The present study had several limitations. First, we did not examine dietary intake, which may affect the GNRI and the development of sarcopenia. Second, this was a cross-sectional study; therefore, the morbidity and mortality were not evaluated. In the future, we will investigate the relationship between the GNRI and prognosis in patients with cirrhosis. Lastly, GNRI values may be overestimated in patients with ascites due to weight gain from ascites. However, this discrepancy was reduced in this study, since patients with massive ascites were excluded from this study.
## Conclusions
This study revealed that muscle strength, muscle mass, and gait speed are positively correlated with the GNRI and that the prevalence of sarcopenia and slow gait speed increases with a reduction in the GNRI, suggesting that the GNRI could be a convenient and helpful screening tool for sarcopenia in patients with cirrhosis. Appropriate nutrition-related risk assessment and early therapeutic intervention based on the GNRI may be useful for preventing sarcopenia or inhibiting disease progression in patients with cirrhosis.
## Participants and study design
We enrolled 202 consecutive patients with cirrhosis who attended the Jikei University School of Medicine and Fuji City General Hospital between February 2017 and March 2021. This study cohort included 192 patients analyzed in our previous report28. Cirrhosis was diagnosed on the basis of laboratory tests and radiological imaging findings, including the presence of esophageal/gastric varices and ascites, and liver deformation and surface irregularities. Liver functional reserve was assessed according to the Child–Pugh classification and modified albumin-bilirubin (mALBI) grade30,31. The ALBI score was calculated using the following formula: ALBI score = (log 10 bilirubin [mg/L] × 17.1 × 0.66) + (albumin [g/dL] × 10 × − 0.085). The mALBI grading system classifies individuals into the following four groups: Grade 1, ≤ − 2.60; Grade 2a, > − 2.60 to − 2.27 ≤; Grade 2b, − 2.27 > to ≤ − 1.39; and Grade 3, > − 1.39, with Grade 3 being the most advanced liver disease31. Serum total bilirubin, albumin, estimated glomerular filtration rate (eGFR), prothrombin time (PT), zinc, branched-chain amino acid (BCAA), and Mac-2 binding protein glycosylation isomer (M2BPGi) were measured using standard laboratory methods. This study complied with the 2013 Declaration of Helsinki and was approved by the Ethics Committee of the Jikei University School of Medicine (approval no. 28-196) and Fuji City General Hospital (approval no. 156). Written informed consent was obtained from all the participants.
## Assessment of sarcopenia and gait speed
Sarcopenia was diagnosed according to the criteria advocated by the Japan Society of Hepatology (1st edition)6. The average handgrip strength (HGS) of the left and right hands was measured twice in a standing position using a digital Smedley-type hand dynamometer (T.K.K5401 GRIP-D; Takei Scientific Instruments, Niigata, Japan). In addition, the skeletal muscle mass index (SMI) was assessed using bioelectrical impedance analysis (BIA; InBody S10; InBody Japan, Tokyo, Japan). The cutoff values for decreased handgrip strength and SMI were < 26 kg and < 7.0 kg/m2 for men and < 18 kg and < 5.7 kg/m2 for women, respectively. Patients undergoing hemodialysis, with massive ascites, or implants were excluded due to the unreliability of the BIA method7. The 6-m walk was used to assess physical performance, with a slow gait speed defined as < 1.0 m/s.
## Patient grouping based on the GNRI values
The GNRI was calculated based on actual and ideal body weight and serum albumin values using the following formula: GNRI = (14.89 × albumin [g/dL]) + (41.7 × [actual body weight/ideal body weight])16. In the current study, the median GNRI value for all subjects was 102.6 (interquartile range, 94.0–109.5). Subjects were classified into three groups according to these first and third quartiles: low (L)-GNRI group, < 94.0 (first quartile); intermediate (I)-GNRI, between 94.0 and 109.5 (third quartile); and high (H)-GNRI group, > 109.5 (see Supplementary Fig. S2 online).
## Statistical analysis
Categorical variables are presented as numbers and percentages in parentheses. Continuous variables are presented as medians and interquartile ranges in parentheses. For categorical variables, the chi-squared test was used to evaluate the significance of group differences. For continuous variables, the Mann–Whitney U test and Kruskal–Wallis test were used to assess group differences, as appropriate. The Jonckheere–Terpstra test for continuous variables and Cochran–Armitage test for categorical variables were employed to evaluate whether significant trends were present among the groups. The Spearman’s rank correlation test was employed to evaluate the correlations between the GNRI and sarcopenia-related variables. Variables that reached $p \leq 0.10$ in univariate analysis were subsequently entered into multiple logistic regression analysis to identify significantly independent factors related to sarcopenia; however, BMI and albumin were excluded from multivariate analysis given that they are GNRI components. To estimate the optimal cutoff values for predicting sarcopenia, the area under the receiver operating characteristic (ROC) curve of age, GNRI, and BCAA was constructed. SPSS Statistics version 27 (IBM Japan, Tokyo, Japan) was used for each statistical analysis. Statistical significance was set at a p-value of less than 0.05.
## Supplementary Information
Supplementary Figure S1.Supplementary Figure S2.Supplementary Table S1.Supplementary Legends. The online version contains supplementary material available at 10.1038/s41598-023-31065-1.
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|
---
title: Qualitative and quantitative studies on two commercial specifications of Polygonatum
odoratum
authors:
- Yi Nan
- Haizhen Liang
- Xu Pang
- Wei Zheng
- Yuhao Shi
- Xiaojuan Chen
- Jie Zhang
- Juan Song
- Baiping Ma
journal: Frontiers in Chemistry
year: 2023
pmcid: PMC9995655
doi: 10.3389/fchem.2023.1146153
license: CC BY 4.0
---
# Qualitative and quantitative studies on two commercial specifications of Polygonatum odoratum
## Abstract
The rhizoma of *Polygonatum odoratum* (PO) is used to treat yin injuries of the lung and stomach in traditional Chinese medicine. The chemical constituents of this herb are steroidal saponins, homoisoflavanones, and alkaloids. Xiangyuzhu (XPO) and Guanyuzhu (GPO) are available in the market as two specifications of the commodity. Nonetheless, systematic research on the identification and comparison of chemical constituents of these two commercial specifications is yet lacking. Herein, an integrated method combing ultra-high-performance liquid chromatography-quadruple time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) with ultra-high-performance liquid chromatography-charged aerosol detection (UHPLC-CAD) was employed for the comprehensively qualitative and quantitative analyses of PO. A total of 62 compounds were identified by UHPLC-Q-TOF/MS, among which 13 potential chemical markers were screened out to distinguish two commercial specifications. Subsequently, the absolute determination method for polygodoraside G, polygonatumoside F, and timosaponin H1 was established and validated by UHPLC-CAD. The contents of the three compounds were 13.33–236.24 μg/g, 50.55–545.04 μg/g, and 13.34–407.83 μg/g, respectively. Furthermore, the ratio of timosaponin H1/polygodoraside G could be applied to differentiate the two specifications. Samples with a ratio <2 are considered XPO and >5 are considered GPO. Therefore, the above results provide a valuable means for the quality control of PO.
## 1 Introduction
Plants of genus Polygonatum have been extensively used as traditional Chinese medicine. The Chinese Pharmacopoeia records the rhizome of *Polygonatum odoratum* (PO) as a yin-nourishing medicine “Yuzhu”, which can be used for the treatment of diabetes and cough (Zhao et al., 2020; Li et al., 2021). PO is widely distributed in the temperate regions of Eurasia (Xia et al., 2022), growing in the forest or on the shady slopes of mountains (Feng et al., 2022). The major chemical constituents of PO are steroidal saponins, homoisoflavanones, alkaloids, and polysaccharides (Zhao et al., 2018; Zhao et al., 2019). In recent years, PO has gained increasing attention (Ning et al., 2018; Wang H J et al., 2018; Pang et al., 2021; Zhou et al., 2021), but there is no systematic study on the chemical identification of PO. Xiangyuzhu (XPO) and Guanyuzhu (GPO) are available in the market as two main commercial specifications with different origins and prices. In the book “Standard compilation of authentic medicinal materials,” the appearance of XPO is thick, long, translucent, and light yellow in color, and that from *Hunan is* considered an authentic medicinal material, which occupies the majority of the Chinese market. GPO is a wild product in Northeast China, Inner Mongolia, and Hebei, and its cost is lower than XPO. Nonetheless, XPO is more frequently used as the raw material of dietary supplements, while GPO is more inclined to medicinal use. Currently, the quality control of PO was the only focus on the polysaccharide content (Jing et al., 2022), whereas some studies have pointed out that the polysaccharide content of GPO is higher than that of XPO (Tian et al., 2014). However, no comparison has been made between the other small molecule chemicals of these two commercial specifications to date. Therefore, it is essential to develop a specific method to distinguish the two commercial specifications in terms of chemical ingredients.
Ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) is a capital analytical tool with good resolution, excellent sensitivity, and strong structural characterization capability and has also been used in the qualitative analysis of *Polygonatum genus* (Huang et al., 2020; Sui et al., 2022). Off-line solid phase extraction (SPE) has been used in the pretreatment of complex samples to remove polysaccharides that cannot be retained on C18 columns and enrich targeted components for analysis (Li et al., 2019). The present study aimed to establish a qualitative method by coupling off-line SPE with UHPLC-Q-TOF/MS to characterize PO in positive and negative ion modes. The measured molecular weight plus the fragment ion information obtained by MS/MS could identify the structures of the chemical components (Zhao et al., 2013). Multivariate statistical analyses [for example, principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and variable importance in the projection (VIP) plot] were used to identify the potential differential components among XPO and GPO (Pan et al., 2021).
As an aerosol-based universal detector, charged aerosol detector (CAD) can be characterized by high sensitivity, broad dynamic range, less interanalyte response variability, and improved reproducibility, which has gained increasing popularity for LC analysis of organic compounds with poor UV chromophores (Haidar Ahmad et al., 2019; Zheng et al., 2022). The fitting method of regression equation relies on the concentration range. In many cases, the linearity of CAD response is sufficient over the range of interest (Pawellek et al., 2021). Due to its high sensitivity, the limit of detection (LOD) and limit of quantification (LOQ) may be at the nanogram level (Zhang et al., 2019). Saponins are a class of active components used as key quality control indicators for content determination in many studies (Zhang et al., 2019; Nan et al., 2021). The furostonal saponin polygonatumoside F showed the higher content in the PO samples and may be served as the chemical marker for quality control (Liu et al., 2018). In the present study, the other two components polygodoraside G and timosaponin H1 were screened out as major chemical markers in XPO and GPO. In order to assess the quality of PO, UHPLC-CAD method was used, and a content ratio of timosaponin H1/polygodoraside G was proposed to differentiate the two commercial specifications of PO.
## 2.1 Materials and reagents
Acetonitrile (HPLC grade) was purchased from Fisher Scientific Co. (Loughborough, United Kingdom). Distilled water was purchased from Watsons. Formic acid (HPLC grade) was purchased from Acros Co. Ltd. (NJ, United States). The other reagents of analytical grade were also obtained commercially (Beijing, China).
A total of 27 batches of representative PO were collected or purchased from Hunan (XPO, S1-S14) and Heilongjiang (GPO, S15-S27) provinces of China. The identity of all samples was confirmed by Prof. Bao-lin Guo of the Institute of Medicinal Plant Development, Chinese Academy of Medical Science&Peking Union. A total of 22 reference standards (purity >$95\%$), polygodoraside A-H, typaspidoside H, typaspidoside L, polygonatumoside F, timosaponin H1, (25S)-26-O-(β-D-glucopyranosyl)-furost-5-en3β,22α,26-triol 3-O-β-D-glucopyranosyl-(1→2)-β-D-glucopyranosyl-(1→4)-β-D-glucopyranoside, officinalisnin II, 25-epi-officinalisnin II, (3R)-5,7-dihydroxy-6,8-dimethyl-3-(4′-hydroxybenzyl)-chroman-4-one, (3R)-5,7-dihydroxy-6-methyl-8-methoxy-3-(4′-hydroxybenzyl)-chroman-4-one, (3R)-5,7-dihydroxy-6-methyl-3-(4′-hydroxybenzyl)-chroman-4-one, N-trans-p-coumaroyloctopamine, N-trans-feruloyloctopamine, (E)-3-(4-hydroxy-3-methoxybenzylidene)-4-(4-hydroxyphenyl)-pyrrolidin-2-one, and 3-(4-hydroxy-3-methoxy-phenyl)-acrylic acid carboxymethyl ester, were isolated in our laboratory and their structures were confirmed by comparing their MS and NMR spectral data with those described previously (Pang et al., 2020).
## 2.2 Preparation of samples and standard solutions
An equivalent of 1.0 g accurately weighed fine powder (<40 mesh) of each sample was mixed with 20 mL of $70\%$ ethanol was added. After ultrasonication for 60 min, the solutions were cooled to room temperature, and the weight loss was replaced with $70\%$ aqueous EtOH. Then, the solutions were filtered through a 0.45 µm membrane before quantitative analysis. A volume of 10 mL of the supernatant was concentrated under reduced pressure and diluted to 4 mL with deionized water. A solid-phase extraction cartridge (C18-SPE, 6 mL) was activated with 10 mL of methanol, rinsed with 10 mL of water, and then reconstituted in 4 mL solution to load the sample; first, 10 mL water was used for elution, followed by 4 mL of $95\%$ ethanol. Finally, the ethanol eluate was collected and filtered through a 0.22-μm membrane for qualitative analysis.
The stock solutions of 22 standard references were prepared in acetonitrile at a final concentration of 0.1 mg/mL and analyzed by UHPLC-Q-TOF/MS. All the solutions were stored at 4°C for further study.
## 2.3 Qualitative analysis by UHPLC-Q-TOF/MS
UHPLC-Q-TOF/MS analysis was performed on an ACQUITY UHPLC™ system (Waters Corp. Milford, MA, United States) coupled with a Synapt G1 MS system (Waters Corp. Manchester, United Kingdom). A Waters ACQUITY UHPLC HSS T3 column (100 × 2.1 mm, 1.8 μm) was used for the analysis with the column temperature at 40°C. The mobile phases were water with $0.1\%$ formic acid (A) and acetonitrile (B). The gradient used was as follows: 0–2 min, $5\%$→$15\%$ B; 2–18 min, $15\%$→$37\%$ B; 18–25 min, $37\%$→$50\%$ B; 25–27 min, $50\%$ B; 27–28 min, $50\%$→$5\%$ B and 28–30 min, $5\%$ B. The flow rate was 0.5 mL/min. The injection volume of the sample was 5 μL.
The data acquisition mode was MSE. The data were obtained at 50–1500 Da. The source temperature was 100°C, the desolvation temperature was 450 °C with desolvation gas flow 850 L/h, leucine enkephaline was used as lock mass, and the capillary voltage was 3 kV. At low CE scan, the cone voltage was 30 V for ESI, and the collision energy was 6 eV (trap) and 4 eV (transfer), while it was 40–60 eV ramp (trap) and 12 eV (transfer) for ESI− and 15–25 eV ramp (trap) and 12 eV (transfer) for ESI+. The instrument was controlled by MassLynx 4.1 software (Waters Corp.).
## 2.4 Quantitative analysis by UHPLC-CAD analysis
UHPLC-CAD analysis was performed on the Thermo Vanquish UHPLC system (ThermoFisher Scientific, Germering, Bavaria, Germany). A Waters ACQUITY™ UHPLC HSS T3 column (100 × 2.1 mm, 1.8 µm) was used at a column temperature at 40°C, and the sample temperature was 10°C. The mobile phases were water with $0.1\%$ formic acid (A) and acetonitrile (B). The gradient was as follows: 0–3 min, $10\%$→$20\%$ B; 3–12 min, $20\%$ B; 12–13 min, $20\%$→$22\%$ B; 13–24 min, $22\%$ B; 24–25 min, $22\%$→$95\%$ B; 25–27 min, $95\%$ B; 27–28 min, $95\%$→$10\%$ B; 28–30 min, $10\%$ B. The injection volume of the sample was 5 µL. The data collection was 5, and the filtration was for 3.6 s.
## 2.5 Validation of UHPLC-CAD analysis
The linearity of regression curves was tested by diluting the mixed stock solution to a series of concentrations of working solutions, and each was subjected to UHPLC analysis. The lower LODs and LOQs were determined by analyzing the serially diluted reference solutions of each compound until the signal-to-noise (S/N) proportion was about 3 and 10, respectively. The samples were analyzed in six replicates. The stability was tested by analyzing the sample solutions at different time points (0, 2, 4, 6, 8, 12, and 24 h). The RSD values of peak areas were calculated to examine the precision, repeatability, and stability of the quantitative method. Its accuracy was evaluated by recovery experiments. A specific amount of individual reference standards was spiked into PO sample. Five fortified samples were extracted and analyzed as described above. The recovery value (%) was calculated by the following equation: recovery (%) = 100 × (detected amount − original amount)/spiked amount.
## 2.6 Multivariate analysis
The ESI-MSE centroid data were processed by MarkerLynx version 4.1 (Waters Corp. Manchester, United Kingdom). The analysis included deconvolution, alignment, and data reduction to obtain a list of mass and retention time pairs with the corresponding areas for all the detected peaks from each file in the dataset. The processed data list was then imported by the PCA and OPLS-DA. All the test groups were discriminated in the PCA to investigate whether different groups could be separated. The method parameters were as follows: retention time range, 1–25 min; mass range, 100–1500 Da; mass tolerance, 0.02 Da; 6.00 for noise elimination level, $5\%$ of the base peak intensity of minimum intensity; 0.20 min for RT tolerance. Moreover, isotopic peaks were excluded from analysis. Then, OPLS-DA was carried out to discriminate the ions contributing to the classification of the samples. The results were visualized in a score plot to show group clusters, and a VIP plot showed variables contributing to the classification.
## 3.1 Characterization of chemical compounds in PO
To obtain satisfactory separation and high analytical efficiency, a series of preliminary experiment conditions, including chromatographic column particle size, mobile phase composition, and column temperature, were optimized. Various columns, such as ACQUITY UHPLC BEH C18 (2.1 mm × 100 mm, 1.7 μm) and CORTECS T3 (2.1 mm × 100 mm, 1.6 μm) were compared. Finally, the result was obtained on UHPLC HSS T3 column (100 × 2.1 mm, 1.8 µm). The acetonitrile-water system showed better separation and a more satisfactory peak shape than the methanol-water system. Moreover, $0.1\%$ formic acid was added to the ACN-water system to enhance the peak capacity and improve the peak shape of saponins and flavonoids. The column showed excellent separation performance at 40°C.
Simultaneously, for the identification of the chemical components in PO by UHPLC-Q-TOF/MS, the MS parameters, such as ionization mode, capillary voltage, and different collision energy ranges, were optimized. The herbal extract samples were analyzed in the positive and negative ion modes with the same LC conditions. As shown in Supplementary Table S1, 62 compounds were identified from the PO extract tentatively. The characterization was validated by the data of high-resolution mass spectrometry (Figure 1). The combination of the measured molecular weight with the fragment ion information obtained by collision-induced dissociation (CID) identified the structures and deduced that alkaloids, flavonoids, and steroidal saponins were the major constituents of the components.
**FIGURE 1:** *Base peak ion (BPI) chromatograms of XPO (A) and GPO (B) in the negative mode by UHPLC-Q-TOF/MS.*
## 3.1.1 Alkaloid derivatives
Alkaloids are nitrogen-containing basic organic compounds existing in nature. A total of four pairs of alkaloid components were identified in Supplementary Table S1; their ion chromatographic signals were well-displayed in positive and negative ion modes. Each group of alkaloids had cis and trans structures, and the isomers could be determined by referring to the retention time of reference materials.
Peak two produced a deprotonated molecular ion at m/z 328.1145 [M-H]- and m/z 310.1048 [M-H-H2O]- in the MS1 spectrum. In the CID spectrum, the peak produced the fragment ion at m/z 161.0241 [M-H-C9H11O3]- was broken at the b bond resulting from McFarland’s rearrangement cleavage, and also obtained the fragment ion m/z 190.0215 [M-H-C8H10O2]-; then, a molecule of CHO was removed to obtain the fragment ion m/z 132.0180 [M-H-C10H12O4]- was generated by the cleavage of the bond based on the loss of a molecule of H2O and McFarland’s rearrangement. Peak two was tentatively identified as N-cis-feruloyloctopamine. The comparison of retention time showed that the alkaloids with the cis structure peaked more than those with trans structures. Also, other alkaloids were identified by this rule, and the mass spectrogram in the negative mode and the cracking pathway are shown in Figure 2.
**FIGURE 2:** *Mass spectrogram in negative mode and fragmentation pathways of N-cis-feruloyloctopamine. (A) MS spectra. (B) CID spectra.*
## 3.1.2 Flavonoids
The homoisoflavanones of PO are critical bioactive compounds (Wang Y et al., 2018; Xia G. H. et al., 2021; Xia G. H. et al., 2021), and the potential biosynthetic pathway for isoflavonoid formation has been defined in the PO (Zhang et al., 2020). It can generate characteristic fragment ions by RAD cleavage in the C ring at high energy levels (Ying et al., 2020), and the bond connecting the two sides of the methylene group between the B and C rings is broken to form other characteristic fragment ions.
Peak 32 produced a deprotonated molecular ion at m/z 301.0716 [M-H]- in the MS1 spectrum. In the CID spectrum, the peak produced characteristic fragment ions m/z 179.0370 [M-H-C7H6O2]- and m/z 121.0269 [M-H-C9H8O4]- due to α-cleavage at the f bond (Ren et al., 2021) and the characteristic fragment ion m/z 191.0311 [M-H-C6H6O2]- due to α-cleavage at the g bond. Finally, peak 32 was determined to be disporopsin, and the mass spectrogram in the negative mode and the potential cracking pathway are shown in Figure 3.
**FIGURE 3:** *Mass spectrogram in negative mode and fragmentation pathways of disporopsin. (A) MS spectra. (B) CID spectra.*
## 3.1.3 Steroidal saponins
Diosgenin and yamogenin are the main parent nucleus types in PO, and the C-3 position is often substituted by 3-4 sugar groups. The sugar group mainly consists of arabinose, glucose, xylose, and rhamnose. The negative ion mode displays deglycosylation fragments, while the positive ion mode presents patent ion characteristic fragments. Thus, these compounds could be identified by the combination of the positive and negative ion modes.
Peak 60 produced a deprotonated molecular ion at m/z 1061.5176 [M-H]- in the MS1 spectrum. In the negative CID spectrum, the peak produced the fragment ions at m/z 899.4650 [M-H-Glc]-, m/z 737.4129 [M-H-Glc-Glc]-, and m/z 575.3652 [M-H-Glc-Glc-Glc]-. In the positive CID spectrum, the peak produced the fragment ions at m/z 739.5706 [M + H-Glc-Glc]+, m/z 577.4858 [M + H-Glc-Glc-Glc]+, and m/z 415.3997 [M + H-Glc-Glc-Glc-Gal]+. The presence of the characteristic ion m/z 271.2585 [M + H-Glc-Glc-Glc-Gal-C8H16O2]+ means that the composition is the same as the parent nucleus of 3-O-β-D-glucopyranosyl-(1→2)-[β-D-xylopyranosyl-(1→3)]-β-D-glucopyranosyl (1→4)-β-D-galacopyranosyl-diosgenin, and the peak is finally determined to be 3-O-β-D-glucopyranosyl-(1→2)-[β-D-glucopyranosyl-(1→3)]-β-D-glucopyranosyl (1→4)-β-D-galacopyranosyl-diosgenin. The mass spectrogram in the positive, negative mode and possible cracking pathway are shown in Figure 4.
**FIGURE 4:** *Mass spectrogram in positive, negative mode and fragmentation pathways of 3-O-β-D-glucopyranosyl-(1→2)-[β-D-glucopyranosyl-(1→3)]-β-D-glucopyranosyl (1→4)-β-D-galacopyranosyl-diosgenin. (A) CID spectra in positive mode. (B) CID spectra in negative mode.*
## 3.1.4 Acetylated steroidal saponins
Acetylated steroidal saponins are primary metabolites that have been reported in the same genus of plants (Ahn et al., 2006). The position of the acetyl group is not fixed and could appear in the C-1 position or the sugar group inside the C-3 position. Such compounds mainly exist in GPO, and it is feasible to classify such compounds by the presence of a neutral loss of 42 Da in the mass spectrum.
For example, peak 36 produced a deprotonated molecular ion at m/z 1253.595 [M-H]- in the MS1 spectrum. Typically, we observed that the precursor ion sheds 42 Da of m/z 1211.5792 [M-H-COCH2]- in the CID spectrum. The peak produced the fragment ions at m/z 1079.5385 [M-H-COCH2-Xyl]-, m/z 1049.5275 [M-H-COCH2-Glc]-, m/z 917.4746 [M-H-COCH2-Glc-Xyl]-, and m/z 755.4301 [M-H-COCH2-2Glc-Xyl]-. The most fragment ion peaks are the same as timosaponin H1, which is the hydroxyacetylated precursor compound acetyl-timosaponin H1. The mass spectrogram in the negative mode of acetyl-timosaponin H1 and timosaponin H1 are shown in Supplementary Figure S1.
## 3.1.5 Identification of novel compounds
The summary of the chromatographic rules of reference substances could be used to deduce the structure of unknown compounds to identify novel compounds. For example, the retention time of compounds with the glucose terminal group is less than that of xylose, and peak 7 can be inferred from this rule. The retention time of peak 7 was close to polygodoraside F and produced a deprotonated molecular ion at m/z 1255.5535 [M-H]- in the MS1 spectrum. In the CID spectrum, the peak produced the fragment ions at m/z 931.4557 [M-H-Glc-Glc]-, m/z 769.3910 [M-H-Glc-Glc-Glc]-, and m/z 571.3401 [M-H-Glc-Glc-Glc-Glc-2H2O]-; these fragments were the same as those of polygodoraside F, and the retention time is relatively close. Therefore, it is inferred that there is difference between peak 7 and polygodoraside F in only the terminal sugar group; the peak 7 is finally determined to be polygodoraside F-Xyl + Glc. The mass spectrogram in the negative mode of polygodoraside F-Xyl + Glc and polygodoraside F are shown in Supplementary Figure S2.
As a result, 62 compounds were identified from PO tentatively, including 43 steroidal saponins, 11 flavonoids, 8 alkaloids, and 10 possible new components.
## 3.2 Discrimination of PO samples with two commercial specifications by PCA and OPLS-DA analysis
Multivariate statistical analysis was carried out on the metabolite data to discriminate the two commercial specifications of PO. First, the obtained multivariate dataset of 27 batches of samples was analyzed by PCA. The results showed that the samples from different specifications were classified into two categories (Figure 5A), indicating a great variation in the chemical profile between XPO and GPO.
**FIGURE 5:** *PCA score plot (A), OPLS-DA score plot (B), and VIP plot (C) of XPO and GPO.*
Then, the OPLS-DA model and VIP plot (Figure 5C) were established to identify the key markers that contribute to the differences between XPO and GPO, and a remarkable separation between these two specifications was also observed in the OPLS-DA score plot (Figure 5B). The model displayed $99\%$ of the variation in the response Y (class) (R2Y = $99\%$) and also predicted $97\%$ of the variations in the response Y (Q2Y = $97\%$). Therefore, the model could satisfactorily distinguish between the two species of samples. The VIP values (VIP >3.5) from OPLS-DA model were utilized to identify the potential differentiated variables, and 13 robust known chemical markers (including 6 flavonoids and 7 steroidal saponins) between XPO and GPO were selected and listed (Table 1). Isomer of (25R,22ξ)-hydroxylwattinoside C (peak 24) and 3β-hydroxy-25S-spiriost-3-O-β-D-glucopyranosyl (1→4)-β-D-galactopyranoside (peak 57) only existed in GPO samples, and their presence could be used to determine the commercial specification of PO. The differences in other components were differentiated in the content. For example, [1] compared to polygodoraside G (peak 17), no hydroxyl substitution was observed at the C-14 position of the parent nucleus of timosaponin H1 (peak 33). Peak 17 was a high content in XPO samples, while peak 33 was high in GPO samples; [2] polygonatumoside G (peak 40) was the product of deglycosylation of peak 17 with higher content in GPO samples; [3] as representative isoflavonoids components, (3R)-5,7-dihydroxy-6-methyl-3-(4′-hydroxybenzyl)-chroman-4-one (peak 46), (3R)-5,7-dihydroxy-6-methyl-8-methoxy-3-(4′-hydroxybenzyl)-chroman-4-one (peak 47), and (3R)-5,7-dihydroxy-6,8-dimethyl-3-(4′-hydroxybenzyl)-chroman-4-one (peak 50) showed high contents in XPO samples. These chemical markers make it possible to distinguish the two groups of PO samples; then, two conspicuously characteristic markers, polygodoraside G and timosaponin H1, were selected for further quantitative analysis to evaluate the quality of PO from two specifications.
**TABLE 1**
| Peak No. | t R (min) | [M-H]- | Identification | Main existing groups |
| --- | --- | --- | --- | --- |
| 17 | 8.03 | 1257.5757 | Polygodoraside G | XPO |
| 24 | 8.71 | 933.4652 | Isomer of (25R, 22 ξ)–hydroxylwattinoside C | GPO |
| 28 | 10.21 | 741.4414 | (22S)-cholest-5-ene-1β,3β,16β,22-tetrol-1-O-α-L-rhamnopyranosyl-16-O-β-D-glucopyranoside | XPO |
| 33 | 10.79 | 1211.5667 | Timosaponin H1 | GPO |
| 40 | 12.82 | 609.3631 | Polygonatumoside G | GPO |
| 42 | 15.19 | 1061.5146 | Isomer of 3-O- β -D-glucopyranosyl-(1→2)-[β-D-glucopyranosyl-(1→3)]-β-D-glucopyranosyl (1→4)-β-D-galacopyranosyl-diosgenin | XPO |
| 46 | 17.47 | 299.0952 | (3R)-5,7-dihydroxy-6-methyl-3-(4′-hydroxybenzyl)-chroman-4-one | XPO |
| 47 | 18.1 | 329.1043 | (3R)-5,7-dihydroxy-6-methyl-8-methoxy-3-(4′-hydroxybenzyl)-chroman-4-one | XPO |
| 50 | 19.29 | 313.1061 | (3R)-5,7-dihydroxy-6,8-dimethyl-3-(4′-hydroxybenzyl)-chroman-4-one | XPO |
| 51 | 19.35 | 329.1071 | Isomer of (3R)-5,7-dihydroxy-6-methyl-8-methoxy-3-(4′-hydroxybenzyl)-chroman-4-one | GPO |
| 56 | 20.69 | 299.0875 | Isomer of (3R)-5,7-dihydroxy-6-methyl-3-(4′-hydroxybenzyl)-chroman-4-one | GPO |
| 57 | 21.28 | 753.4075 | 3β-hydroxy-25S-spiriost-3-O-β-D-glucopyranosyl (1→4)-β-D-galactopyranoside | GPO |
| 59 | 23.46 | 313.1019 | Isomer of (3R)-5,7-dihydroxy-6,8-dimethyl-3-(4′-hydroxybenzyl)-chroman-4-one | GPO |
## 3.3 Quantitative analysis of the PO samples by UHPLC-CAD with two commercial specifications
The furostonal saponin polygonatumoside F showed the higher content in all PO samples and thus could serve as the chemical marker for quality control, while the other components polygodoraside G and timosaponin H1 were screened out as chemical markers in XPO and GPO. To further understand the variation in the contents of the main steroidal saponins, a UHPLC-CAD approach was developed for the quantitative analysis of three furostanol saponins (polygodoraside G, polygonatumoside F, and timosaponin H1) in 27 samples with two commercial specifications; the chemical structures showed in Supplementary Figure S3.
The performance of Waters ACQUITY BEH C18 and ACQUITY CORTECS T3 was compared. Polygodoraside G and polygonatumoside F could be separated by Waters ACQUITY™ HSS T3. Therefore, the ACQUITY™ HSS T3 column was used for further separation. The column temperature and injection volume were also considered, and finally, the gradient was optimized under the condition of isocratic elution for the separation of the compounds. Subsequently, the established method was validated. The regression equations of the three analytes were calculated in the form of Y = aX 2 + bX + c; X and Y indicated the concentrations of the compound and the corresponding peak area, respectively. A good linear correlation of the three analytes was gained (r 2 > 0.999) with a specific concentration range. Due to the high sensitivity of CAD, the LOD and LOQ were in the nanogram level. The results of the method validation are summarized in Table 2. The data indicated that the UHPLC-CAD method possessed good accuracy with recoveries from $97.38\%$ to $106.76\%$, and all the RSDs of precision, stability, and repeatability were <$3\%$.
**TABLE 2**
| No | Analyte | Calibration equation | r 2 | Range (µg/mL) | LOD (ng) | LOQ (ng) | Recovery (%) | Precision (%) | Stability (%) | Repeatability (%) |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | Polygodoraside G | Y = −104.67X 2 +30.31X | 0.9999 | 0.23–30.00 | 0.04 | 2.0 | 105.1 | 0.83 | 1.83 | 2.75 |
| 2 | Polygonatumoside F | Y = −76.39X 2 +32.44X | 0.9998 | 0.47–60.00 | 0.12 | 2.0 | 97.38 | 1.05 | 1.05 | 2.98 |
| 3 | Timosaponin H1 | Y = −9.55X 2 +27.68X | 0.9999 | 0.23–30.00 | 0.1 | 4.0 | 106.76 | 0.86 | 2.7 | 2.13 |
Then, the developed UHPLC-CAD method was applied to determine the three targeted compounds in different batches of PO samples (Supplementary Figure S4). Each sample was determined in triplicate, and the contents of targeted analytes are presented in Table 3. The contents of polygodoraside G, polygonatumoside F, and timosaponin H1 ranged from 13.33 to 236.24 μg/g, 50.55–545.04 μg/g, and 13.34–407.83 μg/g, respectively. Considering the total content of the three compounds (Figure 6), the quality of GPO was stable in the range of 349.19–859.64 μg/g, while the quality of different batches of XPO varied greatly, and the total content was 137.70–1005.55 μg/g. Moreover, polygonatumoside F was the main characteristic compound in the samples of PO. The average content of polygonatumoside F in XPO was 195.49 μg/g and was lower in GPO with 329.35 μg/g, indicating that GPO should be focused upon from the perspective of saponin content. In addition, polygodoraside G was present in XPO with an average content of 113.04 μg/g, and that in GPO was 19.82 μg/g, while timosaponin H1 was mainly present in XPO with an average content of 78.85 μg/g and that in GPO was 291.22 μg/g; the content ratio of the two compounds in different specifications differed markedly. Therefore, it can be concluded that the samples with a ratio <2 of timosaponin H1/polygodoraside G were considered as XPO, and >5 were considered as GPO (Figure 7). In conclusion, the absolute content of the main characteristic components in PO has been investigated. We found that XPO and GPO have significant differences in the quality of medicinal materials. Thus, timosaponin H1/polygodoraside G seems to be a promising indicator to distinguish the two commercial specifications.
## 4 Conclusion
In order to understand the chemical composition of PO comprehensively, an effective and sensitive UHPLC-Q-TOF/MS method was developed for the characterization of PO with two commercial specifications. A total of 62 components were identified, of which 13 robust known chemical markers (including 6 flavonoids and 7 steroidal saponins) were screened out to differentiate Xiangyuzhu (XPO) and Guanyuzhu (GPO). A simultaneous determination method for polygodoraside G, polygonatumoside F, and timosaponin H1 was established and validated by UHPLC-CAD before applying it to determine the differences in the contents of the three components in XPO and GPO samples. The ratio of timosaponin H1/polygodoraside G can be used as an indicator to distinguish the two commercial specifications of PO. The samples with a ratio <2 are considered XPO and >5 are considered GPO.
In this study, the qualitative analysis and quantitative analysis of PO were carried out to elucidate the composition. The findings provided a method for distinguishing the two commercial specifications of PO in the market and laid a theoretical foundation for the appropriate utilization of the resources.
## 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
YN and BM conceived and designed the experiments. YN and XP performed the experiments. YN and BM conceived and designed the experiments. YN and XP performed the experiments. YN and YS analyzed the data, and WZ contributed to the manuscript preparation. HL, JZ, XC, and JS reviewed the manuscript. All authors reviewed the results and approved the final manuscript.
## 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/fchem.2023.1146153/full#supplementary-material
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|
---
title: Neuroprotective effect of Dl-3-n-butylphthalide against ischemia–reperfusion
injury is mediated by ferroptosis regulation via the SLC7A11/GSH/GPX4 pathway and
the attenuation of blood–brain barrier disruption
authors:
- Shuangli Xu
- Xuewei Li
- Yutian Li
- Xiangling Li
- E. Lv
- Xiaojun Zhang
- Youkui Shi
- Yanqiang Wang
journal: Frontiers in Aging Neuroscience
year: 2023
pmcid: PMC9995665
doi: 10.3389/fnagi.2023.1028178
license: CC BY 4.0
---
# Neuroprotective effect of Dl-3-n-butylphthalide against ischemia–reperfusion injury is mediated by ferroptosis regulation via the SLC7A11/GSH/GPX4 pathway and the attenuation of blood–brain barrier disruption
## Abstract
### Background
Stroke is one of the most severe diseases worldwide, resulting in physical and mental problems. Dl-3-n-butylphthalide, a compound derived from celery seed, has been approved for treating ischemic stroke in China. No study has evaluated how Dl-3-n-butylphthalide affects the ferroptosis SLC7A11/GSH/GPX4 signal pathway and blood–brain barrier (BBB) PDGFRβ/PI3K/Akt signal pathways in the rat middle cerebral artery occlusion/reperfusion (MCAO/R) model of ischemic stroke.
### Methods
Sprague–Dawley rats were used to develop the MCAO/R model. Our study used three incremental doses (10, 20, and 30) of Dl-3-n-butylphthalide injected intraperitoneally 24 h after MCAO/R surgery. The neuroprotective effect and success of the model were evaluated using the neurofunction score, brain water content determination, and triphenyl-tetrazolium chloride-determined infarction area changes. Pathological changes in the brain tissue and the degree of apoptosis were examined by hematoxylin and eosin, Nissl, and terminal deoxynucleotidyl transferase dUTP nick end labeling staining. In addition, pathway proteins and RNA expression levels were studied to verify the effects of Dl-3-n-butyphthalide on both pathways. At the same time, commercial kits were used to detect glutathione, reactive oxygen species, and malondialdehyde, to detect oxidative stress in brain tissues.
### Results
The middle dose of Dl-3-n-butylphthalide not only improved MCAO-induced brain dysfunction and alleviated pathological damage, brain inflammatory response, oxidative stress, and apoptosis but also protected against ferroptosis and reduced BBB damage. These changes resulted in improved neurological function in the cerebral cortex.
### Conclusion
We speculate that Dl-3-n-butylphthalide has a neuroprotective effect on focal cerebral ischemia/reperfusion, which may be mediated through ferroptosis-dependent SLC7A11/GSH/GPX4 signal pathway and PDGFRβ/PI3/Akt signal pathway.
## Introduction
Stroke is an acute cerebrovascular disease characterized by high mortality and morbidity and can cause focal neurological dysfunction. Ischemic stroke comprises about $87\%$ of all stroke cases and is characterized by blocked blood vessels resulting in insufficient oxygen and nutrient delivery to brain tissue within minutes of the attack (Yan W. et al., 2021). Cerebral ischemia can cause a series of complex enzyme cascades, including inflammatory responses, energy metabolism disorders, oxidative stress, and disruption of the blood–brain barrier (BBB; Zhu et al., 2021). The primary clinical treatment for ischemic stroke involves thrombolytic drugs, antiplatelet aggregation drugs, and anticoagulants. However, thrombolytic therapy must be carried out within a limited treatment window, dramatically limiting its clinical application (Derek Barthels, 2020; Wei et al., 2021).
Dl-3-n-butylphthalide (DL-NBP) is a relatively new drug independently developed in China to treat cerebrovascular diseases. Its efficacy and safety have been evaluated in several clinical trials in China (Fan et al., 2021; Tan et al., 2021; Zhu et al., 2021).
Ferroptosis is a form of cell death that is distinguishable from apoptosis and autophagy (Yan H. F. et al., 2021). Since its discovery in 2012, it has become a research hotspot in the pathogenesis of various diseases. It is characterized by the production of reactive oxygen species (ROS) and lipid peroxidation (Hirschhorn and Stockwell, 2019). Cystine is transported by cysteine glutamate antiporter (system xc-) which consists of SLC7A11 in cells for the synthesis of glutathione (GSH) (Koppula et al., 2021). GSH is an important intracellular antioxidant and a substrate for the synthesis of GSH peroxidase 4 (GPX4). GPX4 converts lipid peroxides to non-toxic alcohols to protect cellular lipid peroxidation and is a key regulator of ferroptosis (Chen et al., 2021). SLC7A11, GSH, and GPX4 constitute the important ferroptosis signal pathway. Whether DL-NBP plays a role in ferroptosis and its main signaling pathway SLC7A11/GSH/GPX4 (solute carrier family 7 member 11/glutathione/glutathione peroxidase 4) in ischemic brain injury has not been reported.
Stroke caused damage to the BBB by a complex mechanism of injury and is regulated by a variety of mechanisms (Derek Barthels, 2020). Platelet-derived growth factor-BB (PDGF-BB), one of the therapeutic targets for endothelial dysfunction of the BBB, can bind to the PDGFβ receptor (PDGFRβ) and activate phosphatidylinositol-4,5-diphosphate 3-kinase/protein kinase B (PI3K/Akt), which is involved in neuroprotection against cerebral ischemic injury (Zheng et al., 2019). However, whether DL-NBP influences the PDGFR-β/PI3K/Akt signaling pathway is unknown and requires elucidation.
In this study, we evaluated the neuroprotective effect of DL-NBP in a model of ischemic reperfusion injury, and established and investigated its underlying mechanisms by analyzing the expression of important genes and proteins in the SLC7A11/GSH/GPX4 and PDGFR-β/PI3K/Akt signaling pathways.
## Animals
8-week-old Sprague–Dawley (SD) rats (weight 250–280 g) were bought from Jinan Peng Yue Experimental Animal Reproduction Company, Ltd. (Jinan, China) and were fed with pure water and sterile fodder in the Clinical Medicine Research Center of the Affiliated Hospital of Weifang Medical University. The rats’ cages were temperature (20°C–25°C) and humidity ($60\%$–$70\%$) controlled under a 12 h/12 h light/dark cycle. Our experimentations on animals were authorized by the Institutional Animal Care and Use Committee of Weifang Medical University and every attempt was made to reduce damage to the animals during the experiments. The animal study protocol was reviewed and approved by the experimental animal Ethics Committee of Weifang Medical University (2018–037).
## Animal models
The middle cerebral artery occlusion/reperfusion (MCAO/R) rat model was used to carry out the research (Fluri et al., 2015). Rats were anesthetized with pentobarbital sodium (40 mg/kg) by intraperitoneal injection and the pain reflex was detected by the Randall-Selitto deep pressure test (calipers applied to the hind paw of the rat) in the perioperative period. Surgery was performed after the pain reflex disappeared. Briefly, the rat underwent a neck incision, exposing the right external carotid artery (ECA), internal carotid artery (ICA), and common carotid artery (CCA). After ligation of the distal ECA and proximal CCA, we clipped the ICA and made a small cut at the distal end of the CCA ligation. A thread was inserted (0.38–0.40 mm; MSRC40B200PK50, Shen Zhen RWD Life Science Co., Ltd., Shenzhen, China) with a thick head to approximately 16–20 mm and fixed. After 2 h, the thread tail was pulled out but the head was retained to restore blood circulation. The skin incision was then sutured. Sham rats underwent the same procedure but without occlusion. A successful model could be judged by Horner syndrome in the left eye when the rats awakened, bending of its right forelimb on lifting the tail, and the ability to move in a circle as they moved autonomously on the ground. Rats with massive bleeding, subarachnoid hemorrhage, and premature death/drop-out were excluded after cerebral ischemia–reperfusion injury (Feng C. et al., 2020). A total of 198 SD rats were operated on, in this experiment, of which 155 were finally included in the experiment and 16 were excluded due to unsuccessful molding; the mortality rate was $13.64\%$.
## Grouping and drug treatment
The male SD rats were divided into the following five groups using a table of random numbers according to the principle of simple random allocation, and the original liquid of DL-NBP is provided by CSPC NBP Pharmaceutical Co., Ltd.: (i) a Sham group, in which rats underwent a sham operation (Sham); (ii) the MCAO/R group, in which rats underwent MCAO/R; (iii) MCAO/R + low-dose N-butylphthalide group (10 mg/kg; MCAO/R + NBP-L); (iv) MCAO/R + medium-dose N-butylphthalide group (20 mg/kg; MCAO/R + NBP-M); (v) MCAO/R + high-dose N-butylphthalide group (30 mg/kg; MCAO/R + NBP-H). This allocation minimized any selection bias (Hirst et al., 2014; Schulz et al., 2018). We selected the appropriate dose of DL-NBP using body surface area conversion according to the drug instructions and clinical dosage. 24 h after the rats underwent the MCAO/R operation, the treatment was administered at each corresponding dose in each group through intravenous injection into the femoral vein. After 24 h of DL-NBP (CSPC NBP Pharmaceutical Co., Ltd., Shijiazhuang, China) administration, the rats were euthanized after behavioral tests, and the brain tissue was obtained for analysis.
## Modified neurological severity scores
The 18-point Garcia grading score was used to measure neurological function in each group of rats ($$n = 6$$) to assess the behavior 24 h after the drug treatment (Sha et al., 2019). The mNSS test is scored out of 18 points and includes three movement tests and two sensory experiments. The results are interpreted as follows: 1–6, mild injury; 7–12, moderate injury; and 13–18, severe injury. The evaluators were blinded to the different group allocations of the rats.
## Cerebral blood flow measurement
Under the premise of double-blind, the success of MCAO in rats was monitored by laser speckle flow imaging (Heeman et al., 2019). Briefly, after anesthesia and sterilization, and the skull is exposed. The skull is slowly polished by a high-speed electric skull drill until the epidural forms a 10x6mm skull window. Record the blood flow before and after MCAO using laser speckle flow imaging (SIM BFI-HR PRO, Wuhan, China).
## Evaluation of cerebral edema
Brain water content was measured by the standard wet-dry method (Cao et al., 2016). The right cerebral hemisphere in each group ($$n = 6$$) was separated 24 h after the drug treatment and weighed to obtain the wet weight. The dry weight was then obtained by dehydrating the brain in an oven at 105°C for 72 h. The evaluators were blinded to group allocation. The brain water content was calculated as follows: (wet weight–dry weight) /wet weight.
## 2,3,5-Triphenyltetrazolium chloride staining
The brain tissue from five groups ($$n = 3$$) was sliced into 2-mm thick coronal sections (a total of 6 slices) after freezing in a-20°C refrigerator for 20 min. The slices were placed in a pre-prepared $1\%$ TTC (Sigma-Aldrich, St. Louis, MO, USA) solution. After 15–30 min, the slices were stained according to the presence of specific non-ischemic areas (light red) and ischemic necrotic tissue (white) (Sha et al., 2019). This process was done by a member blind to the grouping. Image J software was used to analyze the cerebral infarct volume.
## Hematoxylin and eosin, Nissl, and terminal deoxynucleotidyl transferase dUTP nick-end labeling staining
After anesthetization, the rats, selected by single blinding from each group ($$n = 3$$) were perfused through the left cardiac apex with normal saline followed by $4\%$ paraformaldehyde (PFA). The rats were decapitated post-perfusion and the brains were separated. The tissue was immersed in $4\%$ PFA overnight at 4°C. The tissues were embedded in paraffin and cut into 5-μM thick sections. After dewaxing using xylene and hydration using gradient ethanol, along with ddH2O washing, we stained the specimen using HE, Nissl, and TUNEL (Sha et al., 2019). Finally, the sections were viewed and imaged using a fluorescence microscope (Leica, Wetzlar, Germany).
## Evaluation of BBB permeability
Evans blue (EB) was used to evaluate BBB integrity at 24 h after the drug treatment. The rats were picked by single blinding from each group. EB dye ($2\%$ in saline, 4 ml/kg, Solarbio, Beijing, China, $$n = 4$$) was injected through the right femoral vein 2 h before the brain was collected (Wang et al., 2020). Blood and intravascular dyes were removed by perfusing saline via the left ventricle in the rats. The right infarction cerebral hemispheres were separated on ice and then homogenized for 24 h at a temperature of 60°C in 2 mL dimethylformamide. After centrifugation, the absorbance value of the supernatant was measured at 632 nm. The EB content was calculated by its standard curve. The frozen slice of the cerebrum tissue dyed by EB could be observed with blue excitation light (620 nm) under a fluorescence microscope (Zeiss, Oberkochen, Germany).
## Superoxide dismutase, GSH, and malondialdehyde content
After obtaining brain tissue from rats obtained by the blinded selection, the brain cortex was isolated on ice to prepare brain tissue homogenate ($$n = 3$$). According to the manufacturer’s instructions, the concentrations of ROS (Affandi, Shanghai, China), MDA (Nanjing Jiancheng, Jiangsu, China), and GSH (Meimian, Chengdu, China) in the brain tissue homogenate were measured with commercial kits.
## Western blot analysis
The smashed fresh brain tissues were selected blindly and RIPA Lysis Buffer (Beyotime, Haimen, China) and protease phosphatase inhibitors (PMSF, Beyotime) were mixed to fully ground. The protein concentration was measured using a bicinchoninic acid assay (Beyotime). The belt was transferred to the polyvinylidene fluoride membrane (Beyotime) after an electrophoresis process. Membranes were blocked with $5\%$ skim milk blocking buffer at 37°C for 2 h and incubated with the following ferroptosis-rand BBB-related primary antibodies: anti-SLC7A11 (Abcam, Cambridge, UK; 1: 5,000), anti-GPX4 (Abcam; 1: 1,000), anti-PDGFR-β (Solarbio; 1: 1,000), anti-TFR1 (Abcam; 1: 5,000), anti-GSS (Abcam; 1: 5,000), anti-PI3K (Abcam; 1: 1,000), anti-p-PI3K (Abcam; 1: 1,000), anti-Akt (Abcam; 1: 10,000), anti-p-Akt (Abcam; 1: 1,000), and anti-β-actin (Abcam; 1: 5,000) as an internal control, at 4°C overnight in a thermostat shaker. After being washed by TBST (Tris-HCI buffer salt solution+Tween) buffer, all membranes were incubated with the second antibodies (Proteintech, Rosemont, IL, USA; 1: 5,000) at 37°C for 2 h. Immunoreactive membranes were processed with a chemiluminescence assay (Beyotime) (Wang et al., 2020). ImageJ software was used for analysis.
## Immunofluorescence staining
The sections made from rats selected using single-blinding were incubated with anti-occludin (Solarbio) and anti-ZO-1 (Solarbio) antibodies at 4°C overnight (Jin et al., 2021). After being washed with PBS, they were incubated together with the fluorescence-conjugated secondary antibody (Solarbio) at 25°Cfor for 1 h. In addition, the vascular endothelial cell markers CD31 were co-immunostaining with Zo-1 and occludin to observe the BBB integrity. The histopathological changes in the brain could be observed using a fluorescence microscope. The results were analyzed using ImageJ software.
## Quantitative real-time polymerase chain reaction assay
Total RNA was isolated from the brain tissues of rats selected by blind selection ($$n = 3$$) using an RNA extraction kit (Beyotime, Jiangsu, China). cDNA was generated by reverse transcription at 50°C for 15 min and 85°C for 5 min in T-RNA apparatus (Bio-rad, Hercules, CA, USA). The reaction system, including 10 μL 2xUltraSYBR Mixture, 0.4 μl of PCR Forward Primer (10 μM), 0.4 μL of PCR Reverse Primer (10 μM), 0.8 μL of cDNA template, and 8.4 μL of ddH2O was processed in PCR (Bio-rad) under the following conditions: 95°C for 10 min denaturation, followed by 40 cycles of 95°C for 15 s, and 60°C for 60 s (Liu et al., 2019). The primer sequences (Sangon Biotech, Shanghai, China) used for qRT-PCR are shown in Table 1.
**Table 1**
| Primer sequences | Unnamed: 1 |
| --- | --- |
| SLC7A11 | Forward 5′-ATGCAGTGGCAGTGACCTTT-3′ |
| SLC7A11 | Reverse 5′-GGCAACAAAGATCGGAACTG-3′ |
| GPX4 | Forward 5′-TGTGTAAATGGGGACGATGCC-3′ |
| GPX4 | Reverse 5′-ACGCAGCCGTTCTTATCAATG-3′ |
| TFRC | Forward 5′-AGTAGGAGCCCAGAGAGACGCTTGG-3′ |
| TFRC | Reverse 5′-CACTCAGTGGCACCAACAGCTCCAT-3′ |
| PDGFR-β | Forward 5′-GTGCTCACCATCATCTCCCT-3′ |
| PDGFR-β | Reverse 5′-ACTCAATCACCTTCCATCGG-3′ |
## Statistical analysis
The quantitative data analysis is presented as the mean ± standard deviation (SD) in the presence of at least three independent experiments. T-tests were used for the comparison of the two groups. GraphPad Prism 9 was used to conduct all statistical analyses. p values <0.05 were considered statistically significant.
## Dl-NBP improves neurological scores and reduces brain water content as well as cerebral infarct volume in MCAO/R rat
We observed the cerebral blood flow by laser speckle flow imaging system to determine the success of the MCAO/R rat model (Figure 1E). To examine whether NBP is helpful for neural function in rats with MCAO/R, a single-blind method was used to score each group of rats ($$n = 6$$) 24 h after the corresponding treatment. No significant changes were observed in the Sham group. Compared with the Sham group, rats in other groups showed significantly increased neurological deficit scores. The neurological deficit score in the MCAO/R + NBP-M group was significantly reduced compared to the Sham group ($p \leq 0.01$, 8.667 ± 2.422 vs. 13.83 ± 1.722; Figure 1A). After treatment with different doses of NBP in the three groups, the water content of the brain decreased significantly compared to the MCAO group ($p \leq 0.01$ or $p \leq 0.001$ vs. 0.8500 ± 0.01414; Figure 1B). Through TTC staining, rat cerebral infarction volume analysis showed that no injuries occurred in the Sham group, and white areas of infarction were observed in the remaining groups. Compared with the MCAO group, the cerebral infarct area in the NBP treatment group was significantly reduced ($p \leq 0.01$ or $p \leq 0.001$ vs. 0.3949 ± 0.03035; Figures 1C,D). The results showed that NBP effectively improved the neurological performance of MCAO model rats and reduced the cerebral infarction area and brain water content.
**Figure 1:** *Effects of NBP on neurological deficit scores, brain water content, and cerebral infarct area. (A) Neurological deficit score. (B) Brain water content. (C) TTC staining. (D) Infarct volume. (E) Cerebral Blood Flow Measurement (From left to right, normal, ischemia and reperfusion). MCAO, middle cerebral artery occlusion; NBP, N-butylphthalide; TTC, 2,3,5-Triphenyltetrazolium chloride; MCAO/R + NBP-L, Low-dose N-butylphthalide, 10 mg/kg; MCAO/R + NBP-M, Medium-dose N-butylphthalide, 20 mg/kg; MCAO/R + NBP-H, High-dose N-butylphthalide, 30 mg/kg. *p < 0.05; **p < 0.01; ***p < 0.001. The values represent the mean ± SD, n = 6.*
## Dl-NBP protects against neuronal necrosis and apoptosis after MCAO/R in rat
HE staining of the coronal brain slices showed that the neuronal cells in the cerebral cortex in the Sham group were neatly arranged and structurally normal. In contrast, the damaged side of the brain in the remaining groups showed evident cell disorder, neuronal loss, a large amount of vacuole space, and partial nuclear dissolution and condensation ($p \leq 0.001$, Sham vs. MCAO; Figure 2A; Supplementary Figure 1A). Regarding quantitative analysis, neuronal pathology in the MCAO/R + NBP-M group improved significantly compared with the MCAO group ($p \leq 0.001$, 21.33 ± 3.215 vs. 42.33 ± 3.512; Figure 2D). Nissl bodies in the neurons were stained purple-blue in the cytoplasm and light blue in the nuclei (Song et al., 2022). Compared to the Sham group, the MCAO group had fewer Nissl bodies ($p \leq 0.001$, Sham vs. MCAO; Figure 2B; Supplementary Figure 1B). Quantitative analysis showed that the number of Nissl bodies in the medium DL-NBP treatment group increased compared with the MCAO group ($p \leq 0.001$, 76.00 ± 2.646 vs. 41.00 ± 3.606; Figure 2E). Apoptotic cells (brown-yellow staining) were observed in the cerebral cortex on the side of the infarction (Figure 2C). Less frequent neuronal apoptosis was observed in the DL-NBP compared to the MCAO groups ($p \leq 0.001$, 22.00 ± 3.606 vs. 65.67 ± 4.041; Figure 2F). These findings suggest that DL-NBP therapy improves MCAO-induced neuronal disorders, death, and apoptosis.
**Figure 2:** *The histopathological and structural changes by HE, Nissl, and TUNEL staining. (A) HE staining. (B) Nissl staining. (C) TUNEL staining. (D–F) Quantitative analysis of the staining above (×400). HE, hematoxylin and Eosin; TUNEL, terminal deoxynucleotidyl transferase dUTP nick-end labeling. *p < 0.05; **p < 0.01; ***p < 0.001. Data are presented as the mean ± SD, n = 3.*
## Dl-NBP reduces BBB damage after MCAO/R in rat
EB staining has been used to evaluate BBB permeability in ischemic hemispheres in the coronal plane (Guo et al., 2019) (Figure 3A). In the frozen slice of infarcted brain tissue, red dots appeared after excitation with a blue laser (excitation wavelength 620 nm) under a fluorescence microscope (Figure 3B). The MCAO group showed many red dots compared with the Sham group, while the red dots were significantly reduced after NBP treatment. EB content decreased after applying NBP ($p \leq 0.001$, 2.190 ± 0.1061 vs. 3.673 ± 0.1326; Figure 3C). To find out whether NBP treatment could affect BBB integrity, the tight junction proteins ZO-1 and occludin were observed by immunofluorescence staining (Figure 4A; Supplementary Figure 2A). In Supplementary Figures 3, 4, it is clear that the application of medium dose DL-NBP could protect BBB integrity compared to the MCAO/R group by labeling ZO-1 and occludin with vascular endothelial cell markers CD31 (Supplementary Figures 3–5). It was concluded that DL-NBP could protect the expression of ZO-1 ($p \leq 0.05$, 4.949 ± 0.3181 vs. 3.066 ± 0.4698; Figure 4B) and occludin ($p \leq 0.001$, 5.800 ± 0.4400 vs. 2.447 ± 0.2550; Supplementary Figure 2B).
**Figure 3:** *Evaluation of BBB Permeability. (A) The representative appearance of EB stained rats’ brains. (B) A fluorescence microscope observed the leakage of EB in rats’ frozen slices of brain tissue. (C) Quantitative analysis of EB content in brain tissue. EB, Evans blue; BBB, blood–brain barrier. *p < 0.05; **p < 0.01; ***p < 0.001. The values represent the mean ± SD, n = 4.* **Figure 4:** *The ZO-1 tight junction protein of BBB. (A) The expression of ZO-1. (B) The qualification of ZO-1. BBB, the blood–brain barrier. *p < 0.05; **p < 0.01; ***p < 0.001. The values represent the mean ± SD, n = 3.*
## The protective effect of DL-NBP on BBB may be mediated through PDGFRβ/PI3K/Akt signal pathway
The expression of p-Akt and p-PI3K proteins in the MCAO group was significantly downregulated compared to that in the Sham group ($p \leq 0.05$, 0.7132 ± 0.1293 vs. 1.292 ± 0.2336, 0.6355 ± 0.02536 vs. 0.9193 ± 0.06814; Figures 5A–C). In addition, the expression of p-Akt and p-PI3K proteins in the MCAO/R + NBP-M group was significantly higher than in the MCAO group ($p \leq 0.05$, 1.247 ± 0.1993 vs. 0.7132 ± 0.1293, 0.8556 ± 0.0611 vs. 0.6355 ± 0.0254; Figures 5B,C). After MCAO, p-PDGFRβ expression was upregulated ($p \leq 0.01$, 1.079 ± 0.1097 vs. 0.5305 ± 0.01927; Figures 5D,E). Compared with the MCAO group, the PDGFRβ in the MCAO/R + NBP-M group was significantly lowered ($p \leq 0.01$, 0.5927 ± 0.05741 vs. 1.079 ± 0.1097; Figure 5E). Compared to the Sham group, MCAO group PDGFRβ ($p \leq 0.05$) levels rose in PCR. After applying DL-NBP, PDGFRβ ($p \leq 0.05$, MCAO/R + NBP-M vs. MCAO, 1.368 ± 0.1996 vs. 6.064 ± 1.800; Figure 5F) were reduced. These results are consistent with other experimental methods. These findings suggest that DL-NBP probably mediates the PDGFRβ/PI3K/Akt pathway to promote BBB integrity.
**Figure 5:** *Western blot and PCR of BBB signal protein. (A) Representative western blot image of p-Akt, Akt, p-PI3K, and PI3k. (B,C) The expression ratio of p-Akt/Akt and p-PI3K/PI3k is quantified by image J software and is represented as a histogram. (D) Representative western blot image of PDGFRβ. (E) The expression ratio of PDGFRβ/β-actin is quantified by Image J software and is represented as a histogram. (F) PCR for PDGFRβ levels within cerebral cortex tissues. BBB, the blood–brain barrier; PCR, real-time polymerase chain reaction. *p < 0.05, **p < 0.01, ***p < 0.001. The values represent the mean ± SD, n = 3.*
## Dl-NBP protects against ferroptosis after MCAO/R in rat
TFRC is a critical protein for intracellular iron ion transfer, and our experimental results showed a significant increase in the expression of TFRC ($p \leq 0.01$, 1.220 ± 0.0870 vs. 0.8049 ± 0.0517; Figures 6A,B). This indicates that there are more free iron ions in cells after MCAO. After DL-NBP treatment, there were apparent changes ($p \leq 0.05$, 0.8489 ± 0.0748 vs. 1.220 ± 0.0870; Figure 6B). MCAO group TFRC ($p \leq 0.01$) levels rose compared to the Sham group in PCR. After applying DL-NBP, TFRC ($p \leq 0.01$, MCAO/R + NBP-M vs. MCAO, 1.355 ± 0.02738 vs. 8.338 ± 2.211; Figure 6C) was reduced. The ELISA method detected GSH, MDA, and ROS levels to indicate the ferroptosis-related correlates of cellular oxidative stress. Levels of ROS ($p \leq 0.01$, 94.24 ± 16.17 vs. 45.35 ± 4.174; Figure 6E) and MDA ($p \leq 0.05$, MCAO vs. Sham, 18.15 ± 2.029 vs. 6.424 ± 0.6414; Figure 6F) were significantly higher compared to that in the Sham group, while GSH levels were lower ($p \leq 0.05$, MCAO vs. Sham, 0.4365 ± 0.0683 vs. 1.327 ± 0.2412; Figure 6D). After treatment with DL-NBP, GSH was significantly upregulated ($p \leq 0.01$, MCAO/R + NBP-M vs. MCAO, 1.470 ± 0.1380 vs. 0.4365 ± 0.0683; Figure 6D), and ROS ($p \leq 0.05$, MCAO/R + NBP-M vs. MCAO, 52.08 ± 6.187 vs. 94.24 ± 16.17; Figure 6E) and MDA ($p \leq 0.05$, MCAO/R + NBP-M vs. MCAO, 7.853 ± 2.438 vs. 18.15 ± 2.029; Figure 6F) were downregulated.
**Figure 6:** *The relative levels of ferroptosis-related proteins were examined by Western blotting and ELISA. (A) Representative western blot image of TFRC. (B) Quantification of TFRC. (C) PCR for TFRC. (D–F) The result of GSH, ROS, and MDA by ELISA kit. ELISA, enzyme-linked immunoassay kit; TFRC, transferrin receptor; GSH, glutathione; ROS, Superoxide dismutase; MDA, malondialdehyde; PCR, real-time polymerase chain reaction. *p < 0.05, **p < 0.01, ***p < 0.001. The values represent the mean ± SD, n = 3.*
## Suppression of ferroptosis by DL-NBP is possibly mediated through SLC7A11/GSH/GPX4 signal pathway
According to the western blotting results, SLC7A11 ($p \leq 0.001$, 0.4119 ± 0.0419 vs. 1.340 ± 0.0551), GPX4 ($p \leq 0.01$, 0.4215 ± 0.0498 vs. 0.8374 ± 0.1189), and GSS ($p \leq 0.05$, MCAO vs. Sham, 0.6400 ± 0.0347 vs. 1.244 ± 0.1248) were downregulated (Figures 7A–D). Compared to the MCAO/R group, SLC7A11 ($p \leq 0.001$, 0.8843 ± 0.0248 vs. 0.4119 ± 0.0419), GPX4 ($p \leq 0.05$, 0.7728 ± 0.0661 vs. 0.4215 ± 0.0498), and GSS ($p \leq 0.01$, 1.178 ± 0.1357 vs. 0.6400 ± 0.0347) in MCAO/R + NBP-M were upregulated after DL-NBP treatment. PCR shows the same conclusion. Compared to the Sham group, MCAO group SLC7A11 ($p \leq 0.001$) and GPX4 ($p \leq 0.001$) levels were lowered. After the application of DL-NBP, SLC7A11 ($p \leq 0.01$, MCAO/R + NBP-M vs. MCAO, 0.8129 ± 0.1165 vs. 0.3471 ± 0.1061) and GPX4 ($p \leq 0.01$, MCAO/R + NBP-M vs. MCAO, 0.9076 ± 0.07632 vs. 0.2593 ± 0.1559) were both increased (Figures 7E,F). The results showed that DL-NBP could probably mediate SLC7A11/GSH/GPX4 signal pathway to attenuate ferroptosis.
**Figure 7:** *Western blot and PCR of ferroptosis-dependent SLC7A11/GSH/GPX4 signal pathway. (A) Representative western blot image of SLC7A11, GPX4, and GSS. (B–D) Quantification of SLC7A11, GPX4, and GSS. (E,F) PCR for SLC7A11, GPX4 levels within cerebral cortex tissues. PCR, Real-time polymerase chain reaction. *p < 0.05, **p < 0.01, ***p < 0.001. The values represent the mean ± SD, n = 3.*
## Discussion
Ischemic stroke is one of the most severe diseases worldwide and is associated with a poor prognosis and complications, including central nervous system infection and thrombosis of the deep veins (Pan et al., 2018; Yan W. et al., 2021). Dl-3-NBP, the main active ingredient in NBP, was initially isolated from celery seeds. As a new drug recommended for treating ischemic stroke in China, it has been shown to have antioxidant properties, reduce the inflammatory response, and promote angiogenesis (Zhao et al., 2014). It is a new therapy with multisite and multitarget regulation features, showing apparent neuroprotective effects. In this study, we built a model of brain ischemia–reperfusion to observe whether the SLC7A11/GSH/GPX4 and PDGFRβ/PIK/Akt signal pathways were involved in the process; most importantly, we evaluated whether these two pathways could be potential valid targets of DL-NBP treatment.
The results showed that the medium-dose DL-NBP alleviated MCAO-induced neurologic deficits, brain water content, and infarction area and improved nerve function and ischemic brain injury compared to the MCAO group. Previous research not only reached the same conclusion (Wang J. et al., 2021; Wei et al., 2021) but also proved that BBB destruction could increase tissue swelling and lead to brain damage (Song et al., 2022). These three studies confirm the successful establishment of a model of hypoxic–ischemic brain injury but also serve as a reminder of the damage to the BBB in this injury, which has also been mentioned in other articles (Zhu et al., 2021). In the meantime, these outcomes also remind us that an appropriate dose of DL-NBP is an effective treatment method for ischemic stroke. We also observed through HE staining that nuclear vacuole formation, atomic condensation, contraction, and nuclear lysis were reduced with DL-NBP treatment. The neuronal cells after Nissl staining had a square shape. TUNEL staining showed reduced apoptotic cells (Pi et al., 2021). Ferroptosis is another type of cell death (Gao et al., 2016). Whether DL-NBP could affect ferroptosis in MCAO/R is another question for future research. The conditions of DL-NBP-administered groups improved compared to the MCAO group in the study. Therefore, the targets of BBB and ferroptosis that DL-NBP works on were the main focus of this article.
The PI3K/Akt pathway is an essential intracellular signaling pathway involved in processes such as cell resting, proliferation, and cancer (Xie et al., 2019; Feng H. et al., 2020). PDGF-β is expressed in cerebral mesenchymal cells. PDGFR-β was found to function in endothelial progenitor cells overexpressed by the PI3K/Akt signaling pathway to exert neuroprotective functions in adult mice (Zheng et al., 2019). PDGFRβ activates the PI3K-AKT pathway and has roles on the cell membrane, including promoting actin recombination, directing cell proliferation, stimulating cell growth, improving angiogenesis, and inhibiting apoptosis (Fan et al., 2014). It is also an essential protective pathway for the BBB after MCAO/R-induced brain damage (Shen et al., 2019). The binding of PDGF to PDGFRβ by activating the PI3K-Akt signaling pathway in rat models can enhance cell proliferation and migration, promoting early vascular repair and angiogenesis after cerebral ischemia–reperfusion injury (Zheng et al., 2019). Our experiment observed the decreasing expression of p-PI3K and p-Akt and the increased expression of PDGFRβ after MCAO. We inferred that, along with the weak signal of the PDGFRβ/PIK/Akt pathway, it was unable to promote the binding of PDGF to PDGFRβ, resulting in the upregulation of PDGFRβ expression. This is consistent with previous research (Zhang et al., 2007). After treatment with DL-NBP, PDGFRβ was downregulated and p-PI3K, as well as p-Akt, experienced elevated expression. In addition, DL-NBP preserved the expression of tight junction proteins ZO-1 and occludin in our experiment. The same results were seen in the immunofluorescence double-label CD31 with ZO-1 or occludin. Microvascular endothelial cells are the critical component of BBB, and platelet endothelial cell adhesion molecule-1 (CD31) is expressed in it. Previous research has proved that ZO-1 and occludin are essential tight junction proteins in the BBB structure, maintaining its stabilization, and their absence could destroy the BBB directly (Song et al., 2022). Co-immunostaining ZO-1 (or occludin) with CD31 could detect the integrity of the tight junction of the BBB better. The density of luminous dots was observed from immunofluorescence staining, and we showed coincident results with western blotting after quantification. This conclusion is similar to the BBB permeability shown by EB staining. These outcomes indicate that DL-NBP likely promotes the recovery of BBB via PDGFRβ/PI3K/Akt. We conclude from previous research (Shen et al., 2019) and our experiment that DL-NBP could presumably mediate the PDGFRβ/PIK/Akt signal pathway and alleviate the damage of the BBB caused by ischemia–reperfusion injury.
This study found the downregulation of SLC7A11, GPX4, GSH, and GSS after MCAO and the upregulation of TFRC by western blotting and an ELISA kit. However, the medium DL-NBP treatment group had a better outcome than the MCAO group. Transferrin receptor (TFRC) is a receptor on the cell membrane that transfers free iron ions into cells, which is regarded as a sign of ferroptosis (Feng H. et al., 2020). Owing to neuronal dysfunction, TFRC levels increased as it lost its ability to bond with free iron ions. The ferroptosis-related proteins were also changed, such as the downregulation of the SLC7A11/GSH/GPX4 signal pathway proteins; DL-NBP appeared to have helped in this. Protein expression significantly changed, which was consistent with the histopathological findings. This indicates that SLC7A11/GSH/GPX4 was probably one of the targets for DL-NBP to cure ischemic stroke.
Substrate-specific subunit SLC7A11 and auxiliary regulatory subunit SLC3A2 form the main pump of the cysteine glutamate antiporter (system xc-), which ingests cystine into cells to converts it into cysteine for GSH synthesis and pumps out high concentration glutamate intracellularly (Lin et al., 2022). SLC7A11-imported cystine is the raw material for GSH biosynthesis, which promotes subsequent GPX4-mediated lipid peroxidase detoxification to inhibit ferroptosis (Chen et al., 2021b). SLC7A11 is essential in transferring antioxidant raw material, and GPX4 is a critical cellular enzyme involved in the regulation of ferritin growth, which can directly reduce lipid peroxide to non-toxic alcohols in the membrane to prevent ferroptosis in the process of lipid oxidation (Hambright et al., 2017; Koppula et al., 2021). Research has shown that the deletion of SLC7A11 or GPX4 in mice could cause ferroptosis-like damage, lead to cognitive impairment and neurodegeneration, and even show early embryonic lethality (Koppula et al., 2021).
GSH is the crucial link in the SLC7A11/GSH/GPX4 signal pathway. Under normal conditions, systematic xc-transporters on neuronal cell membranes can exchange the extracellular GSH raw material cystine with intracellular glutamate in cells (Mandal et al., 2010). Under the catalysis of GPX4 and GSS (GSH synthase), two molecules of GSH are oxidized to produce one molecule of GSH disulfide (GSSG) (Chen et al., 2021a). This reaction provided electrons to react with ROS and reactive nitrogen species such as hydrogen peroxide (H2O2) or organic peroxides (ROOH) to reduce into water or non-toxic alcohols (Lin et al., 2022). The process could maintain the intracellular antioxidant system and redox balance. GSS-defective mice cannot survive the embryonic stage, suggesting that GSH is essential for embryogenesis (Aoyama, 2021). In addition, GSH depletion in the brain is a common finding in patients with neurodegenerative diseases such as Alzheimer’s and Parkinson’s disease (Aoyama, 2021; Shi et al., 2022).
The SLC7A11/GSH/GPX4 signal pathway is the fundamental element of intracellular antioxidant function, which regulates the production and balance of antioxidants in the intracellular space (Cao and Dixon, 2016). Our research also confirmed these findings to reinstate the importance of the SLC7A11/GSH/GPX4 antioxidant axis in ferroptosis, which aligns with previous studies (Bu et al., 2021). The appropriate dose of DL-NBP has the probable function of targeting the SLC7A11/GSH/GPX4 signal pathway to treat the disease. This provides a reference for future clinical treatment of ischemic stroke. Whether DL-NBP affects the ferroptosis signal pathway could also be shown by MDA content (Zhao et al., 2014) and ROS. MDA is an aldehyde byproduct of lipid peroxidation, which can help measure the degree of lipid peroxidation and is one of the markers of ferroptosis (Wang Y. et al., 2021). There are several sources of ROS, including the oxygen in restored blood flow during the reperfusion phase, where highly active alkoxy radicals (L-O•) produced by L-OOH are oxidized by free iron ions (She et al., 2020), and a decrease in antioxidants such as GSH (Zhou et al., 2020) is observed. We could infer from the results of the MDA and ROS data in our experiment that DL-NBP could reduce the content of both in the MCAO/R brain tissue. This is indirect evidence that DL-NBP tends to alleviate the ferroptosis pathway.
Through our experiments, the brain tissue GSH in the MCAO group decreased significantly, while ROS and MDA increased compared with the Sham group. After treatment with medium-dose DL-NBP, the GSH upregulation was prominent, while ROS and MDA declined significantly. The oxidative stress state of neuronal cells was reduced following treatment with DL-NBP. Previous studies have also shown the antioxidative stress effects of DL-NBP in rat renal ischemic reperfusion (Dong et al., 2021; Wang B. N. et al., 2021), similar to our experimental findings.
Ferroptosis is evoked by iron-dependent lipid peroxidation, which has the characteristics of intracellular iron overload, GSH deficiency, and GPX4 dysfunction that can lead to redox disorders and partial tissue dysfunction (Cao and Dixon, 2016). After a stroke, increased free iron ions and rich polyunsaturated fatty acid (PUFA) in neuronal membranes provide the conditions for the occurrence of ferroptosis in brain tissue (Yan et al., 2020). The regular operation of the oxidative-resistant SLC7A11/GSH/GPX4 signaling pathway is interrupted in this situation. The increased free iron ions and rich PUFA contribute to ferroptosis (She et al., 2020). DL-NBP could presumably alleviate ferroptosis by affecting SLC7A11/GSH/GPX4 pathway. This is consistent with the comparison of results between the MCAO/R and MCAO/R + NBP-M groups in this experiment.
In summary, a series of oxidative stresses occur after cerebral ischemia and reperfusion. These stresses reduce the cellular antioxidant GSH and lead to the accumulation of ROS and MDA, leading to dysfunction of the SLC7A11/GSH/GPX4 pathway of neuronal cells, which ultimately mediates cell death. At the same time, due to the imbalance of the PDGFRβ/PI3/Akt pathway, the recovery of the endothelial structures of the blood–brain barrier is hindered. DL-NBP has been used in clinical settings to address psychiatric and behavioral functions following acute ischemic stroke in China (Fan et al., 2021). Our research has provided further support for using DL-NBP therapy in this setting (Yang et al., 2021).
DL-NBP could be used as a co-treatment method along with thrombolysis or mechanical thrombectomy treatment. Early initiation of neuron cytoprotecting, especially in the prehospital period (e.g., ambulance), would target the ischemic penumbra to slow down its evolution into the infarct zone (Savitz et al., 2019; Lyden et al., 2021). Meanwhile, it could also be considered to be used in-hospital pre-thrombectomy and for post-thrombectomy cytoprotection (Savitz et al., 2019).
This study had a few limitations. Our experiments show that DL-NBP can influence the SLC7A11/GSH/GPX4 and PDGFRβ/PI3/Akt pathways to alleviate ferroptosis and damage to the BBB, but a specific cell type has not yet been identified. We also did not use inhibitors to provide comparisons in the experiment. Although we had considered the feasibility of experimental surgery and bias based on sex, geriatric disease, and gender incidence are still our limitations in this experiment. These limitations should be addressed in future studies.
## Conclusion
*In* general, our findings suggest that DL-NBP is likely to reduce ferroptosis, inflammatory response, apoptosis, and BBB permeability of brain tissue cells to protect brain tissue from ischemia–reperfusion damage in a rat model of ischemic stroke. We found that DL-NBP could presumably mediate the SLC7A11-GSH-GPX4 and PDGFRβ/PI3/Akt pathways. The expressions of the essential proteins and genes to produce this neuroprotective effect suggests that this signaling pathway may be the mechanism behind this protective effect. The results of this study provide a reference for the clinical neuroprotective effect of DL-NBP.
## 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.
## Ethics statement
The animal study protocol was reviewed and approved by the experimental animal Ethics Committee of Weifang Medical University 77 [2018-037].
## Author contributions
SX, XuL, and YW conceptualized and designed the experiments. SX drafted the article and performed most of the experiments. YL, XuL, XiL, EL, and XZ conducted experiments. YS and YW made the approval of the final manuscript. All authors contributed to the article and approved the submitted version.
## Funding
This work was supported by the National Natural Science Foundation of China (No. 81870943) and Yuan Du Scholars.
## 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/fnagi.2023.1028178/full#supplementary-material
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|
---
title: The effect of systemic hypertension on prostatic artery resistive indices in
patients with benign prostate enlargement
authors:
- Stephen O. Onigbinde
- Christianah M. Asaleye
- Abdulkadir A. Salako
- Bukunmi M. Idowu
- Abimbola O. Onigbinde
- Adeyinka Laoye
journal: Prostate International
year: 2022
pmcid: PMC9995693
doi: 10.1016/j.prnil.2022.09.001
license: CC BY 4.0
---
# The effect of systemic hypertension on prostatic artery resistive indices in patients with benign prostate enlargement
## Abstract
### Background
To investigate the effect of systemic hypertension on the prostatic artery resistive indices by a comparative ultrasonographic evaluation of the prostate gland in normotensive and hypertensive patients with benign prostatic enlargement (BPE).
### Materials and methods
The participants had BPE and presented at the outpatient urologic clinic of a tertiary hospital. They were divided into normotensive and hypertensive groups. Each group had fifty patients. Calculation of international prostate symptom score, measurement of blood pressure, and transrectal ultrasonographic evaluation were done.
### Results
The mean age for the normotensive and hypertensive groups were 66.9 ± 9.8 and 66.0 ± 10.7 years, respectively ($$P \leq 0.662$$). Patients with hypertensive BPE had a significantly higher mean transitional zone volume, transitional zone index, presumed circle area ratio, quality of life score, and prostatic arterial resistive indices than the age-matched normotensive BPE patients.
### Conclusion
Patients with BPE and with hypertension had significantly higher prostate arteries resistive indices than normotensives with BPE. Even in patients with BPE and controlled hypertension, the prostatic artery resistance indices were still elevated than that of normotensive men with BPE.
## Introduction
Histologically, benign prostatic hyperplasia is defined by the presence of non-cancerous nodules composed of smooth muscle and epithelial cells inside the prostatic transitional zone (TZ).1 Benign prostatic enlargement (BPE) is the non-malignant increase in the size of the prostate gland and is the second most common indication for surgery in men >60 years old.2,3 *It is* the most common benign pathological condition affecting elderly men.
The global burden of BPE outweighs that of all other male genitourinary diseases combined.4,5 In 2019, an estimated 11.26 million men were newly diagnosed with BPE throughout the world.6 The lifetime prevalence of BPE is 22.8–$29.6\%$.7,8 Clinical BPE is diagnosed when at least two of these criteria are present: moderate to severe lower urinary tract symptoms (LUTS) with an international prostate symptom score (IPSS) >8, prostatomegaly with prostatic volume >30 mL, and diminished maximum urinary flow rate (Qmax) less than 15 mL/s.2 Systemic hypertension is a global health problem, accounting for substantial morbidity and mortality from heart disease, stroke, and renal failure.9,10 Globally, as of 2019, there were more than 1 billion people with systemic hypertension.11 *Hypertension is* one of the common comorbidities of BPE in the elderly.12 Over $25\%$ of elderly men with BPE also have hypertension.13 Transrectal ultrasound (TRUS) is the standard first-line investigation for prostatic pathologies because of the increased proximity of the transducer to the prostate gland, reduced artifactual interference, improved resolution, and generation of detailed zonal anatomical information.14, 15, 16, 17, 18
In recent years, the prostatic resistive index (PRI) measured by power Doppler imaging has been used to evaluate patients with BPE.19 It has been shown that the PRI is increased in patients with BPE and it is related to the severity of bladder outlet obstruction.19, 20, 21 A previous study revealed that men with hypertension are predisposed to more severe LUTS and larger prostatomegaly than men without high blood pressure (BP).22 In addition, Berger et al indicated that diabetes mellitus might be a risk factor for BPE.23 Chen et al reported a very weak but significant positive correlation ($r = 0.23$, $P \leq 0.05$) between the resistive indices of the periurethral arteries and right neurovascular bundles of the prostate and some cardiovascular risk factors (hypertension, diabetes, hyperlipidemia, obesity, and a history of cardiovascular events).24 Similar findings were also documented by Baykam et al.25 The aim of this study was to find out if there is any significant difference between the prostatic arteries resistive indices of subjects with BPE co-existing with hypertension and that of normotensive subjects with BPE. We hypothesized that the prostate arteries resistive indices would be significantly higher BPE patients with hypertension than normotensives with BPE.
## Materials and methods
This was a descriptive comparative study. The participants were hypertensive and normotensive men, aged 40–90 years, with LUTS and a clinical diagnosis of BPE. They were recruited consecutively from the Urology clinic of our tertiary hospital between October 2017 and September 2018. The hospital's Ethics and Research Committee approved the study protocol (ERC/$\frac{2017}{01}$/01). Written informed consent was obtained from all the participants.
The study included individuals who were diagnosed with BPE based on clinical history, digital rectal examination, PSA testing (<10 ng/ml), and transabdominal prostatic ultrasound (with prostate gland volume >25 cm3 on TRUS). They were categorized into two–hypertensive and normotensives–each group comprised fifty men.
The participants' BP was taken while seated, after he had rested for at least 15 minutes prior to the measurement. Three readings were taken and averaged. Systemic hypertension was defined as a BP ≥$\frac{140}{90}$ mmHg or an elevated BP necessitating antihypertensive therapy.26 The body mass index (BMI) was computed by dividing the weight (Kg) by the square of the height (meters). Participants with BMI of 18.5–24.9 kg/m2, 25.0–29.9 kg/m2, 30–39.9 kg/m2, and >40 kg/m2 were classified as normal, overweight, obese, and morbidly obese.27 Participants with hypertension placed on beta blockers, alpha-1 adrenoceptors blockers (prazosin, doxazosin, tamsulosin, etc), and phosphodiesterase-5 inhibitors (tadalafil) were excluded because they can affect the severity of LUTS. Other exclusion criteria include severe hydronephrosis or serum creatinine >132.5 μmol/L, PSA level of >10 ng/ml, diabetes mellitus, hyperlipidemia, obesity (BMI ≥30 kg/m2), smoking, prostate or bladder cancer, prostatitis, bladder stone, urethral stricture, neurogenic bladder, acute urinary retention, history of transurethral resection of the prostate or previous urinary tract surgery, anal stenosis, and previous major rectal surgery.19 The sonographic measurements were carried out with a Mindray® model DC-7 ultrasound scanner with Doppler functionality (Shenzhen Mindray Bio-medical Electronics, Nanshan, Shenzhen, China). A transrectal biplanar transducer (frequency = 5.0–10.0 MHz) was used for TRUS scan of the prostate, while a curvilinear transducer (frequency = 3.5–5.0 MHz) was used for the transabdominal ultrasound scan of the prostate.
The total volume of the prostate gland was measured using transabdominal and TRUS techniques. All transabdominal measurements of the prostate were performed with full bladder. Measurements were performed with the participants in supine position. The longitudinal (LD), anteroposterior (APD), and transverse (TD) diameters of the prostate were measured. The volume of the prostate on transabdominal USS (TAPV) was calculated using ellipsoid formula (0.523 × LD × APD × TD).19 Transrectal sonography was done with the patients lying in the left lateral decubitus position. Multiple transverse and sagittal sections were obtained and recorded after visualizing the TZ and whole prostate outline. The TD and the APD of the whole prostate and the transition zone, at the largest cross sectional area, were obtained. Also, the craniocaudal diameters (CCD) of the whole prostate and the TZ were measured on the midline sagittal images. Transrectal total prostatic volume and transitional zone volume (TZV) were also estimated using the ellipsoid formula (CCD × APD × TD × 0.523).19 The sonographic measurements were performed by the same sonologist three times and mean values derived in order to reduce intra-observer variability.
The IPSS, quality of life (QOL) score, maximum urine flow rate (Qmax), bladder wall thickness (BWT), transition zone index (TZI), presumed circle area ratio (PCAR), and postvoidal residual (PVR) urine volume, and triplex Doppler resistance indices (RI) of the right capsular artery (RIRCA), left capsular artery (RILCA), and urethral artery (RIUA) were calculated using previously published methods and guidelines.19,28 During power Doppler imaging, care was taken to minimize probe pressure on the rectal wall and an empty or nearly empty bladder was ensured so that compression effect by either the probe or full urinary bladder would not increase the intraprostatic pressure, which could alter the prostatic RI. The power Doppler gain was set to just below the noise threshold, so that blood flow was identified with minimum background noise and the low flow setting was used for optimal visualization of low flow intraprostatic vessels. Then, the pulsed waved spectral Doppler images were obtained from the urethral artery, right capsular artery, and left capsular artery on transverse section of the prostate. Care was taken to select Doppler measurements with angles of insonation of <60°. After the spectral Doppler wave form became stable, it was traced for three pulses. The peak systolic velocity, end-diastolic flow velocity, and RI were automatically calculated by the software of the ultrasound scanner. Three values for RI were measured for each of the three prostatic arteries and averaged to obtain the mean RI.
The data were analyzed using the IBM SPSS Statistics for Windows, version 22 (IBM Corp., Armonk, N.Y., USA). Means and standard deviation were gotten for the continuous variables (TPV, TZV, TZI, PCAR, RIRCA, RIUA, and RILCA). Categorical variables (age group, IPSS category, and QOL category) were presented as frequencies. The continuous data were non-parametric as determined by Kolmogorov–Smirnov test and Shapiro–Wilk test. Thus, Mann U Whitney test was used to compare the mean ranks of TPV, TZV, TZI, PCAR, BWT, PVR, IPSS, systolic blood pressure (SBP), diastolic blood pressure (DBP), QOL, RIRCA, RIUA, and RILCA between the two study groups. Correlation between RIRCA, RILCA, RIUA, SBP, and DBP was determined using Spearman's correlation. The correlation coefficients were classified as follows: $r = 0$–0.2 (very low, negligible, and probably meaningless correlation), r = >0.2–0.4 (low correlation which might warrant further investigation), r = >0.4–0.6 (moderate correlation), r = >0.6–0.8 (high correlation), and r = >0.8–1.0 (excellent/very high correlation).29 At $95\%$ confidence interval, P ≤ 0.05 was considered statistically significant.
## Results
The bio-demographic characteristics of the study population are shown in Table 1. There were one hundred participants in this study. Fifty were normotensive patients with BPE, while 50 were hypertensive patients with BPE. The hypertensive group did not significantly differ in age, height, weight and BMI from the normotensive group. The mean age for the normotensive and hypertensive groups were 66.9 ± 9.8 years and 66.0 ± 10.7 years, respectively ($$P \leq 0.66$$). There was no significant difference in the mean BMI of both groups. The BMI of the normotensive group was 23.7 ± 4.7 kg/m2, while that of the hypertensive group was 23.8 ± 4.4 kg/m2 ($$P \leq 0.98$$). The mean SBP of the hypertensive group was (129.52 ± 10.76) mmHg, while that of normotensive BPE participants was (125.4 ± 6.94) mmHg. The mean SBP was higher in the hypertensive group but the difference was not statistically significant ($$P \leq 0.087$$).Table 1Bio-demographic characteristics of study populationTable 1VariablesNormotensive BPE ($$n = 50$$)Hypertensive BPE ($$n = 50$$)StatisticdfP valueAge (years)Range46.0–88.043.0–89.0Mean ± SD66.9 ± 9.866.0 ± 10.70.438980.662a)Age Group (years)40–49Range46.0–47.043.0–49.00.0004>0.999b)Frequency, n (%)4 [8]4 [8]50–59Range50.0–57.050.0–56.0Frequency, n (%)6 [12]6 [12]60–69Range60.0–69.060.0–68.0Frequency, n (%)21 [42]21 [42]70–79Range70.0–77.070.0–79.0Frequency, n (%)14 [28]14 [28]≥80Range82.0–88.081.0–89.0Frequency, n (%)5 [10]5 [10]Height in (m)Range1.5–1.81.6–1.9Mean ± SD1.7 ± 0.11.7 ± 0.1−0.175980.862a)Weight (kg)Range42.1–121.249.2–121.1Mean ± SD70.7 ± 16.270.5 ± 14.90.090980.928a)BMI (kg/m2)Range15.7–39.517.5–39.6Mean ± SD23.7 ± 4.723.8 ± 4.4−0.023980.982a)BMI, body mass index; BPE, benign prostatic enlargement.a)Independent samples t-test was used to compare means.b)Likelihood ratio test was used to compare proportions.
The mean RIRCA of the hypertensive BPE group was 0.7 ± 0.1, while that of the normotensive BPE group was also 0.7 ± 0.1. However, the mean rank of the hypertensive BPE group (56.6) was significantly higher than ($$P \leq 0.037$$) that of the normotensive BPE group (44.5) (Table 2).Table 2Resistive indices of the prostatic arteriesTable 2Normotensive BPE group ($$n = 50$$)Hypertensive BPE group ($$n = 50$$)UP valueMean ± S.D.Mean RankMean ± S.D.Mean RankRIRCA0.7 ± 0.144.50.7 ± 0.156.5947.5000.037RIUA0.7 ± 0.138.70.7 ± 0.162.3659.500<0.0001RILCA0.6 ± 0.140.90.7 ± 0.160.1769.5000.001BPE, benign prostatic enlargement; RIRCA, resistive index of the right capsular artery; RILCA, resistive index of the left capsular artery; RIUA, resistive index of the urethral artery.
The mean RIUA of the hypertensive BPE group was 0.7 ± 0.1, while the mean RIUA of the normotensive BPE group was also 0.7 ± 0.1. However, the mean rank of RIUA in the hypertensive BPE group (62.3) was significantly higher ($P \leq 0.0001$) than that of the normotensive BPE group (38.69) (Table 2).
The mean RILCA of the hypertensive BPE group subjects was 0.7 ± 0.1, while that of normotensive BPE subjects was 0.6 ± 0.1. The hypertensive BPE group (60.1) had a significantly higher ($$P \leq 0.001$$) mean RILCA than the normotensive BPE group (40.9) (Table 2).
The RIRCA and SBP had a low positive correlation in the normotensive BPE group ($r = 0.34$, $$P \leq 0.017$$) and a stronger moderate positive correlation in the hypertensive BPE group ($r = 0.60$, $P \leq 0.0001$). There was a low positive correlation between the RIRCA and DBP in the normotensive BPE group ($r = 0.38$, $$P \leq 0.006$$) and a stronger moderate positive correlation in the hypertensive BPE group ($r = 0.49$, P value < 0.001).
In the normotensive BPE group, RIUA yielded a low positive correlation with SBP ($r = 0.34$, $$P \leq 0.016$$), but yielded a stronger moderate positive correlation between RIUA and SBP ($r = 0.60$, P value < 0.001) in the hypertensive BPE group. Also, there was a low positive correlation between RIUA and DBP in the normotensive BPE group ($r = 0.29$, $$P \leq 0.039$$), but a stronger moderate positive correlation in the hypertensive BPE group ($r = 0.49$, $P \leq 0.001$).
The RILCA and SBP had a low positive correlation in the normotensive BPE group ($r = 0.38$, $$P \leq 0.007$$) and a stronger moderate positive correlation in the hypertensive BPE group ($r = 0.48$, $P \leq 0.001$). Correlation between RILCA and DBP was low positive in the normotensive BPE group ($r = 0.37$, $$P \leq 0.009$$), but moderately positive in the hypertensive BPE group ($r = 0.55$, $P \leq 0.001$).
Table 3 shows that the mean rank of the BWT, PCAR, TZI, and transition zone volume (TZV) of the hypertensive BPE group were significantly higher than that of the normotensive BPE group. There was no statistically significant difference between the TPV and PVR urine volume of the two groups. Table 3Comparison of B-mode ultrasound parametersTable 3Normotensive BPE group ($$n = 50$$)Hypertensive BPE group ($$n = 50$$)UP valueMean ± S.D.Mean RankMean ± S.D.Mean RankTPV48.5 ± 18.145.557.8 ± 26.355.51000.5000.085TZV19.0 ± 12.244.027.5 ± 19.157.0923.0000.024TZI0.4 ± 0.144.00.4 ± 0.157.0927.0000.026PCAR0.7 ± 0.140.20.8 ± 0.161.0737.000<0.0001BWT2.8 ± 1.542.23.2 ± 1.358.8835.0000.004PVR56.1 ± 24.445.061.1 ± 24.256.1970.0000.054BPE, benign prostatic enlargement; BWT, bladder wall thickness; PCAR, presumed circle area ratio; PVR, postvoidal residual urine; TPV, total prostate volume; TZI, transition zone index; TZV, transition zone volume.
The SBP, DBP, and IPSS yielded no statistically significant difference between the two study groups (Table 4). In contrast, the hypertensive with BPE group had a significantly higher QOL score than the normotensive with BPE group (Table 4).Table 4Comparison of clinical parametersTable 4Normotensive BPE group ($$n = 50$$)Hypertensive BPE group ($$n = 50$$)UP valueMean ± S.D.Mean RankMean ± S.D.Mean RankSBP (mmHg)125.4 ± 6.945.6129.5 ± 10.855.510020.087DBP (mmHg)84.1 ± 4.646.087.1 ± 8.955.01.0240.117IPSS16.0 ± 8.645.319.4 ± 8.155.7990.50.073QOL3.4 ± 1.444.74.0 ± 1.156.3961.50.039BPE, benign prostatic enlargement; DBP, diastolic blood pressure; IPSS, international prostatic symptoms score; QOL, quality of life score; SBP, systolic blood pressure.
Fig. 1, Fig. 2 show the IPSS and QOL scores of the study participants. Fig. 1Clustered bar chart showing the distribution of subjects according to severity of lower urinary tract symptoms (LUTS) measured by the International Prostatic Symptoms Score (IPSS).Fig. 1Fig. 2Clustered bar chart showing the distribution of subjects according to the quality of life score. Fig. 2
## Discussion
Increased arterial resistive indices in enlarged prostate glands have been thought to be due vascular compression against a rigid prostatic capsule, neuronally-mediated vasoconstriction, or vascular damage.20,30 *Hypertension is* associated with increased vascular damage and vasoconstriction.31 The RI can be used to quantify alterations in the blood flow of various target organs. In recent years, the PRI measured by Power Doppler Imaging has been used to evaluate patients with BPE.13,19 Several reports have shown that the PRI is increased in patients with BPE and it is related to the severity of bladder outlet obstruction.13,19 PRI was first proposed as a diagnostic tool to differentiate between BPE and normal patients by Kojima et al.32 *In this* study, the mean RI of the prostatic arteries was significantly higher in participants with hypertension than normotensives. For comparison, the mean RI of the urethral and capsular arteries was less than the values gotten by Abdelwahab et al20 in Egypt (RIRCA = 0.8 ± 0.1; RILCA = 0.8 ± 0.1; RIUA = 0.8 ± 0.1). The Nigerian patients in this study were chosen consecutively, while Abdelwahab et al did a random selection of 86 Egyptian patients. Their mean TPV (75.1 ± 44.7 ml) is larger than that of both groups (Normotensive = 48.5 ± 18.1 ml; Hypertensive = 57.8 ± 26.3 ml) in this study. The Egyptian patients had partially filled urinary bladders during TRUS–compression by the partially filled urinary bladders could have affected the resistive indices obtained.13 In addition, Abdelwahab et al did not exclude patients with systemic comorbidities such as DM which is known to affect the prostate gland.23 The increased mean RI values in the hypertensive group supports the finding by Michel et al33, in a study conducted among Germans, that there is an association between BPE and hypertension. This might be partly explained by a common pathophysiological factor such as an increased sympathetic nervous system activity in both BPE and hypertension. Although Baykam et al found no correlation between prostate arteries RI (PRI) and hypertension specifically; there was a correlation between PRI and metabolic syndrome.25 A review by Abdollah et al confirmed this association with metabolic syndrome.34
A similar pattern of increased PRI has also been reported in patients with diabetes mellitus. Berger et al reported that the RI of the TZ was significantly higher in BPE patients with type 2 diabetes mellitus, coronary artery disease, peripheral arterial occlusive disease than in healthy control patients.23,35 They surmised that vascular damage in the prostate induced prostatic enlargement which, in turn, accounts for the increased RI.
The mean IPSS (19.4 ± 8.1) and PVR (61.1 ± 24.2 ml) of hypertensives in this study were significantly higher than that of normotensives (IPSS = 16 ± 8.6; PVR = 56.1 ± 24.3 ml). This pattern is similar to findings by Sugaya et al36 and Michel et al33 The mean value of IPSS of the index study is close to what Michel et al33 got in their study of German patients, while our mean value of IPSS and PVR of hypertensives were significantly lower than the values reported by Sugaya et al.36 This disparity is probably because the hypertensives in this study and in the study by Michel et al enrolled those that had been commenced on antihypertensive medications. Sugaya et al36 studied Japanese BPE patients who were hypertensive and had not been commenced on antihypertensive medications. Güven et al37 in Turkey also found a positive correlation between benign prostate enlargement-related LUTS (storage and voiding symptoms) and SBP.
There was no statistically significant difference between the BPs of the two groups in this study. This lack of difference likely resulted from the fact that the BPE with hypertension enrolled had well-controlled BP (on antihypertensive medication). It would have been unethical to ask them to discontinue their medication just for the study. Therefore, we can conclude that even in patients with BPE and with controlled hypertension, the prostatic artery RIs are still elevated than that of normotensive men with BPE, as demonstrated by our results. The alternative would have been to recruit patients newly diagnosed with hypertension who had yet to commence antihypertensive treatment. Unfortunately, hypertension is highly prevalent in our environment with the age at first diagnosis often <40 years. At this age, significant BPE is unlikely to have developed.
In conclusion, the mean values of RIRCA, RIUA, and RILCA in hypertensive BPE patients were significantly higher than those of patients with normotensive BPE. The PRI showed low positive correlation with SBP and DBP in the normotensive group and moderate positive correlation with SBP and DBP in the hypertensive group.
A limitation of this study was that the influence of hypertension duration and antihypertensive-naivety (non-treatment or uncontrolled from drug resistance) on prostate blood flow could not be assessed. Also, this was a hospital-based study although we do not foresee a remarkable difference in PRI–hypertension relationship in a community-based study.
## Conflict of interest
All authors have no conflict of interest to declare.
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|
---
title: Melanocortin receptor 3 and 4 mRNA expression in the adult female Syrian hamster
brain
authors:
- Megan A. L. Hall
- Abigail L. Kohut-Jackson
- Anna C. Peyla
- Gloria D. Friedman
- Nicole J. Simco
- Johnathan M. Borland
- Robert L. Meisel
journal: Frontiers in Molecular Neuroscience
year: 2023
pmcid: PMC9995703
doi: 10.3389/fnmol.2023.1038341
license: CC BY 4.0
---
# Melanocortin receptor 3 and 4 mRNA expression in the adult female Syrian hamster brain
## Abstract
Melanocortin 3 receptors (MC3R) and melanocortin 4 receptors (MC4R) are vital in regulating a variety of functions across many species. For example, the dysregulation of these receptors results in obesity and dysfunction in sexual behaviors. Only a handful of studies have mapped the expression of MC3R and MC4R mRNA across the central nervous system, with the primary focus on mice and rats. Because Syrian hamsters are valuable models for functions regulated by melanocortin receptors, our current study maps the distribution of MC3R and MC4R mRNA in the Syrian hamster telencephalon, diencephalon, and midbrain using RNAscope. We found that the expression of MC3R mRNA was lowest in the telencephalon and greatest in the diencephalon, whereas the expression of MC4R mRNA was greatest in the midbrain. A comparison of these findings to previous studies found that MC3R and MC4R expression is similar in some brain regions across species and divergent in others. In addition, our study identifies novel brain regions for the expression of MC3Rs and MC4Rs, and identifies cells that co-express bothMC3 and MC4 receptors within certain brain regions.
## Introduction
Melanocortin receptors 3 and 4 (MC$\frac{3}{4}$R) were discovered by Gantz et al. ( 1993a,b) using human cDNA. The melanocortin system includes five g-protein coupled receptors, with only MC3R and MC4R expressed in the central nervous system, along with their endogenous agonist alpha melanocortin stimulating hormone (alpha-MSH), and antagonist (agouti-related protein) (Ellacott and Cone, 2006). Functionally, MC3Rs regulate body fat to mass ratio. For example, knockout of the MC3R gene in mice results in an increase in body fat mass with a corresponding decrease in lean mass. Melanocortin 3 receptor knockouts have also resulted in a decrease in anxiety-like behaviors (Butler et al., 2000; Chen et al., 2000; Ghamari-Langroudi et al., 2018; Sweeney et al., 2021). These effects of energy regulation in humans have recently been discovered, as mutations that cause a loss of function in MC3R result in a delayed onset of puberty and reduced height (Lam et al., 2021). MC3R is also involved in cardiovascular regulation, thermoregulation, and neuroendocrine processes (Roselli-Rehfuss et al., 1993). Melanocortin receptor 4 regulates food intake and energy expenditure (Balthasar et al., 2005; Tao, 2010). For example, administration of MC4R antagonists or the knockout of the MC4R gene in rodents resulted in an increase in feeding behavior, obesity, and linear growth, the opposite of what is seen with loss of MC3R function (Fan et al., 1997; Huszar et al., 1997; Tao, 2010). Melanocortin receptor 4 also has a role in sexual behaviors through the modulation of luteinizing hormone and prolactin surges in female rodents and erectile activity in male rodents (Van der Ploeg et al., 2002; Tao, 2010).
The distribution of MC3R and MC4R expression in the brain has been mapped by Roselli-Rehfuss et al. [ 1993], Mountjoy et al. [ 1994], and Kishi et al. [ 2003] in rats, and Gantz et al. ( 1993a,b), Liu et al. [ 2003], and Sweeney et al. [ 2021] in mice. Furthermore, a study was recently published that provided a more extensive description of the distribution of the cells that express MC3R mRNA in the central nervous system of mice (Bedenbaugh et al., 2022). The expression of MC3R tends to be isolated in the hypothalamus and thalamus in mice and rats (Roselli-Rehfuss et al., 1993; Bedenbaugh et al., 2022). On the other hand, MC4Rs are expressed more broadly across the central nervous system in mice and rats (Mountjoy et al., 1994; Liu et al., 2003; Tao, 2010). Because determining the distribution of the expression of MC3R and MC4R mRNA in the brain provides a key to understanding the function of this system, additional studies are needed to determine the degree of convergence and differences in the receptor distribution among species. Finally, no studies have simultaneously mapped and compared both MC3R and MC4R mRNA. Thus, this study analyzed the mapping of MC3R and MC4R mRNA across the central nervous system of adult Syrian hamsters, broadening the species studied and advancing our understanding of the relationship between MC3R and MC4R expression.
The utilization of RNAscope and imaging software can be advantageous by detecting expression at lower levels than prior methods, assessing expression and changes in expression across more fine-tuned subregions, and assessing the co-localization/co-expression of MC3R and MC4R mRNA. As an initial approach to cataloging the expression of MC3R and MC4R in the hamster brain, we chose to focus on the telencephalon, diencephalon, and midbrain. We observed expression in 26 brain regions across these levels of the brain. We chose to categorize the distribution within these three levels of the nervous system due to their functionality and evolutionary conservation. Our findings bring novel insights of the distribution and the expression of MC3R and MC4R mRNA in the brain.
## Subjects
Adult female ($$n = 4$$) Syrian hamsters (Mesocricetus auratus) were purchased from Charles River Laboratories (Wilmington, MA, United States) at approximately 60 days of age (120–130 g). Females were housed individually in polycarbonate cages (50.8 × 40.6 × 20.3 cm). All animals were maintained on a reversed 14 h:10 h light/dark photoperiod (lights off at 1300 h). The animal room was maintained at a controlled temperature of 22°C, and food and water were available ad libitum. Subjects were ovariectomized (at approximately 90 days of age) and were approximately 140–150 days old at the time of tissue collection. All animal procedures were carried out in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals (NIH Publications No. 80–23; revised 2011) and approved by the University of Minnesota Institutional Animal Care and Use Committee.
## Histology
Subjects were injected with a lethal dose of sodium pentobarbital (Beuthanasia-D, 0.25 ml i.p., Schering, Union, NJ, United States) and sacrificed by rapid decapitation. Brains were extracted, frozen in optimum cutting temperature compound (VWR Scientific Products) and stored at −80°C. Brains were later sliced coronally on a cryostat at 14 μm thickness and sections were mounted on Colorfrost Plus slides (Fisher Scientific, Waltham, MA, United States). Some studies in rats and mice have provided an extensive analysis of range of the distribution of melanocortin receptor expressing neurons. Here, we limited our focus to the forebrain and midbrain with tissue collected from slices containing the prefrontal cortex through to slices containing the dorsal raphe nucleus. Slides were stored at −80°C until RNAscope procedure treatment.
## RNAscope
For in-situ hybridization, the protocols for fresh-frozen sample preparation and Multiplex fluorescent v2 assay were followed according to manufacturer’s instructions (Advanced Cell Diagnostics, Newark, CA, United States). Briefly, slides were removed from −80°C storage and fixed in $10\%$ neutral buffered formalin (Sigma Aldrich, Burlington, MA, United States) for 15 min at 4°C.
Slides were rinsed twice in phosphate-buffered saline (9.8 g/l PBS), which contains 80 g/l sodium chloride, 14 g/l sodium phosphate dibasic, 3 g/l sodium phosphate monobasic, and 2 g/l potassium chloride (Fisher Scientific), at pH 7.4 and then dehydrated for 5 min each in 50, 70, 100, and $100\%$ ethanol at room temperature. After air-drying for 5 min, hydrophobic barriers were drawn around the tissue to minimize loss of applied solutions. Slides were treated with Protease IV, incubated at room temperature for 30 min, and washed twice for 2 min in PBS. Two probes targeting Syrian hamster sequences were created by Advanced Cell Diagnostics (Newark, CA, United States) and used for the procedure: Mau-MC4R-C1 (mRNA encoding melanocortin-4 receptor, MC4R); GenBank accession number (XM_005074647.3, target nt region 519–1,483) and Mau-MC3R-C2 (mRNA encoding melanocortin-3 receptor, MC3R); GenBank accession number (XM_005074460.4, target nt region 932–1997). Probes were applied such that each tissue received 75 μl of solution that contained a 50:50 ratio of probe diluent (Advanced Cell Diagnostics) and probe mixture. Each probe mixture contained a 50:1 ratio of C1:C2 (each tissue received 37.50 μl of probe diluent, 36.75 μl of C1 and 0.75 μl of C2). Tissues were incubated at 40°C for 2 h. Following incubation, tissues were washed twice for at room temperature for 2 min each with washing buffer solution (8.7 g/l of sodium chloride, 4.41 g/l of sodium citrate, 3 g/l of sodium dodecylsulfate) based on Wang et al. [ 2012]. Tissues were then incubated with amplifying probes (AMP1, AMP2, and AMP3, Advanced Cell Diagnostics) at 40°C for 30 min, 30 min, and 15 min, respectively. Slides were washed with washing buffer solution twice at room temperature for 2 min each between each incubation step. Fluorescently labeled probes (Akoya Biosciences, Marlborough, MA, United States) were applied with the following assignments: MC4R-C1 with Opal 570 nm, concentration 1:750, and MC3R-C2 with Opal 520 nm, concentration 1:750. TSA buffer (Advanced Cell Diagnostics) was mixed with each dye to achieve the desired dilution. For each fluorescent probe, slides were first incubated with horseradish peroxidase (HRP) (Advanced Cell Diagnostics) for the corresponding channel at 40°C for 15 min, then with the probe at 40°C for 30 min, and finally with HRP blocker (Advanced Cell Diagnostics) at 40°C for 15 min. Slides were washed twice with washing buffer solution for 2 min each between each incubation step. Slides were coverslipped with ProLong Gold Antifade Mountant (Advanced Cell Diagnostics) and stored at 4°C. An example of the staining can be seen in Supplemental Figure 1.
## Confocal imaging
Experimenters scanned all the tissue slices with a fluorescent microscope to identify brain regions that expressed either MC3R or MC4R mRNA. Twenty-six brain regions were chosen based on findings in previous literature, functional sites of melanocortin action, and visual inspection of expression within tissue by the experimenter. The 26 brain regions were identified based on A Stereotaxic Atlas of the Golden Hamster Brain (Morin and Wood, 2001). Schematic diagrams from the hamster atlas were used at the level that corresponds to where the region of interest was imaged and overlayed with a color fill-in using BioRender to help illustrate the mRNA labeling within a region. The serial sections were taken at a specific location (matched across brains) within each brain region, as indicated by the atlas plates and represent a rostral-caudal distance of less than 0.5 mm. Anatomical abbreviations are as follows:ARCArcuate nucleus of the hypothalamusBICNucleus of the brachium of the inferior colliculusBNSTBed nucleus of the stria terminalisCPu medial, lateral, dorsalCaudate-putamen medial, lateral, dorsalDEnDorsal endopiriform nucleusDRNDorsal raphe nucleusHPCHippocampus (dorsal/ventral CA1, CA2)ILInfralimbic cortexLHbLateral habenulaLSLateral septumMePDMedial amygdaloid nucleus, Posterodorsal partMDMediodorsal thalamic nucleusMSMedial septumMPOAMedial preoptic areaNAc coreNucleus accumbens coreNAc shellNucleus accumbens shellOTOlfactory tuberclePAGPeriaqueductal grayPrLPrelimbic cortexPVNPeriventricular hypothalamusVMHVentromedial hypothalamusVTAVentral tegmental area All images were acquired on a Leica TCS SPE confocal microscope under the same scanning parameters. An image was collected in the left and right hemisphere from two tissue sections (in series) for all brain regions for each subject (resulting in a sample of four images for each brain region for each subject). Images were collected with a 20X/0.60 advanced correction system objective with a pixel distribution of 1,024 × 1,024 at a frequency of 8 kHz. A solid-state laser with 488 and 532 nm wavelengths and an ultra-high dynamic PMT detector was utilized to capture z-stack images with 1.5 μm spacing for a maximum of 15 steps. The pinhole size was 1 airy unit (AU), 2 frames were averaged, and optical zoom was 1.00X. 3D images produced were 550 × 550 × 14 μm.
## Image analysis
Images were analyzed using Imaris software (Oxford Instruments, version 9.7.2) to investigate [1] the number of puncta of mRNA, [2] the number of cells that express MC3R and MC4R mRNA, and [3] the colocalization of MC3R and MC4R mRNA. First, the Spots feature was utilized to create a model of each channel. The estimated XY diameter was set at 0.650 μm. Next, the “quality” filter was applied, and the threshold was adjusted manually to include all spots that met the diameter in each channel. Then the “average distance to 3 nearest neighbors” filter was applied for all spots that passed the quality filter. The number of spots that were filtered into the model was then recorded for each of the MC3R and MC4R channels (Supplemental Figures 2A–C).
The Surfaces feature was then utilized to create a secondary model of each channel to estimate the number of cells that express MC3R and MC4R mRNA. The threshold for absolute intensity was set with a seed point diameter of 15 μm. First, the “quality” filter was applied, and the threshold was adjusted manually to include a seed point for all possible cells in each channel. Second, the “area” filter was applied such that all cells with an area of 19.63 μm3 were included in the surface model. Third, a “volume” filter was applied in order to include in the model only cells with a volume greater than 65.45 μm3. The number of cells that were filtered into the model was then recorded for each of the MC3R and MC4R channels. The criteria for cell surfaces excluded any non-specific binding that may be present, as the non-specific binding would not cluster in a way that meets the area and volume parameters that we have set (Supplemental Figures 3A–F).
For co-expression analysis, a similar procedure was followed using Imaris software. Receptor expression from the image was filtered such that only the overlap between two channels of interest was displayed. The Surfaces feature was used to create a model of the new channel, again using “quality,” “area,” and “volume” filters. Only cells that met these criteria were counted as colocalized. The percent of total cells that co-expressed MC3Rand MC4R mRNA was calculated by the following formula: cells containing both MC3R and MC4R label/total numbers of cells containing MC3R and/or MC4R label X 100.
## Plus scale conversion
The average number of cells expressing MC3R or MC4R mRNA (i.e., labeled cells) was calculated for each region and used to determine the strength of expression within a brain region (Table 1). The four images per region for all four subjects were averaged together. Five categories were created and are as follows: Regions that had an average of 0 cell count averages are categorized as lack of expression and labeled as −, 1–24 cell count averages are categorized as low and labeled as +, 25–49 cell count averages are categorized as moderate and labeled as + +, 50–74 cell count averages are categorized as high and labeled as + + +, and 75–100 cell count averages are categorized as highest and labeled as + + + +.
**Table 1**
| Brain region | MC3R | MC3R.1 | MC4R | MC4R.1 |
| --- | --- | --- | --- | --- |
| Brain region | Avg. cell count number | Plus conversion | Avg. cell count number | Plus conversion |
| PrL | 1 | + | 28.9 | + + |
| IL | 0 | − | 47.2 | + + |
| NAc Core | 0.8 | − | 15.3 | + |
| NAc Shell | 3.1 | + | 16.9 | + |
| CPu medial | 0 | − | 18.5 | + |
| CPu lateral | 0 | − | 5.9 | + |
| CPu dorsal | 0 | − | 11.9 | + |
| OT | 0 | − | 47.1 | + + |
| LS | 45.6 | + + | 20 | + |
| MS | 11.2 | + | 45.5 | + + |
| BNST | 7.1 | + | 39.1 | + + |
| DEn | 4.2 | + | 56.9 | + + + |
| MePD | 6.3 | + | 47.8 | + + |
| Dorsal CA1 | 3.7 | + | 33.4 | + + |
| Dorsal CA2 | 2.1 | + | 27.3 | + + |
| MD | 64.4 | + + + | 0 | − |
| LHb | 46 | + + | 10.3 | + |
| MPOA | 25.3 | + + | 80.1 | + + + + |
| PVN | 4.3 | + | 67.5 | + + + |
| VMH | 83.9 | + + + + | 8.7 | + |
| ARC | 37.8 | + + | 22.7 | + |
| Ventral CA1 | 1 | + | 90.1 | + + + + |
| PAG | 19.3 | + | 67.9 | + + + |
| BIC | 91.5 | + + + + | 0 | − |
| VTA | 7.3 | + | 62.2 | + + + |
| DRN | 3.8 | + | 67.1 | + + + |
## Telencephalon
In the telencephalon, there was a greater number of cells that expressed MC4R mRNA than MC3R mRNA in all the regions analyzed except the LS (Figure 1A; Table 1). The number of cells that expressed MC3R mRNA within the PrL and NAc shell was low, with an average of 1 and 3.1 cell counts, respectively. The IL of the medial prefrontal cortex, the subregions of the CPu, the NAc core, and the OT all had a lack of cells that expressed MC3R mRNA (0 to 4 average cell counts; Figures 1A, 2, 3). In the PrL and the IL the number of cells that expressed MC4R mRNA was moderate, an average of 28.9 and 47.2 cell counts, respectively, per 550 × 550 μm sampling area (Figure 1A). In the NAc core and shell the number of cells that expressed MC4R mRNA was similar, but low; cell counts were 15.3 for the NAc core and 16.9 for the NAc shell (Figure 1A). Low numbers of MC4R cells were also observed in the medial, the lateral, and the dorsal CPu. The number of cells that express MC4R was three times greater in the medial CPu (23.9) compared to the lateral CPu (7.8) and slightly greater compared to the dorsal CPu (14.5; Figure 1A). The number of cells that express MC4R mRNA within the OT was moderate with an average of 47.1 cells (Figures 1A, 3).
**Figure 1:** *The number of cells that express MC3R mRNA (blue left-hand bar for each pair) and MC4R mRNA (red right-hand bar for each pair) in the regions of the (A) telencephalon, (B) diencephalon, and (C) midbrain. Each symbol indicates a section of the region from each subject. Circles indicate counts from subject one. Squares indicate counts from subject two. Triangles indicate counts from subject three. Diamonds indicate counts from subject four.* **Figure 2:** *The expression of MC3R and MC4R mRNA in the IL and the PrL. For this and remaining figures: Coronal sections were used to estimate the expression of MC3R and MC4R mRNA (2–4 sections/hamster) using RNAscope. A schematic diagram outlining each region is shown on the left, followed by a representative RNAscope image on the right. Red overlay in the schematic depicts the general area of MC4R mRNA expression. Yellow overlay in the schematic depicts the general area of MC3R and MC4R mRNA expression. Blue overlay in the schematic depicts the general area of MC3R mRNA expression. Red labeling depicts MC4R mRNA. Blue labeling depicts MC3R mRNA. Created with BioRender.com* **Figure 3:** *The expression of MC3R and MC4R mRNA in the dorsal, the lateral, and the medial CPu (dCPu, lCPu, and mCPu), the NAc core and shell (NAcc and NAcs), and the OT.*
The LS had a greater number of cells that express MC3R mRNA compared to MC4R mRNA, whereas the MS had a greater number of cells that express MC4R mRNA compared to the number of cells that express MC3R mRNA. The number of cells that express MC3R mRNA within the LS was moderate, with an average of 45.6 cell counts (Figure 1A). The number of cells that express MC4R mRNA within the LS was low (20 average cell counts; Figure 1A). The number of cells that express MC3R mRNA within the MS was low, with an average of 11.2 cell counts (Figure 1A). The number of cells that express MC4R mRNA within the MS was moderate, with an average of 45.5 cell counts (Figures 1A, 4). The number of cells expressing MC3R mRNA was four times greater in the LS compared to the MS, whereas the number of cells expressing MC4R mRNA was two times greater in the MS compared to the LS. The number of cells that expressed MC3R mRNA within the BNST was low (7.1 average cell counts), whereas the number of cells that expressed MC4R mRNA within the BNST was moderate (39.1 average cell counts; Figures 1A, 4).
**Figure 4:** *The expression of MC3R and MC4R mRNA in the LS, the MS, and the BNST.*
There was a low average number of cells that expressed MC3R within the DEn, while the number of cells that express MC4R mRNA was high (4.2 and 56.9 average cell counts respectively; Figures 1A, 5). The number of cells that express MC3R mRNA within the MePD of the amygdala was low (6.3 average cell counts), and the number of cells that express MC4R mRNA within the MePD of the amygdala was moderate (47.8 average cell counts; Figures 1A, 5). The number of cells that express MC3R mRNA in the dorsal CA1 and CA2 regions of the hippocampus was low; there was an average of 3.7 and 2.1 cell counts, respectively, (Figure 1A). The number of cells that express MC4R mRNA in the dorsal CA1 and CA2 regions of the hippocampus were similar and moderate, there was an average of 33.4 and 27.3 cell counts, respectively, (Figures 1A, 5). The ventral CA1 region of the hippocampus had a low average number of cells that expressed MC3R mRNA (an average of 1 cell; Figure 1A). The ventral CA1 region of the hippocampus had the highest number of cells that express MC4R mRNA compared to the other regions analyzed in the hamster (90.1 average cell counts; Figures 1A, 6).
**Figure 5:** *The expression of MC3R and MC4R mRNA in the Dorsal CA1, Dorsal CA2, DEn, and MePD.* **Figure 6:** *The expression of MC3R and MC4R mRNA in the MPOA and the PVN.*
## Diencephalon
The greatest MC3R hybridization was observed within subregions of the diencephalon (Figure 1B; Table 1). However, there was also substantial MC4R hybridization which varied according to diencephalic subregion. The number of cells expressing MC3R mRNA within the MPOA was moderate (25.3 average cell counts), whereas the number of cells expressing MC4R mRNA within the MPOA was highest (80.1 average cell counts; Figures 1B, 7). There was a low average number of cells expressing MC3R within the PVN (4.3 average cell counts), whereas the number of cells expressing MC4R mRNA in the PVN was high (67.5 average cell counts; Figures 1B, 7). Melanocortin receptor 4 hybridization was greatest within the parvocellular regions of the PVN. The number of cells expressing MC3R mRNA within the LHb was moderate (46 average cell counts), whereas the number of cells expressing MC4R mRNA in the LHb was low (10.3 average cell counts; Figures 1B, 8). The number of cells expressing MC3R mRNA within the MD was high, there was an average of 64.4 cell counts (Figure 8). There were no cells expressing MC4R mRNA within the MD (Figures 1B, 8). The VMH had the highest MC3R hybridization for the brain regions analyzed in the diencephalon. There was an average of 83.9 cells within the VMH that expressed MC3R mRNA (Figure 1B). The number of cells that expressed MC4R mRNA within the VMH was low (8.7 average cell counts; Figures 1B, 9). The number of cells expressing MC3R mRNA was moderate while the number of cells with MC4R mRNA expression was low in the ARC (37.8 and 22.7 average cell counts respectively; Figures 1B, 9).
**Figure 7:** *The expression of MC3R and MC4R mRNA in the LHb and the MD.* **Figure 8:** *The expression of MC3R and MC4R mRNA in the VMH and the ARC.* **Figure 9:** *. The expression of MC3R and MC4R mRNA in the Ventral CA1 region of the hippocampus and the PAG.*
## Midbrain
Like the telencephalon, the midbrain generally had higher expression of MC4R mRNA compared to the expression of MC3R mRNA (Table 1). The highest levels of MC3R hybridization across all the regions examined was within the midbrain (Figure 1C). The PAG exhibited low MC3R hybridization (19.3 average cell counts; Figure 6). The number of cells that express MC4R mRNA within the PAG was high, with an average of 67.9 cell counts (Figures 1C, 6). The BIC had the highest number of cells that express MC3R mRNA compared to the other regions analyzed in the hamster (91.5 average cell counts; Figure 1C). There was a lack of cells that express MC4R mRNA within the BIC (Figures 1C, 10). The number of cells that express MC3R mRNA within the VTA was low while the number of cells that express MC4R mRNA was high (7.3 and 62.2 average cell counts respectively; Figures 1C, 10). The DRN exhibited a low average of cells that expressed MC3R mRNA (3.8 average cell counts; Figure 1C). Finally, the number of cells that express MC4R mRNA within the DRN was high (67.1 average cell counts; Figures 1C, 11).
**Figure 10:** *The expression of MC3R and MC4R mRNA in the BIC and the VTA.* **Figure 11:** *The expression of MC3R and MC4R mRNA in the DRN.*
## Colocalization
The colocalization of MC3R and MC4R mRNA was conducted in the BNST, LS, MS, MPOA, ARC, and PAG. These regions demonstrated in situ hybridization signaling of both MC3R and MC4R. There was variability in overlap, though in general there was a relatively low level of colocalization. The percentage of colocalization for cells that expressed both MC3R and MC4R mRNA in the LS was $8.1\%$. The percentage of colocalization for cells that expressed both MC3R and MC4R mRNA in the MS was $3.5\%$. The percentage of colocalization for cells that expressed both MC3R and MC4R mRNA in the BNST was $5.6\%$, with the percentage of colocalization for cells that expressed both MC3R and MC4R mRNA in the PAG at $5.6\%$ (Figure 12). The MPOA and the ARC exhibited the highest percentage of colocalization for cells that expressed both MC3R and MC4R mRNA compared to all the other brain regions analyzed. In the MPOA, $15.5\%$ of the cells that expressed MC3R mRNA also co-expressed MC4R mRNA (Figure 12). In the ARC, $11.2\%$ of the cells that expressed MC3R mRNA also co-expressed MC4R mRNA (Figure 12).
**Figure 12:** *On the left: a colocalization graph illustrating the number of cells that co-express MC3R and MC4R mRNA in select brain regions. On the right: a colocalization percentage graph illustrating the percentage of cells that co-express MC3R and MC4R mRNA in select brain regions. Bars indicate the average number or percentage of cells that co-expressed MC3R and MC4R mRNA. Circles indicate counts from subject one. Squares indicate counts from subject two. Triangles indicate counts from subject three. Diamonds indicate counts from subject four.*
## Discussion
In this study we mapped the expression of MC3R and MC4R mRNA across the central nervous system of Syrian hamsters. Overall, there is more widespread expression of MC4R mRNA in the hamster brain compared to the expression of MC3R mRNA. The expression of MC3R mRNA was contained primarily in the diencephalon. The expression of MC4R mRNA was greatest in the midbrain. These observations are consistent with those described in previous mapping studies (Roselli-Rehfuss et al., 1993; Gantz et al., 1993a,b; Mountjoy et al., 1994; Kishi et al., 2003; Bedenbaugh et al., 2022). Finally, with the exception of the MPOA and the ARC, there was very little co-expression of MC3R and MC4R mRNA in female Syrian hamsters in this study.
There have been multiple MC4R distribution studies – two in rats, one in mice, one in Atlantic salmon (Kalananthan et al., 2020), and one in the human hypothalamus (Siljee et al., 2013) – with a mixture of these studies having qualitative and quantitative data. There have been fewer studies mapping MC3R distribution. Each MC3R/MC4R distribution study has taken a different approach in creating a qualitative scale to compare levels of receptor expression. Aside from Bedenbaugh et al. [ 2022], the remaining studies did not explicitly state how they created their scale. For our study, a qualitative plus scale was derived from the average cell counts of neurons containing each receptor. The plus scale was used to compare the expression of MC3R and MC4R in hamsters with data reported for other species (Table 2). A plus scale was utilized for the MC3R distribution study conducted by Bedenbaugh et al. [ 2022]. The plus scale was generated from the intensity color of MC3R expression, with each color having a natural break in the numbers of labeled cells (Table 2). One study did not generate an expression level scale (Gantz et al., 1993b) and we did not use that study in our comparison.
**Table 2**
| Brain region | MC3R | MC3R.1 | MC3R.2 | MC4R | MC4R.1 | MC4R.2 |
| --- | --- | --- | --- | --- | --- | --- |
| Brain region | Hamster | Rat (Roselli-Rehfuss et al., 1993) | Mouse (Bedenbaugh et al., 2022) | Hamster | Rat (Mountjoy et al., 1994) | Rat (Kishi et al., 2003) |
| PrL | + | | + + + + | + + | | + |
| IL | − | | | + + | + + | + + |
| NAc Core | − | | + + | + | + + | + + |
| NAc Shell | + | | + + | + | + + | + + |
| CPu medial | − | | − | + | + + | + + + + |
| CPu lateral | − | | − | + | + + | + + + + |
| CPu dorsal | − | | − | + | + + | + + + + |
| OT | − | | | + + | + + + | + + + + |
| LS | + + | | + + + | + | + + to + + + + | + to + + + + |
| MS | + | | | + + | + + | + + |
| BNST | + | + | + + | + + | + + (+) | + |
| DEn | + | | | + + + | | |
| MePD | + | | + + + + | + + | + + | + + |
| Dorsal CA1 | + | + | | + + | + (+) | + |
| Dorsal CA2 | + | + | | + + | + + | + |
| MD | + + + | | - | − | + | |
| LHb | + + | | | + | | + |
| MPOA | + + | + (+) | + | + + + + | + + | + |
| PVN | + | + (+) | + + | + + + | + to + + + | + + to + + + + |
| VMH | + + + + | | + + + + | + | + to + + + | + |
| ARC | + + | + + | + + + | + | + | + |
| Ventral CA1 | + | + | | + + + + | | + |
| PAG | + | + | + + | + + + | + + | + to + + |
| BIC | + + + + | | | − | | + + |
| VTA | + | + + (+) | | + + + | + | |
| DRN | + | + + (+) | − | + + + | + | + |
The melanocortin system in the central nervous system is thought to be involved in a limited number of functions (Hill and Faulkner, 2017). Energy homeostasis is vital for the regulation of body weight, body temperature, and hormone balance among other processes. Melanocortin 3 receptors and MC4Rs regulate energy homeostasis and feeding behaviors (Ellacott and Cone, 2006). We observed staining for both receptors within hypothalamic and amygdalar regions that participate in these processes. The PVN, the VMH, and the ARC form a well-known pathway that regulates food intake and energy expenditure (Thomas et al., 2018). Energy homeostasis is regulated by proopiomelanocortin (POMC), neuropeptide Y (NPY), and Agouti-Related Protein (AgRP) expressing neurons in the ARC. POMC serves as a pro-peptide precursor for the melanocortin hormone. Further, AgRP is an endogenous receptor antagonist at MCRs that is expressed in NPY neurons in the ARC. The PVN receives input from POMC and AgRP expressing neurons in the ARC. All three of these regions were found to express MC3Rs and MC4Rs. Melanocortin 4 receptors in the PVN regulate body weight and food intake (Balthasar et al., 2005). Balthasar et al. [ 2005] hypothesized that specifically the parvocellular neurons of the PVN may be the mediators of food intake due to their projections to the nucleus of the solitary tract, which receives signals from the gut. The amygdala also has a role in regulating body weight (Balthasar et al., 2005). Melanocortin 3 receptor dependent roles in dopamine homeostasis and nutritional intake behaviors that occur within the VTA have been observed (Lippert et al., 2014; Dunigan et al., 2021b). Together, the expression pattern of MC3R and MC4R mRNA may be linked to the regulation of body weight.
There are species differences in MC3R mRNA expression in the PVN and the MePD of the amygdala, where the expression levels in hamsters are lower than that reported for the rat (Roselli-Rehfuss et al., 1993). The VMH of the hamster had similar expression levels of MC3R mRNA with that of the rat and mouse (Roselli-Rehfuss et al., 1993; Bedenbaugh et al., 2022). Melanocortin 3 receptor mRNA expression in the hamster ARC was equal to that in the rat and greater than the expression in the mouse (Roselli-Rehfuss et al., 1993; Bedenbaugh et al., 2022). The expression of MC4R within the PVN, the VMH, and the ARC in the hamster was similar to that in the rat, with rats having a range of expression depending on the subnuclei (Mountjoy et al., 1994; Kishi et al., 2003). Melanocortin 4 receptor expression levels within the MePD were equal in the hamster compared to the rat (Mountjoy et al., 1994; Kishi et al., 2003).
A secondary role of MC3Rs and MC4Rs includes sex behavior. Female hamsters serve as good animal models in investigating rewards derived from sex due to their immobility during lordosis (the primary rodent receptive posture), allowing researchers to isolate sex itself from other behaviors such as locomotion (Meisel et al., 1993). Erectile function in male rodents, leptin-stimulated hormone surges in female rodents, and grooming behaviors are all also mediated by the activity of MC3Rs and MC4Rs (Van der Ploeg et al., 2002; Pfaus et al., 2004; Tao, 2010). Lordosis can be inhibited or facilitated by alpha-MSH, an MC$\frac{3}{4}$R agonist; alpha-MSH receives signaling from estradiol to activate the neural pathways required for lordosis (Pfaus et al., 2004; Tsukahara et al., 2014). Pathways between the ARC, VMH, and LS are important for inhibiting lordosis behavior (Tsukahara et al., 2014). AgRP projections to the MPOA can stimulate the release of gonadotropin releasing hormone from the hypothalamus. The MPOA, BNST, and the PAG are involved in precopulatory behaviors such as odor preference and scent marking (Hennessey et al., 1992; Martinez and Petrulis, 2013). The expression of MC3R mRNA was greater than the expression of MC4R mRNA in the VMH and the LS, whereas the expression of MC3R and MC4R mRNA was equal in the ARC. Melanocortin 4 receptor mRNA expression was greater than the expression of MC3R mRNA in the MPOA, the BNST, and the PAG. This distribution of MC3R and MC4R expressing neurons may give insight into which receptor is more functionally represented in certain pathways.
The BNST and the PAG of the hamster had equal expression levels of MC3R mRNA with that of the rat whereas both regions along with the LS of the hamster had lower levels of MC3R mRNA expression compared to that of the mouse (Roselli-Rehfuss et al., 1993; Bedenbaugh et al., 2022). The MPOA of the hamster had higher levels of MC3R expression than that in the rat and mouse. The MC4R expression in the MPOA and PAG was higher in the hamster than that in the rat (Mountjoy et al., 1994; Kishi et al., 2003). The LS and the BNST of the hamster fall within the lower range of MC4R mRNA expression levels compared to the rat (Mountjoy et al., 1994; Kishi et al., 2003).
Studies have identified the involvement of MC3Rs and MC4Rs in reward circuits. Antagonists acting on MC3Rs and MC4Rs result in alterations in reward behaviors and locomotor activity (de Vrind et al., 2021; Dunigan et al., 2021a). A reward is derived from sex behaviors and regions such as the NAc, CPu, and the VTA are involved in the reward circuit. Injections into the VTA of agonists that are more selective for MC3R result in an increase in the release of dopamine in the NAc (Torre and Celis, 1988). In the NAc, CPu, and VTA, the expression of MC4R mRNA was greater than the expression of MC3R mRNA. The VTA has been previously observed as a region of high MC3R expression, though we observed low hybridization of MC3R in our hamsters. This may be due to differences in feeding behavior where hamsters maintain a rigid daily feeding schedule with feeding in compared to mice and rats where feeding is more of a motivated process (Schneider et al., 2002).
The MC3R mRNA expression levels of the hamster VTA are lower than that reported for the rat (Roselli-Rehfuss et al., 1993). The hamster NAc core and shell had lower levels of MC3R mRNA expression, compared to that of the mouse (Bedenbaugh et al., 2022). The CPu had a lack of MC3R expression across both the hamster and mouse. Nucleus accumbens core, shell, and CPuMC4R mRNA expression levels were lower in the hamster when compared to levels in the rat (Mountjoy et al., 1994; Kishi et al., 2003). The VTA of the hamster had higher MC4R expression than that of rats (Mountjoy et al., 1994; Kishi et al., 2003). Overall, the different brain regions have segregated subdivisions with different circuitry. Further, each function (metabolism, feeding, female sexual behavior, reward) has varied components, which may be mapped differently on the varied neural circuits.
Primary functions of melanocortin receptors are feeding and sex behavior, which was part of the basis for our focus on the forebrain and midbrain distribution of melanocortin receptors. Feeding circuits also involve hindbrain regions, suggesting that melanocortin receptors could be located more caudally. Further, melanocortins affect pain regulation which is modulated by hindbrain and spinal cord circuits. In fact, prior studies have demonstrated MCR distribution in the hindbrain and spinal cord. Kishi et al. [ 2003] and Bedenbaugh et al. [ 2022] mapped MC4R and MC3R mRNA across nuclei of the pons, medulla, and spinal cord. Melanocortin 4 receptor mRNA expression in the hindbrain ranged from background density in the nucleus ambiguus to highest density in the dorsal motor nucleus of the vagus and parabrachial nucleus, areas regulating feeding (Kishi et al., 2003). There was low to high density of MC4R mRNA expression within the spinal cord. Melanocortin 3 receptor mRNA expression was absent in the dorsal motor nucleus of the vagus nerve whereas labeling density was greater in the spinal nucleus of the trigeminal nerve (Bedenbaugh et al., 2022). As melanocortin 4 receptors have been linked functionally to pain processing, MC4R antagonists could be used therapeutically to reduce trigeminal neuropathic pain (Korczeniewska et al., 2023).
This study was conducted in four ovariectomized female subjects. Consequently, we do not know if there are differences in the number of MC3R or MC4R expressing neurons across the female’s reproductive cycle or whether there are sex differences for Syrian hamsters. Previous studies have found quantitative sex differences in MC3R and MC4R expression as well as sex differences in function. In the VTA, activation of MC3R neurons results in a significant decrease in food intake for females but not males, whereas the inhibition of MC3R neurons significantly decreases food intake in males but not females (Dunigan et al., 2021b). A knockout of MC3R results in reduced locomotor activity in female mice (Chen et al., 2000). It has been found that female mice have a significantly greater number of MC3R cells in the principal nucleus of the BNST, anteroventral periventricular nucleus, and ventral premammillary nucleus while male mice have a significantly greater number in the ARC (Sweeney et al., 2021; Bedenbaugh et al., 2022). At the same time that genetic sex or reproductive variables such as estrous cycle can affect expression of MC3R or MC4R receptors, these are quantitative differences that do not impact the overall distribution of melanocortin receptive neurons in the forebrain or midbrain. As such, the distribution of neurons we describe in our female hamsters should generalize to male hamsters as well.
One limitation of our study is that many regions are large with heterogenous subdivision and circuits. The MCR cell counts across regions will certainly vary depending on the particular subdivision sampled and where the sampling lies in a rostral-caudal dimension. We intentionally matched our sampling within restricted regions across subjects to ensure that each region was analyzed at the same level. The utility of our study and prior mapping studies is that it points the way for investigators interested in a specific brain region to perform detailed analyses of receptor distribution and circuits. Liu et al. [ 2003] provide an example of this approach where they engineered transgenic mice that expressed GFP in neurons under the control of an MC4R promoter. Following an analysis of the distribution of MC4R expressing neurons, they focused on the paraventricular nucleus of the hypothalamus and dorsal motor nucleus of the vagus. In the paraventricular nucleus MC4R was expressed in thyrotropin-releasing hormone producing neurons, whereas in the dorsal motor nucleus of the vagus MC4R was expressed in cholinergic neurons. Further, the paraventricular neurons were demonstrated to be responsive electrophysiologically to a synthetic melanocortin agonist.
To conclude, this study has added novel insights into the distribution of MC3R and MC4R across the central nervous system of a non-murine rodent and demonstrates the high level of conservation of melanocortin receptors among rodent species. Further, our study has advanced our understanding of the co-expression of MC3R and MC4R cells in the rodent nervous system, paving the way for future functional analyses of the melanocortin system.
## 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.
## Ethics statement
The animal study was reviewed and approved by University of Minnesota Institutional Animal Care and Use Committee.
## Author contributions
MH, RM, and JB designed the research. MH, AK-J, AP, GF, NS, and JB performed research. MH wrote the paper. All authors contributed to the article and approved the submitted version.
## Funding
This work was supported by grants from the National Institutes of Health to RM (R01 HD100007 and R01 HD100007-03S1). JB was supported by an NIH Training Grant (T32 DA007234) awarded to Paul Mermelstein.
## 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/fnmol.2023.1038341/full#supplementary-material
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|
---
title: Mitigation of Paeoniae Radix Alba extracts on H2O2-induced oxidative damage
in HepG2 cells and hyperglycemia in zebrafish, and identification of phytochemical
constituents
authors:
- Lu Zhang
- Mei Deng
- Si-yu Wang
- Qiao Ding
- Jia-hui Liu
- Xing Xie
- Yun-hong Huang
- Zong-cai Tu
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC9995737
doi: 10.3389/fnut.2023.1135759
license: CC BY 4.0
---
# Mitigation of Paeoniae Radix Alba extracts on H2O2-induced oxidative damage in HepG2 cells and hyperglycemia in zebrafish, and identification of phytochemical constituents
## Abstract
Paeoniae Radix Alba (PRA), as a Traditional Chinese Medicine, is widely used in Chinese cuisine due to high health-benefits and nutrition, but the effect of different polarity of solvents on the extraction of antioxidant and hypoglycemic constituents, as well as the major active compounds remain unclear. In this research, 40, 70, and $95\%$ ethanol were firstly applied to extract the polyphenols from PRA, the extraction yields, total phenolics, and total flavonoids content, free radical scavenging ability, α-glucosidase inhibition ability, and anti-glycation ability of extracts were evaluated spectroscopically. The oxidative damage protection, hypoglycemic activity, and alleviation on peripheral nerve damage were evaluated by H2O2-induced HepG2 cells and hyperglycemic zebrafish models. UPLC-QTOF-MS/MS was used to identify the major chemical constituents. The results showed that 40, 70, and $95\%$ ethanol exhibited insignificant difference on the extraction of phenolics and flavonoids from PRA. All extracts showed promising DPPH⋅ and ABTS⋅+ scavenging ability, α-glucosidase inhibition and anti-glycation ability. In addition, PRA extracts could restore the survival rate of HepG2 cells induced by H2O2, and alleviate the oxidative stress by reducing the content of MDA and increasing the levels of SOD, CAT, and GSH-Px. The $70\%$ ethanol extract could also mitigate the blood glucose level and peripheral motor nerve damage of hyperglycemic zebrafish. Thirty-five compounds were identified from $70\%$ ethanol extract, gallotannins, gallic acid and its derivatives, and paeoniflorin and its derivatives were the dominant bioactive compounds. Above results could provide important information for the value-added application of PRA in functional food and medicinal industry.
## 1. Introduction
Diabetes mellitus (DM) is a chronic metabolic disease characterized by continuous hyperglycemia due to either insulin deficiency or insulin resistance, along with metabolic disturbance of carbohydrate, fat, and protein. According to the International Diabetes Federation, the global diabetes prevalence in 2021 reached $10.5\%$ (536.6 million people), and was expected to be $12.2\%$ in 2045 (783.2 million people) [1]. The economic growth, urbanization, reduction of physical activity, and dietary habits are the main reasons for the rising of DM [2]. Prolonged hyperglycemia will damage multiple vital organs tissues (eyes, kidneys, heart, nerves, and blood vessels), resulting in many complications, such as neuropathy, nephropathy, retinopathy, and hypertension [3]. Currently, hypoglycemic drugs (biguanides, thiazolidinediones, α-glucosidase inhibitors, etc.) and injection of insulin are the major treatment of DM [4]. However, some of these approaches cannot effectively avoid the occurrence of complications and may result in many side effects, such as weight gain, hypoglycemia, gastrointestinal disturbances, flatulence, diarrhea, abdominal pain, and others [5]. Therefore, the development of new natural anti-diabetic resource with low side effects has great application prospect.
Furthermore, strong evidences showed that oxidative stress plays an important role during the development of DM and its complications through activating the metabolic pathways of glycolytic pathway, hexosamine pathway, and polyol pathway [6]. Oxidative stress was activated by the over production of reactive oxygen species (ROS) or the reduction of antioxidants, or both. Under the hyperglycemia conditions, the excessively produced ROS destroyed the steady state of reduction-oxidation (redox), resulting in oxidative damages of biomolecules (proteins, lipid, and DNA), formation of advanced glycation end products (AGEs), and dysfunction of β-cell and endothelial cells [7, 8]. Moreover, oxidative stress was reported to stimulate damage to nerve cell with exhaustion of cell antioxidants and generation of pro-inflammatory signals in the diabetic neuropathy [9]. Based on the close relationship between oxidative stress, DM and its complications, seeking of new therapeutic approaches focusing on oxidative stress are of great significance.
Natural extracts had attracted great attention because of their low side effects and promising protective effect on diabetes, inflammation, cancer, neurodegenerative conditions, etc. Paeoniae Radix Alba (PRA, Bai Shao in China) is the dried root of *Paeonia lactiflora* Pall, which has been used as traditional medicine for centuries in attenuating liver diseases, regulating menstruation and rheumatism, nourishing blood, and relieving pain [10]. It is also widely used in Chinese food due to its nutrition and healthcare function, especially in various stews and soups, such as stewed PRA with pig’s feet, stewed PRA with pigeon, oyster-PRA soup, et al. Recently, PRA extracts were found to exhibit diverse pharmacological activities, such as anticancer, antioxidant, immunomodulation, anti-inflammatory, and anti-diabetic properties due to its rich bioactive compounds, including monoterpene glycosides, tannins, volatile oils, polysaccharides, etc. [ 10, 11]. Juan et al. [ 11] indicated that PRA extracts exhibited anti-diabetic functions by stimulating glucose uptake, inhibiting glucose absorption and gluconeogenesis transcription. The total glucosides of PRA showed a protective effect on renal function in diabetic nephropathy by increasing T-AOC, SOD, and CAT activities, and reducing the level of MDA [12]. However, systematic research on the antioxidant and hypoglycemic activities of different PRA extracts in vitro and in vivo, and the identification of primary active compounds are still needed further study, which will provide more sufficient basis for the scientific application of PRA.
In this article, PRA was extracted with 40, 70, and $95\%$ ethanol. The antioxidant and hypoglycemic activities in vitro were evaluated by DPPH⋅ and ABTS⋅+ scavenging ability, α-glucosidase inhibition, and bovine serum albumin (BSA) glycosylation assays. Furthermore, to further evaluate the antioxidant capacity, the oxidative protection and influence on the activities of antioxidant enzymes were measured with HepG2 oxidative injury models induced by H2O2. In vivo hypoglycemic and peripheral motor nerve protective ability were evaluated with hyperglycemic zebrafish model. Finally, the major chemical composition was preliminarily identified by high performance liquid chromatography-high resolution mass spectrometry (HPLC-QTOF-MS/MS), which provided theoretical basis for comprehensive utilization of PRA.
## 2.1. Materials and chemicals
Dried PRA was purchased from Anqing Chunyuan Pharmacy (Anhui, China). Pioglitazone hydrochloride tablets were bought from Deyuan Pharmaceutical Co., Ltd. (Jiangsu, China). Glucose, gallic acid, quercetin, and ethanol were from Aladdin Biotechnology Technology (Shanghai, China). Acarbose, 1-diphenyl-2-picrylhydrazyl (DPPH⋅), 4′-nitrophenyl-beta-D-glucopyranoside (pNPG), 2,2-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS⋅+), and α-glucosidase were purchased from Sigma-Aldrich (St. Louis, MO, USA). HepG2 cell and Dulbecco’s modified Eagle’s medium (DMEM) with high sugar medium were from Beina Biology (Beijing, China). Amino guanidine was from Bio-Rad Laboratories Ltd. (Shanghai, China). Sitagliptin was bought from Macklin (Shanghai, China). All other chemicals were analytical grade and purchased from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China).
## 2.2. Sample preparation
The PRA powder (5.0 g) was homogenized in 40, 70, and $95\%$ ethanol aqueous solution, respectively (1:20, w/v). Then the mixtures were treated with KQ-300DE Ultrasonic cleaner (Kun Shan Ultrasonic Instruments Co., Ltd., China) at 50°C, 400 W for 1 h. After centrifugation at 7,000 rpm for 5 min, the supernatants were collected, the residues were re-extracted according to the same method. Finally, the supernatants were combined and evaporated, the volumes were adjusted to 100 ml with extraction solvent to obtain the extracts for further analysis. E40, E70, and E95 was used to refer to the extracts prepared with 40, 70, and $95\%$ ethanol aqueous solution, respectively. Meanwhile, 2.0 ml of extracts were taken out and lyophilized with LGJ-1D-80 Freeze Drier (Beijing Yatai Kelong Instrument Technology Co., Ltd., China) to calculate the concentration of extract, the yield of extract was calculated as follows:
## 2.3.1. Total phenolics content
The total phenolics content (TPC) was evaluated using Folin–Ciocalteu method as reported [13]. The absorbance at 765 nm was measured using Synergy H1 microplate reader (Biotek Vermont, USA). The calibration curve ($Y = 0.0058$x + 0.0511, R2 = 1.00) was constructed with gallic acid as standard to calculate the TPC in PRA extracts. All results were expressed as μg of gallic acid equivalent (GAE) per milliliter of extraction solution (μg GAE/ml).
## 2.3.2. Total flavonoids content
The total flavonoids content (TFC) was measured using AlCl3-NaOH-NaNO3 method according to the report of Zhang et al. [ 14]. Absorbance at 430 nm was measured using Synergy H1 microplate reader (Biotek Vermont, USA). The calibration curve ($Y = 0.0016$x + 0.056, R2 = 0.9985) was constructed with quercetin as standard to calculate the TFC in PRA extracts. All results were expressed as μg of quercetin equivalent (QuE) per milliliter of extraction solution (μg QuE/ml).
## 2.4. Determination of radical scavenging ability
The DPPH⋅ and ABTS⋅+ scavenging activity of samples were analyzed according to the method reported by Jia et al. [ 15]. The PRA extracts at different concentrations (50 μl) and DPPH⋅ or ABTS⋅+ working solution (150 μl) were mixed in 96-well microplate, then the mixtures were incubated for 30 min or 6 min at room temperature in darkness. The absorbance at 510 or 734 nm was measured using a Synergy H1 microplate reader (Biotek Vermont, USA). Quercetin and ethanol were used as positive and negative control, respectively. The radical scavenging activity was calculated as follows: where Ac was the absorbance of control group; Ab was the absorbance of blank group without samples and free radical solution; Ai was the absorbance of sample group, and Aib was the absorbance of sample blank group without free radical solution only. The IC50 values were calculated based on the curves of sample concentration vs. scavenging activity by Origin 2019 (OriginLab Co., Ltd., USA).
## 2.5.1. Cell culture and cells viability assay
The HepG2 cells were cultured in DMEM medium containing $10\%$ fetal bovine serum (FBS), 100 units/ml penicillin and 100 μg/ml streptomycin. To determine the toxicity of PRA extracts and H2O2, the viability of HepG2 cells was measured by CCK-8 assay [16]. Briefly, the HepG2 cells (8.0 × 103 cells/well) were incubated in 96-well plates at 37°C for 24 h in an MCO-18AC incubator (Panasonic, Japan) containing $5\%$ CO2, followed by the addition of PRA extracts (25, 50, 100, 200, 400, and 800 μg/ml) for 24 h cultivation or H2O2 (400, 500, 600, and 700 μM) for 6 h cultivation. Then, CCK-8 solution (10 μl/well) was added for another 2 h of incubation, the absorbance of each well at 450 nm was determined with SMR60047 microplate reader (USCNK, China).
## 2.5.2. Cytoprotective effect on H2O2-induced cell damage
After incubation in a 96-well plate (8.0 × 103 cells/well) for 24 h, HepG2 cells were pre-treated with PRA extracts (37.5, 75, and 150 μg/ml) for 24 h, followed by incubation with H2O2 (600 μM) for 6 h. Cells only treated with H2O2 were used as model group (oxidative stress). The CCK-8 assay kit was used to determine the cytoprotective effects of PRA extracts against H2O2-induced oxidative damage.
## 2.5.3. Measurement of antioxidant enzymes
After the establishment of oxidative damage in H2O2-induced HepG2 cells as described in section “2.5.2. Cytoprotective effect on H2O2-induced cell damage,” cells were lysed and centrifuged, the supernatant was used to measure antioxidant enzyme activities. The antioxidant activities of PRA extracts on HepG2 cells were detected using SOD, MDA, CAT, and GSH-Px assay kits (Jiancheng Bioengineering Institute, Nanjing, China) according to the instructions. The protein content of HepG2 cells was detected by the BCA assay kit (Jiancheng Bioengineering Institute, Nanjing, China).
## 2.6. Inhibitory activity on α-glucosidase
The α-glucosidase inhibitory activity was detected as previously reported [13]. Briefly, 50 μl PRA extraction solutions at different concentrations and 50 μl phosphate buffered saline (PBS) were incubated with 100 μl α-glucosidase solution (pH 6.9, 0.2 U/ml, in 0.1 M PBS) in 96-well plates at 25°C for 10 min. Then, 50 μl, 5 mM pNPG solution was added to each well. After 10 min of incubation at 25°C, Na2CO3 solution (100 μl, 0.2 M) was mixed to terminate the reaction, and absorbance at 405 nm was measured using a Synergy H1 microplate reader (Biotek Vermont, USA). Acarbose was used as positive control, the IC50 values were used to reflect the inhibition ability.
## 2.7. Inhibition on bovine serum albumin-fructose glycosylation
The glycosylation inhibition was measured with BSA-fructose glycosylation model [17]. The BSA solution (1.0 ml, 625 mM), fructose solution (1.0 ml, 20 mg/ml), and PRA extracts (100 μl, 1.0 mg/ml) were mixed and reacted at 55°C for 24 h. All sample solutions were 10-fold dilution prior to the measurement of fluorescence intensity at an excitation and emission wavelength of 350 and 425 nm, respectively. Hitachi F-7000 fluorescence spectrometer (Tokyo, Japan) was used to record the data, amino guanidine was taken as positive control. The inhibition rate of PRA extracts on BSA glycosylation was calculated as following: where FIs, FIc, FIb, and FInb were the fluorescence intensities of PRA extracts group, control group (without PRA extracts), blank group (without fructose and PRA extracts) and sample blank group (without fructose), respectively.
## 2.8.1. Determination of maximum detection concentration
Wild type AB zebrafish [390] aged 5 days were propagated and breed by Hunter Biotechnology Co., Ltd. accredited by the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC) International. They were randomly selected in a beaker containing 25 ml water with 30 in each group, and freely feed at 28°C under light/dark cycle conditions. The hyperglycemic zebrafish model was established with high-sugar and high-fat diet. Then, the E70 PRA extracts at 62.5, 125, 250, 500, 1,000, and 2,000 μg/ml were added into the water, respectively. During the experiment period, the numbers of zebrafish deaths in each group were calculated and removed in time. After 2 days of continuous treatment for 7.5 h/day, maximum detection concentration (MTC) of zebrafish with hyperglycemia model was determined. All procedures were approved by the Institutional Animal Care and Use Committee at Hunter Biotechnology, Inc. [approval number: IACUC-2020-2574-01, use license number: SYXK (zhe) 2022-0004]. The feeding and management were accredited by the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC) International (No. 001458).
## 2.8.2. Evaluation of hypoglycemic effects
After the determination of MTC, the hyperglycemic zebrafish model was established as described in section “2.8.1. Determination of maximum detection concentration.” The zebrafish without any treatment was taken as normal group. The zebrafish treated with 500, 1,000, and 2,000 μg/ml of E70 PRA extract were taken as treatment groups, while the zebrafish treated with 18 μg/ml pioglitazone (PGTZ) was used as positive control. The blood glucose level (G) was determined with blood glucose meter, the hypoglycemic effect was calculated as follows:
## 2.8.3. Evaluation of peripheral motor nerve protection
The transgenic motor neuron green fluorescent strain zebrafish (NBT, 270) aged 5 days (dpf) were randomly selected and feed in a beaker containing 50 ml water (30 zebrafish in each group). The hyperglycemic zebrafish model was established as described in section “2.8.1. Determination of maximum detection concentration,” following by the treatment with 1,000, 1,500, and 2,000 μg/ml E70 PRA extract at 28°C for 7.5 h/day. After 2 days treatment, 10 zebrafish in each group were randomly selected and photographed with AZ100 fluorescence microscope (Nikon, Japan). The fluorescence intensity (S) of peripheral motor nerve in the area of two segments above the ventral pores of zebrafish was analyzed by NIS-Elements D 3.20. Sitagliptin (STGP, 350 μg/ml) was used as positive control, the protective effect on peripheral nerve was calculated as follows:
## 2.9. Identification of phytochemical profiling
Isolation and identification of the major phytochemical constituents in the E70 of PRA was carried out on a UPLC LC30 SYSTEM (Shimadzu, Japan) coupled to a Hybrid Quadrupole-TOF Mass Spectrometer Triple TOF 5600+ system (AB SCIEX, USA). Compounds were separated on a YMC C18 column (4.6 × 250 mm, 5 μm, GL Science, Japan) at a flow rate of 0.8 ml/min. Acetonitrile and $0.1\%$ formic acids were used as mobile phases A and B, respectively. The optimized gradient elution program was: 0 min, $5\%$ A; 6 min, $9\%$ A; 7 min, $18\%$ A; and 30 min, $40\%$ A. All samples were filtered through a 0.22 μm membrane prior to injection (10 μl). The MS and MS/MS data were collected under negative ion mode in the scan range of m/z 100–1,500 and 50–1,500, respectively, with electrospray ionization (ESI) resource. The MS and MS/MS data were processed with software PeakView 1.2.
## 2.10. Statistical analysis
The experiments in vitro were repeated for three times. The results were expressed as mean standard deviation. One-way analysis of variance (ANOVA) with Tukey’s b test was carried out by SPSS 22.0 (Armonk, NY, USA) to assess the statistical significance difference among data, $P \leq 0.05$ refers significant difference.
## 3.1. Comparison of yield, TPC, and TFC
Phenolics and flavonoids were the main active components of PRA [18], their extraction efficacy will impact its further processing efficacy and the bio-activity of extracts, thus, influence of different polarity of solvents on the extraction of phenolics and flavonoids from PRA were evaluated. As indicated in Table 1, $70\%$ ethanol showed the highest extraction yield, with the percentage rate of $18.1\%$, followed by $95\%$ ethanol, and $40\%$ ethanol. While, the TPC in the extraction solutions were determined to be 281.94 ± 3.36, 276.82 ± 8.11, and 273.20 ± 9.55 μg GAE/ml, respectively, for 40, 70, and $95\%$ ethanol solution, insignificant difference was observed ($P \leq 0.05$). Meanwhile, different ethanol concentrations also have insignificant influence on the concentration of total flavonoids, the values were 37.92 ± 5.64, 36.67 ± 6.91, and 40.63 ± 4.88 μg QuE/ml ($P \leq 0.05$), respectively, when 40, 70, and $95\%$ ethanol were used. These indicated that 40, 70, and $95\%$ ethanol solution exerted insignificant difference on the extraction of phenolics and flavonoids in PRA.
**TABLE 1**
| Ethanol concentration (%) | Extraction yield (%) | Phenolics (μg GAE/ml) | Flavonoids (μg QuE/ml) | DPPH⋅ (IC50, μg E./ml) | ABTS+ (IC50, μg E./ml) |
| --- | --- | --- | --- | --- | --- |
| E40 | 11.9 | 281.94 ± 3.36a | 37.92 ± 5.64a | 165.77 ± 14.11a | 94.73 ± 2.06b |
| E70 | 18.1 | 276.82 ± 8.11a | 36.67 ± 6.91a | 151.31 ± 13.40a | 119.89 ± 8.30a |
| E95 | 15.5 | 273.20 ± 9.55a | 40.63 ± 4.88a | 165.88 ± 4.68a | 90.00 ± 7.02b |
| Quercetin | | | | 37.02 ± 0.24b | 7.97 ± 0.61c |
## 3.2. In vitro antioxidant abilities
Research had indicated that the activation of oxidative stress was closely associated with the pathological development of diabetic complications, and long-term hyperglycemia would in turn lead to the production of excessive ROS [8]. Antioxidants can alleviate oxidative stress by clearing the over-produced free radicals or activating the endogenous antioxidant defense systems, thereby eliminating the progression of diabetes and related complications [19]. Hence, it was meaningful to investigate the antioxidant activity of PRA extracts.
Two classic antioxidant models (DPPH⋅ and ABTS⋅+) were carried out to evaluate the antioxidant ability of PRA extracts. The DPPH⋅ and ABTS⋅+ scavenging capacity and corresponding IC50 values of PRA extracts and standard are shown in Figure 1 and Table 1, all ethanol aqueous extracts gave obvious radical scavenging ability in a dose-dependent manner, the IC50 values ranged from 151.31 to 165.88 μg E./ml for DPPH⋅ scavenging ability, and ranged from 90.00 to 119.89 μg E./ml for ABTS⋅+ scavenging ability, suggesting certain antioxidant potency. Currently, a variety of tannins, monoterpenoid, and flavonoids, especially for tannins have been identified in PRA, which might contribute to its antioxidant activity [20]. But the activity was all lower than that of positive control quercetin. In addition, no significant difference was observed among the DPPH⋅ scavenging ability of extracts prepared with 40, 70, and $95\%$ ethanol ($P \leq 0.05$). The 40 and $95\%$ ethanol extracts exhibited stronger ABTS⋅+ scavenging ability than $70\%$ ethanol extract, and insignificant difference was observed between E40 and E95 ($P \leq 0.05$).
**FIGURE 1:** *DPPH⋅ (A) and ABTS⋅+
(B) scavenging ability of 40% (E40), 70% (E70), and 95% (E95) ethanol extracts of PRA.*
## 3.3.1. Effect of PRA extracts on cell viability
It is necessary to evaluate the toxic concentration of PRA extracts on HepG2 cells before determining its oxidative protective effect. Cell viability is often used as an indicator of cytotoxicity, and it was generally accepted that when the survival rate of cells exceeds $80\%$ upon treated with a reagent, suggesting a non-cytotoxic effect [21]. As shown in Figure 2A, exposure HepG2 cells to 25, 50, and 100 μg/ml of PRA extracts did not reduce the cell viability. Oppositely, induction with 25 μg/ml of E70 and E95 enhanced the viability. The viability of HepG2 cells remained above $80\%$ when 200 μg/ml of E40, E70, or E95 was applied. While, the cell viability significantly decreased to 69.84 ∼ $78.41\%$ when the concentration of PRA extracts arrived at 400 μg/ml, and dramatically to 18.79 ∼ $25.08\%$ at 800 μg/ml, indicating severe HepG2 cells damage. Therefore, PRA extracts showed almost no cytotoxicity on HepG2 cells when the concentration was lower than 200 μg/ml, 37.5, 75.0, and 150 μg/ml of PRA extracts were thus selected for subsequent oxidative protection assays.
**FIGURE 2:** *The viability of HepG2 cells treated with PRA extracts (A) and H2O2
(B), effect of PRA extracts on cells viability of HepG2 cells induced by H2O2
(C). Different letters (a, b, c, etc.) in each column indicate significant difference among the data (P < 0.05). *P < 0.05.*
## 3.3.2. PRA extracts enhanced the viability of H2O2-induced HepG2 cells
H2O2, as a considerably active oxygen molecule with relatively stable properties, is one of the main factors causing excessive oxidative stress, which is widely used to establish cell oxidative damage model [16, 22]. In this research, 400 ∼ 700 μM of H2O2 were used to induce HepG2 cells for 6 h to establish the oxidative stress model. As shown in Figure 2B, compared with the untreated group, the cell viability declined in a concentration-dependent manner with the increasing of H2O2 concentration. The cell viability of HepG2 cells was reduced to $52.2\%$ when induced with 600 μM H2O2 for 6 h ($P \leq 0.01$). Therefore, the concentration of 600 μM was selected to induce oxidative stress for subsequent experiment.
The protection effect of PRA extracts against oxidative damage were determined with the H2O2-induced HepG2 cells oxidative stress model. As shown in Figure 2C, the cell viability of model group (induced by H2O2) significantly decreased ($P \leq 0.01$) compared to the normal group (without H2O2 and extracts treatment), and reached to $49.56\%$, indicating the successful establishment of the oxidative damage model. It was greatly restored to 69.79 ∼ $96.16\%$ when the cells were pre-treated with 37.5 ∼ 150 μg/ml of PRA extracts for 24 h prior to H2O2 incubation ($P \leq 0.01$). The highest viability was found on the cells pre-treated with 37.5 μg/ml of E40 or 75 μg/ml of E70 or E95, with the values enhanced by 1.87 ∼ 1.94-folds, indicating good alleviation of PRA extracts on cell oxidative damage. While, when the concentration was at 37.5 μg/ml, E40 and E70 treatment showed higher cell viability than E95, but no significant difference ($P \leq 0.05$) was observed among E40, E70, and E95 when the treatment concentration was at 75 or 150 μg/ml. The results disclosed that PRA extracts could significantly decrease the oxidative stress of HepG2 cells induced by H2O2, 40, 70, and $95\%$ ethanol extracts showed similar oxidative protection, and the activity will be weaker when the induction concentration over 150 μg/ml.
## 3.3.3. Effects on the levels of MDA and antioxidant enzymes
SOD, CAT, and GSH-Px are crucial endogenous antioxidant enzymes that provide the first line of defense against damage mediated by oxidative stress. The SOD responsible to catalyze O2– to H2O2, O2 and less reactive H2O2, CAT can directly convert H2O2 into H2O and O2, GPXs are a large family of diverse isozymes that use glutathione to reduce H2O2 [21, 23]. Intracellular MDA is a typical degradation product of lipid peroxidation in biological membranes, it can aggravate membrane damage and is often regarded as a biomarker of oxidative stress [22]. Therefore, changes in the level of antioxidant enzymes (SOD, CAT, and GSH-Px) and MDA are regarded as pivotal indicator of antioxidant ability evaluation.
Previous radical experiments showed that PRA could effectively scavenge free radicals in vitro, which meant that it might alleviate oxidative damage in HepG2 cells. As indicated in Figure 3A, the MDA level of HepG2 cells increased to 60.11 nmol/mg protein ($P \leq 0.01$) after being incubated with H2O2 for 6 h, indicating the occurrence of lipid peroxidation caused by H2O2-induced oxidative stress [24]. Addition of PRA extracts could apparently prevent the release of MDA as compared to the model group ($P \leq 0.01$), the MDA levels were markedly reduced to 6.28 ∼ 12.6 nmol/mg protein when pre-treated with 75 μg/ml PRA extracts for 24 h, the best suppression was detected on 75 μg/ml of E95 (6.28 nmol/mg protein), followed by 37.5 μg/ml of E40 (8.08 nmol/mg protein). The suppression on MDA production was weaken when the pre-treatment concentration of PRA extracts reached 150 μg/ml, but the MDA levels were still much lower than that of model group.
**FIGURE 3:** *Effects of different concentrations of PRA extracts on the levels of MDA (A), SOD (B), GSH-Px (C), and CAT (D) in H2O2-induced HepG2 cells. The different letters (a, b, c, etc.) upper each column indicate that there are significant differences among the data (P < 0.05).*
As shown in Figures 3B–D, SOD, GSH-Px, and CAT levels decreased significantly in H2O2 induced HepG2 cells, the enzyme activity was reduced from 71.56, 605.41, and 57.03 U/mg protein in normal group to 10.28, 71.10, and 11.63 U/mg protein ($P \leq 0.05$), respectively, suggesting the generation of oxidative stress. Pre-treatment with 37.5 ∼ 150 μg/ml of PRA extracts for 24 h significantly restored the antioxidant enzyme activity, especially for extracts at the concentration of 75 μg/ml. The highest SOD, GSH-Px, and CAT level was individually found on the HepG2 cells pre-treated with 75 μg/ml of E95, 37.5 μg/ml of E40 and 75 μg/ml of E95 ($P \leq 0.01$), and 37.5 μg/ml of E40, the enzyme level was improved by 5.2, 6.7 ∼ 6.8, and 4.3-folds, respectively, as compared with the model group. Consistent with the results of cell viability, at a low pre-treatment concentration (37.5 μg/ml), E40 exhibited much better mitigation effect than E70 and E95 on H2O2-induced reduction on SOD, GSH-Px, and CAT levels. The ability did not vary greatly at middle treatment concentration, while, at high concentration of 150 μg/ml, the SOD, CAT, and GSH-Px levels were the lowest, indicating the presence of certain cytotoxicity, but they were still significantly higher than that of model group ($P \leq 0.05$). Therefore, it can be concluded that PRA extracts showed good protection on HepG2 cells against H2O2 induced oxidative stress through decreasing MDA formation and improving the levels of endogenous antioxidant enzymes SOD, CAT, and GSH-Px.
## 3.4.1. Inhibition on the activity of α-glucosidase
α-*Glucosidase is* a critical carbohydrate hydrolase secreted by intestinal epithelium, and responsible for the degradation of disaccharides, trisaccharides, and oligosaccharides into absorbable monosaccharides. Inhibiting the activity of α-glucosidase has been regarded as one of the important methods to control diabetes and diabetic complications [25]. Hence, α-glucosidase inhibitory experiment was carried out to appraise the hyperglycemic activity of PRA extracts. Results were presented in Figures 4A, B. Obviously, all ethanol extracts displayed good α-glucosidase inhibitory activity, the percentage inhibition all over $85\%$ when the concentration of PRA extracts arrived at 200 μg E./ml. Moreover, the α-glucosidase inhibition of E40, E70, and E95 all much higher than that of positive control acarbose, with the IC50 value of 68.58, 69.01, 91.07, and 163.56 μg/ml, respectively. The strongest inhibitory activity was found on E40 and E70 ($P \leq 0.05$), which was about 2.4 times of that of acarbose. Which is consistent with the results of Sun et al. [ 26], who found that ethanol extract of PRA can effectively inhibit α-glucosidase activity.
**FIGURE 4:** *The α-glucosidase inhibition (A), IC50 values for inhibiting α-glucosidase (B), and anti-glycative ability (C) of PRA extracts. Different letters (a, b, c, etc.) upper each column indicate significant differences among the data (P < 0.05).*
## 3.4.2. Suppression on BSA glycosylation
Advanced glycation end products (AGES) are a class of heterogeneous compounds spontaneously formed during the advanced stage of protein glycosylation. The accumulation of AGEs in body tissues will increase the oxidative stress and contribute to the pathology of various diabetic complications, such as retinopathy, neuropathy, and cardiovascular complications [27]. Inhibiting protein glycosylation and AGEs accumulation have been considered as one of effective approach to alleviate diabetic complications. In this research, the inhibition of PRA extracts on protein glycosylation was evaluated with BSA-fructose model. As indicated in Figure 4C, the E40, E70, and E95 all exhibited stronger suppression on BSA glycosylation than standard amino guanidine, and displayed an obvious does-dependent relationship. While, no significant difference was found among E40, E70, and E95 at tested concentrations ($P \leq 0.05$). At the concentration of 1.0 mg/ml, the suppression ratio on AGEs formation of E40, E70, and E95 reached to 78.51, 82.40, and $80.63\%$, respectively, which was 1.82 ∼ 1.91 times of that of amino guanidine, suggesting the potential of PRA extracts in preventing diabetic complications.
## 3.5.1. Hypoglycemic effect
During decades, zebrafish (Danio rerio) has been one of the favorite and validated model organism in screening of drug against metabolic diseases due to the advantage of high human genetic homology, easier visualization of tissues and organs, short drugs induction time, allowing the use of small amount of compound, etc. [ 28]. In case of glucose metabolism, the biological mechanisms of zebrafish to regulate glucose homeostasis are very similar to those of humans, feeding on high glucose solution persistently can induce hyperglycemic symptoms and impair glucose metabolism [29]. Therefore, hyperglycemic zebrafish model was developed in this research to evaluate the in vivo hypoglycemic activity of PRA extract.
According to the results displayed in Table 1 and Figures 1–4, the E70 exhibited the highest yield, good radical scavenging ability, excellent prevention on oxidative stress, promising α-glucosidase inhibition and anti-glycation. It was thus chosen as the representative sample for in vivo hypoglycemic activity evaluation. The toxicological concentration of E70 was analyzed with 5 dpf zebrafish reared in dechlorinated tap water containing 62.5 ∼ 2,000 μg/ml of E70 at 25°C. After 2 days (7.5 h/day) of treatment, no dead zebrafish was detected, suggesting the feasibility to use the concentration below 2.0 mg/ml in subsequent experiments.
Effect of 0.5, 1.0, and 2.0 mg/ml of E70 on the blood glucose level of hyperglycemic zebrafish model induced by high-sugar and high-fat diet are shown in Figure 5. The blood glucose level of zebrafish in model group increased significantly from 0.86 mmol/L in normal group to 2.06 mmol/L ($P \leq 0.001$), which indicated that the model was successfully established. But it was reduced to 1.98, 1.80, and 1.20 mmol/L, respectively, upon treatment with 0.5, 1.0, and 2.0 mg/ml of E70, suggesting an obvious dose-dependent relationship. But no significant difference was observed between the glucose level of model group and 0.5 mg/ml of E70 treatment group ($P \leq 0.05$). When the concentration reached to 2.0 mg/ml, the blood glucose level decreased by $42\%$ ($P \leq 0.001$), which was similar to that of 18 μg/ml of pioglitazone ($47\%$), suggesting promising potential of high concentration of E70 in alleviating hyperglycemia. Studies have shown that paeony total glucosides could reduce blood glucose level by improving insulin sensitivity and lipid metabolism [30].
**FIGURE 5:** *Blood glucose of zebrafish treated with E70 PRA extracts (A), the fluorescence intensity of peripheral motor nerve of transgenic zebrafish (B), and fluorescence diagram of peripheral motor nerve of zebrafish (C). Compared with the model control group, **P < 0.01, ***P < 0.001. The red frame was the peripheral motor nerve, and the green fluorescence was the zebrafish motor neuron.*
## 3.5.2. Protective effect of peripheral motor nerve
Peripheral neuropathy is one of the typical chronic complications of diabetes, approximately $50\%$ of adult patients suffering from diabetes developed various degrees of peripheral neuropathy in their lifetime [31]. Dorsemans et al. [ 32] found that acute and chronic hyperglycemia impaired the regeneration and de novo formation of zebrafish neuronal cells, resulting to adverse effects on neurogenesis and brain healing. Alteration in the metabolic physiology associated with neurodegeneration was also found in the insulin resistance zebrafish larvae model by Razip et al. [ 33]. To evaluate the prevention of E70 on diabetic complication, the protective effect of PRA on peripheral nerve was evaluated by detecting the fluorescence intensity of peripheral motor nerve of normal and hyperglycemic zebrafish. As indicated in Figures 5B, C, the fluorescence intensity of model group was 139,884 ± 5,656, which was much lower than that of normal group (168,917 ± 6,706), indicating the appearance of peripheral motor nerve damage in high glucose zebrafish model. Upon incubation with 1,000, 1,500, and 2,000 μg/ml of E70, the fluorescence intensities were increased to 173,347 ± 4,977, 185,685 ± 5,149, and 179,153 ± 5,248, respectively ($P \leq 0.001$), the value for these incubated with 350 μg/ml of standard sitagliptin was 179,871 ± 7,772. Above results suggested that the E70 of PRA could effectively mitigate peripheral motor nerve damage of hyperglycemic zebrafish. Huang et al. [ 34] showed that PRA extract might be a potential nerve growth promoting factor and could promote the growth of damaged peripheral nerves through in vivo and in vitro experiments.
## 3.6. Identification of phytochemical profiling
In this work, the phytochemical constituents of E70 were further evaluated by HPLC-QTOF-MS/MS due to its excellent antioxidant and anti-diabetic activities. The composition was tentatively identified based on the molecular weight, fragment ions, retention time and formula. The total ion chromatogram and MS/MS information of E70 extract was respectively displayed in Figure 6 and Table 2. With the help of database and relevant literatures, a total of 35 compounds were identified in E70, which consisted of 5 organic acids, 4 phenolic acids, 12 tannins, 3 flavonoids, 8 terpenoids, and 3 other compounds.
**FIGURE 6:** *The total ion chromatography of $70\%$ ethanol extract of PRA under negative mode.* TABLE_PLACEHOLDER:TABLE 2
## 3.6.1. Organic acid
Peak 2 was identified as gluconic acid, the MS/MS ion at 159.0250 indicated the loss of two H2O. Peak 6 with fragment ion at 111.0082 ([M-H-CO2-2H2O]–) was assigned as citric acid. Peak 3 was proposed as trimethylhydroxycitric acid, the MS/MS ion at 161.0445 was produced by loss of two -CO2. Peak 7 was suggested as succinic acid resulted from the characteristic fragment ion at 73.0330 ([M-H-CO2]–). Peak 28 was proposed as 4-hexosylmethyl-5-oxo-2-pentyltetrahydro-3-furancarboxylic acid by matching the MS/MS ion with MassBank database [35].
## 3.6.2. Phenolic acids
Peak 11 was identified as gallic acid by comparing with standard due to the characteristic fragment ion at 169.0211 ([gallic acid-H]–). Peak 8 was proposed as galloylquinic acid based on the MS/MS ions at 191.0544 and 169.0134 [36]. Peak 13 with MS/MS ion at 153.0213 [M-162-H]– implied the presence of protocatechuic acid and loss of hexoside, and was suggested as protocatechuic acid hexoside [37].
## 3.6.3. Tannins
Peaks 9, 10, 17, and 23 were identified as gallic acid derivatives due to the diagnostic MS/MS ion at 169. Based on the MS/MS information, peaks 9 and 10 existed a hexose and sucrose moieties, and were characterized as monogalloyl-hexoside and galloylsucrose, respectively. Peaks 17 and 23 were suggested as digalloylglucopyranose and trigalloylglucose, respectively, the MS/MS ions at 313 and 465 indicated the presence of two and three galloyl groups. Peak 14 was proposed as corilagin, the MS/MS ion at 300.9992 was generated by the loss of a gallic acid and a glucose residue [18]. Peaks 22 and 29 with characteristic fragment ion at 124.02 (C6H4O3) demonstrated the loss of trihydroxyphenyl group, and were individually proposed as methyl gallate and ethyl gallate according to literature [38]. Peak 31 with MS/MS ions at 301.00, 283.99, and 229.02 was identified as ellagic acid by comparing with literature [39]. Peak 24 ([M-H]–, 463.0531, C20H16O13) was suggested as ellagic acid-4-O-glucoside, and was the glucoside substituted compound of peak 31. Peaks 35 and 38 showed the same molecular formula (C24H18O15) were assigned as dihydroxybenzoic acetate-digallate isomers, the MS/MS ions at 469.0553 [M-H-72]– and 393.0469 [M-H-152]– corresponded to the loss of hydroxyacetyl group and galloyl groups [18]. Peak 36 was galloyl substituted compound of peak 22, and was identified as methyl digallate.
## 3.6.4. Flavonoids
Three flavonoids were detected in E70. Peak 16 was suggested as (epi)catechin glucopyranoside, the MS/MS ion at 289.0734 represented the existence of (epi)catechin [40]. Peak 21 with MS/MS ions at 245.0885, 179.0586, and 109.0332 accounted for the characterization of (epi)catechin. Peak 30 with MS/MS ion at 271.0609 displayed the loss of glucoside, the characteristic fragment at 151.0034 was produced by naringenin, which was identified as naringenin-C-glucoside [41].
## 3.6.5. Terpenoids
Peaks 20, 25, 26, and 39 possessed the same MS ion at 479.1559, and was suggested as paeoniflorin isomers. The MS/MS ions at 283 and 121 indicated the loss of benzoic acid and glucose moieties [42]. The molecular weight of peaks 19 and 27 was 16 Da higher than that of peak 20, and were tentatively identified as oxypaeoniflorin isomers. Similar, peaks 32 and 34 gave the same molecular formula (C30H32O15), the molecular weight was 152 Da higher than peak 20 due to the loss of a galloyl group, which were proposed as galloylpaeoniflorin isomers.
## 3.6.6. Others
Peak 4 was characterized as sucrose through standard. Peak 15 with MS/MS ion at 101.0242 ([M-H-C16H23O8]–) was identified as penstemide [40]. Peak 37 was assigned as picrocrocinic acid by matching the MS/MS ions with that reported by Zhu et al. [ 43].
## 4. Discussion
In recent years, the prevalence of diabetes, especially type 2 diabetes, has been increasing. Uncontrolled diabetes can lead to many complications (neuropathy, nephropathy, retinopathy, cardiovascular diseases, etc.), which is also the cause of its high mortality. Oxidative stress has always been considered as a key factor in the development and progress of diabetes and its related complications, many studies are devoted to the role of antioxidants in diabetes [44]. It has been proved that plant extracts were excellent antioxidants and had potential applications in reducing oxidative damage of diabetes [45]. PRA contained a variety of bio-active compositions, which was a natural product with high antioxidant and hypoglycemic potential [11, 20].
In our research, $70\%$ ethanol exhibited the highest extract yield as compared with $40\%$ ethanol and $95\%$ ethanol. The E70 presented excellent free radical scavenging ability, anti-glycation ability, as well as the best α-glucosidase inhibition. In addition, the oxidative damage protection and in vivo hypoglycemic activity of PRA were evaluated by H2O2-induced HepG2 cells oxidative damage model and diabetic zebrafish model, respectively. Markedly, the cell survival rate of H2O2–induced HepG2 cells was increased when pre-treatment with PRA. The activities of SOD, CAT, and GSH-Px were obviously enhanced, and the level of MDA in cells was remarkably decreased. The results turned out that PRA extracts had good protection effect on HepG2 cells against damage induced by H2O2. UPLC-QTOF-MS/MS analysis showed that the main components of PRA were tannins (gallotannins, gallic acid, and its derivatives) and terpenoids (paeoniflorin and its derivatives), which were consistent with the found of Xiong et al. [ 18].
Tannins, especially for hydrolysable tannins, have been proved to show antioxidant, anti-glycation, and hypoglycemic activity by a multiple of researchers [46, 47]. Yamini Kosuru et al. [ 48] summarized that gallic acids and their derivatives could improve the antioxidant capacity by increasing antioxidant enzymes (SOD, CAT, and GSH-Px) and decreasing lipid per-oxidant. Ding et al. [ 49] indicated that ellagic acid ameliorated oxidative stress by reducing ROS and MDA levels and increasing SOD activity via activating miR-223-mediated keap1-Nrf2 pathway in high glucose-induced HepG2 cells. In case of terpenoids, Parker et al. [ 20] summarized that paeoniflorin possessed the highest concentration in PRA, it was also found abundance in E70. Yuan et al. [ 50] reported that paeoniflorin alleviated oxidative stress by increasing SOD and CAT levels in H2O2-induced HepG2 cells.
Oxidative stress was highly associated with DM and its complications [6]. The results indicated that E70 PRA extract significantly reduced the blood glucose level and alleviated peripheral motor nerve damage in diabetic zebrafish. Similarly, paeoniflorin was reported to be able to protected pancreatic β cells from streptozotocin-induced damage by inhibiting the p38 MAPK and JNK signaling pathways, and then maintained blood glucose level [51]. Further, Laddha and Kulkarni [52] reported that hydrolysable tannins, such as gallic acid played a vital role in diabetic peripheral neuropathy because of its antioxidant and insulin secretion promoting effects.
Based on above results, tannins, and terpenoids had excellent antioxidant and hypoglycemic activities, the abundance gallotannins, gallic acid and its derivatives, and paeoniflorin and its derivatives contributed to the promising antioxidant, anti-glycation, and hypoglycemic activities of PRA extracts.
## 5. Conclusion
In summary, our research showed that 40, 70, and $95\%$ showed insignificant difference on the extraction of phenolics and flavonoids from PRA, all ethanol aqueous extracts of PRA could effectively scavenge free radical, inhibit the activity of α-glucosidase and BSA glycosylation. Meanwhile, PRA extracts pre-treatment alleviated the H2O2-induced oxidative damage of HepG2 cells by increasing the activity of antioxidant enzymes SOD, CAT, and GSH-Px and reducing the production of MDA. Furthermore, E70 extract exhibited excellent hypoglycemic activity in vivo, the blood glucose level of diabetic zebrafish induced by high sugar and high fat was reduced by $42\%$ when treatment with 2.0 mg/ml of E70, which was similar to that of 18 μg/ml of pioglitazone ($47\%$). The peripheral motor nerve damage of hyperglycemic zebrafish was also obviously mitigated by 1.0 ∼ 2.0 mg/ml of E70. Totally, 35 compounds were identified or tentatively identified from E70 by HPLC-QTOF-MS/MS, terpenoids (paeoniflorin and its derivatives) and tannins (gallotannins, gallic acid and its derivatives) were the dominant antioxidant and hypoglycemic active constituents of E70. To sum up, these results indicated that PRA ethanol extracts may be a promising potential natural antioxidant and hypoglycemic resource to prevent the chronic degenerative disease caused by oxidative stress and prolonged high blood sugar.
## Data availability statement
The original contributions presented in this study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
## Ethics statement
This animal study was reviewed and approved by the Institutional Animal Care and Use Committee at Hunter Biotechnology, Inc., [approval number: IACUC-2020-2574-01, use license number: SYXK (zhe) 2022-0004].
## Author contributions
LZ: investigation, methodology, formal analysis, and writing—review and editing. MD: investigation, validation, formal analysis, and writing. S-YW: methodology, investigation, and original draft. QD: software, investigation, and statistical analysis. J-HL: investigation and validation. XX: supervision, methodology, and writing—review and editing. Y-HH: conceptualization, supervision, and methodology. Z-CT: funding acquisition and project administration. All authors contributed to the article and approved the submitted version.
## Conflict of interest
LZ was employed by Jiangxi Deshang Pharmaceutical Co., Ltd. The remaining 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.
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|
---
title: NLRP6 deficiency expands a novel CD103+ B cell population that confers immune
tolerance in NOD mice
authors:
- James A. Pearson
- Jian Peng
- Juan Huang
- Xiaoqing Yu
- Ningwen Tai
- Youjia Hu
- Sha Sha
- Richard A. Flavell
- Hongyu Zhao
- F. Susan Wong
- Li Wen
journal: Frontiers in Immunology
year: 2023
pmcid: PMC9995752
doi: 10.3389/fimmu.2023.1147925
license: CC BY 4.0
---
# NLRP6 deficiency expands a novel CD103+ B cell population that confers immune tolerance in NOD mice
## Abstract
### Introduction
Gut microbiota have been linked to modulating susceptibility to Type 1 diabetes; however, there are many ways in which the microbiota interact with host cells, including through microbial ligand binding to intracellular inflammasomes (large multi-subunit proteins) to initiate immune responses. NLRP6, a microbe-recognizing inflammasome protein, is highly expressed by intestinal epithelial cells and can alter susceptibility to cancer, obesity and Crohn’s disease; however, the role of NLRP6 in modulating susceptibility to autoimmune diabetes, was previously unknown.
### Methods
*We* generated NLRP6-deficient Non-obese diabetic (NOD) mice to study the effect of NLRP6-deficiency on the immune cells and susceptibility to Type 1 diabetes development.
### Results
NLRP6-deficient mice exhibited an expansion of CD103+ B cells and were protected from type 1 diabetes. Moreover, NLRP6-deficient CD103+ B cells express regulatory markers, secreted higher concentrations of IL-10 and TGFb1 cytokines and suppressed diabetogenic T cell proliferation, compared to NLRP6-sufficient CD103+ B cells. Microarray analysis of NLRP6-sufficient and -deficient CD103+ B cells identified 79 significantly different genes including genes regulated by lipopolysaccharide (LPS), tretinoin, IL-10 and TGFb, which was confirmed in vitro following LPS stimulation. Furthermore, microbiota from NLRP6-deficient mice induced CD103+ B cells in colonized NLRP6-sufficient germ-free mice; however, the long-term maintenance of the CD103+ B cells required the absence of NLRP6 in the hosts, or continued exposure to microbiota from NLRP6-deficient mice.
### Discussion
Together, our data indicate that NLRP6 deficiency promotes expansion and maintenance of a novel TGF -dependent CD103+ Breg population. Thus, targeting NLRP6 therapeutically may prove clinically useful.
## Introduction
The NLR family pyrin domain containing 6 (NLRP6) protein forms an inflammasome complex in human [1] and mouse [2, 3] cells, and is important in cleaving IL-1β and IL-18. NLRP6 is expressed in epithelial cells (2–6) and at a lower level in immune cells [1, 4, 6]. NLRP6 functions range from modulating immunity to bacteria (3, 6–8) and viruses [9], to regulating metabolic disease [10] and inducing protection from cancer [4, 11]. Little is known of the role of NLRP6-expressing immune cell types in autoimmune diabetes. NLRP6-associated effects are attributed to modulation of the gut microbiota composition [2, 3, 10]. However, other studies suggested that the altered microbiota composition may not be due to an intrinsic NLRP6-deficiency but extrinsic factors that include how the mice are reared for study [12, 13]. Thus, understanding how NLRP6 modulates disease development is important.
B cells are antigen-specific antigen presenting cells (APCs), in addition to producing (auto)antibodies and importantly, regulating immune responses. Regulatory B cells (Bregs) are potent producers of IL-10, TGFβ and IL35 and have phenotypes found in marginal zone (precursor) B cells, CD1dhiCD5+ B cells, plasmablasts and plasma cells (14–18). In addition, microbial products such as lipopolysaccharide (LPS) (19–21), or microbiota-driven IL-1β and IL-6, produced by splenocytes [22], can induce Bregs, indicating the importance of microbial influences on Breg development.
In Type 1 diabetes (T1D), T cells destroy the beta cells; however, B cells are important in modulating disease development. In B cell-deficient non-obese diabetic (NOD) mice, a mouse model of human T1D, diabetes incidence is greatly reduced (23–25). Furthermore, B cell depletion immunotherapy in patients with T1D and NOD mice, helps preserve islet β-cells from T cell-mediated destruction and reduces autoimmunity [26, 27]. B cells can also protect NOD mice from T cell-mediated autoimmunity [19, 21, 28]. However, the role of inflammasomes in B cells in the immunopathogenesis of T1D has not been studied. In addition, to our knowledge, the role of NLRP6 in B cells, in any disease setting, has not been elucidated. Therefore, we investigated the role of B cells in NLRP6-deficient NOD mice and hypothesized that NLRP6 deficiency would alter B cell development and functions, and thus, modulate susceptibility to T1D.
## Mice
NOD/Caj mice have been maintained at Yale University for over 30 yrs. NLRP6-/- C57BL/6 mice [2] were backcrossed onto the NOD background for more than 10 generations, and the NOD genetic background was verified by Illumina mouse whole genome SNP scan (DartMouseTM, Hanover, NH, USA). All the mice studied were bred from homozygous breeders. Recombinase-activating-gene deficient NOD (Rag-/-NOD) mice, NY8.3 T cell receptor transgenic (TCR-Tg) NOD and BDC2.5 TCR-Tg NOD, obtained from the Jackson Laboratory, have been maintained at Yale University for nearly 20 years. TLR4-deficient NOD mice, (kindly provided by A.V. Chervonsky, University of Chicago) were maintained at Yale for over a decade. All mice were maintained in a 12-hr dark/light cycle, in specific pathogen–free (SPF) individually-ventilated filter cages with access to water and autoclaved food ad libitum at the Yale Animal Resource Center. Most mice were females at 12-16-weeks unless specified, and studied in the morning. Mice were randomly assigned to experiments from multiple breeders. Germ-free (GF) NOD mouse breeders were generously provided by A.V. Chervonsky (bred and maintained at the gnotobiotic facility of the Yale Animal Resource Center). The Yale University Institutional Animal Care and Use Committee approved the procedures used in this study.
## Diabetes monitoring
Mice were monitored weekly for glycosuria with glucose strips (Bayer, Whippany, NJ, USA) from 10-weeks-old until termination (up to 30-weeks-old). Diabetes was confirmed following two consecutive positive glycosuria tests, 24-hrs apart, with a blood glucose ≥250mg/dl (13.9mmol/L), measured using a FreeStyle glucose meter (Abbott, Chicago, IL, USA).
## Intestinal immune cell isolation
Peyer’s patches were first removed from the small intestine (duodenum, jejunum and ileum) and large intestine (cecum and colon). The intestinal sections were flushed with 1xPBS, opened longitudinally, then cut into 1cm sections, washed in PBS, followed by incubation in RPMI-1640 +1mM Dithiotriol (DTT) for 10mins (both Sigma-Aldrich (St Louis, MI, USA)). Cells were pelleted and suspended in HBSS (Calcium- and Magnesium-free) containing 25mM HEPES, 1mM DTT and 1mM EDTA (all Sigma-Aldrich), followed by incubation for 30 minutes at 37°C on a shaker at 140 rpm. Post-incubation, cells were vortexed vigorously for 10 seconds and filtered through a 100μm nylon membrane, to obtain the intestinal epithelial lymphocytes (IELs). Remaining tissues were further digested for 1hr at 37°C, with shaking at 250 rpm, in RPMI-1640 containing 1mg/ml Collagenase from Clostridium Histolyticum (Sigma-Aldrich). Post-incubation, cells were vortexed vigorously for 10 seconds and nylon membrane-filtered, to obtain the lamina propria lymphocytes (LPs). IELs and LPs were washed twice and resuspended in $30\%$ Ficoll (GE HealthCare, Chicago, IL, USA), then layered on a $70\%$ Ficoll solution prior to density centrifugation (469 xg, 20 mins, room temperature). Isolated IELs were washed and resuspended in RPMI complete media (RPMI-1640 + L-glutamine containing $5\%$ FBS, 1mM HEPES, 1x MEM NEAA, 1mM Sodium pyruvate, 50μM 2-mercaptoethanol and 25μg Gentamycin sulfate (all Sigma-Aldrich)).
## Islet histology analysis
Formalin-fixed pancreata were embedded in paraffin and stained with hematoxylin and eosin. Insulitis was scored under a light microscope. 110-140 islets from 5 mice were individually blind scored.
## Pancreatic immune cell isolation
The pancreas was inflated with 3ml cold collagenase (Sigma; St Louis, MO, USA) solution (0.3mg/ml) through the bile duct with a 20G needle, then dissected and transferred into a 2 ml collagenase solution (1 mg/ml) in a siliconised glass tube followed by at 37°C in a water bath for 12–15 min. Following 3 washes, islets were hand-picked under a light microscope. Islets were treated with Cell Dissociation Solution (Sigma) and the single-cell suspension was harvested for flow cytometry.
## Cell staining and flow cytometry
Cells were pre-incubated with a Fc-blocking mAb (2.4G2, Biolegend), then incubated with pre-titrated antibody combinations for 30mins at 4°C, washed and stored at 4°C until analysis. For intracellular staining, cells were stimulated with PMA (50ng/ml, Sigma-Aldrich) and Ionomycin (500ng/ml, Sigma-Aldrich) in the presence of 1μl/ml GolgiPlug™ (BD, Franklin Lakes, NJ, USA) for 4-hrs. Post-stimulation, cells were stained for surface makers as above, then washed before fixing (20mins at RT in the dark) and permeabilization (eBioscience™ intracellular fixation and permeabilization buffer kit). Cells were then incubated with a Fc-blocking antibody (2.4G2; 15 minutes, 4°C), prior to staining for intracellular markers (30mins, 4°C). Samples were analyzed on a BD LSRFortessa Flow Cytometer with *Fluorescent minus* one (FMO) controls and isotype controls for gating. Results were analyzed by FlowJo v10.4 (BD). Antibodies to B220 (RA3-6B2; AB_313007), CD1d (K253; AB_10643277), CD4 (GK1.5; AB_493647), CD8 (53-6.7; AB_312751), CD5 (53-7.3; AB_312735), CD11b (M$\frac{1}{70}$; AB_755986), CD11c (N418; AB_493568), CD19 (6D5; AB_439718), CD$\frac{21}{35}$ (7E9; AB_940405 or AB_940413), CD23 (B3B4; AB_312831), CD24 (M$\frac{1}{69}$; AB_439716), CD40 (FGK45; AB_2860731), CD44 (IM7; AB_312957), CD80 (16-10A1; AB_313127), CD86 (GL-1; AB_493342), CD103 (2E7; AB_2128621), CD138 (281-2; AB_10962911), IgA (mA-6E1; AB_465917; eBioscience, San Diego, CA, USA), TCRβ (H57-597; AB_893625), TLR4 (SA15-21; AB_2561874), I-AK (cross reacts to I-Ag7; 10-3.6; AB_313457), H2-Kd (SF1-1.1; AB_313741), IL-4 (11B11; AB_493320), IL-6 (MP5-20F3; AB_10694868), IL-10 (JES5-16E3; AB_2566331), IL-17A (TC11-18H10.1; AB_315464), IL-21 (MHALX21; eBioscience; AB_2784739), IFNγ (XMG1.2; AB_2295770), TNFα (MP6-XT22; AB_493328) and TGFβ (TW7-16B4; AB_10898159) were used, in addition to a Zombie Aqua™ fixable viability dye. All antibodies were purchased from BioLegend (San Diego, CA, USA) unless specified.
## B cell isolation
Purified splenic B cells were isolated by magnetic (negative) selection using an EasySep™ Mouse B cell Isolation kit (StemCell Technologies, Vancouver, Canada), with >$98\%$ purity routinely confirmed by flow cytometry. CD103+ and CD103- B cells were gated from live, single CD19+ cells after gating out TCRβ+, CD11b+ and CD11c+ cells, followed by gating on CD103+ and CD103- cells and purification by Fluorescent-activated cell sorting (FACS).
## T cell isolation
Total splenocytes were incubated with hybridoma supernatants containing mAbs to CD8 (TB105; for isolation of CD4+ T cells) or CD4 (GK1.5; for isolation of CD8+ T cells), together with MHC-II (10.2.16) for 30mins at 4°C (kindly provided by the late Charles Janeway (Yale University). After washing with PBS, the cells were further incubated for 45mins on ice with magnetic beads (QIAGEN (Hilden, Germany) conjugated with goat anti-mouse IgG and IgM (to remove B cells) or goat anti-rat IgG (to remove CD4+ or CD8+ T cells and MHC-II+ cells). CD4+ or CD8+ T cells were then separated using a magnetic plate with a purity >90-$95\%$ as verified by flow cytometry.
## Adoptive transfer
Purified splenic T cells from diabetic (<1-week post-diagnosis) NOD mice were isolated and co-transferred [107] with purified splenic total or CD103- B cells (7.5x106), from 12-16-week-old non-diabetic mice, intravenously into 4-week-old Rag-/-NOD mice.
## Proliferation
Purified B cells [105] were stimulated with either anti-mouse IgM (VWR International, Radnor, PA, USA) in the presence of anti-CD40 (FGK4.5, BioXcell, Lebanon, NH, USA)) or LPS from E.coli O111:B4 (Sigma-Aldrich). For T-B cell co-culture (105/each), the splenic B cells were isolated and mitomycin-c-treated (Sigma-Aldrich), prior to co-culture with T cells in the presence of IGRP peptide for NY8.3 transgenic CD8+ T cells (VYLKTNVFL [29];) or BDC mimotope peptide for BDC2.5 transgenic CD4+ T cells (RTRPLWVRME [30];). After 48-hrs at 37°C, culture supernatants were collected prior to adding 3H-Thymidine, then a further 18-hr incubation was followed by harvest and analysis on a β-counter (Perkin Elmer, Waltham, MA, USA). Proliferation was measured as ΔCPM (counts per minute) with the background proliferation without antigen subtracted.
## Transwell
Purified splenic CD103+ B cells [105] were cultured in the insert of the transwell plate, while mitomycin-c-treated APCs [105] were directly co-cultured with BDC2.5 transgenic CD4+ T cells [105], in the presence of BDC mimotope peptide in the lower chamber of the transwell plate. Cells were cultured for 48-hrs at 37°C prior to supernatant collection and 3H-Thymidine addition, and assessed as described above.
## Cytokine ELISA
IL-10, IL-17a, IL-21 and TGFβ1 were measured using the ELISA MAX™ kit (BioLegend), Ready-SET-Go! ELISA kits (eBioscience) and mouse TGFβ-1 DuoSet ELISA kit (R&D Systems, Minneapolis, MN, USA), respectively, according to the protocols provided by the manufacturers and the results were analyzed on a microplate spectrophotometer (Perkin Elmer).
## Microarray
RNA was extracted from FACS-sorted splenic CD103+ or CD103- B cells, followed by cRNA synthesis and whole genome microarray analysis (Yale Center for Genomic Analysis). GeneChip® WT Plus Reagent Kit was used for sample preparation and ss-cDNA generation (Thermo Fisher Scientific, Waltham, MA, USA). 150 ng of total RNA were used for input. Affymetrix GeneChip Mouse Gene 2.0 ST arrays were washed using the GeneChip® Fludics Station 450 and scanned with the GeneChip Scanner 3000. All the reactions and hybridizations were carried out according to the manufacturer’s protocol. Data were normalized to control probes and adjusted p values were calculated. Microarray analysis was conducted using Ingenuity Pathway Analysis (IPA) software (QIAGEN). For heat map generation, the data for each gene was normalized to 1 (i.e. all 8 samples analyzed for each gene gave a sum of 1). RNA microarray data are available at GEO accession number GSE224472.
## Quantitative PCR
Intestinal tissues were stored in Trizol (Sigma-Aldrich) at -80°C, prior to RNA extraction and clean up using the RNeady Mini Kit (QIAGEN). Purified B cells were directly processed for RNA extraction. Equimolar concentrations of RNA were subsequently used for cDNA synthesis using an iScript™ cDNA Synthesis Kit (Bio-Rad, Hercules, CA, USA). qPCR was performed using an iQ5 qPCR detection system (Bio-Rad) according to the manufacturer’s instructions. The relative mRNA abundance was determined using the 2−ΔΔCt method by normalization with the housekeeping gene GAPDH. All primer sequences are listed in Supplementary Table 1.
## Fecal bacterial DNA extraction and sequencing
Fecal samples from 12-week-old mice were collected and stored at −20°C. Bacterial DNA was extracted as previously described [31]. Briefly, fecal samples were resuspended in 300 µl TE buffer (10 mM Tris and 1 mM EDTA, pH 8) containing 7.5 µl SDS ($0.5\%$) and 3 µl proteinase K (20 mg/ml) and incubated for 1 hour at 37°C (all Sigma-Aldrich). One volume of phenol/chloroform/isoamyl alcohol (25:24:1), 200 µl of $20\%$ SDS, and 0.3 g of zirconium/silica beads (0.1 mm; BioSpec, Bartlesville, OK, USA) were added, and samples were mixed with a Mini bead-beater (BioSpec) for 2 min. The samples were then mixed with 820 µl phenol/chloroform/isoamyl alcohol (25:24:1) and centrifuged, and the aqueous layer was collected into a new tube. The bacterial DNA was precipitated with 0.6 volume of isopropanol, washed with $70\%$ ethanol, air dried, and resuspended in 100 µl of sterile water. The V4 region of the bacterial 16S ribosomal gene was amplified from each DNA sample with barcoded broadly conserved bacterial primers (forward (5′-GTGCCAGCMGCCGCGGTAA-3′) and reverse primer (5′-GGACTACHVGGGTWTCTAAT-3′)). The PCR products were purified with a gel extraction kit (QIAGEN) and quantified with a spectrophotometer (Nanodrop), and equimolar amounts of each sample were pooled and pyrosequenced on an Ion Torrent personal genome machine sequencing system (Thermo Fisher Scientific). The results were analyzed using the QIIME software package (version 1.8) and UPARSE pipeline (version 7.0). After removing the primer sequences, the sequences were demultiplexed, quality filtered using QIIME, and further quality and chimera filtered in UPARSE pipeline. Operational taxonomic units (OTUs) were picked with $97\%$ identity in UPARSE pipeline. In QIIME, the Greengenes reference database was used for taxonomy assignment, which was performed at various levels using representative sequences of each OTU. β-Diversity was calculated to compare differences between microbial community profiles, and the data are shown as a principal coordinate analysis (PCoA).
## Intestinal permeability assay
Mice were fasted overnight before gavage with FITC-dextran (600 mg/kg; ~4000 MW, Sigma-Aldrich), with food supply restored 2-hrs post-gavage. 2-hrs later, serum was separated from blood samples by centrifugation (2300 xg, 5mins, room temperature (RT)), and diluted 1:1 in PBS in a 96-well plate. Serum FITC-dextran concentrations were determined using a fluorescence spectrophotometer (Perkin Elmer), with serum samples from non-FITC-dextran-gavaged mice used as baseline. Standard curves were generated using known concentrations of FITC-dextran diluted in control serum. Concentrations were determined using linear regression of a standard curve.
## Endotoxin measurement
Lipopolysaccharide (LPS) endotoxin content in the serum samples were detected using Pierce LAL Chromogenic Endotoxin Quantitation Kit (Thermo Fisher Scientific), using the manufacturer’s protocol.
## Immunoglobulin ELISA
Immunoglobulins in the culture supernatants or luminal gut were measured by direct ELISA. Briefly, samples or standards were used to coat the 96-well plate overnight at 4°C. After washing and blocking (1-hr, RT) with $1\%$ BSA in PBS on a shaking platform (400 rpm), the samples were then incubated with AP–conjugated goat anti-mouse IgA, IgG or IgM (Southern Biotech, Birmingham, AL, USA) for 2-hrs RT on a shaking platform). Samples were subsequently washed and PNPP substrate (Sigma-Aldrich) was added. The reaction was stopped upon addition of 1M NaOH. Samples were analyzed on a microplate spectrophotometer (Perkin Elmer) at 405 nm (OD). Antibody concentrations were determined by linear regression of standard curves.
## Oral gavage
Fresh fecal pellets were resuspended in sterile PBS and homogenized using a Mini bead-beater (30secs; BioSpec). Fecal material was then centrifuged for 2-mins at low speed (52xg) to remove dietary residue. The supernatant was transferred to a new tube and spun. Following 2 further repeats, the combined supernatant was centrifuged at 469xg to remove mammalian cells. Bacteria in the supernatant were pelleted by centrifugation at high speed (1876xg, 5 min) and resuspended in PBS. Bacterial colony forming units (CFUs) were determined by optical density (OD), pre-determined using an E. coli strain, with a spectrophotometer (Bio-Rad). Four-week-old GF NOD mice were colonized with 200μl sterile PBS containing 2x108 CFUs of stool bacteria by oral gavage. Colonized mice were terminated 2-4 weeks after gavage.
## In vitro LPS stimulation
Splenic B cells (5x106) were isolated and stimulated with or without LPS at different concentrations, for 20mins at 37°C. The cells were then pelleted and RNA was extracted immediately for cDNA synthesis and qPCR.
## In vitro TGFβ1 stimulation and blocking assays
Splenic B cells (5x106) were stimulated with 10ng/ml LPS for 12-hrs in the presence of either TGFβ1 or anti-TGFβ1 (BioXCell, clone 1D11.16.8; or a control antibody) at different concentrations. CD103 expression was evaluated by flow cytometry.
## Statistics
Statistical significance was determined using either a Gehan-Breslow-Wilcoxon test, a two-tailed Student’s T-test, a Two-way ANOVA or ANOSIM. All statistical analysis was conducted in GraphPad Prism V9, except microarray analysis which was analyzed using Ingenuity Pathway Analysis.
## NLRP6-deficient B cells exhibit increased CD103 expression, and are associated with protection from both spontaneous and transferred diabetes
To explore the role of NLRP6 in T1D, we generated NLRP6-/-NOD mice, backcrossing NLRP6-/-C57BL/6 mice onto the NOD genetic background for over 10 generations with $99.2\%$ NOD purity. We observed NLRP6-/-NOD and NLRP6+/+ littermates for spontaneous diabetes, and observed significantly delayed and reduced T1D development in female NLRP6-/- mice with reduced immune infiltration of the pancreas (Figure 1A and Supplementary Figure 1A) compared to NLRP6+/+ mice. Additionally, we studied mice at 12-16-weeks old, when some NLRP6+/+NOD, but not NLRP6-/-NOD mice, had developed diabetes. Whilst NLRP6 in intestine is defined (2–5, 11), the role of NLRP6 in the immune cells within the intestinal tissues is not clear. We observed increased intestinal B cells in both the intestinal epithelial layer and lamina propria (LP) of the small and large intestine in NLRP6-/- mice, compared to NLRP6+/+ mice (Figures 1B, C); however, the B cell proportion in other peripheral lymphoid tissues was comparable between the NLRP6-/- and NLRP6+/+ mice (Supplementary Figure 1B). Further phenotypic analysis of the B cells revealed an expanded CD103+ B cell population in NLRP6-/- mice in all intestinal and peripheral lymphoid tissues studied, except the pancreas (Figures 1D-G and Supplementary Figure 1C). This may suggest a dominant role of these CD103+ B cells in limiting entry of immune cells to the pancreas. Similarly, we also observed an increase in CD103 median fluorescent intensity (MFI) in all tissues examined from NLRP6-/- mice, compared to NLRP6+/+ mice (Supplementary Figures 1D-F). Interestingly, we also found differences in the intestinal and peripheral IgA+ B cells in NLRP6-/- mice (Figures 1H-J). Coinciding with the expansion of CD103+ B cells, CD11b+CD11c+, CD11c+CD11b- dendritic cells (DCs) cell subsets and CD11b+CD11c- macrophages were reduced in some intestinal and peripheral lymphoid tissues in NLRP6-/-NOD mice compared to NLRP6+/+NOD mice (Supplementary Figures 2A-I); however, no differences were seen in CD103+ DCs between NLRP6-/- or NLRP6+/+ mice in any tissues studied (Supplementary Figures 2J-L). We observed no differences in T cell or Treg proportion (Supplementary Figure 3). To determine if NLRP6-/- B cells contributed to β-cell immune tolerance, we co-transferred splenic B cells isolated from either NLRP6+/+ or NLRP6-/- donor mice with splenic T cells from newly diagnosed diabetic NOD mice (NLRP6+/+) into immunodeficient Rag-/-NOD mice. We found that NLRP6-/- B cells significantly delayed diabetes development in the Rag-/-NOD recipients, compared to the recipients that were co-transferred with NLRP6+/+ B cells (Figure 1K). Moreover, this delay was associated with an increased frequency of CD103+ B cells in NLRP6-/- B cell recipients (Figure 1L). To verify the role of CD103+ B cells in the protection, we repeated the experiment with CD103+ depleted B cells from both NLRP6+/+ or NLRP6-/- mice and found no difference in diabetes development (Figure 1M), suggesting CD103+ B cells delayed development of T1D. Together, our data indicated that NLRP6-/- CD103+ B cells were potentially associated with immune tolerance to autoimmune diabetes.
**Figure 1:** *Increased CD103+ B cells, found in NLRP6-deficient mice exhibit are associated with protection from autoimmune diabetes development. (A) NLRP6+/+NOD (n=19) and NLRP6-/-NOD (n=14) female mice were observed for spontaneous diabetes development until 30-weeks-old. (B, C) The proportion of B cells were investigated by flow cytometry in the intestinal epithelial layer (B) or lamina propria (C) from 12-16-week-old NLRP6+/+NOD and NLRP6-/-NOD mice. B cells were gated from single, live CD45+TCRβ-CD11b-CD11c- cells prior to gating on CD19+ cells. (D) Representative flow cytometric plots of CD103+ B cells, gated from single, live CD45+CD19+TCRβ-CD11b-CD11c- cells prior to gating on CD103+ B cells. (E-G) CD103+ B cells from 12-16-week-old NLRP6+/+NOD and NLRP6-/-NOD mice from the intestinal epithelial layer (E), lamina propria (F) and peripheral lymphoid tissues (G). (H-J) Proportion of IgA+ B cells from the intestinal epithelial layer (H), lamina propria (I) and peripheral lymphoid tissues (J). (K, L) Splenic B cells from 12-16-week-old NLRP6+/+NOD and NLRP6-/-NOD mice were co-transferred with T cells from newly diabetic NLRP6+/+ NOD mice into 4-6-week old Rag-/-NOD mice (7.5x106 B cells and 10x106 T cells). (K) The incidence of diabetes development in the Rag-/- NOD recipients (n=10/group). (L) CD103+ B cell proportion and number in non-diabetic Rag-/- NOD mice, 2-weeks after transfer. (M) The incidence of diabetes development in the Rag-/- NOD recipients of splenic CD103- B cells either from 12-16-week-old NLRP6+/+NOD or NLRP6-/-NOD mice co-transferred with T cells from newly diabetic NLRP6+/+ NOD mice (7.5x106 CD103- B cells and 10x106 T cells). Abbreviations include pancreatic lymph nodes (PLN), mesenteric lymph nodes (MLN), Peyer’s patches (PP) and Fluorescence minus one (FMO). Data are pooled from 2-3 independent experiments (n=6 unless specified) with lines indicating the median value. Data were assessed for significance using a Gehan-Breslow-Wilcoxon test (A, K, M) or a two-tailed Student’s T-test.*
## CD103+ B regulatory subsets are expanded in NLRP6-deficient mice
We further examined the CD103+ B cells, comparing their phenotype with features commonly associated with Bregs. A number of Breg populations have been suggested, including CD1dhiCD5hi B10 cells, splenic Transitional 2 (T2) marginal zone (MZ) precursor Bregs (CD21+CD23+), MZ Bregs (CD21+CD23-), antigen-experienced T2 MZ Bregs (CD21intCD24int) and plasmablast Bregs (CD44+CD138+) (14–17, 32). We found few proportional changes in any of these Breg subsets (Figures 2A-F and Supplementary Figure 4) between NLRP6+/+ and NLRP6-/- mice; however, comparison of the overall phenotype of CD103+ and CD103- B cells between NLRP6-sufficient and NLRP6-deficient NOD mice demonstrated that NLRP6-deficient mice had more CD103+ than CD103- Bregs than their wild-type counterparts (Supplementary Figure 5). In contrast, CD103- B cells between NLRP6-sufficient and -deficient mice showed minimal differences (Supplementary Figure 6), suggesting that NLRP6-deficiency contributes to the increased CD103+ B cell population with several Breg phenotypes.
**Figure 2:** *Characterization of CD103+ B cells and their function. CD103+ and CD103- B cells from 12-16-week-old NLRP6+/+NOD and NLRP6-/-NOD mice were studied for phenotypic and functional changes. CD19+ B cells were gated from live, single CD19+TCRβ-CD11b-CD11c- cells prior to gating on CD103+ cells and subsequent gating on the specific regulatory B cell subsets. Proportion of CD1dhiCD5hi
(A), splenic CD21+CD23+
(B), splenic CD21+CD23-
(C), splenic CD21hiCD24hi
(D), splenic CD21intCD24int
(E) and CD138+CD44+
(F) B cells. CD103+ B cell secreted cytokines were investigated following brief 4-hr PMA and Ionomycin stimulation in the presence of Golgi Plug. Cytokine-secreting B cells were gated from live, single CD19+TCRβ-CD11b-CD11c-CD103+ cells prior to gating on the specific cytokine. (G) Summarised cytokine results from the PLN of all cytokines investigated in both CD103+ and CD103- B cells from NLRP6-sufficient and -deficient mice. Individual CD103+ B cell secreted cytokines are shown for TGFβ1- (H), IL-10- (I), IL-17a- (J) and IL-21 (K) from all tissues studied. CD103+
(L) or CD103-
(M) B cells were stimulated with anti-IgM (in the presence of 1μg/ml anti-CD40). Proliferation was assessed by 3H-Thymidine incorporation. Culture supernatants from (L-M) taken at 48-hrs were measured for IL-10 and TGFβ1 (N-Q) concentrations by ELISA. (R-U) CD103+ B cells (R, S) or CD103- B cells (T, U) were mitomycin-c-treated and co-cultured 1:1 with NY8.3 CD8+ T cells or BDC2.5 CD4+ T cells in the presence of IGRP peptide (designated NY8.3 peptide) or BDC2.5 mimotope peptide (designated BDC mimotope) respectively, and assessed for proliferation by 3H-Thymidine incorporation, averaged from triplicates. (V) CD103+ B cells were cultured in a transwell with BDC2.5 CD4+ T cells and mitomycin-c-treated NOD splenocytes in the presence of BDC mimotope, and assessed for proliferation by 3H-Thymidine incorporation, averaged from triplicates. All data shown (L-V) were corrected for background (baseline proliferation CPM or cytokine production cytokine in the absence of stimulation). Abbreviations include pancreatic lymph nodes (PLN), mesenteric lymph nodes (MLN) and Peyer’s patches (PP). Data were pooled from 2-3 independent experiments (n=6-10), with lines indicating the median value (A-K) or mean and SD (L-V). Data were assessed for significance using a Student’s T-test (A-K) or a Two-way ANOVA (L-V).*
## NLRP6-deficiency enhances CD103+ B cell cytokine secretion and reduces B cell and antigen-specific T cell proliferation
As CD103+ regulatory B cells were expanded, to assess the regulatory function of CD103+ B cells, we first determined the cytokine production profile by intracellular cytokine (ICC) staining. Interestingly, we found a significantly higher proportion of cytokine-secreting CD103+ B cells, compared with the CD103- B cells (Figure 2G and Supplementary Figure 7). Furthermore, much higher proportions of NLRP6-/- CD103+ B cells were TGFβ1-, IL-10- and IL-17a-producing cells but IL-21-producing CD103+ B cells were reduced, compared to the CD103+ B cells from NLRP6+/+ mice (Figures 2H-K). No differences were found in IL-4-, IL-6-, IFNγ- or TNFα-producing CD103+ B cells with or without NLRP6 (Supplementary Figures 8, 9). To further elucidate functional differences, we FACS-sorted splenic CD103+ and CD103- B cells from NLRP6-sufficient and -deficient mice and stimulated the cells with anti-IgM (BCR) and anti-CD40 (coreceptor) in vitro. Both NLRP6-deficient CD103+ and CD103- B cells had impaired proliferative responses to anti-IgM/CD40 stimulation, compared to their NLRP6-sufficient counterparts (Figures 2L, M). However, NLRP6-deficient CD103+ B cells secreted much higher concentrations of IL-10 and TGFβ1 (Figures 2N-Q). As B cells are also potent antigen presenting cells, we next assessed the CD103+ and CD103- B cells for presentation of autoantigen to diabetogenic BDC2.5 CD4+ (recognizing insulin/chromogranin A hybrid peptide) and NY8.3 CD8+ (recognizing an islet-specific-glucose-6-phospatase catalytic subunit related protein) T cells. Interestingly, NLRP6-/- CD103+ B cells, but not NLRP6-/- CD103- B cells, had significantly reduced ability to promote the antigen-specific proliferation of both diabetogenic CD4+ and CD8+ T cells, compared to the NLRP6-sufficient CD103+ and CD103- B cells respectively (Figures 2R-U). There were no differences in MHC or the proportion of costimulatory molecule expression (Supplementary Figure 10); however, anti-inflammatory cytokine secretion by NLRP6-deficient CD103+ B cells, compared to NLRP6-sufficient CD103+ B cells, were sufficient to reduce antigen-specific T cell activation when cultured in a transwell system with BDC2.5 CD4+ T cells (Figure 2V). Together, our data suggested that the NLRP6-deficiency led to an expansion of novel CD103+ Breg cells that have enhanced tolerogenic cytokine production which dampens autoreactive T cell responses.
## NLRP6-deficient CD103+ B cells are intrinsically different from NLRP6-sufficient CD103+ B cells
We investigated the molecular signature of the novel CD103+ Breg cells, especially the effect of NLRP6 on these cells, with an unbiased approach, performing an RNA microarray analysis of FACS-sorted CD103+ and CD103- B cells from NLRP6+/+ and NLRP6-/- splenocytes. We observed 79 differences in gene expression between NLRP6-sufficient and -deficient CD103+ B cells, while only 5 genes were different between NLRP6-sufficient and -deficient CD103- B cells (Figure 3A). In agreement with our earlier data at the cellular level, we also found that NLRP6-sufficient and -deficient CD103+ B cells were significantly different to the respective CD103- B cells at the gene expression level (Supplementary Figure 11A and Supplementary Table 2). To determine potential regulators of CD103+ B cells we conducted an upstream pathway analysis using Ingenuity Pathway Analysis in NLRP6-deficient CD103+ B cells compared to NLRP6-deficient CD103- B cells. Of the top 20 upstream regulators, we found multiple cytokines including IL-10 and TGFβ, all of which were secreted in higher concentrations from CD103+ B cells compared to CD103- B cells (Figure 3B and Figures 2H, I). TGFβ, IL-6, 17-alpha-ethinylestradiol, LPS and IL-4 were also in the top 20 upstream regulators when comparing NLRP6-sufficient CD103+ to NLRP6-deficient CD103+ B cells (Supplementary Figure 11B). Interestingly, comparison between NLRP6-sufficient CD103- and CD103+ B cells resulted in very different upstream regulators, suggesting different pathways of modulation were used (Supplementary Figure 11C). Our microarray data provided evidence that the absence of NLRP6 resulted in the promotion of tolerogenic CD103+ B cells in response to different upstream regulators, compared to CD103- B cells and NLRP6-sufficient CD103+ B cells. In the comparisons between NLRP6-deficient CD103+ and CD103- B cells (Figure 3B) and NLRP6-sufficient CD103+ and NLRP6-deficient CD103+ B cells (Supplementary Figure 11B), we identified LPS to be an upstream regulator, suggesting that gut microbiota may modulate CD103+ B cells.
**Figure 3:** *Identification of pathways involved in inducing regulatory NLRP6-deficient CD103+ B cells. FACS-sorted CD103+ and CD103- B cells from 12-16-week-old NLRP6+/+NOD and NLRP6-/-NOD mice were investigated for gene expression changes by RNA microarray (n=2/group from 2 different experiments). (A) Heat map of normalized gene expression data showing the significant differences between CD103+ B cells from NLRP6+/+NOD and NLRP6-/-NOD mice (79 changes; top) or CD103- B cells from NLRP6+/+NOD and NLRP6-/-NOD mice (5 changes marked with a *); 1 change shared with CD103+ B cell comparison (Nxep2)). High gene expression is shown in red, while green indicates low gene expression. Data are organized by genes increased or decreased in NLRP6+/+NOD vs NLRP6-/-NOD mice, in order of significance. (B) Upstream analysis using IPA software showing the activation z-score of the top 20 regulators of CD103+ B cells or CD103- B cells using data from NLRP6-/-NOD CD103+ B cells vs CD103- B cells. Data are organized from left to right in order of significance. (C-G)
Tlr4
(C), Aldh1a1
(D), Aldh1a2
(E), Aldh3a1
(F) and Ahr
(G) gene expression from NLRP6-sufficient or -deficient CD103- or CD103+ B cells, investigated by qPCR. The relative mRNA abundance was determined using the 2−ΔΔCt method by normalization, with the housekeeping gene Gapdh. (H) Proportion of CD103+ B cells from TLR4-sufficient and TLR4-deficient NOD mice. Abbreviations include Toll-like receptor 4 (Tlr4), Aldehyde dehydrogenase (Aldh), Aryl hydrocarbon receptor (Ahr), pancreatic lymph nodes (PLN), mesenteric lymph nodes (MLN) and Peyer’s patches (PP). Data were pooled from 2-3 independent experiments (n=6), with the line indicating the median value. Data were assessed for significance using a two-tailed Student’s T-test (C-H).*
## TLR4 is upregulated in NLRP6-deficient CD103+ B cells
To test this hypothesis, we investigated Tlr4 expression in different B cell subsets by qPCR, and our results revealed that Tlr4 expression was significantly higher in NLRP6-deficient CD103+ B cells compared with CD103- B cells (Figure 3C), suggesting an increased ability to detect bacterial LPS. This was also confirmed by flow cytometry (Supplementary Figure 12). Similar to CD103+ DCs in mice [33] and humans [34], we also found tretinoin (retinoic acid) was increased (Figure 3B). As retinal dehydrogenases convert retinal to retinoic acid, which in turn induces CD103 expression [35], we determined whether NLRP6 deficiency altered retinal dehydrogenase expression. We investigated 3 different retinal dehydrogenase isozymes that influence B cell development [36], in CD103+ and CD103- B cells, by qPCR. Whilst we observed no significant changes in aldh1a1 expression, aldh1a2 expression was increased in CD103+ B cells (Figures 3D, E), in line with previous findings in CD103+ DCs [33]. Whereas the enhanced aldh1a2 expression in CD103+ B cells was independent of NLRP6 expression (Figure 3E), we found increased expression of aldh3a1 in CD103+ B cells was NLRP6-dependent (Figure 3F). Aryl hydrocarbon receptor (AHR) activation induces aldh3a1 [37] and promotes IL-10-producing Bregs [38]. However, unexpectedly, we found that *Ahr* gene expression was significantly decreased in CD103+ B cells compared to CD103- B cells, and the expression was even lower in NLRP6-deficient CD103+ B cells, compared to NLRP6-sufficient CD103+ B cells (Figure 3G). To confirm the potential importance of LPS for CD103+ B cell generation, we investigated CD103+ B cells from TLR4-deficient and TLR4-sufficient NOD mice. We found TLR4-deficient mice had reduced CD103+ B cells compared to TLR4-sufficient mice, confirming TLR4, the dominant receptor for LPS, was required for their expansion (Figure 3H). Together, these results of the altered gene expression of Tlr4, retinal dehydrogenases and Ahr, and TLR4-deficiency reducing the CD103+ B cell population, suggested that microbiota recognition and signaling may influence the development of the CD103+ Bregs in NLRP6-deficient mice.
## Gut microbiota are influenced by NLRP6 deficiency
Previous reports suggested that altered microbiota in NLRP6-deficient C57BL/6 mice changed microbe-derived metabolites, which altered AHR activation and modulated NLRP6 inflammasome signaling in intestinal epithelial cells [2, 3, 10]. Interestingly, the novel CD103+ Bregs identified in this study reside in highest proportion in the intestinal tissues (Figures 1E, F), compared to all other tissues studied (Figure 1G). Moreover, the highest expression of Tlr4 was in NLRP6-/- CD103+ B cells (Figure 3C), suggesting a link between gut microbiota and CD103+ B cells. To investigate whether the microbiota could modulate CD103+ Breg development, we firstly studied the intestinal microbiota composition in the NLRP6-deficient and -sufficient mice. We found significant differences in the microbial β-diversity between NLRP6-sufficient and -deficient mice (12-weeks old, Figure 4A); however, no single microbial species was identified as significantly different, after performing multiple T-tests and corrections. However, we found that NLRP6-deficient mice had a more permeable intestine and a higher concentration of circulating LPS in the serum compared to NLRP6-sufficient mice (Figures 4B, C). Similar to previous studies, we found that the absence of NLRP6 altered the antimicrobial peptide expression in the small intestine, as well as Il-18, Muc2 and *Zonulin1* gene expression (Figures 4D-K). We also identified that IgA and IgM antibodies were increased in the gut luminal fluid, whereas IgG antibodies were decreased in NLRP6-deficient mice compared to NLRP6-sufficient mice (Figures 4L-N). Furthermore, we detected increased concentrations of gut luminal TGFβ1 and IL-17a (Figures 4O, P), both of which were also increased in NLRP6-deficient CD103+ B cells (Figures 2H, J). There were no significant changes in intestinal IL-21 and IL-23 (Figures 4Q, R) or IL-10 (below detection level; data not shown). Together, our data suggested that the absence of NLRP6 altered intestinal immune responses, which may contribute to altering the microbial β-diversity, enhancing gut leakiness and increasing the circulating LPS.
**Figure 4:** *NLRP6-deficiency changes microbial β-diversity and promotes increased gut permeability, systemic microbial exposure and intestinal immunity. Microbial DNA was extracted from the fecal pellets of 12-week-old NLRP6+/+NOD (n=30; green) and NLRP6-/-NOD mice (n=8; red) and subjected to 16s rRNA deep sequencing (A). β-diversity is shown as a principal component analysis. Intestinal permeability of 12-16-week old NLRP6+/+NOD (n=9) and NLRP6-/-NOD mice (n=10) following oral gavage of FITC-dextran (B). Serum LPS Endotoxin measurements from NLRP6+/+NOD and NLRP6-/-NOD mice (n=8/group) measured by ELISA, with concentrations determined by linear regression (C). Crp-ductin
(D), Defcr
(E), Reg3β
(F), Reg3γ (G), Relmb
(H), Il-18
(I), Muc2
(J)
and Zonulin-1
(K) gene expression from the intestinal tissue of 12-16-week old NLRP6-sufficient or -deficient mice conducted by qPCR (n=6-8). The relative mRNA abundance was determined using the 2−ΔΔCt method by normalization with the housekeeping gene Gapdh. Intestinal IgA (L), IgM (M), IgG (N), TGFβ (O), IL-17a (P), IL-21 (Q) and IL-23 (R) measured from the luminal flush of 12-16-week old mice by ELISA, with concentrations determined by linear regression (n=16-18). Data were assessed for significance using ANOSIM (A) or a two-tailed Student’s T-test (B-R), with lines indicating the median value.*
## Regulatory CD103+ B cells can be induced by gut microbiota from NLRP6-deficient mice but NLRP6-deficiency is required to maintain their regulatory function
We further investigated gut microbiota modulation of CD103+ B cells. As LPS was identified as an upstream regulator of NLRP6-deficient CD103+ B cells (Figure 3B), we stimulated total splenic B cells from NLRP6-deficient and NLRP6-sufficient mice with LPS in vitro, and assessed the gene expression by qPCR. We found that NLRP6-deficient B cells maintained or increased their regulatory profile in response to increasing LPS concentrations, as evidenced by a regulatory profile in Aldh isozyme, Il-10, Tgfβ1 and Ahr expression, compared to both their own baseline (no stimulation) and NLRP6-sufficient B cells (Figures 5A-F). To confirm that microbiota promoted the development of CD103+ B cells, we compared splenic CD103+ B cells from specific pathogen-free (SPF; with microbiota) and germ-free (GF; without microbiota) NLRP6-sufficient mice. We found that GF mice had significantly fewer CD103+ B cells, suggesting that the presence of microbiota boosted the expansion of CD103+ B cells (Figure 5G).
**Figure 5:** *Gut microbiota, LPS and TGFβ1 induce regulatory NLRP6-deficient CD103+ B cells but a NLRP6 deficiency is required to maintain them. (A-F)
Aldh1a1
(A), Aldh1a2
(B), Aldh3a1
(C), Il-10
(D), Tgfβ1
(E) and Ahr
(F) gene expression from NLRP6-sufficient or -deficient B cells following 20min LPS stimulation investigated by qPCR. The relative mRNA abundance was determined using the 2−ΔΔCt method by normalization with the housekeeping gene Gapdh. (G) The proportion of CD103+ splenic B cells in SPF vs GF mice. (H-J) Isolated fecal microbiota was pooled from 12-16-week-old NLRP6+/+NOD and NLRP6-/-NOD mice (n=4) and transferred to germ-free NLRP6+/+NOD mice by oral gavage (2x108 CFUs/recipient). Representative CD103+ B cell gating from bacterial-colonized mice (H) and summarized data from colonized mice 2- (I) and 4-weeks (J) post-gavage (NLRP6+/+ left column and NLRP6-/-NOD right column). (K) Diabetes incidence of cohoused NLRP6-sufficient and -deficient mice. (L) qPCR of the Nlrp6 gene from CD103+ and CD103- B cells from 12-16-week old NLRP6+/+NOD mice. (M-O) Proportion of CD103+ B cells post-overnight stimulation with LPS (10ng/ml) and TGFβ1 (M) or in the presence of an anti-TGFβ1 antibody (N) or control antibody (O). Abbreviations include aldehyde dehydrogenase (aldh), aryl hydrocarbon receptor (ahr), specific pathogen-free (SPF), germ-free (GF), pancreatic lymph nodes (PLN), mesenteric lymph nodes (MLN) and Peyer’s patches (PP). Data were pooled from 2-3 independent experiments (n=6-8) with the exception of K, with lines representing the median value. Data were assessed for significance using a two-way ANOVA (A-F), a two-tailed Student’s T-test (I-N) or a Gehan-Breslow-Wilcoxon test (K).*
To determine whether gut microbiota could induce CD103+ B cells in vivo, we performed fecal material transplant experiments by oral gavage of donor microbiota from SPF NLRP6-sufficient and -deficient mice into GF NLRP6-sufficient mice. We found that 2-weeks post-transfer, the recipients of NLRP6-deficient microbiota had increased proportions of CD103+ B cells compared to NLRP6-sufficient microbiota recipients (Figure 5H (top), I). However, NLRP6 deficiency was required to maintain the increased abundance of CD103+ B cells as the difference diminished by 4-weeks post-transfer (Figure 5H (bottom), J). To confirm whether continued NLRP6-deficient bacteria exposure influenced diabetes development, we co-housed NLRP6-sufficient and -deficient mice and observed their diabetes development. We found that NLRP6-sufficient NOD mice developed a similar low incidence of diabetes as NLRP6-deficient NOD mice (Figure 5K), suggesting that long-term exposure to NLRP6-deficient microbiota was able to protect NLRP6-sufficient mice from developing diabetes, compared to mice housed separately (Figure 5K vs. Figure 1A). To determine whether CD103+ B cells expressed NLRP6, we performed qPCR from FACS-sorted CD103- and CD103+ B cells from non-cohoused NLRP6-sufficient mice. We found that CD103+ B cells had higher Nlrp6 expression compared to CD103- B cells (Figure 5L), suggesting that NLRP6 may have a direct effect on the CD103+ B cells. As TGFβ1 has been associated with CD103+ cell generation [33, 39], we investigated whether TGFβ1 induced CD103+ B cells. We found that TGFβ1 addition to LPS-stimulated B cells, induced CD103+ B cells in both NLRP6-sufficient and -deficient mice, with the latter maintaining a higher proportion (Figure 5M). In contrast, when an anti-TGFβ1 antibody was used, we found a reduction in NLRP6-deficient CD103+ B cells and no change in NLRP6-sufficient CD103+ B cells, which was not observed in the presence of a control antibody (Figures 5N, O). Together, our data suggest that the absence of NLRP6 may directly affect the development and maintenance of novel regulatory CD103+ B cells, induced through altered responses to microbiota and LPS with a requirement for TGFβ1 for their expansion. Importantly, these effects can be induced transiently in NLRP6-sufficient B cells when exposed to NLRP6-deficient dysbiotic microbiota, suggesting the potential for suppressing NLRP6 expression to boost Breg development.
## Discussion
Our study has highlighted that, in the absence of NLRP6, CD103+ B cells, which have regulatory properties, are expanded in many lymphoid tissues and this novel subset of Breg contributes to immune tolerance to islet beta cell autoimmunity. CD103+ B cells were represented in cell subsets that express B10 Breg markers and secreted higher concentrations of IL-10 and TGFβ1. Moreover, LPS maintained and increased the regulatory function of the novel subset of CD103+ Bregs. Finally, we found that the gut microbiota from NLRP6-deficient mice induced peripheral CD103+ B cells in NLRP6-sufficient mice; however, the absence of NLRP6 was required to maintain the expansion of the CD103+ B cells, unless exposure to microbiota from NLRP6-deficient mice was continuous (e.g. cohousing of mice). Bregs are important immune modulators in autoimmune diseases, cancer and infections. Bregs are influenced by gut microbiota and gut microbiota-induced cytokines and AhR signaling [22, 38, 40], and here we have demonstrated that inflammasomes also modulate Breg development.
CD103 (integrin αE) is a surface glycoprotein and is highly expressed by a subset of DCs and regulatory T cells (Tregs). Our data showed that B cells also express CD103. However, the function of these CD103+ B cells was previously unknown. CD103 forms a complex with the β7 integrin (αEβ7). A deficiency in the β7 integrin significantly reduced B cell recruitment to the lamina propria [41, 42]. We have found that CD103+ B cells were increased in the intestinal tissues, suggesting enhanced recruitment of CD103+ B cells to the intestine. In humans, CD103 expression on B cells is not typically seen [43]; however, CD103+ B cells are expanded in hairy cell leukemia (HCL) and are used as a diagnostic marker, although their function was not clear [44]. In our study, these cells promoted immune tolerance, and immune regulation by limiting pancreatic β-cell destruction by autoreactive T cells in type 1 diabetes. Our study suggests that NLRP6 deficiency leads to an expansion of CD103+ B cells, which can be induced by the gut microbiota from NLRP6-deficient mice. Thus, it is possible that the CD103+ B cell expansion observed in HCL patients may be modulated by gut microbiota and NLRP6 inflammasome signaling.
The role of gut microbiota in NLRP6-deficient mice is controversial, some studies have indicated their importance [2, 3, 10], while others did not find differences from NLRP6-sufficient mice [12, 13]. We found altered β-diversity of the gut microbiota in NLRP6-deficient NOD mice but were unable to identify individual bacteria to which this change could be attributed, after statistical corrections. However, the absence of NLRP6 promoted a more permeable gut barrier, which may predispose the mice to type 1 diabetes development [45, 46] (as has also been suggested in humans [47, 48]); however, in contrast to published work, here, the mice with a more permeable gut were more protected from developing diabetes. We believe that the increased gut permeability in the NLRP6-deficient mice leads to increased LPS in the circulation, which protects NOD mice from diabetes development [19]. Further, the increased gut permeability results in increased microbial exposure to the immune system and primes the Breg cells.
Interestingly, CD103+ B cells express NLRP6, albeit at a low level. Moreover, NLRP6 directly impacts on CD103+ B cells, as shown by the altered response to LPS and upregulation of anti-inflammatory genes in the NLRP6-deficient B cells. The Ahr and associated genes have been clearly linked to anti-inflammatory responses and regulation in the immune system [49]. The reduction of *Ahr* gene expression and the enhanced Aldh3a1 gene expression in CD103+ B cells from NLRP6-deficient NOD mice suggest that Aldh3a1 may be regulated independently of Ahr; however, upon LPS stimulation, Aldh3a1 was upregulated which coincided with the recovery of the expression of Ahr (from baseline). Thus, our results suggest that LPS may activate both Ahr and *Aldh* gene expression upon stimulation. Furthermore, fecal microbiota from NLRP6-deficient mice were able to induce CD103+ B cell expansion in colonized NLRP6-sufficient germ-free mice. However, maintaining the expanded CD103+ B cells required the absence of NLRP6 in the host. Although no specific microbial species were identified, we have demonstrated the important functional changes of B cells in response to the microbiota. Whether this is due to the microbiota or microbial metabolites [3], remains to be further studied. Inosine, one of the upstream regulators of NLRP6-deficient CD103+ Bregs, can also be produced by the microbiota and is important in inhibition of severe autoimmunity [50], and improving the efficacy of checkpoint inhibitor therapy in colorectal cancer [51]. Thus, this would be a logical future investigation.
A limitation of our study is that we have not directly demonstrated the reverse interaction, i.e., if CD103+ B cells modulate the gut microbiota and whether the interaction is dependent on NLRP6 expression from tissue cells, all of which will require to be tested in cell-specific NLRP6-deficient mice. We have provided evidence that NLRP6-deficient mice have altered intestinal antibody titers and this most likely contributes to the modulation of the gut microbiota, such as by antibody-coating of microbiota, known to have important implications in autoimmunity, including type 1 diabetes [52, 53]. Future studies of cell-specific NLRP6-deficient mice will greatly aid in understanding the role of each cell type in regulation of the gut microbiota and whether NLRP6-deficient CD103+ B cells are reliant on cross-talk with other cells for their regulatory functions.
In summary, we present a novel regulatory B cell subset, characterized by the expression of CD103, increased in NLRP6 deficiency and are potent TGFβ- and IL-10 producers, all of which delayed and prevented diabetes development. Further investigation into the above upstream regulators and the small molecules associated with the microbial metabolites which enhance the regulatory function of CD103+ B cells may prove very valuable in devising future therapies to prevent autoimmune disease development.
## Data availability statement
The data presented in the study are deposited in the GEO repository, accession number GSE224472.
## Ethics statement
The animal study was reviewed and approved by Yale University Institutional Animal Care and Use Committee, Yale University.
## Author contributions
JAP, JP, JH, NT, YH and SS conducted the experiments. JAP, JP, XY and HZ analyzed the data. JAP, FW and LW designed the experiments. RF provided the mice for study. JAP, FW and LW wrote and edited the manuscript. 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.2023.1147925/full#supplementary-material
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title: Altered infective competence of the human gut microbiome in COVID-19
authors:
- Laura de Nies
- Valentina Galata
- Camille Martin-Gallausiaux
- Milena Despotovic
- Susheel Bhanu Busi
- Chantal J. Snoeck
- Lea Delacour
- Deepthi Poornima Budagavi
- Cédric Christian Laczny
- Janine Habier
- Paula-Cristina Lupu
- Rashi Halder
- Joëlle V. Fritz
- Taina Marques
- Estelle Sandt
- Marc Paul O’Sullivan
- Soumyabrata Ghosh
- Venkata Satagopam
- Geeta Acharya
- Geeta Acharya
- Gloria Aguayo
- Wim Ammerlaan
- Ariane Assele-Kama
- Christelle Bahlawane
- Katy Beaumont
- Nadia Beaupain
- Lucrèce Beckers
- Camille Bellora
- Fay Betsou
- Sandie Boly
- Dirk Brenner
- Eleftheria Charalambous
- Emilie Charpentier
- Manuel Counson
- Brian De Witt
- Olivia Domingues
- Claire Dording
- Bianca Dragomir
- Tessy Fautsch
- Jean-Yves Ferrand
- Ana Festas Lopes
- Joëlle Véronique Fritz
- Manon Gantenbein
- Laura Georges
- Jérôme Graas
- Gael Hamot
- Anne-Marie Hanff
- Maxime Hansen
- Lisa Hefele
- Estelle Henry
- Margaux Henry
- Eve Herkenne
- Christiane Hilger
- Judith Hübschen
- Laetitia Huiart
- Alexander Hundt
- Gilles Iserentant
- Stéphanie Kler
- Pauline Lambert
- Sabine Lehmann
- Morgane Lemaire
- Andrew Lumley
- Monica Marchese
- Sophie Mériaux
- Maura Minelli
- Alessandra Mousel
- Maeva Munsch
- Mareike Neumann
- Magali Perquin
- Achilleas Pexaras
- Jean-Marc Plesseria
- Lucie Remark
- Bruno Santos
- Aurélie Sausy
- Margaux Schmitt
- Sneeha Seal
- Jean-Yves Servais
- Florian Simon
- Chantal Snoeck
- Kate Sokolowska
- Hermann Thien
- Johanna Trouet
- Jonathan Turner
- Michel Vaillant
- Daniela Valoura Esteves
- Charlène Verschueren
- Tania Zamboni
- Pinar Alper
- Piotr Gawron
- Enrico Glaab
- Clarissa Gomes
- Borja Gomez Ramos
- Vyron Gorgogietas
- Valentin Groues
- Wei Gu
- Laurent Heirendt
- Ahmed Hemedan
- Sascha Herzinger
- Anne Kaysen
- Jacek Jaroslaw Lebioda
- Tainà Marques
- François Massart
- Christiane Olesky
- Venkata P. Satagopam
- Claire Pauly
- Laure Pauly
- Lukas Pavelka
- Guilherme Ramos Meyers
- Armin Rauschenberger
- Basile Rommes
- Kirsten Rump
- Reinhard Schneider
- Valerie Schröder
- Amna Skrozic
- Lara Stute
- Noua Toukourou
- Christophe Trefois
- Carlos Vega Moreno
- Maharshi Vyas
- Xinhui Wang
- Anja Leist
- Annika Lutz
- Claus Vögele
- Linda Hansen
- João Manuel Loureiro
- Beatrice Nicolai
- Alexandra Schweicher
- Femke Wauters
- Tamir Abdelrahman
- Estelle Coibion
- Guillaume Fournier
- Marie Leick
- Friedrich Mühlschlegel
- Marie France Pirard
- Nguyen Trung
- Philipp Jägi
- Henry-Michel Cauchie
- Delphine Collart
- Leslie Ogorzaly
- Christian Penny
- Cécile Walczak
- Rejko Krüger
- Guy Fagherazzi
- Markus Ollert
- Feng Q. Hefeng
- Patrick May
- Paul Wilmes
journal: Microbiome
year: 2023
pmcid: PMC9995755
doi: 10.1186/s40168-023-01472-7
license: CC BY 4.0
---
# Altered infective competence of the human gut microbiome in COVID-19
## Abstract
### Background
Infections with SARS-CoV-2 have a pronounced impact on the gastrointestinal tract and its resident microbiome. Clear differences between severe cases of infection and healthy individuals have been reported, including the loss of commensal taxa. We aimed to understand if microbiome alterations including functional shifts are unique to severe cases or a common effect of COVID-19. We used high-resolution systematic multi-omic analyses to profile the gut microbiome in asymptomatic-to-moderate COVID-19 individuals compared to a control group.
### Results
We found a striking increase in the overall abundance and expression of both virulence factors and antimicrobial resistance genes in COVID-19. Importantly, these genes are encoded and expressed by commensal taxa from families such as Acidaminococcaceae and Erysipelatoclostridiaceae, which we found to be enriched in COVID-19-positive individuals. We also found an enrichment in the expression of a betaherpesvirus and rotavirus C genes in COVID-19-positive individuals compared to healthy controls.
### Conclusions
Our analyses identified an altered and increased infective competence of the gut microbiome in COVID-19 patients.
Video Abstract
### Supplementary Information
The online version contains supplementary material available at 10.1186/s40168-023-01472-7.
## Background
Coronavirus disease 2019 (COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was declared a global pandemic by the World Health Organization (WHO). COVID-19 exhibits a high degree of clinical heterogeneity, ranging from asymptomatic to severe disease, and may be accompanied by a poor outcome and a relatively high mortality rate [1]. As of 17 October 2022, more than 621 million confirmed SARS-CoV-2 infections and 6.5 million COVID-19-related deaths have been reported [2]. Although COVID-19 is primarily considered a respiratory disease, it clinically often presents with general (fever, myalgia, and/or fatigue) and respiratory symptoms (cough and/or dyspnea). Moreover, an emergence of new variants has led to the more frequent presentation of gastrointestinal symptoms (appetite loss, nausea, vomiting, and diarrhea) [3], indicating a potential involvement of the gastrointestinal tract in COVID-19. More specifically, SARS-CoV-2 has been shown to be able to infect and replicate in enterocytes in vitro [4]. In fact, viral RNA can be detected in fecal samples even after resolution of respiratory symptoms [5]. Additionally, SARS-CoV-2 infections are associated with alterations to the gut microbiome composition that persist for at least 6 months after the initial infection [6]. Thus, an imbalance in the gut microbiome can be linked to disease severity and increased concentrations of inflammatory markers, as well as an increased post-COVID-19 risk, understood as a wide range of symptoms persisting four or more weeks after the initial SARS-CoV-2 infection [6, 7].
Stable ecosystems are important for colonization resistance to pathogens [8]. As such, host and SARS-CoV-2-mediated immune dysregulation and dysbiosis may predispose patients to co-infections or secondary infections of the respiratory and gastrointestinal tracts. In addition, co-infecting microorganisms may alter the intensity of the host immune response [9], thus significantly influencing severity and outcome of the disease. For instance, co-infections with viruses (rhinovirus/enterovirus, respiratory syncytial virus, influenza virus, non–SARS-CoV-2 Coronavirus) [10], bacteria (Mycoplasma pneumoniae, Pseudomonas aeruginosa, Haemophilus influenzae, Klebsiella pneumoniae, Streptococcus pneumoniae, Staphylococcus aureus) [11, 12], or fungi (Candida spp., Aspergillus spp.) [ 13] have been described among SARS-CoV-2-positive cases in different study set-ups. In particular, bacterial co-infections in hospitalized and intensive care unit patients with COVID-19 are associated with prolonged ventilation and an increased mortality rate [11, 14]. Furthermore, hospital-acquired infections with multi-drug-resistant (MDR) pathogens are also linked with increased mortality in COVID-19 patients [15]. These reports collectively suggest a clear shift in COVID-19 patients with respect to an increased abundance of pathogens and potential for harm. Moreover, these shifts may further manifest themselves in relation to the infective competence, i.e., the propensity for virulence and increased antibiotic resistance, in the gut microbiome as a consequence of an increased capacity to cause infections.
Major factors that contribute to the success of some of the pathogens highlighted above are virulence factors (VFs). Virulence factors including cell-surface structures, adhesins, siderophores, endo-, and exotoxins enable pathogens to undergo quick adaptive shifts, invade and colonize host niches, as well as evade innate and adaptive immune mechanisms of the host, resulting in inflammation and clinical manifestations of the disease. Another factor facilitating colonization of pathogens, through prevention of effective treatment, is antimicrobial resistance (AMR). Even though AMR is an ancient and natural phenomenon [16], it is usually linked to the human influence on the environment and the use of antibiotics. Overuse of antibiotics is hypothesized to also contribute to the broader problem of antimicrobial resistance [17]. Moreover, although not a VF by itself, AMR shares common characteristics with VFs [18]. Specifically, AMR and VFs: [1] are necessary for the survival of pathogens under unfavourable conditions [19]; [2] can be transmitted between species by horizontal gene transfer [20]; and [3] both processes make use of similar systems, e.g., cell wall alterations, efflux pumps, porins, and two-component systems to activate or repress expression of various genes [18, 21]. Thus, in response to host defence mechanisms and environmental challenges, communities of microorganisms, i.e., microbiomes, may alter their “infective competence”. The infective competence is defined as the ability of microorganisms to constantly adapt and evolve, utilizing VFs and AMR mechanisms, resulting in increased survival, invasion, or growth. Importantly, the combination of host-driven factors, i.e., immune system-mediated effects and antimicrobial peptides, and unfavourable gastrointestinal conditions, e.g., low pH, disruption of the mucus layer, niche competition with other taxa, may confer transiently a selective advantage to a pathogenic lifestyle [22, 23]. This may be reflected in the entire gut microbiome, possibly altering the infective competence of the endogenous taxa and subsequently giving rise to pathobiont-dominated communities.
Here, we addressed questions pertaining to the effect of SARS-CoV-2 infection on the endogenous gut microbiome in COVID-19 cases compared to healthy controls using systematic, high-resolution multi-omic data, including metagenomics and metatranscriptomics with a particular focus on VFs and antimicrobial resistance genes (ARGs). We find that mild, i.e. asymptomatic-to-moderate, COVID-19 does not alter the overall composition of the gut microbiome, unlike the drastic microbiota changes reported previously in severe cases. Importantly, we find that a mild progression of COVID-19 affects the infective competence of gut microbiota, wherein taxa encode and express genes facilitating their survival and/or growth. We find specific families such as Acidaminococcaceae and Erysipelatoclostridiaceae to be encoding for and expressing VFs and ARGs, significantly more in individuals with COVID-19. Collectively our data also demonstrates a significantly higher infective competence of the endogenous microbiome, suggesting that infection with SARS-CoV-2 may mediate co-infections in the longer term.
## Taxonomic and functional profiles indicate minimal changes in COVID-19
COVID-19 studies have reported an altered gut microbiota composition of hospitalized and critical COVID-19 patients. However, limited attention has been paid to milder forms of COVID-19. Thus, we assessed whether gut microbiota composition was altered in COVID-19 individuals compared to healthy controls. Overall, the gut microbiome compositions, based on the alpha- and beta-diversity metrics, of 61 COVID-19 and 57 individuals from the control group were similar (Fig. 1 and Supplementary Figure S1), with an increased abundance of species belonging to the Lachnospiraceae, Ruminococcaceae, Bacteroidaceae, and Bifidobacteriaceae families in COVID-19 (Fig. 2a). We found specific taxonomic differences within the metagenomes, such as an increase in the abundance of AM10 47 (Firmicutes phylum), Prevotella sp. CAG 520, *Prevotella stercorea* and Roseburia sp. CAG 471 in the COVID-19 group (Fig. 2b), along with a decrease in CAG 145 (Firmicutes phylum), *Roseburia faecis* and *Turicibacter sanguinis* (Fig. 2c). Despite these taxonomic differences, we did not observe any significant changes in the overall functional profile of the microbiome between the COVID-19 and control groups. Along similar lines, we did not find a significant correlation between covariates such as age, sex, COVID-19 severity, and other variables in the COVID-19 and control groups in relation to the taxonomic and functional features. Fig. 1Sample collection and study design. Schematic of the project study design, including cohort composition, and data analysesFig. 2Composition of the microbial community. a Cladogram representing the microbial community profiles in COVID-19 patients (red) and control group (green). The outer rings represent the relative abundance (%) of the microbial community. b Relative abundance of bacterial species significantly enriched in COVID-19 patients compared to the control group [adj.$p \leq 0.05$; Wilcoxon rank-sum test]. c Relative abundance of bacterial species significantly decreased in COVID-19 patients compared to the control group [adj. $p \leq 0.05$; Wilcoxon rank-sum test] In light of reports, indicating the potential co-infections with viruses along with SARS-CoV-2, we also assessed the virome (Methods) within the COVID-19 patient and the control groups. We did not observe large differences between the groups. However, we found that genes associated with a specific betaherpesvirus and rotavirus were enriched (adj. $p \leq 0.05$; one-way ANOVA) in the COVID-19 group (Supplementary Table S2).
## SARS-CoV-2 is associated with increased abundance and expression of virulence factors
SARS-CoV-2 infections have been suggested to predispose patients to co-infections or secondary infections of the respiratory and gastrointestinal tracts. Virulence factors in particular enable (pathogenic) microorganisms to colonize host niches and establish infections. We used PathoFact [24] to assess the prevalence of VFs in the co-assembled metagenomic and metatranscripomic data. PathoFact was designed to contextualize the genomic data and classify VFs and ARGs, allowing to assess the infective competence of taxa. To obtain a comprehensive overview of actual gene expression, we complemented metagenomic analyses with metatranscriptomic information conferring information regarding the transcription levels of identified VFs. Based on the metagenomic data, we found a significant increase (adj. $p \leq 0.05$; Wilcoxon rank-sum test) in alpha diversity (Supplementary Figure S2) as well as the overall abundance of VFs in the COVID-19 group compared to the control group (Fig. 3a). The metatranscriptomic information further confirmed that these VFs demonstrated significantly increased expression levels (adj. $p \leq 0.05$; Wilcoxon rank-sum test) in the COVID-19 group compared to the control group (Fig. 3b).Fig. 3Abundance of virulence factors in the microbial community. a Overall abundance (metagenome) of virulence factors encoded by the microbiome of COVID-19 patients and control group. The significance of the differential abundance is indicated with the adjusted p value [adj.$p \leq 0.05$; Wilcoxon rank-sum test]. b Overall expression levels (metatranscriptomics) of virulence factors encoded by the microbiome in COVID-19 patients and the control group [adj.$p \leq 0.05$; Wilcoxon rank-sum test]. c Abundance and expression levels of MAG families where a significant increase in encoded and expressed virulence factors was observed in COVID-19 patients [adj.$p \leq 0.05$; Wilcoxon rank-sum test, * < 0.05, ** < 0.01, *** < 0.001]. d Abundance and expression levels of virulence factors in MAGs depicting taxonomic families only demonstrating an increased expression of virulence factors, with no significant difference observed at a metagenomic level [adj.$p \leq 0.05$; Wilcoxon rank-sum test, * < 0.05, ** < 0.01, *** < 0.001] To link the prevalence and expression of the identified VFs to the taxa within the microbial community, we reconstructed metagenome-assembled genomes (MAGs) and further leveraged the iterative workflow of the integrated meta-omic pipeline (IMP) [25]. Overall, we found a significant increase in encoded and expressed VFs between the COVID-19 and control groups (adj. $p \leq 0.05$; Wilcoxon rank-sum test). Our analyses further linked families such as Acidaminococcaceae, Erysipelatoclostridiaceae, and Erysipelotrichaceae with increased expression of VFs in the COVID-19 group (Fig. 3c). Interestingly, the control group exhibited higher gene abundances and expression of VFs only in the Dialisteraceae family. Furthermore, we found that some families (Acutalibacteraceae, Coriobacteriaceae, Lachnospiraceae, and Ruminococcaceae) demonstrated an increased expression of VFs in the COVID-19 group (Fig. 3d; adj. $p \leq 0.05$; Wilcoxon rank-sum test), although their respective gene abundances were not different from those found in the control group.
## Expression of antimicrobial resistance increases together with virulence factors
While co-infections or secondary infections in COVID-19 may exacerbate the disease, the presence of ARGs may limit treatment options. Since the overall abundance and expression of VFs was increased in COVID-19 individuals, we assessed the antimicrobial resistance profile of the microbial community in the COVID-19 and control groups. Specifically, using PathoFact, we characterized the prevalence and relative expression of ARGs (22 categories). While we did not find any significant differences in the overall gene abundances and the normalized expression levels of all ARGs contributing to the resistome, we observed a significant increase (adj. $p \leq 0.05$; Wilcoxon rank-sum test) in ARG alpha diversity (Supplementary Figure S3) between COVID-19 and the control groups (Fig. 4a). Importantly, when investigating individual AMR categories, we found that peptide resistance wash significantly higher in terms of gene abundance and also more highly expressed within the COVID-19 group (Fig. 4b; adj. $p \leq 0.05$; Wilcoxon rank-sum test). In addition, we observed that the expression of multi-drug resistance was enriched (adj. $p \leq 0.05$; Wilcoxon rank-sum test) in the COVID-19 group, while macrolides, lincosamides and streptogramins (MLS) and beta-lactam resistance both exhibited a higher gene abundance in the same group (adj. $p \leq 0.05$; Wilcoxon rank-sum test).Fig. 4Abundance levels of antimicrobial resistance genes. a Overall ARG abundance and expression levels for COVID-19 and control groups (boxplot), coupled with a breakdown of the respective abundance and expression levels to individual AMR categories [adj.$p \leq 0.05$; Wilcoxon rank-sum test, * < 0.05, ** < 0.01, *** < 0.001]. b ARG abundance (top) and expression levels (bottom) of individual AMR categories significantly increased in COVID-19 patients compared to the control group [adj.$p \leq 0.05$; Wilcoxon rank-sum test, * < 0.05, ** < 0.01, *** < 0.001] As described above, we leveraged the MAGs to correlate the differentially abundant and expressed ARGs to the microbial community. In line with our observations with the VFs, we found a significant increase (adj. $p \leq 0.05$; Wilcoxon rank-sum test) in ARGs encoded and expressed by the Acidaminococcaceae and Erysipelatoclostridiaceae in the COVID-19 group (Fig. 5a, b). Furthermore, an additional family, i.e., Tannerellaceae was also associated with increased abundance and expression of ARGs in the COVID-19 group (Fig. 5a, b). Specifically, in relation to the above-reported AMR categories, we identified a significant increase in multi-drug resistance encoded and expressed by all three of these taxonomic families. In addition, the Acidaminococcaceae also were found to encode a significant increase in ARGs contributing to peptide resistance. Interestingly, we found that several other taxonomic families were also associated with increased ARG expression in the COVID-19 group (adj. $p \leq 0.05$; Wilcoxon rank-sum test), although their gene abundances did not demonstrate any significant differences (Fig. 5b). These included Barnesiellaceae, Lachnospiraceae, Ruminococcaceae, and Rikenellaceae. Fig. 5Association of AMR with the microbial community. Abundance (a) and expression (b) levels of ARGs and corresponding to AMR categories linked to MAGs. On top (boxplot) depicting the overall ARG abundance, below the average abundance of selected AMR categories per taxonomic family. The plot depicts taxonomic families in which overall a significant increase in abundance or expression of ARGs was observed [adj.$p \leq 0.05$; Wilcoxon rank-sum test, * < 0.05, ** < 0.01, *** < 0.001] To further validate our findings, especially those linking VFs and ARGs with taxa, we used bias correction-based analyses for microbial compositions (ANCOM-BC). For both abundances and relative expression, across VFs and ARGs, ANCOM-BC revealed similar taxonomic families were enriched in the COVID-19 group, as identified in our initial analyses using MaAsLin2 and subsequent non-parametric tests. Based on the ANCOM-BC analyses, the COVID-19 group had a higher log2 fold-change of Erysipelatoclostridiaceae, Acidaminococcaceae, and Erysipelotrichaceae in the VFs compared to the control group (Supplementary Figure S4a-b). Similarly, ARGs in the Acidaminococcaceae and Erysipelatoclostridiaceae families were both abundant and showed higher relative expression in the COVID-19 group (Supplementary Figure S4 c-d).
## Infective competence of the gut microbiome
Our analyses collectively indicated that both VFs and ARGs were enriched in abundance and expression in the COVID-19 group. Specifically, we found that the abundances of ARGs were correlated with those of the VFs (Fig. 6a, $R = 0.52$ and $p \leq 0.01$; Spearman’s correlation). Complementing this observation, we found that the expression profiles of ARGs and VFs also correlated with each other (Fig. 6b, $R = 0.46$ and $p \leq 0.01$; Spearman’s correlation) suggesting a higher propensity for infectious capacity. To further characterize the infective competence of the various taxa within the gut microbiome, we estimated the log2 fold-change of the abundance and expression of VFs and ARGs across taxonomic families found in the COVID-19 group and the control group. We found that ~ $62\%$ ($\frac{21}{34}$) of the families had a higher infective competence and were enriched in abundance and expression within the COVID-19 group, whereas only ~ $9\%$ ($\frac{3}{34}$) of the families showed increased infective competence in the control group (Fig. 6c). In particular, these analyses highlighted the Acidaminococcaceae and Erysipelatoclostridiaceae families, in line with our earlier observations, suggesting a higher infective competence, where the abundances and expression levels of the VFs and ARGs were significantly higher in COVID-19 compared to the control group ($p \leq 0.05$; two-way ANOVA). In the control group, Dialisteraceae, which was also observed earlier, showed increased infective competence (Fig. 6c). Collectively, our data suggests that the infective competence of taxa found in the COVID-19 group is increased compared to controls. Fig. 6Assessing the infective competence of the gut microbiome. a Correlation of gene abundances of AMR and virulence factors [$R = 0.52$ and $p \leq 0.01$; Spearman’s correlation] in COVID-19 patients (red) and the control group (green). b Correlation of AMR and virulence factors gene expression levels [$R = 0.46$ and $p \leq 0.01$; Spearman’s correlation] in COVID-19 patients (red) and negative controls (green). c Bubble plot depicting the infective competence via the log2 fold change of AMR and virulence factors between COVID-19 patients (red) and control group (green)
## Discussion
COVID-19 has become a common condition for which the manifold effects however remain a challenge [26]. Since the onset of the pandemic, the presentation of gastrointestinal symptoms has indicated the involvement of the gastrointestinal tract in COVID-19 [7]. As we uncover and understand the potential effects of COVID-19 in humans, it is important to also elucidate the concomitant consequences of the disease on the gut microbiome. To this end, several studies have focused on the drastic shifts in the microbiome of COVID-19 patients with severe symptoms. These include changes in diversity including stark enrichments and/or loss of specific taxa [27]. Several Studies have focused on differences in the gut microbiome between patients with severe COVID-19 and controls [7, 28]. Though these findings are essential, the effect on the larger population, wherein the infection is asymptomatic-to-moderate, is not readily represented. To address this particular gap in knowledge, we focused on the effect of COVID-19 in cases with asymptomatic-to-moderate symptoms in comparison to controls. Interestingly, we found that the diversity and overall shifts in community composition, highlighted in previous reports between severe and control patients [29], did not manifest themselves when comparing asymptomatic-to-moderate cases to the control group of individuals. However, this was associated with an increased abundances of specific taxa such as Prevotella spp., AM10 and CAG145 (Firmicutes phylum), Roseburia spp. and a Turicibacter spp. in the COVID-19 group. This is in contrast to existing reports [27, 30], suggesting the loss of beneficial taxa such as Faecalibacterium, Bifidobacterium and Roseburia in the context of COVID-19. Since our study did not include patients with severe COVID-19 or those that were hospitalized, it is likely that the lower disease severity does not lead to significant changes in the abundance of beneficial commensals. Along similar lines, major differences in the virome profile of the COVID-19 group in our study were not observed when compared to the control group. Nevertheless, and importantly, we found that genes associated with rotavirus C were increasingly expressed in the COVID-19 group, despite no differences in overall abundance of this virus between the patient groups. Rotavirus is a known enteric pathogen causing gastroenteritis in the pediatric population; however, their capacity to cause infections in adults is underappreciated and poorly characterized due to only mild symptoms including nausea, headaches, and diarrhea [31]. Importantly, at the time of writing only one report by Wang et al. [ 32], indicated the possibility of increased rotavirus A-mediated enteric infections in COVID-19 patients. These findings are intriguing given the propensity for COVID-19 patients to suffer from enteric symptoms [3], including nausea [33] and diarrhea [34]. Whether the rotavirus, especially in adults, is associated with COVID-19 gastrointestinal symptoms, or the enteric effects exacerbate the expression of rotavirus C-associated genes is still unknown and will have to be investigated in dedicated follow-up studies.
In line with the above observations, early in the pandemic, the role of COVID-19 in enhancing co-infections was documented extensively [14, 35]. This is not only limited to mucormycosis [36] which was amplified in certain parts of the world, but also bacterial and viral co-infections that were reported in severe COVID-19 patients [32]. Despite these observations and case studies, the effect of COVID-19 on the infective competence of the existing and endogenous microbiota has never been characterized. Our findings, therefore, bridge an important and broad chasm in knowledge, suggesting that COVID-19-mediated shifts may lead to higher microbiome-linked burden with potentially manifold effects. Importantly, we not only found an increased abundance in VFs in the COVID-19 group, but also a concomitant increase in expression of genes associated with virulence. Although it is plausible that a positive correlation between VFs and ARGs exists in de facto pathogens, such infection-linked shifts have not been reported beforehand. Furthermore, this phenomenon has not been reported in commensal organisms. Thereby, infective competence may be used to monitor and understand potential future infections in the context of COVID-19-mediated effects. Simultaneously, we found that these VFs were associated with taxa from families such as Acidaminococcaceae, Erysipelatoclostridiaceae, and Erysipelotrichaceae. Though Acidaminococcus was recently reported to be associated with a disease-related group in a large-scale meta-analysis [37], the exact role of Acidaminococcaceae in virulence is undocumented. Members of the Erysipelatoclostridiaceae family are typically seen as typical members of the microbiome; however, in specific cases species such as *Erysipelatoclostrium ramosum* have been associated with systemic infection and systemic inflammatory response syndrome [38], while Erysipelotrichaceae have been positively correlated with colorectal cancer [39]. Our observations, especially the increased expression of VF genes associated with these taxa, may pave the way in future explorations to serve as indicators of diseases. Importantly, it is still unclear whether the enriched infective competence is a COVID-19-specific hallmark or one found in all infections. For example, it has previously been hypothesized that selection of pathobionts result from inflammatory responses and/or a dysregulation of the tolerant immune system [40]. Future studies will need to address the extent to which various underlying factors such as a dysbiotic microbiome and an impaired immune system affect the infective competence of the gut microbiome. Furthermore, with larger cohorts which would include severe cases of COVID-19, supervised learning analyses may be employed to predict disease status based on the infective competence of individuals’ microbiome. Further work may also involve the heterologous expression of VF and AMR genes to assess predicted versus realized infective competence.
Another important aspect of COVID-19, in particular early on in the pandemic, was the overuse and misuse of antibiotics for treating SARS-CoV-2 [41] which was also associated with the potential increase of AMR [17]. Based on our findings and a recent report from the European Centre for Disease Prevention Control showing a North-to-South as well as a West-to-East AMR gradient in Europe during the COVID-19 pandemic [42], it is imperative to undertake future and detailed analyses accounting for socioeconomic and geographic factors contributing to AMR. In this context, Luxembourg may constitute an important reference population given its geographic location and its diverse demographic composition. Recent studies have reported on the higher incidence of AMR [43] and increased ARGs in COVID-19 patients [44]. However, these reports either refer to patients who were administered antibiotics [44] or include a meta-analyses observing datasets which were generated pre- and post-pandemic, specifically associated with travel [45], or limit characterization of antibiotic-mediated differences at a broad and low-resolution [46]. In contrast to these studies, antibiotic usage was a clear exclusion criterion in our study where individuals included were not administered any antibiotics 3 months prior to sampling. To our knowledge, our findings are the first report to systematically analyze the resistome of COVID-19 and control individuals and importantly to demonstrate that several of these ARGs are indeed expressed significantly higher in the COVID-19 group compared to the control group, regardless of antibiotic treatment. We observe that resistance genes include MLS, multi-drug and peptides, resistance classes where treatments of resistant bacteria are known to be inherently challenging with conventional antibiotics [47]. Strikingly, we found that the increased ARG expression in the COVID-19 group was further associated with the same taxa encoding and expressing VFs. This suggests that combinatorial effects of VFs and ARGs may exacerbate the infective competence of these taxa. This is further supported by our analysis identifying that taxa from the Acidaminococcaceae and Erysipelatoclostridiaceae families demonstrated a predicted higher infective competence in the COVID-19 group.
## Conclusions
Our findings suggest that it is imperative to elucidate all the implications of SARS-CoV-2 infection, especially its effect on the gut microbiome community and functions. Although other studies have involved the severe cases of COVID-19 [7, 48], none of these studies include both metagenomic and metatranscriptomic sequencing data. We found that the VFs and ARGs were indeed expressed in higher levels in the COVID-19 group compared to the controls. These key findings would have not been possible by only focussing on metagenomic data. Our collective findings, indicating the enriched abundance and expression of both VFs and ARGs, suggest that COVID-19 may yet have unknown effects that may come to light in the longer term including the shaping of the microbiome across the population. Moreover, we find that none of the commonly reported pathogens (Salmonella, Shigella, Klebsiella etc.) are enriched in the COVID-19 group in our study. In contrast, we find changes in Prevotella spp, AM10 and CAG145 (Firmicutes phylum), Roseburia spp and a Turicibacter spp. Therefore, it will be critically important to evaluate and further validate the effects of COVID-19 on the gut microbiome also in relation to infections by other viral and other pathogens. In particular, it remains unclear at this time, whether infections with other viruses, known to cause respiratory and gastrointestinal distress, e.g., Adenoviruses, respiratory syncytial virus (RSV), influenza viruses, norovirus, would lead to similar community and functional changes within the gut microbiome. Overall, it must be reiterated that pandemic preparedness coupled to the monitoring of VFs in tandem with antibiotic stewardship may be essential components for future strategies to mitigate the longer-term effects of COVID-19 and possibly other viral infections.
## Cohort description and patient involvement
Between May and October 2020, stool samples were collected from 61 participants with COVID-19 confirmed by positive SARS-CoV-2 RT-qPCR (Supplementary Table S1) within the framework of the Predi-COVID study [49]. In order to be eligible to participate in the study, an individual must have been residing in Luxembourg and met all the following criteria: [1] signed informed consent form; [2] individuals ≥ 18 years old with confirmed SARS-CoV-2 infection as determined by PCR, performed by one of the certified laboratories in Luxembourg; and [3] hospitalized or at home. In addition to the criteria specific to the Predi-COVID study, samples were excluded if antibiotic treatment was reported. From the individuals, relevant clinical data was collected using a modified version of the International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) case report form. The participants to be included in the study were classified using an adapted version of the National Institute of Healthy symptom severity scheme [50]. Briefly, the classification of COVID-19 severity was based on the classification of the National Institute of Health (NIH) in the USA. Disease was classified as moderate if an individual had SpO2 ≥ $94\%$, and shortness of breath and/or evidence of lower respiratory disease. If SpO2 < $94\%$, a ratio of arterial partial pressure of oxygen to fraction of inspired oxygen (PaO2/FiO2) < 300 mmHg, a respiratory rate > 30 breaths/min, or lung infiltrates > $50\%$, disease was classified as severe. Subsequently, only asymptomatic to moderate symptoms were reported.
Along with the samples from the COVID-19 confirmed participants, stool samples were collected from a group of 57 individuals who tested negative for SARS-CoV-2 by RT-qPCR, who were participants of the CON-VINCE study, a population-based cohort study which recruited a representative sample of the Luxembourg population, to serve as age-matched controls. Participation in the control group was excluded if matching any of the following criteria: [1] infection of SARS-CoV-2 prior to the study; [2] presence of fever and respiratory distress/cough not attributable to other known chronic disease; [3] usage of antibiotics up to three months prior to enrolment or first SARS-CoV-2 infection. The study design is presented in Fig. 1. Demographic characteristics of the study groups are summarized in Table 1 while additional metadata are included in Supplementary Table S1. Patients were not involved in setting the research questions or the outcome measures of this study. Table 1Demographic characteristics of the study groupsCOVID-19($$n = 61$$)Controls($$n = 57$$)P valueAge43.85 ± 11.9242.12 ± 3.320.297Sex Female22 ($36.07\%$)22 ($38.6\%$)0.776 Male39 ($63.93\%$)35 ($61.4\%$)COVID-19 severity Asymptomatic4 ($6.56\%$)N/AN/A Mild45 ($73.77\%$) Moderate12 ($19.67\%$)*Hospitalization status* Hospitalized1 ($1.64\%$)N/AN/A Not hospitalized60 ($98.36\%$)COVID-19 symptoms Fever35 ($57.38\%$)N/AN/A Runny nose11 ($18.03\%$) Sore throat22 ($36.06\%$) Smell and/or taste loss32 ($52.46\%$) Fatigue40 ($65.57\%$) Headache40 ($65.57\%$) Cough33 ($54.1\%$) Shortness of breath12 (19.67) Diarrhea15 ($24.59\%$) Abdominal pain1 ($1.64\%$) Chest pain9 ($14.75\%$) Ear pain5 ($8.2\%$) Joint pain6 (9.84) Muscle pain30 ($49.18\%$) Vomiting/nausea/vertigo7 ($11.47\%$)N Number of participants, N/A Not applicable
## Sample collection and processing
Stool samples were collected at home by individuals in Fecal Collection Tubes (Zymo Research). Samples and data were collected at the Integrated BioBank of Luxembourg (IBBL). Around 1 g of stool was sampled, diluted in 9 ml DNA/RNA Shield according to the manufacturer’s instructions. Prior to DNA/RNA extraction, stool samples were thawed on ice and aliquoted as follows: 250 µl of sample was aliquoted for DNA extraction, to which 250 µl of lysis solution (ZymoBIOMICS DNA Miniprep Kit; Zymo Research) was added and the sample was subsequently kept frozen at − 80 °C until DNA extraction was performed. Furthermore, another 700 µl was aliquoted for RNA extraction using ZR BashingBead Lysis Tubes (Zymo Research) and the RNeasy Mini Kit (QIAGEN).
## DNA and RNA extractions
DNA was extracted using the ZymoBIOMICS DNA Miniprep Kit according to the manufacturer’s instructions with the following modifications: samples were inactivated for 7 min at 70 °C prior to homogenization by milling for 3 cycles (5 min of cooling on ice between cycles) for 60 s at 6 m/s in a FastPrep-24 5 G (MP Biomedicals). Prior to DNA purification, a Proteinase K incubation step was performed: 5 µl of 20 mg/ml Proteinase K (New England Biolabs GmbH) was added to each sample and incubated for 30 min at 40 °C. The extraction was performed following the manufacturer’s instructions and DNA was eluted in 50 µl DNase/RNase-Free Water (prewarmed to 60 °C). An RNase treatment was performed by adding 2.4 µl of 20 mg/ml Monarch RNase A (New England Biolabs GmbH) to each sample followed by incubation for 10 min at 56 °C. DNA was purified and concentrated using ZR-96 DNA Clean-Up Kit (Zymo Research) following the manufacturer’s instructions and DNA was eluted in 50 μl DNase/RNase-Free water (prewarmed to 60 °C). DNA was quantified using Qubit dsDNA BR assay kit (Invitrogen) and purity determined using Nanodrop 2000C (Thermo Scientific). Samples were frozen at − 80 °C until further use.
Samples for RNA extraction were inactivated for 7 min at 70 °C and 600 µl of cold RLT Buffer (containing 10 µl/ml 2-mercaptoethanol) was added to the samples prior to homogenization by milling for 3 cycles (5 min of cooling on ice between cycles) for 60 s at 6 m/s in a FastPrep-24 5 G (MP Biomedicals). Samples were centrifuged for 3 min at full speed and the supernatant was mixed with 1 volume of $70\%$ Ethanol. Lysates were loaded onto a RNeasy Mini Spin Column and centrifuged at 8000 × g for 1 min. This last step was repeated until all supernatants had passed through the filters. Columns were washed according to the manufacturer’s instructions whereby 50 μl RNase-free water was added to the centre of the filter and incubated at room temperature for 1 min. RNA was eluted by centrifugation at 8000 × g for 1 min. RNA extracts were filled up to 87.5 μl with RNase-free water, 2.5 µl DNase I stock solution and 10 µl Buffer RDD (both RNase-Free DNase Set, QIAGEN) were added, mixed and incubated for 10 min at room temperature. RNA was purified and concentrated using RNA Clean & Concentrator-5 kit (Zymo Research) following the manufacturer’s instructions. RNA was eluted in 15 μl DNase/RNase-Free water. One microliter of obtained RNA was heat‐denatured for 2 min at 72 °C and quality-checked using Agilent RNA 6000 Nano kit (Agilent Technologies). RNA was quantified using Qubit RNA HS assay kit (Invitrogen). RNA extracts were frozen at − 80 °C for further use.
## Metagenomic and metatranscriptomic sequencing
DNA and RNA were extracted from all collected stool samples and sequenced for metagenomic and metatranscriptomic analysis, respectively. One hundred nanograms of DNA was used for metagenomic library preparation using Swift 2S turbo Flexible DNA library kit (cat. no. 45096). The genomic DNA was enzymatically fragmented for 10 min and DNA libraries were prepared without PCR amplification. The average insert size of libraries was 600 bp. Prepared libraries were quantified using Qubit (DNA HS kit, ThermoFischer) and quality-checked with a DNA HS kit on a Bioanalyzer 2100 (Agilient). Sequencing was performed at the LCSB sequencing platform (RRID: SCR_021931) on a NextSeq2000 instrument using 2 × 150 bp read lengths.
500 ng of RNA was rRNA depleted using the Illumina Ribo-Zero Plus rRNA Depletion kit (Illumina, 20,037,135). rRNA depleted samples were further processed using the TruSeq Stranded mRNA library preparation kit (Illumina, 20,020,594) which includes the fragmentation and priming steps. The fragmentation time was reduced to 3 min. Prepared libraries were quantified using Qubit (DNA HS kit, ThermoFischer) and quality checked with DNA HS kit on a Bioanalyzer 2100 (Agilient). Sequencing was performed at the LCSB sequencing platform (RRID: SCR_021931) on a NextSeq500 instrument using 2 × 150 bp read lengths. In total, this resulted in ~ 6 Gbp per sample for the metagenomics and ~ 21 Gbp per sample for the metatranscriptomics.
## Data processing, including genome reconstruction
The Integrated Meta-omic Pipeline (IMP; v3-commitID #b6f9da0e for preprocessing and #c04edbe for downstream assemblies) [25] was used for the processing and iterative co-assembly of metagenomic and metatranscriptomic reads. The workflow includes pre-processing, assembly, genome reconstruction, and functional and taxonomic annotation based on public and custom databases in a reproducible manner. For the data preprocessing, raw metagenomic reads were first trimmed to the maximal read length of 150 bases using Cutadapt (v3.4) [51]. The preprocessed metagenomic and raw metatranscriptomic reads were further processed using IMP: reads were trimmed using Trimmomatic (v.39) [52], reads mapping to the human genome (hg38 genome) or PhiX genome (gi|9,626,372|ref|NC_001422.1, Enterobacteria phage phiX174 sensu lato, complete genome) were removed using BWA (v. 0.7.9a) [53], and the metatranscriptomic reads were further filtered using SortMeRNA (v.4.2.0–238-g90cdf6c) [54]. In addition, alpha-diversity was calculated based on metagenomic reads using Nonpareil (v. 3.4.1) [55] as part of the IMP preprocessing step. Quality control was performed on the processed reads by running FastQC (v. 0.11.9) [56] and summarizing the reports using MultiQC (v. 1.10.1) [57]. In addition, Kraken2 (v. 2.1.2) [58] was used with a database containing only the human and PhiX genomes (https://ndownloader.figshare.com/files/24658262, from 11.09.2020, provided by Mike Lee) to confirm the successful removal of these contaminants from the processed sequencing data. The tool bbmap (v. 38.90) [59] was used on the preprocessed FASTQ files to extract reads mapping to SARS-CoV-2 reference genomes (same genomes as provided by fastv). Pairwise sample (dis)similarity was calculated using Mash (v. 2.3) [60].
De novo co-assembly of the processed metagenomic and metatranscriptomic reads was performed by running Megahit (v2.0) [61] included in IMP, followed by gene calling using an in-house modified Prokka version also allowing for incomplete ORFs [62]. Concurrently, MetaBAT2 [63] and MaxBin2 [64] together with an in-house binning methodology, binny [65], were used to reconstruct metagenome-assembled genomes (MAGs). Subsequently, we obtained a non-redundant set of MAGs using DAS Tool (v1.1.4) [66] with a score threshold of 0.7 for downstream analyses, and those with a minimum completion of $90\%$ and less than $5\%$ contamination as assessed by CheckM (v1.1.3) [67]. Taxonomy was assigned to the MAGs using gtdbtk (v1.7.0) [68]. Finally, MetaQUAST (v. 5.0.2) [69] was run on the created contig FASTA files to compute assembly statistics such as the number and maximal length of contigs, total assembly length, and the N50 and L50 values.
## Virome analyses
The co-assemblies built using metagenomic and metatranscriptomic data were used for the subsequent identification of viruses and to determine their functional activity. Briefly, the co-assembly was first processed through VIBRANT [70] and CheckV [71]. The CheckV assessment was repeated and any viral contigs with less than $70\%$ completion were removed from further analyses. Subsequently, the complete viral contigs and those passing the $70\%$ completion filter were merged and their respective taxonomies were determined using the IMGVR3 database [72]. To detect other viruses and confirm the status of SARS-CoV-2 infection in the processed reads, we also used fastv (v. 0.8.1, data for SARS-CoV-2 and for other viruses was downloaded on September 11th, 2021) [73]. Taxonomic consensus between the IMGVR3 and the fastv databases were determined to obtain overlapping, robust classification, and subsequently were used for the downstream analyses, where differentially abundant genes were further assessed for differential relative gene expression.
## Prediction of microbial composition, virulence factors, and antimicrobial resistance
Profiling of the microbial community was performed on the processed reads using MetaPhlAn3.1 (v3.1.0, database “mpa_v31_CHOCOPhlAn_201901”) [74]. Simultaneously, profiling of antibiotic resistance factors was done using RGI (v5.2.0, CARD data v3.1.4, prevalence, resistomes and variants data v3.0.9) [75]. To obtain additional in-depth details of ARGs, in addition to the detection of VFs and mobile genetic elements (MGEs), PathoFact (v1.0; modified branch allowing the input of ORFs, #6fa64961) was run [24]. PathoFact is a pipeline for the prediction of ARGs and VFs, and their localization to MGEs, in metagenomic data. PathoFact was run on the contigs assembled by IMP together with their predicted protein sequences (ORFs) for each sample separately. PathoFact uses DeepARG [76] and RGI [75] for the prediction of ARGs, DeepVirFinder [77] and VirSorter [78] for the prediction of phages and PlasFlow [79] for the prediction of plasmids. Additionally, PathoFact uses its own developed tool, a combination of a HMM database (built on the VFDB [80]) and a random forest model, for the prediction of VFs. To run PathoFact, the input protein sequences were first processed to remove any trailing stop codon symbols (“*”) and to remove any sequence having an internal stop codon symbol as this is required for the tool RGI for ARG detection. For analyses of the predictions, FeatureCounts (v1.6.4) (Liao et al. 2014) was used to extract the number of reads per functional category. Thereafter, the relative abundance of genes and general expression levels was calculated using the Rnum_Gi method described by Hu et al. ( Hu et al. 2013) which normalizes for both gene length and library size. Subsequently, metatranscriptomic expression levels were further normalized using the respective gene abundances from the metagenomic data (normalized gene expression = gene expression/ gene abundance).
## Statistical testing and data analysis
Statistical analyses of the taxonomic and functional data, as well as further visualizations, were performed using version 4.1.1 of the R statistical software package [81]. The R package MaAsLin2 [82] was used to determine associations between the cohort data and microbial features (e.g., functional and taxonomic profiles). Furthermore, MaAsLin2 identified significant differences were further validated by Wilcoxon rank-sum tests with adjustments using the ‘Benjamini-Hochberg’ method for multiple testing, specifically the ‘p.adjust’ function from the stats R package was used. To additionally, validate our findings with respect to the VF and ARGs, we used ANCOM-BC [83]. The tidyverse, microbiomeViz, tidytree, and ggtree packages were used to visualize the microbiome data, including using cladogram visualizations. The tidyverse package, including ggplot2, was used to generate all violin plots, box plots, and bubble plots. Finally, the hmisc and corrplot packages were used for all correlation plots.
## Supplementary Information
Additional file 1: Supplementary Figure S1. Dissimilarity of COVID-19 and control groups’ microbiome profiles. Alpha- (Shannon) (a) and beta-diversity (b) metrics of the gut microbiome compositions of the COVID-19 and control groups. Additional file 2: Supplementary Figure S2. VF diversity between the COVID-19 and control groups. Alpha- (a) and beta-diversity (b) metrics of the VF abundances are depicted along with the VF relative expression alpha- (c) and beta-diversity (d) between the COVID-19 and control groups. Additional file 3: Supplementary Figure S3. AMR diversity between the COVID-19 and control groups. Alpha- (a) and beta-diversity (b) metrics of the AMR abundances. AMR relative expression alpha- (c) and beta-diversity (d) between the COVID-19 and control groups. Additional file 4: Supplementary Figure S4. Analysis of VF and ARG compositionality using ANCOM-BC. Log2 fold change (LFC) of (a) abundance and (b) normalised gene expression of VFs per taxonomic family between the COVID-19 and control groups. ARG abundance (c) and normalised gene expression (d) differences between the COVID-19 and control groups. Additional file 5: Supplementary Table 1. Cohort metadata and description. Additional file 6: Supplementary Table 2. Identified and significantly enriched viral genes in the COVID-19 group compared to control individuals.
## The CON-VINCE Consortium
Geeta Acharya, Luxembourg Institute of Health, Strassen, Luxembourg; Gloria Aguayo, Luxembourg Institute of Health, Strassen, Luxembourg; Wim Ammerlaan, Luxembourg Institute of Health, Strassen, Luxembourg; Ariane Assele-Kama, Luxembourg Institute of Health, Strassen, Luxembourg; Christelle Bahlawane, Luxembourg Institute of Health, Strassen, Luxembourg; Katy Beaumont, Luxembourg Institute of Health, Strassen, Luxembourg; Nadia Beaupain, Luxembourg Institute of Health, Strassen, Luxembourg; Lucrèce Beckers, Luxembourg Institute of Health, Strassen, Luxembourg; Camille Bellora, Luxembourg Institute of Health, Strassen, Luxembourg; Fay Betsou, Luxembourg Institute of Health, Strassen, Luxembourg; Sandie Boly, Luxembourg Institute of Health, Strassen, Luxembourg; Dirk Brenner, Luxembourg Institute of Health, Strassen, Luxembourg; Eleftheria Charalambous, Luxembourg Institute of Health, Strassen, Luxembourg; Emilie Charpentier, Luxembourg Institute of Health, Strassen, Luxembourg; Manuel Counson, Luxembourg Institute of Health, Strassen, Luxembourg; Brian De Witt, Luxembourg Institute of Health, Strassen, Luxembourg; Olivia Domingues, Luxembourg Institute of Health, Strassen, Luxembourg; Claire Dording, Luxembourg Institute of Health, Strassen, Luxembourg; Bianca Dragomir, Luxembourg Institute of Health, Strassen, Luxembourg; Tessy Fautsch, Luxembourg Institute of Health, Strassen, Luxembourg; Jean-Yves Ferrand, Luxembourg Institute of Health, Strassen, Luxembourg; Ana Festas Lopes, Luxembourg Institute of Health, Strassen, Luxembourg; Joëlle Véronique Fritz, Luxembourg Institute of Health, Strassen, Luxembourg; Manon Gantenbein, Luxembourg Institute of Health, Strassen, Luxembourg; Laura Georges, Luxembourg Institute of Health, Strassen, Luxembourg; Jérôme Graas, Luxembourg Institute of Health, Strassen, Luxembourg; Gael Hamot, Luxembourg Institute of Health, Strassen, Luxembourg; Anne-Marie Hanff, Luxembourg Institute of Health, Strassen, Luxembourg; Maxime Hansen, Luxembourg Institute of Health, Strassen, Luxembourg; Lisa Hefele, Luxembourg Institute of Health, Strassen, Luxembourg; Estelle Henry, Luxembourg Institute of Health, Strassen, Luxembourg; Margaux Henry, Luxembourg Institute of Health, Strassen, Luxembourg; Eve Herkenne, Luxembourg Institute of Health, Strassen, Luxembourg; Christiane Hilger, Luxembourg Institute of Health, Strassen, Luxembourg; Judith Hübschen, Luxembourg Institute of Health, Strassen, Luxembourg; Laetitia Huiart, Luxembourg Institute of Health, Strassen, Luxembourg; Alexander Hundt, Luxembourg Institute of Health, Strassen, Luxembourg; Gilles Iserentant, Luxembourg Institute of Health, Strassen, Luxembourg; Stéphanie Kler, Luxembourg Institute of Health, Strassen, Luxembourg; Rejko Krüger, Luxembourg Institute of Health, Strassen, Luxembourg; Pauline Lambert, Luxembourg Institute of Health, Strassen, Luxembourg; Sabine Lehmann, Luxembourg Institute of Health, Strassen, Luxembourg; Morgane Lemaire, Luxembourg Institute of Health, Strassen, Luxembourg; Andrew Lumley, Luxembourg Institute of Health, Strassen, Luxembourg; Monica Marchese, Luxembourg Institute of Health, Strassen, Luxembourg; Sophie Mériaux, Luxembourg Institute of Health, Strassen, Luxembourg; Maura Minelli, Luxembourg Institute of Health, Strassen, Luxembourg; Alessandra Mousel, Luxembourg Institute of Health, Strassen, Luxembourg; Maeva Munsch, Luxembourg Institute of Health, Strassen, Luxembourg; Mareike Neumann, Luxembourg Institute of Health, Strassen, Luxembourg; Markus Ollert, Luxembourg Institute of Health, Strassen, Luxembourg; Marc Paul O'Sullivan, Luxembourg Institute of Health, Strassen, Luxembourg; Magali Perquin, Luxembourg Institute of Health, Strassen, Luxembourg; Achilleas Pexaras, Luxembourg Institute of Health, Strassen, Luxembourg; Jean-Marc Plesseria, Luxembourg Institute of Health, Strassen, Luxembourg; Lucie Remark, Luxembourg Institute of Health, Strassen, Luxembourg; Estelle Sandt, Luxembourg Institute of Health, Strassen, Luxembourg; Bruno Santos, Luxembourg Institute of Health, Strassen, Luxembourg; Aurélie Sausy, Luxembourg Institute of Health, Strassen, Luxembourg; Margaux Schmitt, Luxembourg Institute of Health, Strassen, Luxembourg; Sneeha Seal, Luxembourg Institute of Health, Strassen, Luxembourg; Jean-Yves Servais, Luxembourg Institute of Health, Strassen, Luxembourg; Florian Simon, Luxembourg Institute of Health, Strassen, Luxembourg; Chantal Snoeck, Luxembourg Institute of Health, Strassen, Luxembourg; Kate Sokolowska, Luxembourg Institute of Health, Strassen, Luxembourg; Hermann Thien, Luxembourg Institute of Health, Strassen, Luxembourg; Johanna Trouet, Luxembourg Institute of Health, Strassen, Luxembourg; Jonathan Turner, Luxembourg Institute of Health, Strassen, Luxembourg; Michel Vaillant, Luxembourg Institute of Health, Strassen, Luxembourg; Daniela Valoura Esteves, Luxembourg Institute of Health, Strassen, Luxembourg; Charlène Verschueren, Luxembourg Institute of Health, Strassen, Luxembourg; Tania Zamboni, Luxembourg Institute of Health, Strassen, Luxembourg; Pinar Alper, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Piotr Gawron, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Soumyabrata Ghosh, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Enrico Glaab, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Clarissa Gomes, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Borja Gomez Ramos, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Vyron Gorgogietas, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Valentin Groues, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Wei Gu, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Laurent Heirendt, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Ahmed Hemedan, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Sascha Herzinger, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Anne Kaysen, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Jacek Jaroslaw Lebioda, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Tainà Marques, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; François Massart, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Patrick May, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Christiane Olesky, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Venkata P. Satagopam, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Claire Pauly, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Laure Pauly, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Lukas Pavelka, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Guilherme Ramos Meyers, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Armin Rauschenberger, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Basile Rommes, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Kirsten Rump, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Reinhard Schneider, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Valerie Schröder, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Amna Skrozic, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Lara Stute, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Noua Toukourou, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Christophe Trefois, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Carlos Vega Moreno, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Maharshi Vyas, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Xinhui Wang, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg; Anja Leist, University of Luxembourg, Esch-sur-Alzette, Luxembourg; Annika Lutz, University of Luxembourg, Esch-sur-Alzette, Luxembourg; Claus Vögele, University of Luxembourg, Esch-sur-Alzette, Luxembourg; Linda Hansen, Centre Hospitalier de Luxembourg, Luxembourg, Luxembourg; João Manuel Loureiro, Centre Hospitalier de Luxembourg, Luxembourg, Luxembourg; Beatrice Nicolai, Centre Hospitalier de Luxembourg, Luxembourg, Luxembourg; Alexandra Schweicher, Centre Hospitalier de Luxembourg, Luxembourg, Luxembourg; Femke Wauters, Centre Hospitalier de Luxembourg, Luxembourg, Luxembourg; Tamir Abdelrahman, Laboratoire National de Santé, Dudelange, Luxembourg; Estelle Coibion, Laboratoire National de Santé, Dudelange, Luxembourg; Guillaume Fournier, Laboratoire National de Santé, Dudelange, Luxembourg; Marie Leick, Laboratoire National de Santé, Dudelange, Luxembourg; Friedrich Mühlschlegel, Laboratoire National de Santé, Dudelange, Luxembourg; Marie France Pirard, Laboratoire National de Santé, Dudelange, Luxembourg; Nguyen Trung, Laboratoire National de Santé, Dudelange, Luxembourg; Philipp Jägi, Laboratoire Réunis, Junglinster, Luxembourg; Henry-Michel Cauchie, Luxembourg Institute of Science and Technology, Luxembourg, Luxembourg; Delphine Collart, Luxembourg Institute of Science and Technology, Luxembourg, Luxembourg; Leslie Ogorzaly, Luxembourg Institute of Science and Technology, Luxembourg, Luxembourg; Christian Penny, Luxembourg Institute of Science and Technology, Luxembourg, Luxembourg; Cécile Walczak, Luxembourg Institute of Science and Technology, Luxembourg, Luxembourg.
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|
---
title: The telemedicine community readiness model—successful telemedicine implementation
and scale-up
authors:
- Lena Otto
- Hannes Schlieter
- Lorenz Harst
- Diane Whitehouse
- Anthony Maeder
journal: Frontiers in Digital Health
year: 2023
pmcid: PMC9995762
doi: 10.3389/fdgth.2023.1057347
license: CC BY 4.0
---
# The telemedicine community readiness model—successful telemedicine implementation and scale-up
## Abstract
To successfully scale-up telemedicine initiatives (TIs), communities play a crucial role. To empower communities fulfilling this role and increase end users' acceptance of TIs, support tools (from now on entitled artifacts) are needed that include specific measures to implement and scale up telemedicine. Addressing this need, the article introduces the Telemedicine Community Readiness Model (TCRM). The TCRM is designed to help decision-makers in communities to create a favorable environment that facilitates the implementation and scale-up of TIs. The TCRM is a practical tool to assess communities' readiness to implement TIs and identify aspects to improve this readiness. The development process follows a design-science procedure, which integrates literature reviews and semi-structured expert interviews to justify and evaluate design decisions and the final design. For researchers, the paper provides insights into factors that influence telemedicine implementation and scale-up (descriptive role of knowledge) on the community level. For practitioners, it provides a meaningful tool to support the implementation and scale-up of TIs (prescriptive role of knowledge). This should help to realize the potential of telemedicine solutions to increase access to healthcare services and their quality.
## Introduction
Telemedicine solutions can increase accessibility to and quality of healthcare services, especially in rural and remote areas [1, 2]. The term “telemedicine” describes information and communication technologies that support the delivery of healthcare services as well as medical education by health professionals over a (geographical) distance [3]. Telemedicine applications range from synchronous teleconsultation between patient and provider; sending computed tomographies from an ambulance to a hospital before patient arrival; or telemonitoring vital signs, e.g., blood pressure [4]. While the initial implementation of telemedicine initiatives (TIs) works well in most cases, their scale-up, i.e., their progress from the pilot stage towards reaching an increased number of people [5], has often been unsuccessful [6, 7].
In other theories described as diffusion, the scale-up process depends on the users' decision to adopt a specific solution [8]. Telemedicine users and their decisions are influenced by the specific social, legal, or infrastructural environment [9] they are embedded in. One entity that can actively change the parameters of an environment is the community, whether it is a community of place or one of interest [10]. In a community of place, a group of people is connected by a shared geographic and social context, e.g., a city or health network. A community of interest is characterized by common interest independent of the people's location or social group, e.g., people who share conditions resulting from the same disease [10, 11]. The community can influence various factors of telemedicine implementation and scale-up [12]. Therefore, it is essential to understand the factors influencing communities' readiness to implement and scale up TIs. Readiness describes “the relative level of acceptance of a program, action or other form of decision-making activity that is locality-based” [13], i.e., that shares a common context. In this paper, we investigate community readiness by tackling the following research question: •How should an artifact be designed to support the implementation and scale-up process of telemedicine initiatives (TIs) in communities?The results are reflected in the Telemedicine Community Readiness Model (TCRM), whose design and development follow a design science-oriented process incorporating literature reviews and semi-structured expert interviews. The construction of artifacts is one area of information systems research, whereby an artifact can be, e.g., a model or method, something that “has, or can be transformed into, a material existence as an artificially made object […] or process” [14].
Our research contributes to information systems (IS) research and practice in different ways. First, we consolidate the community-related factors influencing telemedicine implementation and scale-up. Second, we provide and demonstrate an artifact that helps telemedicine researchers and community practitioners to create a favorable environment for TIs.
We structure the paper as follows: After the introduction, the background section provides information and knowledge that informs the design artifact. The method is part of Section 3, where the procedure for designing and evaluating the TCRM is outlined. The TCRM and its building blocks are presented in Section 4. Section 5 summarizes the evaluation results, and the implications for adapting the TCRM are outlined. The paper ends with a discussion of the results, their limitations, and future research.
## Background
The background section provides knowledge about TIs and maturity models required to design the TCRM and offer practical support to its users.
## Telemedicine initiatives (TIs)
TIs enable care delivery regardless of location or time and provide a means to overcome healthcare disparities regarding access to healthcare services, especially in rural or underserved areas [3, 15]. Current research streams on telemedicine range from investigations that put the individual at the center of research [16] to studies that highlight the complexity of influences on, e.g., the scale-up and sustainability of healthcare technologies by pointing to the importance of readiness for change or the broader societal system [7, 17]. Communities can be placed within the latter. Few studies [9, 18] explicitly examine the influence of communities on the implementation and scale-up of TIs. Our approach follows this stream. We focus on the role of communities in affecting aspects that positively influence TI implementation and scale-up.
Despite the availability of a variety of technological solutions, studies [9, 19, 20] show that factors like the acceptance by users (e.g., patients or healthcare providers) and their social, technical, or legal environments can influence the implementation of TIs. Figure 1 illustrates the relationship between these aspects and highlights the broader societal context of TIs on the micro, meso, and macro levels [9, 12].
**Figure 1:** *The wider societal context surrounding TIs.*
Individuals such as patients and professionals, in their various institutional environments, constitute the micro level of the context around the TI: they decide whether to adopt the TI or not. This decision is influenced, e.g., by the individual's motivation, the usability of the technology, and the organizational regulatory framework in the institutional environment [20]. On the macro level, which reflects the overarching framework, the legal and regulatory constraints are defined, e.g., by a federal government or health insurance schemes/companies that define standards and quality guidelines to be followed or funding and reimbursement opportunities [21]. Between the micro and the macro levels, on the meso level, the community is located. It represents the social system surrounding the individual. As the users of TIs are locality-dependent, they can be influenced by actions on the meso (community) level.
According to Edwards et al. [ 10], we understand a community as one of place or one of interest. Other types of communities, such as communities of practice or virtual advocacy groups [22], are not within the scope of this paper. In a community of place, a group of people is connected by a shared geographic and social context, e.g., a city or health network. A community of interest is characterized by common interest independent of the people's location or social group, e.g., people who share conditions resulting from the same disease [10].
There are various influences on communities, depending on their nature. The community is generally affected by macro-level constraints, as it acts within the boundaries of the countries` or systems` overarching framework. The individuals also characterize it on the micro level, which forms the community: the community can affect and support these people by providing them, e.g., with financial and human resources [23], conducting campaigns for raising awareness, or diminishing existing inequalities [18] by setting up support programs, e.g., for financially disadvantaged community members.
## Maturity models
In IS research, maturity models (MMs) are used as tools to assess the current situation of the subject under study and further improve this situation by indicating a path for scale-up [24]. A certain number of levels typically characterizes MMs (e.g., initial, defined, managed). These levels show a simplified evolutionary path to reach higher “maturity” [25, 26]. The levels are accompanied by dimensions, describing activities or key elements relevant at each level [25]. MMs are investigated and classified differently, e.g., regarding possible development methods [24, 26, 27], maturation paths [25], or the level of support the MM provides [27]. When individual scores can be assigned to different activities on a level, the model's maturation path is called “continuous”, while “staged” models describe the performance of all activities in a single inclusive level [25]. Regardless of the specific characteristics used, MMs help different stakeholders to collaborate by providing a common domain understanding [28].
According to de Bruin et al. [ 27], three types of MMs can be distinguished: descriptive, prescriptive, and comparative. Descriptive MMs consist solely of a description of the status quo. Beyond that, prescriptive MMs include recommendations for possible improvement steps. When sufficient data is collected to benchmark, the model can become a comparative MM enabling the comparison of various industries or regions [27].
MMs have already been applied in the field of telemedicine. An example is the descriptive model of [29], who present a 5-point, Likert-like questionnaire, which focuses on hospital staff as end users. However, the model lacks clear documentation on how to apply it. Additionally, Likert-like questionnaires are generally intensely subjective as no information is given for each score [25]. Another example is the maturity grid of [30], which is also descriptive but omits a clear statement of who the target audience is and lacks a focus on the community perspective [31]. It was further developed by the same authors [32] into a more substantial but even more complex MM, which makes it hard to easily understand and use it [33].
An existing MM that considers the influence of communities is the Community Readiness Model (CRM) for prevention programs [10, 34]; it concentrates on community efforts or the community climate [34]. Since telemedicine specifics, such as the focus on technical infrastructure and support when using digital solutions, are not part of the CRM, they cannot be applied directly to TIs but are used as a basis for considering the community.
In summary, existing telemedicine MMs either focus on specific aspects of telemedicine (leaving aside the complexity of the context and the supportive role of communities) or have some shortcomings regarding the support of the improvement process [12]. However, MMs are a promising tool to guide implementation and scale-up processes [30]. As existing approaches are not sufficient, the TCRM combines a staged (type of MM), prescriptive MM approach (guidance character) with both TI and community characteristics (scope).
## Method
Inspired by the MM development procedure of [24], we conducted eight steps that implement a specific procedure along these cycles. This paper documents the design and application of the artifact (second and third cycles indicated by a black frame in Figure 2).
**Figure 2:** *Methodical process to design the TCRM (refined research process based on (35).*
## Prior research and problem justification
Our research addresses the problem (step 1) of the slow implementation and scale-up of TIs worldwide [36], and the context-sensitivity of Tis [37]. To understand the domain (step 2), community-specific [10] and telemedicine-specific barriers and enablers were analyzed based on two literature reviews [see the previous study by [12]]. For example, while missing collaboration culture or lacking knowledge about the existence and use of TIs can impede the implementation and scale-up of telemedicine, the provision of adequate resources and the involvement of qualified stakeholders can enable this [12]. The need (step 3) for a prescriptive MM focusing on the community was shown by [31]. Having investigated eight prior MMs, including their shortcomings, they conclude that a new MM for telemedicine should incorporate elements such as “community”, core readiness, barriers, and adequate guidance materials [31]. All these elements were included in the TCRM to provide an artifact advancing the field of MMs for TIs, ready to be applied.
## Designing the TCRM
The levels and dimensions of the staged TCRM (step 4) are inspired by other MM approaches [10, 20, 30, 34] and integrate knowledge about evidence for TIs (38–40). After combining levels and dimensions in analogy to existing models (e.g., van Dyk and Schutte 2012), barriers and enablers for implementing and scaling-up TIs [12] were added to the maturity levels. The model got a prescriptive character by including advice on evolving towards a higher maturity level (step 5).
## Demonstration and evaluation of the TCRM
According to [41], MMs are seen as in-between the artifact types model and method. Demonstrating and evaluating a MM should therefore consider the evaluation criteria for models and methods alike [41]. 17 qualitative, semi-structured expert interviews addressed these evaluation criteria (see Table 1) to reflect the TCRM with potential key users (steps 6–7). The interviews were conducted in two rounds (first round: 12 interviews, second round: 5 interviews) with experts in Australia and Germany, and are further described in Section 5.
**Table 1**
| Round of evaluation (by expert interviews) | Object of evaluation (42) | Evaluation criteria (43) | Artifact type (43) |
| --- | --- | --- | --- |
| First (Section 5.1) | Structure | Completeness | Model |
| First (Section 5.1) | Structure | Fidelity with real-world phenomena | Model |
| First (Section 5.1) | Structure | Internal consistency | Model |
| Second (Section 5.2) | Goal and environment | Level of detail | Method |
| Second (Section 5.2) | Goal and environment | Generality | Method |
| Second (Section 5.2) | Goal and environment | Ease of use | Method |
| Long-term | Evolution and activity | Efficiency | Model |
| Long-term | Evolution and activity | Operationality | Model |
| Long-term | Evolution and activity | Robustness | Model |
## The telemedicine community readiness model (TCRM)
The TCRM consists of three parts: An assessment part to define the current readiness, an improvement part (as it is a prescriptive MM) that helps communities shift to higher levels, and a procedure model guiding the use of the model.
## How to assess communities with the TCRM?
According to [44], the scope of a MM needs to be defined by describing the model's focus, its target audience, and the relevant stakeholders. As shown in Table 2, the TCRM focuses on communities of place and/or interest, the target audience are decision-makers in a community (also called multipliers). Still, we underline the advantage of involving experts with a heterogenous background.
**Table 2**
| Focus | Target audience (people who are interested in the results) | Relevant stakeholders (people able to assess the as-is situation) |
| --- | --- | --- |
| - Telemedicine readiness in communities ○ of place (e.g., a municipality, region, or healthcare network) or○ of interest (e.g., a network of patients with a particular disease) or○ a combination of both (e.g., people with a certain condition within a geographic boundary) | - Decision-makers on an administrative level (e.g., representatives in communities, payers, legislative institutions or service providers) | - All people within the community involved in managing, delivering, and using telemedicine, e.g., healthcare professionals, technicians, or patients- Ideally, different experts from various backgrounds will together apply the TCRM to ensure the reliability of results |
The TCRM depicted in Figure 3 includes all the factors related to and influenceable on the community level (vertical). For example, the individual's unwillingness to use telemedicine should be addressed on the micro level, while regulatory issues have to be dealt with on the macro level.
**Figure 3:** *The assessment part of the TCRM.*
In the TCRM, three dimensions (status of telemedicine (ST), community involvement (CI), and evaluation measures (EM)) and six levels structure the assessment part (Figure 3). Process-related and structural descriptions characterize the six levels. The levels describe an evolutionary path towards successfully implemented and scaled-up TIs, where all levels need to be reached consecutively. Thus, the TCRM is additive, i.e., every aspect considered at a lower level also needs to be fulfilled at all subsequent levels. To illustrate the components of the model and their interplay, we use the following two examples.
Example 1—Progress from Level 1 (Preplanning) to Level 2 (Preparation): At Level 1, the environment for implementing TIs in the community is chaotic, i.e., there is no structure provided to the community. Only a small proportion of the community members (e.g., patients, other citizens, or healthcare providers) participate in sporadically developed telemedicine pilots (e.g., applications are tested in only one hospital). No empirical evidence has been gathered, but initial evaluation studies have been planned. When the environment becomes more coordinated, i.e., the community starts to take responsibility for coordinating the development strategy for TIs, community readiness evolves toward the second level. The number of community members using the existing initiatives increases, but the solutions do not convince everyone. Evaluation studies are now designed to incorporate the needs of the individual TI.
Example 2—Progress from Level 5 (Confirmation/Expansion) to Level 6 (Professionalization): At the fifth level, where the focus is on quality and productivity, most initiatives are completed successfully (e.g., applications are implemented for all the potential patients in the community). The majority of community members actively use TIs. Existing TIs are expanded to other diseases or community members who are accustomed to using TIs. Evaluation activities are steadily conducted in real-world settings, and positive results are gained in the long term. When the focus shifts to continuous improvement, this indicates the sixth level: TIs are established in the community and regularly maintained and improved as a joint initiative among all stakeholders involved. Almost all community members have access to TIs and use them. New initiatives can easily be included and are available to all community members. The evaluation activities are conducted in real-world settings to generate constant evidence about the TIs.
## How to improve guided by the TCRM?
The TCRM also provides improvement aspects (Figure 4) that support moving from lower to higher maturity levels. Each improvement aspect (see Supplementary Annex A1) influences the status of TIs (ST), community involvement (CI), or both, as shown in brackets for each aspect in Figure 4. To illustrate the improvement process, the two examples cited above are used again.
**Figure 4:** *The improvement part of the TCRM.*
Example 1: Aiming to progress from the first to the second level, community actors should consider all aspects on the first level and monitor their fulfillment. For example, a community can have a holistic objective to implement telemedicine, the basic infrastructural requirements are clear, and essential infrastructure is provided. Furthermore, risk management is ensured, and contractual arrangements are documented in written form. Based on this initial situation, the community should ensure that all the relevant stakeholders (especially patients) are involved when implementing new TIs. The community also needs to be aware of existing ethical guidelines and guarantee they are continuously followed. When these measures are implemented to fit the community's needs, this community is ready to advance to the second readiness level.
Example 2: To progress from Level 5 to Level 6, the respective improvement aspects on Level 5 need to be considered. A community at the fifth level could have supportive policies in place to ensure continuous improvement/performance management. As a next step, programs should be set up to support increasing the (health/digital) literacy of community members and diminish any inequalities in the community. Measures for training and qualification need to be provided permanently to ensure that all relevant stakeholders can adequately use TIs and help others to do so. As the model is additive, the improvement aspects depicted in Levels 1 to 4 need to be monitored continuously. Accordingly, in case of maturing from the first level onwards, all improvement aspects can be assumed to have been considered. In case the initial assessment of the current status of community readiness results in a higher level, such as the fifth level, the fulfillment of all improvement aspects on the previous levels needs to be checked. If, for example, the community has already implemented awareness campaigns but did not include users' peers as a target group, this aspect needs to be worked on. After that, it is possible to progress to the sixth level.
## How to apply the TCRM?
The procedure model of the TCRM (Figure 5) describes the detailed activities and decision points that should be dealt with during the use of the TCRM. This assures compliance with the intentions and mechanisms of the TCRM.
**Figure 5:** *Procedure model of the TCRM.*
First, the community (of place and/or interest) to which the TCRM shall be applied has to be defined. Afterwards, the applicability of the model is checked based on two preconditions to be fulfilled: 1.In the community exists a core readiness for change, which means there is a common desire to use TI and to change traditional healthcare processes [18, 45].2.The community has implemented or started implementing at least one TI. ( In the case that no TI is implemented, the improvement aspects in the model can still be used during the telemedicine planning process.)When the preconditions are fulfilled, experts (external auditors or community members) have to qualitatively assess the as-is situation regarding the three assessment dimensions (ST, CI, EM). In the case that the individual assessments for the dimensions differ, the following rules apply: Generally, the lower rating of the two ratings of ST or CI should be taken as the overall rating, representing the dimension that must be improved first. For example, if the ST is on Level 3, the CI on Level 2, and EM on Level 3, the (telemedicine) community readiness is at Level 2. Lower ratings of EM are not directly included in the assessment as evaluation measures can be initiated directly. For example, if ST and CI are on Level 3 but EM is on Level 2, the overall rating is Level 3, with evaluation measures being one of the improvement aspects to reach higher levels. The same applies if no evaluation studies are planned. A rating cannot be given in that case as EM is below Level 1. Since the other two dimensions need to be at least on Level 1 (as one TI must be implemented already according to the preconditions), the overall readiness of the community would be at Level 1, with EMs being the most important aspect to focus on.
Afterward, all improvement aspects described on the rated level and the levels below need to be checked to judge their fulfillment. If all aspects are addressed, the aspects of higher levels can help identifying measures for improvement. Whenever improvement potential is identified, the responsible persons who can guide the improvement process need to be identified to help implementing the improvement aspects with measures that fit the specific community characteristics. Persons who may offer guidance include professionals, technicians, or other users of TIs. Once all improvement measures have been implemented, the process can be repeated for continuous improvement. Undertaking this action supports the scale-up of the ST and the CI and helps the community moving to higher levels of TI readiness.
## Evaluation
The TCRM was assessed regarding its structural characteristics, goal and environment [42]. To assess these characteristics, two rounds of qualitative, semi-structured expert interviews with potential user groups were the most feasible approach. In the first round, the focus was on the structure; in the second round, the focus was on the goal and effectiveness of the TCRM, which was then modified based on the experts' feedback.
## Evaluating the TCRM's structure
The first version of the TCRM was evaluated with twelve experts to obtain their opinions on the completeness, fidelity with real-world phenomena, and internal consistency of the model structure to adjust it, if necessary. During the interviews, the “think aloud” method [46, 47] was used to understand the interviewees' impressions of the descriptive model.
The twelve interviews were conducted in Germany ($$n = 7$$) and Australia ($$n = 5$$) between March and July 2019. Germany and Australia were chosen as both countries rank similarly on international scales of socio-economic comparability [high-income developed countries [48]] but have substantially different contextual settings. While *Australia is* characterized by a definite contrast between its urban and rural areas and a National Health Insurance System, *Germany is* densely populated throughout and has a Social Health Insurance System in place. In Australia, the state is responsible for regulating and financing the healthcare system, whereas in Germany both tasks are carried out by societal actors [49]. In both countries, care provision is carried out by private actors. Thus, the TCRM was tested in different environments. The following criteria were applied during the recruitment process: all experts needed to represent members of the target audience or stakeholders for future use of the model (see Table 2). Furthermore, they had to have personal experience in implementing or using TIs in their job. The experts assessing the structure of the TCRM included healthcare professionals, representatives of health insurance companies, and/or representatives of network organizations in healthcare (Table 3).
**Table 3**
| Country | Germany | Germany.1 | Germany.2 | Germany.3 | Germany.4 | Germany.5 | Germany.6 | Australia | Australia.1 | Australia.2 | Australia.3 | Australia.4 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| SIE no. Role | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
| Healthcare professional | | x | | | | | x | | | x | x | |
| Representative of a health insurance company | | | x | | | x | | | | | | |
| Representative of a network organization in healthcare | x | | | x | x | | | x | x | x | x | x |
All interviewees stated that the process described by the TCRM's levels and dimensions was similar to their real-world experience in their communities, for example: “it's true, we started off […] in the planning […] phase and then […] we have improved. […] Now we’ve got a few more sites […] and became a bit larger” (SIE11). Some adaptions to the initial TCRM were made to address the experts' feedback. The adaptions concerned the descriptions of levels and dimensions as well as the wording and the assignment of improvement aspects to levels (see Supplementary Annex A2). Each expert was asked to assign each improvement aspect to one of the six levels in the model. The median of this assignment was then calculated across all interview results (see Supplementary Annex A3). Adaptations to the model were made by two authors based on this calculation and the explanations the interviewees offered while thinking aloud. Given the small number of interviewees, extreme median values carry the risk of biased allocations of improvement aspects to the steps of the model. Therefore, all assignments were weighted in line with each interviewee's expertise and her or his statements during the allocation process. For some improvement aspects, the median resulting from the assessment by the Australian and German interviewees differed. As the number of interviews and countries was not high enough to assume that the TCRM needs to be country-specific, this needs to be explored in more detail in future work.
Most interviewees remarked that the model represents an idealistic path to the scale-up of telemedicine. Nevertheless, it is “useful to have an ideal […] model, because in a process where you are guided by it, you do not run the risk of forgetting things that are essential” (SIE6).
## Evaluating the TCRM's goal and environment
Revised on the basis of interview round no. 1, the TCRM was afterward applied in real-world settings to ensure that it is understandable to its potential users and can easily be applied. For this, the evaluation criteria were level of detail, generality and ease of use [42]. Therefore, five interviews with different experts were conducted in Australia ($$n = 3$$) and Germany ($$n = 2$$) later in 2019. Following the selection scheme of the first iteration, healthcare professionals and/or representatives of network organizations in healthcare were interviewed [45]. The TCRM was applied by the interviewees to their communities using the procedure model (see Figure 5). This process was supervised by the interviewer to identify weaknesses of the TCRM's documentation (level of detail) or the documentation of the process model (ease of use).
The communities described by the five experts for applying the model (AIE1—AIE5) varied across states (in Australia) and across different conditions in diverse types of cities (in Australia and Germany). Through this procedure, it could be shown that the TCRM can be applied to communities of place and of interest (generality). Four of the five communities were assessed as Level 1 by the experts (Table 4). One community was rated on Level 4. The TCRM could help identifying improvement aspects for each community, e.g., AIE2 stated that “culturally appropriate service response, particularly for the aboriginal community”, is needed, which corresponds to the “culturally appropriate communication” aspect.
**Table 4**
| Unnamed: 0 | AIE no. | 1 | 2 | 3 | 4 | 5 |
| --- | --- | --- | --- | --- | --- | --- |
| | Country | Australia | Australia | Australia | Germany | Germany |
| Role | Healthcare professional | x | | x | x | x |
| Role | Representative of network organization in healthcare | x | x | x | x | x |
| Definition of community | State as geographic boundary (community of place) | x | x | | | |
| Definition of community | City as geographic boundary (community of place) | | | x | x | x |
| Definition of community | Condition (community of interest) | Stroke diagnosis | Need to receive rehabilitation | Not specified | Not specified | People living in nursing homes in that city |
| | Overall level of readiness | 1 | 4 | 1 | 1 | 1 |
The interviewees were also asked to assess the TCRM's usability. AIE3 expressed general doubts as to whether the model provides a one-size-fits-all solution: “I hesitate that the model suits everybody or every condition” (AIE3)). This concern can be addressed by more evaluation activities in the future to find out how universally the TCRM is applicable. However, the model is based on international evidence, e.g., related to barriers for TI implementation worldwide, and it is therefore assumed to be widely applicable for further evaluation.
More detailed feedback was given by the last set of interviewees concerning the suggested improvement activities (“examples would be very helpful” (AIE1), “the contractual arrangements need to be documented in written form” (AIE5)). Wherever possible, the interviewees' feedback was incorporated into the model or in the documentation accompanying it. AIE4 commented that the model is structured in a logical way and can support argumentation with decision-makers. Additionally, AIE2 confirmed the usefulness of the model as “it's been reassuring to know […] the activities of my team […] are wrapped into your model” (AIE2), i.e., the “idealistic” path identified in research points towards the same direction as the activities already conducted in the community of AIE2.
## Scientific implications
The TCRM contributes to theory and research by focusing on the role of communities in TI implementation, thereby bridging the gap between individual adoption decisions and a society-wide effort to implement TIs. Communities and their influence on innovation acceptance are also considered in theories and models of behavioral change and technology acceptance (e.g., the UTAUT2 [50] or the Diffusion of Innovations theory [8]). In the context of these models, however, the community is labeled “social influence” as one predictor variable for individual acceptance and adoption of health technologies [51]. In contrast, the TCRM places the community on the intermediate (meso) level and thereby mediates between the individual adoption decision and a society-wide decision to implement TIs.
By utilizing the TCRM in five different communities, we showed that the TCRM provides a valuable tool for the maturity development concerning community readiness for TIs. The prescriptive character of the TCRM helps to integrate best practice knowledge as potential measures to improve the situation [27]. Having based the suggested measures of the TCRM on barriers and enablers identified in international studies [12], and learnings from the successful application of the CRM [10], an adequate evidence base is ensured.
The focus on the community for successful TI implementation and scale-up also seems feasible. This is underlined by the expert interviewees' feedback and prior literature. In the NASSS framework of [17], for example, the authors recognize the role of communities in the implementation process by considering socio-cultural aspects as part of the wider system influencing an adoption decision. Also [52], considered the community an essential actor when supporting eHealth tools. However, the TCRM goes beyond these approaches by providing an artifact that emphasizes the role of communities and helps empower communities to make a change toward successful TI implementation and scale-up.
Compared to existing approaches, such as the CRM [10], the TCRM also incorporates knowledge about barriers related to TI, such as an absence of infrastructural conditions, interoperability challenges, or health sector barriers such as an inadequately skilled workforce.
Especially the improvement aspects in the TCRM can help defining outcomes to consider when evaluating implementation as suggested in the evidence standards framework for digital health technologies suggested by the NHS England. In this framework, the seamless integration of any healthcare technology into existing processes is considered as basic requirement whose fulfillment needs to be proven in any evaluation process [53].
## Practical implications
The TCRM was evaluated during the design and application phase by seventeen expert interviews. The experts confirmed that the TCRM is a valuable artifact to support the implementation and scale-up of TIs and help communities to increase their readiness for TIs. Notwithstanding a longer-term evaluation of the model's effectiveness, it can be assumed that the model helps TIs to move beyond the pilot phase [54].
To appropriately communicate the research results [55, 56] and to ensure the TCRM is accessible and usable by practice, it has been published as an easily applicable online tool free of charge, including an option to provide ongoing feedback1. To date, the TCRM has already been used by experts in other countries beyond Germany and Australia, e.g., for one community in Croatia (Level 4), one in Norway (Level 3), and one in the United Kingdom (Level 4). Even though embedded in different healthcare systems, each community could define its readiness status and identify improvement measures. The higher readiness levels in these three countries may indicate that the low levels of readiness in the Australian and German demonstration cases for the TCRM are not representative for the state of telemedicine internationally [57]. Benchmarked different countries regarding their digital health index and placed Australia and Germany in groups three and four out of four groups ranking their digital health development, while the Nordic countries or NHS England are placed in the first two groups. Interestingly, even in the same national Australian or German framework, their readiness levels differ, which supports the influencing role of the community regarding TI implementation.
The TCRM will need to be continuously maintained to ensure its ongoing relevance [27]. As the TCRM has been applied to communities in five different countries with various settings and is based on international evidence, it can be assumed that the TCRM can be applied in other countries as well. However, more extensive and longer-term evaluation studies would be necessary to prove this assumption. Larger scale application of the TCRM could also trigger macro-level activities, e.g., if communities in specific regions or countries all have lower levels of readiness that could alert a country's policy-makers to adapt legal or regulatory provisions.
## Limitations
Our approach comes with three limitations. First, designing the TCRM included subjective decisions. We reduced bias by conducting each step in the design process in pairs of two researchers, except for holding the interviews and real-world application sessions. Inconsistencies were resolved through discussion to reach a consensus. Second, we showed the importance of communities in TI implementation and scale-up and validated the TCRM based on expert feedback. We could not, however, evaluate implemented change measures based on the usage of our artifact. Therefore, the artifact's evolution and activity [42] need further longer-term evaluation. Third, the evaluation of Levels 5 and 6 of the TCRM is limited. The expert group consisted of people who rated their community to be on the first four levels. However, all of them stated that the model could be helpful to further increase the readiness of the communities they represented.
## Future research
To further validate the model, more extensive and longer-term evaluation studies with different experts will be necessary to focus on the following four aspects: First, it is essential to evaluate the TCRM's efficiency, robustness, and operationality, i.e., its evolution and activity [42] in the longer-term, as well as, its impact on telemedicine readiness at all. We also assume that there are some constraints related to the type of telemedicine solution, which should be analyzed. Second, more extensive studies could reveal whether country-specifics [e.g. [58],] cultural dimensions) need to be incorporated into the TCRM, which could then lead to the formulation of a comparative maturity model [59]. Such a model would enable benchmarking of different regions on a more objective level [27]. Third, further demonstration and evaluation activities should focus explicitly on communities of interest. This type of community was, in the current paper, only included in relation to an additionally shared geographic context. Fourth, broadening the range of case examples should also include communities on higher readiness levels to further validate Level 5 and 6. Such studies could also reveal if different kinds of payer systems make a difference in how far the community can support TI implementation. While the improvement aspects in the TCRM represent an impetus for enhancing the current readiness status, each community needs to identify and implement measures that fit its specific context and structure. As a next step, an exchange of best practices between comparable communities would help implementing specific improvement measures—as has been done with similar tools (Grooten et al. 2019).
## Conclusion
The paper was motivated by the need for a community perspective aiming to successfully implement and scale up TIs. We showed the shortcomings of prior research, calling for a suitable tool that addresses the community's readiness to apply and scale-up TIs to provide value for the citizens and decrease disparities in healthcare systems. The TCRM has the potential to develop community readiness and to drive TIs in a direction where they can generate value for the people, which is the central concern of design-oriented research. It can interest payers, healthcare professionals, and key community stakeholders and can be explicitly used in health services research to expand needs analyses. In the sense of an evolutionary concept of design work, we hope the TCRM is seen as a proposal to evolve in the community and to foster the discussion on how we can speed up digital health generally and TIs specifically.
## 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.
## Ethics statement
Ethical review and approval was not required for the study of human participants in accordance with the local legislation and institutional requirements. Written informed consent from the participants was not required to participate in this study in accordance with the national legislation and institutional requirements.
## Author contributions
The paper is the result of highly collaborative work. Conceptualization, Design, and Evaluation were mainly driven by LO, HS, LH. DW: helps to position the work in current research and discuss further research directions. AM: helps to evaluate the Australian setting which is one of the evaluation cases. 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/fdgth.2023.1057347/full#supplementary-material.
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|
---
title: Discovery of genomic loci associated with sleep apnea risk through multi-trait
GWAS analysis with snoring
authors:
- Adrian I Campos
- Nathan Ingold
- Yunru Huang
- Brittany L Mitchell
- Pik-Fang Kho
- Xikun Han
- Luis M García-Marín
- Jue-Sheng Ong
- Michelle Agee
- Michelle Agee
- Stella Aslibekyan
- Adam Auton
- Elizabeth Babalola
- Robert K Bell
- Jessica Bielenberg
- Katarzyna Bryc
- Emily Bullis
- Briana Cameron
- Daniella Coker
- Devika Dhamija
- Sayantan Das
- Sarah L Elson
- Teresa Filshtein
- Kipper Fletez-Brant
- Pierre Fontanillas
- Will Freyman
- Pooja M Gandhi
- Karl Heilbron
- Barry Hicks
- David A Hinds
- Karen E Huber
- Ethan M Jewett
- Yunxuan Jiang
- Aaron Kleinman
- Katelyn Kukar
- Keng-Han Lin
- Maya Lowe
- Marie K Luff
- Jennifer C McCreight
- Matthew H McIntyre
- Kimberly F McManus
- Steven J Micheletti
- Meghan E Moreno
- Joanna L Mountain
- Sahar V Mozaffari
- Priyanka Nandakumar
- Elizabeth S Noblin
- Jared O’Connell
- Aaron A Petrakovitz
- G David Poznik
- Anjali J Shastri
- Janie F Shelton
- Jingchunzi Shi
- Suyash Shringarpure
- Chao Tian
- Vinh Tran
- Joyce Y Tung
- Xin Wang
- Wei Wang
- Catherine H Weldon
- Peter Wilton
- Matthew H Law
- Jennifer S Yokoyama
- Nicholas G Martin
- Xianjun Dong
- Gabriel Cuellar-Partida
- Stuart MacGregor
- Stella Aslibekyan
- Miguel E Rentería
journal: Sleep
year: 2022
pmcid: PMC9995783
doi: 10.1093/sleep/zsac308
license: CC BY 4.0
---
# Discovery of genomic loci associated with sleep apnea risk through multi-trait GWAS analysis with snoring
## Abstract
### Study Objectives
Despite its association with severe health conditions, the etiology of sleep apnea (SA) remains understudied. This study sought to identify genetic variants robustly associated with SA risk.
### Methods
We performed a genome-wide association study (GWAS) meta-analysis of SA across five cohorts (NTotal = 523 366), followed by a multi-trait analysis of GWAS (multi-trait analysis of genome-wide association summary statistics [MTAG]) to boost power, leveraging the high genetic correlation between SA and snoring. We then adjusted our results for the genetic effects of body mass index (BMI) using multi-trait-based conditional and joint analysis (mtCOJO) and sought replication of lead hits in a large cohort of participants from 23andMe, Inc (NTotal = 1 477 352; Ncases = 175 522). We also explored genetic correlations with other complex traits and performed a phenome-wide screen for causally associated phenotypes using the latent causal variable method.
### Results
Our SA meta-analysis identified five independent variants with evidence of association beyond genome-wide significance. After adjustment for BMI, only one genome-wide significant variant was identified. MTAG analyses uncovered 49 significant independent loci associated with SA risk. Twenty-nine variants were replicated in the 23andMe GWAS adjusting for BMI. We observed genetic correlations with several complex traits, including multisite chronic pain, diabetes, eye disorders, high blood pressure, osteoarthritis, chronic obstructive pulmonary disease, and BMI-associated conditions.
### Conclusion
Our study uncovered multiple genetic loci associated with SA risk, thus increasing our understanding of the etiology of this condition and its relationship with other complex traits.
## Graphical Abstract
Graphical Abstract
## Introduction
Sleep apnea (SA) is a disorder characterized by episodes of halted breathing during sleep, which leads to frequent arousal and intermittent hypoxia [1]. The most common type of SA is obstructive SA, which affects $9\%$–$55\%$ of adults and $1\%$–$9.5\%$ of children [2–5]. Obstructive SA is a complex disease with multiple underlying mechanisms and risk factors; these include craniofacial structure differences, decreased width of the upper airways, increased body mass index (BMI), or a reduced function of the pharyngeal dilator muscles, all of which contribute to the collapse of the upper airways and subsequent apneas and hypopneas [6–8].
SA is associated with several factors, including BMI, male sex, older age, craniofacial, or upper-airway abnormalities, smoking, alcohol consumption, cardiovascular disease, and family history of sleep apnea [9]. Furthermore, SA can lead to mental and physical fatigue, which is associated with an increase in the risk of motor accidents [10], and a decrease in mental well-being and overall quality of life [11]. In addition, SA has also been associated with an increased risk of hypertension [12], stroke [13], and increased levels of reactive oxygen species in blood, which increase oxidative stress in the body [14, 15].
Obesity (i.e. commonly determined as BMI > 30) is correlated with a higher SA risk [16]. In fact, one of the most important modifiable risk factors for SA is BMI. Obesity increases the risk for SA through the aggregation of fat deposits in the upper respiratory tract, which narrows the throat and induces a decrease in muscle activity, potentially leading to hypoxic and apneic episodes that lead to SA [17]. Therefore, it is important to consider the potential influences of BMI while studying SA.
The heritability of SA is estimated to be between $35\%$ and $75\%$ [18, 19], but familial aggregation seems to be partially independent of bodyweight [20], suggesting an independent germline component. Despite an estimated population prevalence of at least $5\%$, many SA cases go undiagnosed until other related diseases begin to display [21, 22]. Therefore, an increased understanding of the genetic architecture of SA could help generate risk prediction models, prompting earlier detection, and providing an important groundwork for the development of interventions and therapies. In addition, having information on the effect of genetic variants on SA risk could enable inference of its causal relationship with other conditions using methods such as Mendelian randomization [23]. Although some candidate gene studies for SA have yielded a few putatively associated genes [24, 25], genome-wide association studies (GWAS) have failed to replicate those associations [26–28]. In fact, GWAS have identified very few genome-wide significant loci robustly associated (i.e. with evidence of replication in an independent cohort) with SA to date.
SA is likely a highly polygenic trait, with many variants of small effect size contributing to the genetic liability of developing this condition. Thus, most studies with modest sample sizes will be underpowered to identify the majority of these risk variants and are susceptible to false-positive associations. Furthermore, the number of diagnosed cases of SA within existing large population cohorts is low. In a sample of 500 000 individuals, the expected number of SA cases (assuming a conservative prevalence of ~$5\%$) would be ~25 000. However, in the UK Biobank (UKB) (~500 000 individuals), only ~8000 SA cases have been recorded. That is likely explained by the fact that SA is recognized as an underdiagnosed condition because those affected are unable to gain awareness about their condition or may confuse it with habitual snoring [21, 22]. Underdiagnosis further reduces power as many real cases may be labeled as unaffected controls in a standard analysis. Thus, combining large samples through meta-analysis and replicating findings in large, independent studies are essential steps to uncover reliable results.
Here, we conducted a GWAS meta-analysis of SA across five cohorts. Then, we employed multi-trait analysis of genome-wide association summary statistics (MTAG) to combine our results with a snoring GWAS meta-analysis across five cohorts to boost statistical power by leveraging the high genetic correlation between SA and snoring [29]. We also performed additional sensitivity analyses to control for the genetic effects of BMI and identify loci associated with SA independently from BMI. We sought to replicate lead single nucleotide polymorphisms (SNPs) in an independent sample from 23andMe, Inc. and further explored the genetic underpinnings of SA through gene-based tests and genetic correlation analyses. Finally, we constructed polygenic scores and predicted SA using a leave-one cohort-out (LOO) cross-validation framework. Our analyses can be interpreted as a proxy for obstructive SA, given its higher prevalence than central SA [3, 30].
## Sample information and phenotype ascertainment
This study analyzed GWAS data from five cohorts from the United Kingdom (UKB), Canada (Canadian Longitudinal Study of Aging; CLSA) [31, 32], Australia (Australian Genetics of Depression Study; AGDS), the United States (Partners Healthcare Biobank), and Finland (FinnGen). The total sample size for each cohort, and the number of cases and controls are listed in Table 1. For each cohort, SA cases were defined using participant-reported diagnosis or ICD diagnostic codes available in electronic health records (ICD-9: 327.23 and ICD-10: G47.3). In CLSA and AGDS, SA was defined based on the answer to the item “Stop breathing during sleep” (see Supplementary Methods for individual cohort details). Self-reported snoring cases were excluded from the analyses for the SA GWAS across the UKB, CLSA, and AGDS cohorts. An overview of the analysis pipeline used for SA discovery analysis is available in the Supplementary Figure S1.
**Table 1.**
| Cohort | Total sample size | Apnea GWAS cases | Apnea GWAS controls | Snoring GWAS cases | Snoring GWAS controls |
| --- | --- | --- | --- | --- | --- |
| UK-Biobank | 408 317 | 7902 | 248 112 | 152 303 | 256 014 |
| Finngen | 66 216 | 9096 | 57 120 | 4270 | 61 946 |
| Partners Biobank | 20 047 | 3102 | 16 945 | 4175 | 15 872 |
| CLSA | 18 427 | 3391 | 9615 | 6852 | 10 736 |
| AGDS | 10 359 | 1517 | 5838 | 4450 | 5907 |
| Total | 523 366 | 25 008 | 337 630 | 172 050 | 350 475 |
## GWAS
All GWA studies included the following covariates, namely, age, sex, batch (where relevant), and genetic ancestry principal components derived from genotype data. Standard quality control filters were applied at both the sample and variant levels. Variants were excluded from the analyses if they had a low minor allele frequency (MAF < 0.01) or low imputation quality score (INFO < 0.6). Individuals were excluded based on excess missingness, heterozygosity, or evidence of a deviation from European ancestry based on genetic principal components. For each cohort, a GWAS was performed using logistic regression models and including random effects to account for cryptic relatedness where relevant (Supplementary Methods). For the UKB snoring GWAS, we used the summary statistics from our previously published GWAS for snoring [33]. We obtained FinnGen GWAS results for SA and snoring from the open-access FinnGen resource (Freeze 3).
## GWAS meta-analyses
Sample-size weighted (p-value-based) meta-analyses for SA and snoring were performed (separately for each phenotype) across the five cohorts described above using METAL (v2020-05-05) [34]. Studies were weighted according to their effective sample size as described by the equation: Neff = 4/(1/Ncases + 1/Ncontrols), as recommended for studies with different levels of case-control imbalance (Supplementary Methods).
## Multi-trait GWAS analyses
We used MTAG to boost the statistical power for the discovery of SA-associated loci. MTAG performs a generalized meta-analysis of GWAS summary statistics for different but high genetically correlated traits while accounting for potential sample overlap [29]. For this study, we performed MTAG analyses combining our SA and snoring meta-analyses. That is possible given the high genetic correlation between these traits (rg ~ 0.8) [33] and the observation that snoring is one of the primary symptoms of SA, the most common type of SA [7].
## BMI adjustment
Given the clear relationship between SA, snoring, and BMI, we performed a secondary analysis adjusting our GWAS results (both the meta-analysis and the MTAG) for the effect of BMI. To adjust for BMI while avoiding biases due to collider bias, (i.e. the emergence of a spurious association between a pair of variables when a common outcome is modeled as a covariate) [35], we used multi-trait-based conditional and joint analysis (mtCOJO) [36, 37]. As a sensitivity analysis, we also repeated the SA meta-analysis adjusting for BMI as a covariate. Including BMI as a covariate was only done for the AGDS, CLSA, and UK-Biobank cohorts. These results were compared to the unadjusted and mtCOJO-adjusted analyses using bivariate LD-score regression and by comparing the effect size of SNPs with suggestive evidence of association.
## 23andMe replication GWAS
We sought to replicate variants identified in the discovery phase in an independent sample of participants from the 23andMe cohort ($$n = 1$$ 477 352). Cases were ascertained based on the question “Have you ever been diagnosed with, or treated for any of the following conditions?” with one of the choices being “Sleep apnea” (Yes = 175 522; No = 1 301 830). Methods and results from this GWAS have been presented at the 2018 American Society for Human Genetics annual conference [38]. Briefly, a logistic regression GWAS was performed using SA as the dependent variable while adjusting for sex, age, BMI, genetic principal components, and genotype array. Participants provided informed consent and participated in the research online, under a protocol approved by the external AAHRPP-accredited IRB, Ethical & Independent Review Services (E&I Review). Only unrelated participants of European ancestry who provided consent were included in the analysis.. We defined evidence of replication after correcting for the number of significant variants with data available for replication per GWAS analysis. That is $p \leq .01$ for the SA meta-analysis, $p \leq .0016$ for the SA plus snoring MTAG and $p \leq .002$ for the SA plus snoring MTAG adjusted for BMI.
## Gene-based association tests and eQTL colocalisation
We used the “set-based association analysis for human complex traits” fastBAT method, which performs a set-based enrichment analysis using GWAS summary statistics while accounting for linkage disequilibrium (LD) between SNPs [39]. Statistical significance was defined using the Bonferroni method for multiple testing correction ($p \leq 2.07$e−6). Genes identified as statistically significant were further assessed for expression quantitative trait loci (eQTL) colocalisation using the COLOC [40] package in R. Briefly, we integrated our GWAS summary data with cis-eQTL data from whole blood, esophagus, adipose, and lung tissue from GTEx V8 [41] to estimate the posterior probability that GWAS signals co-occur with eQTL signals while accounting for LD structure. This method estimates the posterior probabilities (PP) for five different scenarios. The scenario of interest is colocalisation due to associations with both traits through the same SNPs (PP4). A threshold of PP4 ≥ 0.8 was considered as evidence for colocalisation of GWAS signals and eQTL signals at the region of interest (Supplementary Methods).
## S-MultiXcan-based eQTL integration
Integration of eQTL with GWAS results interrogates whether the associations observed are consistent with changes in gene expression mediating the trait under study. This study integrated our GWAS results with eQTL data from GTEx using S-MultiXcan [42], as implemented in the Complex Traits Genetics Virtual Lab (CTG-VL). This method employs a multiple-regression of the phenotype on the predicted gene expression across multiple tissues based on eQTL data. When using only GWAS summary statistics, single-tissue associations are performed using S-PrediXcan, and joint effects from the single-tissue results are estimated using an approximation similar to that of the conditional and joint multiple-SNP analysis [43]. Contrary to the eQTL colocalisation described above, this analysis employs the whole GWAS summary statistics and is not restricted only to genes identified using fastBAT or other gene-based tests.
## Heritability and genetic correlations
We used LD score regression to estimate the SNP-based heritability (hSNP2) for the SA meta-analysis. Given that samples were not specifically ascertained for SA, we assumed the overall sample and population prevalence for SA to be the prevalence estimated across cohorts (0.05) that are consistent with reported epidemiological estimates [2]. Genetic correlations (rg) between SA and 1522 phenotypes (with available GWAS summary statistics) were estimated using bivariate LD score regression in CTG-VL [44] based on a common set of HapMap3 variants. The Benjamini–Hochberg false discovery rate (FDR) at $5\%$ was used to define statistical significance.
## Polygenic risk scoring
To assess the external validity of the GWAS, we performed polygenic-based prediction on a target sample of 9221 unrelated Australian adults from the AGDS [45] with complete data. Briefly, the meta and MTAG analyses were repeated, leaving out the AGDS cohort to avoid sample overlap. We employed the SBayesR method to obtain the conditional effects of the studied variants, thus avoiding inflation due to correlated SNPs in LD [46]. SBayesR estimates the SNP multivariate effect sizes using GWAS summary statistics and SNP correlations using an LD-matrix. Here, we used the LD-matrix for 2.8M variants reported in Lloyd-Jones and Zeng et al. [ 27, 46], which is publicly available (10.5281/zenodo.3350914). SBayesR parameters included four mixture components (starting values = 0.95, 0.01, 0.02, 0.01) with default scaling factors (0, 0.01, 0.1, 1), chain length of 25 000, and burn-in of 5000. The SNP conditional effect sizes obtained from SBayesR were then used for polygenic scoring using HRCr1.1 imputed genotype dosage data in plink v1.9. Polygenic risk scoring (PRS) were calculated by multiplying the effect size of a given risk allele (obtained from the GWAS summary statistics) by the imputed number of risk alleles (using dosage probabilities) present in each individual. SNP scores were then summed across all loci. The association between PRS and SA in AGDS was assessed using a logistic regression model (python statsmodels). SAPRS was the predictive variable of interest, with age, sex, and the first 10 genetic principal components included as covariates in Nagelkerke’s pseudo R2. Finally, binary classifiers based on logistic regression were built, including age and sex (base model) or age, sex, and the PRS of interest (SAPRS or SAmtagSnoringPRS). These classifiers were used to assess the polygenic predictive ability further. The sample was divided randomly into training and testing datasets of equal sizes. Then, the classifier’s ability to predict SA was assessed using the area under the receiver operating characteristic (ROC) curve. To avoid potential biases from the random division of training and testing datasets, the procedure was repeated 100 times to estimate a mean area under the curve (Supplementary Methods).
PRS based on either of our results were significantly associated with SA in a leave one out polygenic prediction analysis. Odds ratios (OR) per standard deviation of PRS increased with the number of hits. For example, the meta-analysis-based PRS (SAPRS) showed an OR = 1.15 (1.08–1.21), whereas the PRS based on the SA plus snoring showed an OR = 1.21 (1.14–1.28). A similar pattern was observed for variance explained and significance (Table 3). These PRS were significantly associated with SA even after adjusting for BMI measures in the AGDS cohort (Table 3), suggesting that signals independent from BMI contribute to polygenic prediction. Participants in the highest PRS decile showed between $50\%$ and $87\%$ higher odds of reporting SA than participants in the lowest decile (Figure 3, A). Classifier models based on PRS showed a prediction ability higher than a random guess for the meta-analysis. The MTAG results showed an even higher predictive ability than the meta-analysis alone (Figure 3, B and C and Supplementary Figure S10).
## Latent causal variable analysis
The latent causal variable (LCV) method leverages GWAS summary statistics to estimate whether a causal association can explain a genetic correlation between traits rather than horizontal pleiotropy (i.e. shared genetic pathways) [47–49]. LCV conceptually relies on a latent variable L, assumed to be the causal factor underlying the genetic correlation between both traits [47–49]. LCV estimates the genetic causality proportion (GCP). A higher absolute GCP value indicates more evidence of a causal association among a pair of genetically correlated phenotypes. In contrast, a GCP value of zero would imply that horizontal pleiotropy underlies the genetic correlation between the phenotypes. However, the LCV method will be biased towards the null (a GCP value of 0) if a bi-directional association exists between traits. An absolute value for GCP < 0.60 indicates only partial genetic causality. Multiple testing correction was applied using Benjamini–Hochberg’s FDR (FDR < $5\%$). We performed a phenome-wide hypothesis-free LCV analysis to identify traits causally associated with SA. Given the limitations of the LCV method (see “Discussion” section), we consider this a hypothesis-generating approach. These hypotheses should be tested in follow-up studies that include relevant Mendelian randomization analyses and a synthesis of the available literature on the association between SA and the trait of interest. In addition, as a sensitivity analysis, we performed two sample MR analyses for SHBG and vitamin D with SA (see Supplementary Methods).
## GWAS meta-analysis
The prevalence of both SA and snoring showed some variation across the five cohorts included in this study (Table 1 and Supplementary Material). Nonetheless, all the genetic correlation estimates were high, albeit with large standard errors (Supplementary Table S1). Our meta-analysis identified five independent (LD r2 < 0.05) genome-wide significant ($p \leq 5$e−8) loci associated with SA (Figure 1, A). The signals spanned chromosomes 5, 11, 12, and 16 near genes ANKRD31, STK33, BDNF, KDM2B, and PRIM1 (Supplementary Figure S2). The LD-score regression SNP-based heritability on the observed scale was $13\%$ (S.E. = $0.087\%$). Using a transformation that is more suitable for biobank structure [50], we estimate the heritability on the liability scale might range between $55\%$ and $87\%$ (based on an assumed population prevalence range of $9\%$–$55\%$). LD-score regression intercept suggested most inflation (λGC = 1.21) was due to polygenic signal (intercept = 1.012, S.E. = 0.009) rather than population stratification. Upon adjusting for the effect of BMI using mtCOJO, one new genome-wide hit on chromosome 15, located near genes HDGFL3, TM6SF1, and BNC1, was identified (Supplementary Figure S2). A sensitivity analysis, including BMI as a covariate (see “Methods” section) also identified one single hit in chromosome 13. However, the evidence of association for all other loci was reduced below genome-wide significance upon adjustment for BMI (Figure 1, A). *The* genetic correlation between mtCOJO and covariate adjustment was 1.02 (S.E. = 0.024). Overall, the effects of most loci with suggestive evidence of association were consistent across the unadjusted, mtCOJO and covariate-adjusted meta analysis (Supplementary Figure S3). The only exception was the FTO locus that showed a statistically significant shrinking of effect upon BMI adjustment.
**Figure 1.:** *Discovery of genetic associations with sleep apnea (SA) risk. Miami plots depict the meta-analysis results for SA before and after adjusting for BMI using mtCOJO (A) or MTAG for SA plus snoring before and after adjusting for BMI using mtCOJO (B). Each dot represents a genetic variant. The x-axis represents the variant’s genomic position, and the y-axis depicts the significance of the association with SA. In the BMI-adjusted analyses, highlighted variants show the genome-wide hits of the unadjusted GWAS.*
## MTAG
We used MTAG to boost statistical power and increase loci discovery by leveraging the genetic correlation between SA and snoring. This analysis had an effective sample size of 159 255 participants and identified 43 independent genome-wide significant loci associated with SA (Figure 1, B). The direction and effect sizes of the independent hits were highly consistent across the SA meta-analysis and the MTAG analysis with snoring (R2 > 0.95 Supplementary Figure S4). After adjusting for BMI using mtCOJO, 25 hits were genome-wide significant; most overlapped with the unadjusted results (Figure 1, B). We assessed whether previous genetic association studies of SA or related traits [26, 27, 51–55] align with our results and survive adjustment for BMI. We found some evidence of association for 5 of the 22 loci assessed. Two of the previously reported loci showed evidence of association after adjusting for BMI (Supplementary Table S2).
## Independent sample replication
We sought replication of our GWAS results in an independent sample ($$n = 1$$ 477 352) from 23andMe. Notably, the 23andMe SA replication GWAS was adjusted for BMI (see “Methods” section). Overall, 10 of the independent variants identified by our analyses showed evidence of association beyond the genome-wide significance threshold (Supplementary Table S3) in the replication. After multiple testing corrections, three out of the five loci for SA meta-analysis were replicated. Furthermore, the variant that became significant after adjusting for BMI was also replicated. For the SA plus snoring MTAG, 30 out of 43 variants available in the 23andMe dataset were replicated. Finally, 22 out of 25 variants from the SA plus snoring MTAG adjusted for BMI analysis were also replicated. This higher replication rate was expected since the 23andMe GWAS had been adjusted for BMI (see “Discussion” section). Overall, 29 significant independent loci with evidence for replication were identified (Table 2). Furthermore, there was a large concordance in the direction and magnitude of effect sizes between our analyses and the 23andMe replication results (Supplementary Figure S5) and across cohorts (Supplementary Figures S6 and S7). Due to power, replication rates, and the interest in studying the etiology of SA beyond BMI effects, we focus below on the meta-analysis, the MTAG analysis, and the MTAG analysis adjusted for BMI.
**Table 2.**
| SNP | CHR | BP | A1 | A2 | P_23&me | BETAa | SEa | P_META | Signal source |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| rs1537818 | 1 | 39647038 | G | A | 2.76E−05 | −0.01755 | 0.004182 | 1.31E−09 | MTAG |
| rs633715 | 1 | 177852580 | T | C | 4.60E−07 | −0.02466 | 0.004898 | 3.49E−08 | MTAG_BMIadj |
| rs72902175 | 2 | 157013035 | T | C | 9.30E−10 | 0.035999 | 0.005866 | 3.67E−14 | MTAG |
| rs1403848 | 3 | 77609655 | C | A | 7.51E−05 | −0.01569 | 0.003962 | 9.30E−09 | MTAG |
| rs4076077 | 5 | 170863509 | T | C | 3.70E−06 | −0.01797 | 0.003882 | 4.26E−09 | MTAG |
| rs1428381 | 5 | 122693901 | G | A | 0.000369 | 0.015265 | 0.004283 | 4.83E−09 | MTAG |
| rs2715039 | 7 | 84094964 | C | A | 4.61E−05 | −0.01611 | 0.003952 | 2.04E−08 | MTAG |
| rs7005777 | 8 | 78233600 | T | G | 5.18E−05 | 0.017513 | 0.00433 | 1.12E−08 | MTAG |
| rs8176749 | 9 | 136131188 | T | C | 1.47E−05 | −0.03212 | 0.007433 | 3.78E−09 | MTAG |
| rs10756798 | 9 | 16739763 | T | C | 3.70E−09 | −0.02425 | 0.004115 | 3.28E−08 | MTAG_BMIadj |
| rs1444789 | 10 | 9064361 | T | C | 2.40E−13 | −0.03701 | 0.005042 | 1.10E−09 | MTAG |
| rs6265 | 11 | 27679916 | T | C | 1.12E−05 | −0.02198 | 0.00501 | 1.79E−14 | MTAG |
| rs1815739 | 11 | 66328095 | T | C | 1.19E−06 | 0.018979 | 0.003906 | 2.10E−08 | MTAG |
| rs4923536 | 11 | 28422496 | G | A | 1.52E−10 | 0.025071 | 0.003915 | 7.51E−11 | MTAG_BMIadj |
| rs28758996 | 12 | 121960480 | G | A | 0.00122 | −0.01282 | 0.003963 | 1.21E−08 | META |
| rs1389799 | 12 | 65824846 | G | A | 3.57E−25 | 0.04184 | 0.004032 | 1.38E−18 | MTAG_BMIadj |
| rs4554968 | 12 | 4372609 | G | A | 0.000854 | 0.013381 | 0.004011 | 4.47E−08 | MTAG_BMIadj |
| rs592333 | 13 | 51340315 | G | A | 9.04E−23 | −0.03997 | 0.004068 | 1.69E−14 | MTAG |
| rs11852496 | 15 | 83817559 | T | C | 3.17E−05 | −0.01918 | 0.004605 | 1.71E−06 | META |
| rs11634019 | 15 | 76634680 | T | C | 4.44E−10 | 0.027288 | 0.00438 | 1.84E−09 | MTAG |
| rs11075985 | 16 | 53805207 | C | A | 1.13E−05 | 0.017161 | 0.003907 | 5.41E−20 | META |
| rs8045335 | 16 | 60607116 | G | A | 1.41E−09 | −0.02374 | 0.003922 | 1.24E−08 | MTAG |
| rs9933881 | 16 | 1740691 | T | C | 3.68E−07 | −0.03664 | 0.007183 | 2.54E−08 | MTAG |
| rs12603115 | 17 | 46248994 | T | C | 3.95E−06 | −0.01812 | 0.003926 | 8.14E−10 | MTAG |
| rs227731 | 17 | 54773238 | T | G | 2.40E−11 | −0.02603 | 0.003896 | 3.96E−09 | MTAG |
| rs4987719 | 18 | 60960310 | T | C | 1.28E−08 | 0.061095 | 0.01068 | 4.72E−09 | MTAG |
| rs35445111 | 19 | 32172047 | G | A | 2.62E−12 | 0.04751 | 0.006817 | 1.62E−11 | MTAG |
| rs6113592 | 20 | 22229505 | G | A | 6.42E−07 | 0.019892 | 0.003998 | 7.82E−11 | MTAG |
| rs6038517 | 20 | 6458205 | G | A | 0.000217 | −0.01703 | 0.0046 | 2.19E−08 | MTAG |
## Gene-based tests and colocalization
*The* gene-based association analyses identified 22, 132, and 74 genes beyond the significance threshold ($p \leq 2.07$e−6) for the SA meta-analysis, the SA plus snoring MTAG, and the SA plus snoring MTAG adjusted for BMI respectively. As expected, many of these genes overlapped. *Identified* genes included DLEU1, DLEU7, MSRB3, CTSF, and SCAPER (Supplementary Figure S8 and Supplementary Tables S4–S6). Some of these genes were located within the same locus and in high LD. Thus, to identify genes linked to SA through potential changes in gene expression, we performed eQTL colocalization analyses for any of the genes mentioned above. Of the 151 genes with available eQTL data, only 18 showed strong evidence of eQTL colocalization with either SA, SA plus snoring or SA plus snoring adjusted for BMI (Supplementary Tables S7–S9).
## eQTL integration
We used S-MultiXcan to integrate our GWAS summary statistics with eQTL data and identify genes associated with SA through changes in predicted gene expression. These analyses identified 5 and 65 genes (Supplementary Table S10), for which evidence of association with SA meta-analysis or SA plus snoring MTAG reached statistical significance. *These* genes included DLEU7, PRIM1, COPZ2, SKAP1, DNAJB7, ACTBP13, and ZBTB6, among others. Although the results of S-MultiXcan partially overlapped those of the gene-based positional analysis, this approach identified 4 and 33 new genes that are likely associated with SA through changes in gene expression. Genes with convergent evidence through gene-based association and S-MultiXcan include FTO, STK33, ETFA, SKAP1, MAPT, BAZ2A, DCAF16, MACF1, NSF, COPZ2, SP6, LACTB2, LRRC4, and HOXB3 among others (Supplementary Figure S8).
## Genetic correlations
Bivariate LD score regression was used to assess the genetic correlation between SA and other complex traits. The trait with the highest genetic correlation (rg = 0.92) with SA was a SA GWAS performed on the UK-Biobank from a public GWAS repository (http://www.nealelab.is/uk-biobank/); this is essentially a subset of the UK-Biobank GWAS used in our meta-analysis. *Other* genetically correlated traits (p-value <.05) included respiratory diseases, type 2 diabetes, obesity, eye disorders, stroke, depression, alcohol addiction, smoking history, and musculoskeletal disorders such as arthritis and spondylosis, among others (Supplementary Tables S11–S13). The SA meta-analysis and the SA plus snoring analyses showed a highly concordant pattern of genetic correlations. While also showing overall agreement, the SA plus snoring adjusted for BMI results showed lower genetic correlations with BMI-related traits such as obesity, diabetes, and stroke (Figure 2).
**Figure 2.:** *Sleep apnea (SA) is genetically correlated with psychiatric, behavioral, and cardiorespiratory traits. Forest plots showing genetic correlations calculated using CTG-VL [44] between SA meta-analysis, MTAG between SA and snoring (SAmtagSnoring) and MTAG between SA and snoring adjusted for BMI (SAmtagSnoringBMIadj). Markers depict the genetic correlation estimate (rg), whereas lines represent 95% confidence intervals derived from the rg standard error. Not all traits with a significant association (FDR < 0.05) are shown. See the Supplementary Data for other traits.*
## Predicting traits causally associated with SA
We used LCV to perform a hypothesis-free screening to assess whether the potential genetic overlap between SA and >400 traits and diseases can be explained by a causal relationship. To this end, we employed the results of the MTAG GWAS with snoring, given its increased statistical power. We did not identify any potential outcomes of SA. Nonetheless, we identified 103 potential causal determinants of SA (Supplementary Table S14). For instance, traits that purportedly increase the risk for SA, based on our analysis, included hypertension, asthma, lung cancer, obesity, having a period of mania, and hernia. Conversely, we found evidence for levels of vitamin D and sex hormone-binding globulin (SHBG) (from either a male- or female-only GWAS) to potentially reduce the risk for SA (Supplementary Figure S11). Two-sample Mendelian randomization sensitivity analyses did not identify evidence for a causal effect of vitamin D, but there was some evidence for a protective effect of SHBG quantile (females only) on SA (Supplementary Table S15). We repeated the LCV analysis approach using our BMI-adjusted summary statistics to test how many of these associations were explained by the large overlap with BMI. This identified 29 traits associated with SA (Supplementary Table S14; see “Discussion” section), six of which overlapped with the BMI-unadjusted-analysis mentioned above. These traits were medication taken for anxiety, angina pectoris, testosterone quantile (males), taking ibuprofen, walking for pleasure as physical activity, and depression diagnosed by a professional.
## Discussion
This study aimed at increasing our understanding of the genetic etiology of SA risk, an area that has stagnated due to the difficulty in achieving the required sample size for GWAS studies. Our SA GWAS meta-analysis combined data across five cohorts and identified five independent loci (Supplementary Figure S7). The evidence of association for most these loci decreased below statistical significance upon adjustment for BMI using both mtCOJO or including BMI as a covariate. Adjusting for BMI identified a new locus on chromosome 15 near HDGFL3 when adjusting through mtCOJO and one on chromosome 13 near DLEU1 and DLEU7 when adjusting for BMI as a covariate. While this manuscript was under review, another study describing a GWAS for SASA in FinnGen and the UKB was published [51]. That study identified five genome-wide significant loci associated with SA and a clear, strong causal component of BMI. The strong influence of BMI is consistent with our observation of genome-wide hits showing weaker evidence of association upon adjustments for the effect of BMI [43]. We used MTAG to boost power and identify additional loci likely to confer SA risk by combining our SA meta-analysis with a snoring meta-analysis. We also identified several variants linked to SA over and above the effect of BMI and sought replication in an independent sample from 23andMe. The 23andMe GWAS adjusted for BMI, and we could replicate 29 loci associated with SA, suggesting our results are robust signals linked to other SA pathways.
We employed gene-based tests and identified several genes associated with SA, including DLEU1, DLEU7 CTSF, MSRB3, FTO, and TRIM66. The association with FTO is likely due to this loci’s strong effect on BMI and adiposity [56]. Loss-of-function of MSRB3, which encodes a methionine sulfoxide reductase, has been associated with human deafness. This finding is consistent with reported associations between hearing impairment and SA [57]. CTSF has been linked to the airway wall area (Pi10) as measured quantitatively using CT chest images [58]. That is consistent with the fact that small airway dimensions have been linked to SA measures in a COPD comorbid sample [59] and that obesity is believed to increase SA risk increasing the fat levels of upper airway structures and the compression of airway walls [60]. DLEU1 and DLEU7 are both located within a region associated with leukemia. While DLEU7 is a protein-coding gene, DLEU1 was recently discovered to be part of a bigger gene, BCMS, that has a potential tumor-suppressing function [61]. Although this locus has been linked to snoring [33], its role in the pathogenicity for SA remains to be clarified.
Genes with evidence from positional gene mapping and gene-expression integration included SKAP1, MAPT, STK33, and ETFA, among others. SKAP1, STK33, and MAPT are genes related to the MAPK signaling pathway. MAPT is genetically and neuropathologically associated with neurodegenerative disorders, including Alzheimer’s disease and frontotemporal dementia [62]. Furthermore, ETFA expression has been observed to change in an Alzheimer’s disease mouse model in response to aducanumab, an amyloid beta antibody [63]. There is a known link between SA and Alzheimer’s disease [64]. Recent studies with mouse models suggest that intermittent hypoxia induces cholinergic forebrain degeneration [65]. Furthermore, other observations suggest SA severity might be linked to increased amyloid-beta plaques [66]. Although informative, these studies still lack the ability to distinguish whether a true causal association underlies SA and Alzheimer’s disease in humans. Our results should enable the exploration of this question by enabling causal inference studies using instrumental variable analysis.
We did not replicate previously reported candidate gene associations such as TNFA, APOE, PTGER3, and LPAR124. This could be explained by differences between our analysis and those identifying the candidate genes. For example, the LPAR1 association was observed in participants of African ancestry [67]. Nonetheless, studies assessing the support for candidate gene associations using GWAS have found poor consistency [68]. Our results suggest a similar trend for candidate gene studies of SA. Our study should be powered to detect previously reported candidate-gene effect sizes; for instance, polymorphisms within TNFA were reported to show an odds ratio of 2.01 for SA [69]. Future studies should systematically evaluate candidate gene studies and GWAS concordance in SA, an objective that was outside the scope of the current study.
As a proof-of-principle of the utility of having well-powered GWAS summary statistics, we performed a hypothesis-free inference of causal associations between >400 traits and our SA MTAG. Consistent with previous findings [51], our approach inferred obesity to likely increase the risk for SA. Similar results were found for asthma, lung cancer, hernia, hypertension, a period of mania, and stroke. Conversely, we found that SHBG levels derived from male-only, female-only, and combined-sex GWAS decreased the risk for SA. A similar finding was observed for endogenous testosterone levels derived from a male-only GWAS. This is consistent with observations of SHBG and testosterone levels negatively correlating with SA severity [70]. However, continuous positive airway pressure therapy does not seem to reverse these abnormal changes [71, 72], which would be consistent with the direction of causality predicted through LCV (from hormone level to phenotype). LCV also identified vitamin D levels as causal determinants of SA risk. That is consistent with reports linking vitamin D with SA [73]. Nonetheless, it is also possible that this result is explained by BMI. Given that vitamin D levels increase with sun exposure [74], and exposure increases with physical activity, the well-documented inverse relationship between obesity (or BMI) and vitamin D concentrations might better explain the observed association [75, 76]. The extent to which hypertension, hernia, and stroke are associated with SA above and beyond obesity as a shared causal component was unclear. We tested this by performing our causal analyses using BMI-adjusted summary statistics. Our results suggest most of these associations are potentially mediated through BMI, as these associations were no longer significant after adjusting for BMI. Interestingly, a lifetime diagnosis of depression was consistently associated with an increased risk for SA, even after adjusting for BMI. Overall, our LCV analysis identified a set of testable hypotheses, which can be further explored through multivariable MR analyses contrasting the observational associations with SA, and genetically derived effect sizes for SA and BMI. We performed MR as sensitivity analyses for LCV and found further evidence for a protective effect of SHBG. The SHBG GWAS for which evidence was identified was performed within females only, thus, we do not anticipate these results to be explained by a bias arising from sex differences on SHBG. On the contrary, SHBG levels may be one of the factors involved in the differential prevalence of SA between males and females. Two sample MR studies should be performed on GWAS without sample overlap and including a range of sensitivity checks to rule out heterogeneity and pleiotropy. Such comprehensive analyses are outside the scope of this study.
This study was performed using cohorts of European ancestry. Thus, generalizations and comparisons with other ancestry groups should be performed with caution. In order to maximize sample size, we included cohorts with different definitions of SA, including ICD codes and patient-reported diagnosis. The effect of these definitions is not negligible, as SA prevalence displayed marked differences (i.e. up to sixfold) between cohorts. The AGDS and CLSA cohorts use a single question that assesses whether a participant stops breathing during sleep. This item could also capture cardiopulmonary diseases. Furthermore, although ICD-10 codes may be considered a gold standard for ascertaining cases in GWAS studies, there are reports of low specificity [77] when identifying cases for sleep disorders. To avoid contamination from potentially undiagnosed cases in the control group, we have strived to remove participants that report loud snoring from the control set. While combining multiple sources for phenotype definition is warranted to achieve the required sample sizes for GWAS, minimal phenotyping might introduce heterogeneity. Future studies should explore using novel advances in natural language processing [78] of electronic health records to increase the accuracy of biobank-based phenotyping and compare the accuracy and genetic concordance of the different phenotyping approaches used here. We found the combined effect of the SNPs in our meta-analysis to explain ~$13\%$ of the variance of SA on the observed scale. Estimating heritability on the liability scale is challenging given (i) the wide range of reported prevalence in the population ($9\%$–$55\%$) and (ii) the fact that the current adjustments for transforming between the observed and liability scale assume an overrepresentation rather than an underrepresentation of cases. To avoid this issue, we have used a recently developed model to estimate liability scale heritability on samples with these characteristics [50].
Our results for cross-cohort pairwise genetic correlations suggested that despite using different phenotype ascertainment methods, the underlying genetics represent a common trait. Nonetheless, this analysis suffered from reduced power, and the large standard errors do not allow us to rule out heterogeneity across cohorts. Ideally, any SA study would ascertain cases employing a robust measure such as the apnea–hypopnea index or oxygen saturation; GWAS of complex traits require enormous sample sizes, making such approach challenging. Although MTAG has proven successful in boosting the discovery of loci associations, even in the presence of known or unknown sample overlap [29], combining traits with extreme power differences might inflate signals related to the most powered phenotype [29]. *The* genetic correlation between snoring and our MTAG analysis was higher than that with the SA meta analysis alone. This increase in correlation is not unexpected as MTAG can only boost power for the shared genetic component between the traits included in the multivariate analysis. However, the addition of snoring through MTAG nearly doubled the effective sample size of the SA GWAS (Neff increase from ~90 000 to ~159 000), which resulted in identifying several novel loci with evidence of replication even after adjusting for BMI and the generation of summary statistics of utility for analyses such as MR and PRS.
In our study, adjusting for BMI seemed to affect the pattern of genetic correlations, particularly decreasing the correlations with BMI and related traits such as stroke and obesity. Replication of the SA plus snoring adjusted for BMI results was higher than in the other analyses. This result is expected for two reasons: First, it benefited from the increased power of combining GWAS for apnea and snoring through MTAG and adjusted for BMI using mtCOJO. Second, the GWAS performed by 23andMe included BMI as a covariate. As such, it resembles a phenotype in line with those for which the SA plus snoring adjusted for BMI is boosting power. Finally, some limitations of the approach used for causal inference need to be acknowledged. LCV is still dependent on the power of the original GWAS for both traits. Traits with a potential causal association with SA might not have been included in the tested traits. Finally, this method assumes no bi-directional causality and will likely be biased towards the null in such cases. Thus, a null finding in our study does not reflect a lack of association, especially if bidirectional relationships are suspected.
In summary, we performed a GWAS meta-analysis of SA across five European-ancestry cohorts and identified five independent genome-wide significant loci. Conditional analyses suggested a large contribution of BMI to SA; most of the discovered genome-wide hits in the meta-analysis were explained by BMI. After adjusting for BMI, the meta-analysis identified one genome-wide significant locus. MTAG of SA with snoring identified 43 independent hits and 23 after conditioning on BMI. Overall, 29 independent significant hits were replicated in an independent SA GWAS from 23andMe. All analyses showed a significant polygenic prediction of SA in a leave-one-out PRS analysis. Our results largely confirm the previously observed overlap with BMI and highlight genetic overlap with traits such as stroke, asthma, hypertension, glaucoma, and cataracts. We further found evidence of a potential causal role of SHBG and vitamin D levels in decreasing the risk for SA. If confirmed by multivariable MR and interventional studies, new treatments based on modifying these risk factors might be used for SA treatment or early intervention. *This* general hypothesis-free framework can be used to generate testable hypotheses of risk factors for complex traits [49]. Also, the associations identified here can be used as instrumental variables in targeted MR studies aiming at understanding the relationship between SA and hypothesized causally related traits. Identifying robust loci associated with SA is an important step towards a deeper biological understanding, which can translate into novel treatments and risk assessment strategies.
## Funding
The Government of Canada provides funding for the Canadian Longitudinal Study on Aging (CLSA) through the Canadian Institutes of Health Research (CIHR) under grant reference: LSA 94473 and the Canada Foundation for Innovation. This research has been conducted using the CLSA dataset [Baseline Comprehensive Dataset version 4.0, Follow-up 1 Comprehensive Dataset version 1.0], under Application Number 190225. The CLSA is led by Drs Parminder Raina, Christina Wolfson and Susan Kirkland. Data collection for the Australian Genetics of Depression Study was possible thanks to funding from the Australian National Health & Medical Research Council (NHMRC) to N.G.M. (GNT1086683). L.M.G-M. are supported by UQ Research Training Scholarships from The University of Queensland (UQ). M.E.R. thanks the support of the NHMRC and Australian Research Council (GNT1102821). S.M. is supported by a research fellowship from the Australian NHMRC.
## Author Contributions
M.E.R. and S.M. conceived and directed the study. A.I.C. performed the AGDS GWAS, the meta and MTAG analyses, and post-GWAS gene-based tests, genetic correlation, PRS analyses, and LCV with help from LMGM and BLM. N.I. performed sleep apnea and Snoring GWAS for UK-B and CLSA. P.F.K. and G.C.P. performed the COLOC expression analyses. M.E.R. and X.D. performed the Partners Biobank GWAS. X.H., M.H.L., J.S.O., N.G.M., G.C.P., and J.S.Y. provided technical expertise and helped interpret results. Y.H. and S.A. performed the replication GWAS. All co-authors contributed to manuscript drafting and provided relevant intellectual input for this manuscript.
## Disclosure Statement
Financial disclosure: The funding agencies had no input or involvement in the design, execution, or reporting of the present study. Stella Aslibekyan, Yunru Huang, and Gabriel Cuéllar-Partida are current or former employees of 23andMe, Inc. and may hold stock or stock options. Yunru Huang also shares stock options of Roche, Inc. All other authors have nothing to declare.
Non-financial disclosure: Nothing to declare.
## Ethics Statements
This study was performed under the oversight of the QIMR Berghofer Human Research Ethics Committee. All participants provided informed consent.
UK Biobank: The UK Biobank study was approved by the National Health Service National Research Ethics Service (ref. 11/NW/0382), and all participants provided written informed consent to participate in the UK Biobank study. Information about ethics oversight in the UK Biobank can be found at https://www.ukbiobank.ac.uk/ethics/.
CLSA: All participants provided informed consent. The CLSA abides by the Canadian Institutes of Health Research (CIHR) requirements. The protocol of the CLSA has been reviewed and approved by 13 research ethics boards across Canada. A complete and detailed list is available at: https://www.clsa-elcv.ca/participants/privacy/who-ensures-high-ethical-standards/research-ethics-boards.
FinnGen: The Ethical Review Board of the Hospital District of Helsinki and Uusimaa approved the FinnGen study protocol (HUS/$\frac{990}{2017}$).
Partners Healthcare Biobank: AGDS: all participants provided informed consent prior to participating in the study. This study and all questionnaires used for AGDS were approved by the QIMR Berghofer Human Research Ethics Committee.
23andMe Inc: Participants provided informed consent and participated in the research online, under a protocol approved by the external AAHRPP-accredited IRB, Ethical & Independent Review Services (E&I Review).
## Data Availability
The full GWAS summary statistics for this study will be available through the NHGRI-EBI GWAS Catalogue (https://www.ebi.ac.uk/gwas/downloads/summary-statistics). Data are available from the Canadian Longitudinal Study on Aging (www.clsa-elcv.ca) for researchers who meet the criteria for access to de-identified CLSA data. UK Biobank and FinnGen data are also accessible through their respective application procedures. The full GWAS summary statistics for the 23andMe discovery data set can be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. Please visit research.23andme.com/collaborate/#publication for more information and to apply to access the data.
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|
---
title: Histone deacetylase 8 inhibition prevents the progression of peritoneal fibrosis
by counteracting the epithelial-mesenchymal transition and blockade of M2 macrophage
polarization
authors:
- Xun Zhou
- Hui Chen
- Yingfeng Shi
- Jinqing Li
- Xiaoyan Ma
- Lin Du
- Yan Hu
- Min Tao
- Qin Zhong
- Danying Yan
- Shougang Zhuang
- Na Liu
journal: Frontiers in Immunology
year: 2023
pmcid: PMC9995794
doi: 10.3389/fimmu.2023.1137332
license: CC BY 4.0
---
# Histone deacetylase 8 inhibition prevents the progression of peritoneal fibrosis by counteracting the epithelial-mesenchymal transition and blockade of M2 macrophage polarization
## Abstract
### Background
Peritoneal dialysis (PD) is an effective replacement therapy for end-stage renal disease patients. However, long-term exposure to peritoneal dialysate will lead to the development of peritoneal fibrosis. Epigenetics has been shown to play an important role in peritoneal fibrosis, but the role of histone deacetylases 8 (HDAC8) in peritoneal fibrosis have not been elucidated. In this research, we focused on the role and mechanisms of HDAC8 in peritoneal fibrosis and discussed the mechanisms involved.
### Methods
We examined the expression of HDAC8 in the peritoneum and dialysis effluent of continuous PD patients. Then we assessed the role and mechanism of HDAC8 in peritoneal fibrosis progression in mouse model of peritoneal fibrosis induced by high glucose peritoneal dialysis fluid by using PCI-34051. In vitro, TGF-β1 or IL-4 were used to stimulate human peritoneal mesothelial cells (HPMCs) or RAW264.7 cells to establish two cell injury models to further explore the role and mechanism of HDAC8 in epithelial-mesenchymal transition (EMT) and macrophage polarization.
### Results
We found that HDAC8 expressed highly in the peritoneum from patients with PD-related peritonitis. We further revealed that the level of HDAC8 in the dialysate increased over time, and HDAC8 was positively correlated with TGF-β1 and vascular endothelial growth factor (VEGF), and negatively correlated with cancer antigen 125. In mouse model of peritoneal fibrosis induced by high glucose dialysate, administration of PCI-34051 (a selective HDAC8 inhibitor) significantly prevented the progression of peritoneal fibrosis. Treatment with PCI-34051 blocked the phosphorylation of epidermal growth factor receptor (EGFR) and the activation of its downstream signaling pathways ERK$\frac{1}{2}$ and STAT3/HIF-1α. Inhibition of HDAC8 also reduced apoptosis. In vitro, HDAC8 silencing with PCI-34051 or siRNA inhibited TGF-β1-induced EMT and apoptosis in HPMCs. In addition, continuous high glucose dialysate or IL-4 stimulation induced M2 macrophage polarization. Blockade of HDAC8 reduced M2 macrophage polarization by inhibiting the activation of STAT6 and PI3K/Akt signaling pathways.
### Conclusions
We demonstrated that HDAC8 promoted the EMT of HPMCs via EGFR/ERK$\frac{1}{2}$/STAT3/HIF-1α, induced M2 macrophage polarization via STAT6 and PI3K/Akt signaling pathways, and ultimately accelerated the process of peritoneal fibrosis.
## Introduction
Peritoneal dialysis (PD) is an effective renal replacement therapy [1], however, the proportion of ESRD patients treated with PD is lower than hemodialysis in developed countries [2]. Clinical studies have demonstrated that peritoneal ultrafiltration declines gradually 2 - 4 years after the initiation of PD (3–5). The induction of peritoneal fibrosis is a complex pathological event, and is characterized by epithelial-to-mesenchymal transition (EMT), activation of fibroblasts, deposition of extracellular matrix (ECM) components and angiogenesis [6]. The release of several growth factors/cytokines, especially transforming growth factor-1 (TGF-β1) and vascular endothelial growth factor (VEGF), and activation of various signaling pathways contribute to the progression of peritoneal fibrosis. Our previous studies have demonstrated that epidermal growth factor receptor (EGFR) promotes the development and progression of peritoneal fibrosis via the activation of multiple pro-fibrosis signaling pathways, inflammatory responses and angiogenesis [7]. The phosphorylation of EGFR subsequently leads to the activation of several intracellular signaling pathways, including extracellular signal-regulated kinases $\frac{1}{2}$ (ERK $\frac{1}{2}$) and signal transducer and activator of transcription 3 (STAT3) during peritoneal fibrosis [8]. Thus, targeting EGFR may be an effective approach to preserve peritoneal membrane ultrafiltration capacity.
Macrophages are key components of the peritoneal immune system [9]. Macrophages are usually divided into two functional subtypes (M1 and M2), which play different roles in various physiological and pathological environments [10]. Analysis of experimental evidences from PD patient samples demonstrates that the majority of peritoneal macrophages tend to be M2 macrophages in phenotype and function [11]. Predominance of M2 macrophage response leads to induction of EMT, and upregulate the production of ECM protein, angiogenesis, and fibrosis [12]. Our previous studies have demonstrated that M2 macrophage polarization in the peritoneum is regulated by the phosphorylation of signal transducer and activator of transcription 6 (STAT6) and phosphatidylinositol-3-kinase (PI3K)/Akt pathways [13].
According to previous studies by our group, epigenetic regulation plays an important role in the process of peritoneal fibrosis [6, 7, 13, 14]. Histone deacetylase 8 (HDAC8) is a class I HDAC that catalyzes the deacetylation of histones and non-histones [15]. It has been demonstrated that HDAC8 plays a multifunctional role in cancer progression, such as stimulating tumor growth and metastasis by enhancing cell proliferation and activating EMT via acting on histones and non-histone substrates [15]. However, the specific mechanism and target proteins of HDAC8 in regulating peritoneal fibrosis have not been revealed.
In this research, we examined the expression of HDAC8 in the peritoneum and dialysis effluent of continuous PD patients. Then we assessed the role and mechanism of HDAC8 in peritoneal fibrosis progression in mouse model of peritoneal fibrosis induced by high glucose peritoneal dialysis fluid (PDF) by using PCI-34051. In vitro, TGF-β1 and IL-4 were used to stimulate human peritoneal mesothelial cells (HPMCs) and RAW264.7 cells to establish two cell injury models to further explore the role and mechanism of HDAC8 in EMT and macrophage polarization.
## Materials and methods
Additional details for all methods are provided in the Supplementary Material and Methods.
## Clinical sample collection and ethics statement
We collected peritoneal tissue during catheterization initiation operations ($$n = 6$$) and refractory peritonitis-induced catheter migration ($$n = 6$$) at Shanghai East Hospital affiliated with Tongji University and completed co-immunofluorescent staining of HDAC8 and α-SMA. Human PD effluents from patients with different durations were collected at Shanghai East Hospital, Shanghai Baoshan Hospital and Shanghai Songjiang District Central Hospital from September 2017 to March 2022. These patients were divided into 5 groups according to duration: group 1 (duration ≤ 1 month, $$n = 16$$), group 2 (1<duration ≤ 12 months, $$n = 22$$), group 3 (12<duration ≤ 24 months, $$n = 20$$), group 4 (24<duration ≤ 36 months, $$n = 16$$), and group 5 (duration>36 months, $$n = 14$$). This study was approved by the Medical Ethics Committee of Shanghai East Hospital and was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from each patient. And we have obtained the registration number from the Chinese Clinical Trial Register (ChiCTR): ChiCTR2100052103.
## Animals and treatment
Male C57BL mice (purchased from Shanghai Super-B&K Laboratory Animal Corp. Ltd) weighed 20-25g were used in this study. The peritoneal fibrosis model was created by daily i.p. injection of 100 ml/kg peritoneal dialysis fluid with $4.25\%$ glucose for 28 days [6]. To examine the efficacy of PCI-34051 in peritoneal fibrosis, two different concentrations of PCI-34051 (10 or 20 mg/kg) in DMSO was intraperitoneally every day and the mice were sacrificed on day 28 to collect peritoneum. The animal protocol was reviewed and approved by the Institutional Animal Care and Use Committee at Tongji University (Shanghai, China). The details of animals and treatment are provided in the Supplementary Material and Methods.
## Cell culture and treatments
Cells were collected and cultured as described previously [13, 16]. HPMCs (American Type Culture Collection, ATCC; Rockville, MD, United States) were cultured in Dulbecco’s modified Eagle’s medium (DMEM) with F12 containing $10\%$ fetal bovine serum (FBS), $1\%$ penicillin, and streptomycin. Raw264.7 cells (American Type Culture Collection, ATCC; Rockville, MD, United States) were cultured in RPMI-1640 medium containing $10\%$ FBS, $1\%$ penicillin and streptomycin. All cells were cultured in an atmosphere of $5\%$ CO2, and $95\%$ air at 37°C and were experimented after three generations. To examine the role and mechanisms of HDAC8 in TGF-β1-induced EMT of HPMCs, we starved HPMCs for 24 hours with DMEM/F12 containing $0.5\%$ FBS and then exposed to TGF-β1 (2ng/ml) in the presence or absence of PCI-34051 (5mM). for 36 hours. To examine the role and mechanisms of HDAC8 in IL-4-induced macrophage polarization of Raw264.7 cells, we starved Raw264.7 cells for 24 hours with RPMI-1640 containing $0.5\%$ FBS and then exposed to IL-4 (10 ng/ml) for 24 hours in the presence or absence of PCI-34051 (5μM). Then, cells were harvested for further immunoblot analysis or immunofluorescent staining. All the in vitro experiments were repeated at least three times.
## HDAC8 is highly expressed in peritoneum from long-term PD patients, positively correlated with TGF-β1 and VEGF and negatively correlated with CA125 in human PD effluent
We collected peritoneal tissue during catheterization initiation operations ($$n = 6$$) and refractory peritonitis-induced catheter migration ($$n = 6$$) and completed co-immunofluorescent staining of HDAC8 and α-SMA. As shown in Figure 1A, HDAC8 was highly expressed in the thickened peritoneum of long-term PD patients with PD-related peritonitis, and HDAC8 was co-expressed with α-SMA positive cells.
**Figure 1:** *HDAC8 is highly expressed in peritoneum from long-term PD patients, positively correlated with TGF-β1 and VEGF and negatively correlated with CA125 in human PD effluent (A) Immunofluorescent co-staining of α-SMA and HDAC8 in the peritoneum from patients with non-PD and PD-related peritonitis. And HDAC8 was co-expressed with α-SMA-positive cells (white arrows). (B–E) Levels of HDAC8 and cytokines in dialysis effluent according to ELISA kits. The dialysis effluents from 88 PD patients were divided into 5 groups according to duration: group 1 (duration ≤ 1 month, n=16), group 2 (1<duration ≤ 12 months, n=22), group 3 (12<duration ≤ 24 months, n=20), group 4 (24<duration ≤ 36 months, n=16), and group 5 (duration>36 months, n=14). The expression levels of HDAC8 (B), TGF-β1 (C), VEGF (D), and CA125 (E) were indicated in each group. (F–I) HDAC8 was positively correlated with enhanced expression of TGF-β1, VEGF and 4h D/P creatinine, and negatively with CA125 in dialysis effluent of PD patients. Correlation analysis were conducted between HDAC8 and TGF-β1 (F), HDAC8 and VEGF (G), HDAC8 and CA125 (H), HDAC8 and 4h D/P creatinine (I). Data were expressed as means ± SEM. *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001. N.S., statistically not significant, with the comparisons labeled. All scale bars = 50 μm.*
ELISA kit assays showed that the expressions of HDAC8, TGF-β1 and VEGF in dialysis effluent were significantly increased, while the level of CA125 decreased with the prolongation of dialysis time (Figures 1B–E). CA125 is reported to be a marker of peritoneal mesothelial cell, with the severity of peritoneal fibrosis, the expression of CA125 is decreased due to the loss of peritoneal mesothelial cell [17]. Further correlation analysis showed that HDAC8 was positively correlated with TGF-β1 ($r = 0.2905$, $$p \leq 0.0060$$) and VEGF ($r = 0.3231$, $$p \leq 0.0021$$), and negatively correlated with CA125 (r= −0.3046, $$p \leq 0.0039$$) (Figures 1F–H). In addition, it has been reported that patients characterized as high transporters had an increased sub-mesothelial fibrous layer [18]. According to the results of PET tests in PD patients (Figure 1I), HDAC8 was positively correlated with the peritoneal transport rate of patients ($r = 0.2138$, $$p \leq 0.0455$$). Table S1 showed the clinical characteristics of the enrolled PD patients. There was no significant difference between different groups of PD patients except creatinine and serum sodium. These data suggest that HDAC8 might be a clinically noninvasive biomarker in dialysis efflux for predicting peritoneal injury and transport status in PD patients.
## Inhibition of HDAC8 suppresses development of peritoneal fibrosis and improves functional impairments of peritoneal membrane in the high glucose PDF-injured peritoneum
In the preliminary experiment, we used two different concentrations of PCI-34051 (10mg/kg or 20mg/kg) to intervene high glucose PDF induced peritoneal fibrosis in mice. Our results showed that two different concentrations of PCI-34051 could prevent the development of peritoneal fibrosis and improve functional impairments of peritoneal membrane in the high glucose PDF-injured peritoneum, and high concentration of PCI (20mg/kg) had a better effect on inhibiting peritoneal fibrosis and conserving peritoneum function (Figure S1). Therefore, we chose a concentration of 20mg/kg PCI-34051 to treat mice for further study. As shown by Masson trichrome staining in Figure 2A, we successfully established a mouse model of peritoneal fibrosis with continuous intraperitoneal injection of $4.25\%$ glucose PDF, which was characterized by an increase in the thickness of the sub-mesothelial area. Treatment of PCI-34051 (20mg/kg/d) after injection of $4.25\%$ glucose PDF significantly reduced these pathological changes (Figures 2A–C), suggesting that HDAC8 is a key mediator of peritoneal fibrosis. EMT is an important mechanism of peritoneal fibrosis [19, 20]. We examined the expression of α-SMA, collagen I and E-cadherin by western blot. As shown in Figures 2D–G, expressions of α-SMA and collagen I were significantly increased, and expression of E-cadherin was decreased after exposure to $4.25\%$ glucose PDF. Treatment of PCI-34051 blocked the EMT process.
**Figure 2:** *Inhibition of HDAC8 suppresses development of peritoneal fibrosis and improves functional impairments of peritoneal membrane in the high glucose PDF-injured peritoneum (A) Masson trichrome staining of the peritoneum in different groups of mice. (B) The thickness of peritoneum according to Masson trichrome staining. (C) The positive area of Masson trichrome staining-positive sub-mesothelial area (blue). (D) Western blot analysis showed the protein levels of α-SMA, collagen I, E-cadherin, HDAC8, cortactin, acetyl-cortactin and GAPDH in peritoneum from different groups of mice. Expression levels of (E) α-SMA, (F) collagen I, (G) E-cadherin, (H) HDAC8, (I) acetyl-cortactin, (J) cortactin in different groups were quantified by densitometry and normalized with GAPDH and cortactin respectively. (K) Immunofluorescent co-staining of α-SMA and HDAC8 (white arrows) in the peritoneum from mice with or without high glucose PDF injection. (L) The dialysate-to-plasma (D/P) ratio of blood urea nitrogen (BUN). (M) Ratio of dialysate glucose at 2 h after PDF injection to dialysate glucose at 0 hour (D/D0). Data were expressed as means ± SEM. *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001. N.S., statistically not significant, with the comparisons labeled. All scale bars = 50 μm.*
To demonstrate the effectiveness of PCI-34051 in vivo, we examined the impact of PCI-34051 on the expression of HDAC8 and its substrate protein cortactin. The expression of HDAC8 increased significantly after the injection of $4.25\%$ glucose PDF for 28 days, while the expression of acetyl-cortactin decreased (Figures 2D, H–J). Treatment of PCI-34051 decreased the expression of HDAC8 to the base level in mice receiving $4.25\%$ glucose PDF, and increased acetyl-cortactin. Immunofluorescent staining showed that HDAC8 was mainly expressed in cells present in the sub-mesothelial zone and co-localized with α-SMA (Figure 2K). These results suggested that PCI-34051 may prevent the progression of peritoneal fibrosis by suppressing EMT. In addition, PCI-34051 improved high glucose PDF-associated peritoneal functional impairments according to the PET test results (Figures 2L, M).
## Inhibition of HDAC8 blocks the activation of EGFR/ERK1/2/STAT3/HIF-1α signaling pathway in the peritoneum exposed to high glucose dialysate
Studies from our group have demonstrated that the activation of EGFR/ERK/$\frac{1}{2}$/STAT3 signaling pathway is related to the progression of peritoneal fibrosis [21, 22]. Therefore, we investigated the effect of HDAC8 on the activation of EGFR and its downstream signaling molecules ERK$\frac{1}{2}$ and STAT3. Immunofluorescent staining showed that HDAC8 was observed in EGFR positive cells (Figure 3A). As shown in Figures 3B–H, the phosphorylation of EGFR, ERK$\frac{1}{2}$ and STAT3 were significantly increased after exposure to $4.25\%$ glucose PDF, while PCI-34051 administration inhibited their phosphorylation. Recent studies have demonstrated that the expression of HIF-1α in mesenchymal cells is mainly dependent on the activation of STAT3, and the inhibition of STAT3 will further suppressed the activation of HIF-1α, thus affecting the EMT of mesothelial cells [23]. As shown in Figures 3B, I, the expression of HIF-1α was significantly increased after exposure to $4.25\%$ glucose PDF, while PCI-34051 administration inhibited its expression. These data suggested that inhibition of HDAC8 suppressed the activation of the EGFR/ERK$\frac{1}{2}$/STAT3/HIF-1α signaling pathway.
**Figure 3:** *Inhibition of HDAC8 blocks the activation of EGFR/ERK1/2/STAT3/HIF-1α signaling pathway in the peritoneum exposed to high glucose dialysate (A) Immunofluorescent co-staining of EGFR and HDAC8 (white arrows) in the peritoneum from mice with or without high glucose PDF injection. (B) Western blot analysis showed the protein levels of p-EGFR, EGFR, p-ERK1/2, ERK1/2, p-STAT3, STAT3, HIF-1α and GAPDH. Expression levels of (C) p-EGFR, (D) EGFR, (E) p-ERK1/2, (F) ERK1/2, (G) p-STAT3, (H) STAT3, (I) HIF-1α were quantified by densitometry and normalized with GAPDH, EGFR, ERK1/2 and STAT3 respectively. Data were expressed as means ± SEM. *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001. N.S., statistically not significant, with the comparisons labeled. All scale bars = 50 μm.*
## Blockade of HDAC8 with PCI-34051 or siRNA abrogates EMT by inhibition of EGFR/ERK1/2/STAT3/HIF-1α signaling pathway
TGF-β1 is an important cytokine that can stimulate EMT and induce peritoneal fibrosis [7]. HPMCs exposure to TGF-β1 increased the expression of α-SMA and collagen I, and decreased the expression of epithelial cell marker E-cadherin, indicating that TGF-β1 promoted the EMT of HPMCs (Figures 4A–D, 5A–D). Treatment of PCI-34051 or siRNA, decreased the expression of HDAC8 and increased the expression of acetyl-cortactin (Figures 4A, E–G, 5A, E–G). In addition, TGF-β1 induced the phosphorylation of EGFR and activation of its downstream signaling pathways ERK$\frac{1}{2}$ and STAT3/HIF-1α (Figures 4H–O, 5H–O). Blockade of HDAC8 suppressed all of these responses. These data supported our in vivo observation that HDAC8 is a key protein in regulating peritoneal fibrosis via EGFR/ERK$\frac{1}{2}$/STAT3/HIF-1α signaling pathway.
**Figure 4:** *Blockade of HDAC8 with PCI-34051 abrogates EMT by inhibition of EGFR/ERK1/2/STAT3/HIF-1α signaling pathway (A) Serum-starved HPMCs were pretreated with PCI-34051 (5μM) and then exposed to TGF-β1 (2 ng/ml) for 36 h. Cell lysates were subjected to immunoblot analysis with specific antibodies against α-SMA, collagen I, E-cadherin, HDAC8, cortactin, acetyl-cortactin and GAPDH. Expression levels of (B) α-SMA, (C) collagen I, (D) E-cadherin, (E) HDAC8, (F) acetyl-cortactin, (G) cortactin in different groups were quantified by densitometry and normalized with GAPDH and cortactin respectively. (H) Cell lysates were subjected to immunoblot analysis with specific antibodies against p-EGFR, EGFR, p-ERK1/2, ERK1/2, p-STAT3, STAT3, HIF-1α and GAPDH. Expression levels of (I) p-EGFR, (J) EGFR, (K) p-ERK1/2, (L) ERK1/2, (M) p-STAT3, (N) STAT3, (O) HIF-1α were quantified by densitometry and normalized with GAPDH, EGFR, ERK1/2 and STAT3 respectively. Data were expressed as means ± SEM. *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001. N.S., statistically not significant, with the comparisons labeled.* **Figure 5:** *Blockade of HDAC8 with siRNA abrogates EMT by inhibition of EGFR/ERK1/2/STAT3/HIF-1α signaling pathway (A) Serum-starved HPMCs were pretreated with siRNA and then exposed to TGF-β1 (2 ng/ml) for 36 h. Cell lysates were subjected to immunoblot analysis with specific antibodies against α-SMA, collagen I, E-cadherin, HDAC8, cortactin, acetyl-cortactin and GAPDH. Expression levels of (B) α-SMA, (C) collagen I, (D) E-cadherin, (E) HDAC8, (F) acetyl-cortactin, (G) cortactin in different groups were quantified by densitometry and normalized with GAPDH and cortactin respectively. (H) Cell lysates were subjected to immunoblot analysis with specific antibodies against p-EGFR, EGFR, p-ERK1/2, ERK1/2, p-STAT3, STAT3, HIF-1α and GAPDH. Expression levels of (I) p-EGFR, (J) EGFR, (K) p-ERK1/2, (L) ERK1/2, (M) p-STAT3, (N) STAT3, (O) HIF-1α were quantified by densitometry and normalized with GAPDH, EGFR, ERK1/2 and STAT3 respectively. Data were expressed as means ± SEM. *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001. N.S., statistically not significant, with the comparisons labeled.*
## Inhibition of HDAC8 prevents M2 macrophage polarization via suppressing STAT6 and PI3K/Akt signaling pathways in the high glucose PDF-injured peritoneum
Macrophages play a pivotal role in peritoneal fibrosis process [13]. Therefore, we detected the infiltration and polarization of macrophages in a mouse peritoneal fibrosis model established by $4.25\%$ glucose PDF. Western blot showed increased expressions of arginase-1 (Arg-1) and CD163 expression [cell markers of M2 phenotype [13]] after exposure to $4.25\%$ glucose PDF, while PCI-34051 administration effectively decreased their expressions (Figures 6A, C, D). These results suggested that M2 macrophage polarization was involved in the process of peritoneal fibrosis, and the inhibition of HDAC8 by PCI-34051 could prevent M2 macrophage polarization.
**Figure 6:** *Inhibition of HDAC8 prevents M2 macrophage polarization via suppressing STAT6 and PI3K/Akt signaling pathways in the high glucose PDF-injured peritoneum (A) Western blot analysis showed the protein levels of CD163, Arg-1 and GAPDH in peritoneum of mice. (B) Western blot analysis showed the protein levels of p-STAT6, STAT6, p-PI3K, PI3K p-Akt, Akt and GAPDH in peritoneum of mice. Expression levels of (C) CD163, (D) Arg-1, (E) p-STAT6, (F) STAT6, (G) p-PI3K, (H) PI3K, (I) p-Akt, (J) Akt were quantified by densitometry and normalized with GAPDH, STAT6, PI3K and Akt respectively. Data were expressed as means ± SEM. *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001. N.S., statistically not significant, with the comparisons labeled.*
The phosphorylation of STAT6 and activation of PI3K/AKT signaling pathway are involved in the regulation of M2 macrophage polarization [13]. In our research, the phosphorylation of STAT6, PI3K and Akt were significantly increased after exposure to $4.25\%$ glucose PDF, while PCI-34051 administration inhibited their phosphorylation (Figures 6B, E, G, I). In addition, injection of $4.25\%$ glucose PDF increased the expression of total STAT6, PI3K and Akt, and PCI-34051 administration did not decrease their expressions (Figures 6B, E–J). These data suggested that inhibition of HDAC8 might inhibit the M2 macrophage polarization via STAT6 and PI3K/Akt pathways.
## Blockade of HDAC8 with PCI-34051 or siRNA prevents M2 macrophage polarization via suppressing STAT6 and PI3K/Akt signaling pathways
IL-4 stimulation can induce M2 macrophage polarization [13]. As shown in Figures 7A and 8A, immunofluorescent staining of CD163 indicated that IL-4 induced M2 polarization in RAW264.7 cells, while blockade of HDAC8 prevented M2 macrophage polarization. Western blot further verified this result (Figures 7B, C, E–I, 8B, C, E–I). In addition, IL-4 induced the phosphorylation of STAT6 and activation of PI3K/Akt pathway (Figures 7D, J–L, 8D, J–L, S2, S3). Blockade of HDAC8 with PCI-34051 or siRNA suppressed all of these responses. Immunofluorescent staining of p-STAT6 further verified that blockade of HDAC8 could suppress the phosphorylation of STAT6 (Figures 7M, 8M). These data supported our in vivo observation that HDAC8 could regulate M2 macrophage polarization via STAT6 and PI3K/Akt signaling pathways.
**Figure 7:** *Blockade of HDAC8 with PCI-34051 prevents M2 macrophage polarization via suppressing STAT6 and PI3K/Akt signaling pathways (A) Immunofluorescent staining of CD163 in Raw264.7 cells with different treatments (B–D) Serum-starved RAW264.7 were pretreated with PCI-34051 (5μM) and then exposed to IL-4 (10 ng/ml) for 24 h. Cell lysates were subjected to immunoblot analysis with specific antibodies against HDAC8, acetyl-cortactin, cortactin, CD163, Arg-1, p-STAT6, STAT6, p-PI3K, PI3K p-Akt, Akt and GAPDH. Expression levels of (E) HDAC8, (F) acetyl-cortactin, (G) cortactin, (H) CD163, (I) Arg-1, (J) p-STAT6, (K) p-PI3K, (L) p-Akt were quantified by densitometry and normalized with GAPDH, cortactin, STAT6, PI3K and Akt respectively. (M) Immunofluorescent staining of p-STAT6 in Raw264.7 cells with different treatments. Data were expressed as means ± SEM. *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001. N.S., statistically not significant, with the comparisons labeled. All scale bars = 50 μm.* **Figure 8:** *Blockade of HDAC8 with siRNA prevents M2 macrophage polarization via suppressing STAT6 and PI3K/Akt signaling pathways (A) Immunofluorescent staining of CD163 in Raw264.7 cells with different treatments (B–D) Serum-starved RAW264.7 were pretreated with siRNA and then exposed to IL-4 (10 ng/ml) for 24 h. Cell lysates were subjected to immunoblot analysis with specific antibodies against HDAC8, acetyl-cortactin, cortactin, CD163, Arg-1, p-STAT6, STAT6, p-PI3K, PI3K p-Akt, Akt and GAPDH. Expression levels of (E) HDAC8, (F) acetyl-cortactin, (G) cortactin, (H) CD163, (I) Arg-1, (J) p-STAT6, (K) p-PI3K, (L) p-Akt were quantified by densitometry and normalized with GAPDH, cortactin, STAT6, PI3K and Akt respectively. (M) Immunofluorescent staining of p-STAT6 in Raw264.7 cells with different treatments. Data were expressed as means ± SEM. *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001. N.S., statistically not significant, with the comparisons labeled. All scale bars = 50 μm.*
## Inhibition of HDAC8 reduces cell apoptosis in vivo and vitro
BAX and cleaved-caspase-3 are both pivotal proteins involved in cell apoptosis [24, 25]. In vivo and in vitro experiments (Figure S4), we demonstrated that exposure to $4.25\%$ glucose PDF or TGF-β1 stimulation would promote the cell apoptosis, while blockade of HDAC8 could reduce cell apoptosis.
## Discussion
Peritoneal fibrosis is one of the most significant complications for PD patients, however, up to now there is no effective solution [3, 26]. In this research, we demonstrated that inhibition of HDAC8 with PCI-34051 or siRNA suppressed EMT, apoptosis and M2 macrophage polarization, ultimately attenuated peritoneal fibrosis and peritoneal dysfunction. Thus, inhibition of HDAC8 may be a potential therapeutic strategy for prevention and treatment of peritoneal fibrosis in long term PD patients.
HDAC8 is a sex-linked gene located at chromosomal position Xq13.1 [27] and expressed both in nucleus and cytoplasm (28–30). HDAC8 has a variety of histone and non-histone (SMC3, α-tubulin, cortactin, HSP20, p53, PKM2, AKT, ERRα and c-Jun) substrates (31–34). As one of the target substrates of HDAC8, cortactin is a ubiquitous multidomain protein involved in the regulation of actin cytoskeleton, integrin signaling and ECM degradation [35]. Acetyl-cortactin is localized in the nucleus and can be stimulated by growth factors to transport into the cytoplasm [36, 37]. In our study, we found that HDAC8 expression was increased in peritoneum stimulated by high glucose PDF or HPMCs exposed to TGF-β1, and overexpression of HDAC8 inhibited the acetylation of cortactin. Since cortactin is only expressed in the cytoplasm of cells [37], we hypothesized that HDAC8 regulated peritoneal fibrosis by participating in cytoplasmic protein regulation.
EGFR is a tyrosine kinase receptor that binds to ligands and phosphorylates, subsequently leading to the activation of several signaling pathways, including ERK$\frac{1}{2}$ and STAT3 [8]. Our previous studies have demonstrated that activated EGFR promotes peritoneal fibrosis by regulating EMT, inflammation, and angiogenesis [8]. In addition, previous studies have demonstrated that EGFR activation is regulated by several histone deacetylases, including HDAC1 [38], HDAC4 [39] and HDAC6 [40, 41]. In this research, we found that overexpression of HDAC8 could promote the phosphorylation of EGFR and the activation of its downstream signal molecules. Although the mechanism by which HDAC8 regulates EGFR is still unknown, we speculate that HDAC8 may act by affecting the endocytic trafficking and degradation of EGFR. Activation of EGFR signaling is terminated by endocytosis, and vesicles containing the receptor-ligand complex target lysosomes for degradation along microtubule tracks [42]. Acetylation of microtubule component α-tubulin affects the stability of microtubule and further regulates intracellular cargo (such as EGFR-containing vesicles) transport [42]. The deacetylation of α-tubulin is mainly mediated by HDAC6, but HDAC8 has also recently been characterized to be involved in the deacetylation of α-tubulin [43]. HDAC8 is significantly overexpressed in HeLa cells and may take over the function of HDAC6 as a major deacetylase of α-tubulin [43]. Therefore, we speculate that HDAC8 might inhibit EGFR endocytosis by deacetylating α-tubulin in the cytoplasm, and result in the continuous activation of EGFR.
M2 macrophages are believed to be involved in peritoneal fibrosis, and consumption of M2 macrophages in the peritoneum can alleviate peritoneal fibrosis [44]. Our study found that blockade of HDAC8 could prevent M2 macrophage polarization via STAT6 and PI3K/Akt signaling pathways. STAT6 is a major factor in the M2 polarization process of macrophages, and its activation drives M2 polarization [45]. Recent studies have shown that acetylation of STAT6 can inhibit its transcriptional activity and thus inhibit M2 polarization [46]. Although the specific mechanism by which HDAC8 regulates STAT6 has not been reported, several studies have demonstrated the direct regulatory effect of HDACs on STAT transcription factors. A recent study has demonstrated that IL-4-STAT6 signaling is dependent on HDAC3, which performs post-translational modifications and allosteric regulation of STAT6 by occupying STAT6-repressed enhancers [47]. The expression and phosphorylation of STAT3 are also regulated by several HDACs, including HDAC1 and HDAC3 [48, 49]. Whether HDAC8 affects the activation of STAT6 through acetylation remains to be further investigated. The PI3K/Akt pathway can influence the survival, migration and polarization of macrophages [50]. It has been demonstrated that HDAC8 induced tri-methylation of histone H3 lysine 27 through down-regulating the H3K27me3 eraser Jumonji Domain Containing 3 could suppress PTEN expression, thus activating the PI3K/Akt signaling pathway, and further determining susceptibility to cell cycle arrest induced by anthrax lethal toxin [51]. In this research, we also found that knockdown of HDAC8 could suppress the activation of PI3K/Akt signaling pathway. The regulatory effect of HDA8 on Akt, on one hand, is mediated by its upstream signaling molecule PI3K; On the other hand, Soon-Duck Ha et al. have demonstrated that HDAC8 activates Akt through upregulating PLCB1 and suppressing DESC1 expression [52]. In conclusion, targeting HDAC8 could further regulate M2 macrophage polarization.
At present, the development of HDAC8 inhibitors focuses on improving their activity and high selectivity. Meanwhile, the multi-target pharmacological approach on HDAC8 has gained attention for its benefits from achieving the simultaneous modulation of multiple targets, especially in complex diseases such as cancer and fibrosis [53, 54]. PCI-34051 is currently the most widely used HDAC8 inhibitor in the validation of various target diseases due to its considerable subtype selectivity. PCI-34051 in combination with conventional antitumor agents has shown to have a synergistic effect in the reversal of disease [55, 56]. Whether the combination of PCI-34051 and other drugs could play a reverse role in fibrosis diseases, which remains to be further studied.
In conclusion, we demonstrated that blockade of HDAC8 could prevent and reverse peritoneal fibrosis. Mechanistically, HDAC8 promoted EMT by inducing the phosphorylation of EGFR and in turn led to the activation of its downstream fibrotic signaling pathways, including STAT3/HIF-1α and ERK$\frac{1}{2.}$ HDAC8 is also a key protein in regulating M2 macrophage polarization via STAT6 and PI3K/Akt signaling pathways (Figure S5). As such, targeting HDAC8 might be a new strategy to lessen the severity of peritoneal fibrosis.
## Data availability statement
The original contributions presented in the study are included in the article/ Supplementary Materials. Further inquiries can be directed to the corresponding author.
## Ethics statement
This study was approved by the Medical Ethics Committee of Shanghai East Hospital and was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from each patient. And we have obtained the registration number from the Chinese Clinical Trial Register (ChiCTR): ChiCTR2100052103. The animal protocol was reviewed and approved by the Institutional Animal Care and Use Committee at Tongji University (Shanghai, China).
## Author contributions
NL participated in research design. XZ, HC, YS, JL, XM, LD, YH, MT, QZ and DY conducted experiments. XZ, HC, YS and NL contributed new reagents or analytic tools. XZ performed data analysis. XZ, SZ and NL wrote or contributed to the writing of the manuscript. All authors read and approved the final version of the article.
## 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.2023.1137332/full#supplementary-material
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|
---
title: Diet and high altitude strongly drive convergent adaptation of gut microbiota
in wild macaques, humans, and dogs to high altitude environments
authors:
- Junsong Zhao
- Yongfang Yao
- Mengmeng Dong
- Hongtao Xiao
- Ying Xiong
- Shengzhi Yang
- Diyan Li
- Meng Xie
- Qingyong Ni
- Mingwang Zhang
- Huailiang Xu
journal: Frontiers in Microbiology
year: 2023
pmcid: PMC9995840
doi: 10.3389/fmicb.2023.1067240
license: CC BY 4.0
---
# Diet and high altitude strongly drive convergent adaptation of gut microbiota in wild macaques, humans, and dogs to high altitude environments
## Abstract
Animal gut microbiota plays an indispensable role in host adaptation to different altitude environments. At present, little is known about the mechanism of animal gut microbiota in host adaptation to high altitude environments. Here, we selected wild macaques, humans, and dogs with different levels of kinship and intimate relationships in high altitude and low altitude environments, and analyzed the response of their gut microbiota to the host diet and altitude environments. Alpha diversity analysis found that at high altitude, the gut microbiota diversity of wild macaques with more complex diet in the wild environments is much higher than that of humans and dogs with simpler diet ($p \leq 0.05$), and beta diversity analysis found that the UniFrac distance between humans and dogs was significantly lower than between humans and macaques ($p \leq 0.05$), indicating that diet strongly drive the convergence of gut microbiota among species. Meanwhile, alpha diversity analysis found that among three subjects, the gut microbiota diversity of high altitude population is higher than that of low altitude population (ACE index in three species, Shannon index in dog and macaque and Simpson index in dog, $p \leq 0.05$), and beta diversity analysis found that the UniFrac distances among the three subjects in the high altitude environments were significantly lower than in the low altitude environments ($p \leq 0.05$). Additionally, core shared ASVs analysis found that among three subjects, the number of core microbiota in high altitude environments is higher than in low altitude environments, up to 5.34 times (1,$\frac{105}{207}$), and the proportion and relative abundance of the core bacteria types in each species were significantly higher in high altitude environments than in low altitude environments ($p \leq 0.05$). The results showed that high altitude environments played an important role in driving the convergence of gut microbiota among species. Furthermore, the neutral community model trial found that the gut microbiota of the three subjects was dispersed much more at high altitude than at low altitude, implying that the gut microbiota convergence of animals at high altitudes may be partly due to the microbial transmission between hosts mediated by human activities.
## Introduction
The structure of gut microbiota is the result of the interaction and coevolution of the host, as well as environmental factors (Blaut et al., 2002). *Animal* genetic relationships (Goodrich et al., 2014), dietary (De Filippo et al., 2010; Goodrich et al., 2014), altitude (Zhao et al., 2018; Zeng et al., 2020), season (Xia et al., 2021), and other environmental factors have a profound impact on the composition and structure of gut microbiota. The diet structure of the host directly influences the gut microbiota (De Filippo et al., 2010; Huang et al., 2022), and similar diets drive the convergence evolution of gut microbiota in animals (Huang et al., 2021). The gut microbiota of pandas (Ailuropoda melanoleuca) and red pandas (Ailurus fulgens) differ significantly from those of other species in the Carnivora order, but they share a similar core gut microbiota as insects consuming bamboo, indicating that their diet is a major driving force for the convergence of gut microbiota in these species (Yao et al., 2021). However, other studies also found that the composition of the gut microbiota of giant pandas is more similar to that of bears and completely different from that of other herbivores, with a low level of cellulose digesting bacteria (Xue et al., 2015). Further research has confirmed that the gut microbiota of giant pandas cannot adapt well to the degradation of cellulose and lignin in the high-fiber bamboo diet, but has evolved to utilize more digestible carbohydrates to maximize the intake of nutrients and energy from bamboo (Zhang et al., 2018). Through the fecal microbial transmission (FMT) of germ-free (GF) mice, it was also found that in the first few days after FMT, the difference of gut microbiota among GF mice with different donor microbiota would decrease, but when the gut microbiota was stable, the difference would increase, which proved that the gut microbiota had greater impact than diet (Zhang et al., 2022). These studies show that diet has limited influence on gut microbiota composition of giant pandas.
Meanwhile, in vertebrates, gut microbiota compositional differences among species are positively correlated with the evolutionary divergence time of the host, and the gut microbiota composition is more similar within host species than among species (Moeller et al., 2017; Song et al., 2020; Dillard et al., 2022). Additionally, environmental factors have been confirmed to be closely related to the gut microbiota structure (Ley et al., 2008; Zhao et al., 2018) and the extreme cold, the dry, hostile climate of high altitude environments, high ultraviolet radiation, and low oxygen content have important effects on the cardiovascular system, energy metabolism, and body temperature retention of animals (Simonson et al., 2010; Yu et al., 2016; Zhu et al., 2021). A variety of mammalian gut microbiota also respond to this environmental pressure, forming a composition of gut microbiota that adapts to the high altitude environments, and play an important role in host food digestion, energy metabolism, nutritional homeostasis, immune regulation, signal transduction, and other physiological activities (Bäckhed et al., 2005; Ley et al., 2006; Yan et al., 2021). Studies on humans (Li and Zhao, 2015; Li K. et al., 2016), macaques (Zhao et al., 2018; Wu et al., 2020), pigs (Zeng et al., 2020), and other mammals (Li H. et al., 2016) showed that altitude differences in environmental factors have an important influence on gut microbiota composition. The high altitude rumen microbiota of yak and Tibetan sheep exhibited a convergent phenomenon, with significantly lower levels in production of methane and volatile fatty acids (VFAs) (Zhang et al., 2016). The high altitude environments drive the diversity of gut microbiota composition and convergent evolution of indicator microbiota in ungulates (Wang et al., 2022). Studies on a variety of ungulates living in high altitude environments, such as the Tibetan antelope (Pantholops hodgsoni) and Tibetan sheep, also found that they have a similar composition of gut microbiota (Ma et al., 2019). These studies fully illustrate that high altitude, extreme environments have important driving effects on the compositional structure of the gut microbiota of animals.
The animals gut microbiota are shaped by the dispersal of organisms into habitats, followed by natural selection (i.e., habitat filtration), drift, and in situ diversification (Vellend, 2010). Studies have found that the mammalian microbiota is acquired vertically from mother to offspring (Dominguez-Bello et al., 2010; Vaishampayan et al., 2010) through genetic effects and horizontally among non-relatives through social interactions and shared environments (Tung et al., 2015; Moeller et al., 2016). The contact between host species results in the widespread dissemination of bacteria and homogenisation of microbial communities within mammalian communities (Moeller et al., 2017). In addition, there are routes of gut microbiota transmission between distantly related vertebrate species through humans and urban environments (Dillard et al., 2022), and the wildlife gut microbiota in its urban environment is also gradually humanized (Dillard et al., 2022). The gut microbiota composition of hosts is influenced by migratory dispersal among different species and that activity patterns in humans also drive the humanization of gut microbiota composition in wildlife, which may have important consequences for the host phenotype and environmental fitness (Fackelmann et al., 2021; Dillard et al., 2022), however, the natural resource-dependent lifestyle of human beings in high altitude areas is mainly based on grazing and collection under forests, which means they have a high temporal and spatial overlap with wild animals in the area. Little is known about the impact of gut microbiota community composition of wild and domestic animals, in particular, the degree of integration of wild animals, domestic animals, and the human gut microbiota in the extreme environment at high altitudes has not been fully explored.
Studies on phylogenetic relationships showed that dogs are different from humans and monkeys about 85 million years ago, while humans differed from monkeys about 23.5–34 Ma ago (Dos Reis et al., 2012). Meanwhile, rhesus macaques have high homology with humans in morphology, physiology, biochemistry, genetics, and reproduction (Chan et al., 2001) and are also one of the most widely distributed animals in the natural environment. Dogs, as important companion animals for humans, were domesticated approximately 40,000 to 14,000 years ago and have a more similar diet to humans and close contact with (Wang et al., 2013, 2016). They are also one of the animals more deeply influenced by human activities, the typical representative animals inhabited cultural environments. Therefore, wild macaques, humans, and dogs are ideal for evaluating the effects of diet and altitude environments on the hosts gut microbiota. Here, we compared the gut microbiota composition of humans, dogs and wild macaques housed at high altitude (altitude >3,000 m) and low altitude (altitude <1,000 m) environments, revealed the effects of diet and altitude environments on the host gut microbiota, and assessed the effects of extreme conditions at high altitude on the gut microbiota of wild macaques, humans, and dogs. The results are important for understanding the mechanism of environmental adaptation to high altitude for humans and animals, as well as for the conservation of wildlife, domestic animal feeding, and guiding the rational use of natural resources by humans.
## Ethics statement
Before sample collection, all the animal work was approved by the Animal Welfare and Animal Ethics Committee of Sichuan Agricultural University (SKY-S20171006). The human samples and the relevant information were kept confidential. All fieldwork was granted permission by the Administration of Wild Animal and Plant Protection, Nature Reserves, The Department of Forestry in Tibet provincial region and Chongqing provinces.
Before sample collection, all the human work was approved by the Animal Welfare and Animal Ethics Committee of Sichuan Agricultural University (SKY-S20171006). The patients/participants provided their written informed consent to participate in this study. Before sample collection, all the animal work was approved by the Animal Welfare and Animal Ethics Committee of Sichuan Agricultural University (SKY-S20171006). Written informed consent was obtained from the owners for the participation of their animals in this study.
## Faecal sample collection
Due to the genetic relationship between coyotes and dogs, we also downloaded 18 coyote data at low altitudes for comparative analysis. A total of 152 fecal samples (40 human, 40 dog, 54 wild macaque, and 18 coyote) were enrolled in our diversity study of gut microbiota through a 16S rRNA gene V3–V4 high-throughput sequencing approach. One hundred and nine samples were newly collected in this study, and the data from 25 macaques and 18 coyotes at low altitude were retrieved from previously published studies (SequenceRead Archive number: PRJNA535368, PRJNA528764, and PRJNA528765) (Sugden et al., 2020, 2021; Wu et al., 2020). A total of 7 groups were divided according to altitude and animal species. The number of newly collected samples was determined based on the number of individuals from most groups of macaques, which, through our previous knowledge, had been found to consist mostly of 40–50 individuals, including a certain number of juvenile individuals. Therefore, we planned to collect a sample size of 10–20 adult individuals per population, with the number of samples from humans and dogs also determined with reference to the number of samples from wild macaques. Among them, wild macaque samples were collected from wild populations, including those in Linzhi County of Tibet and Jiangjin County of Chongqing. The dog samples were collected at a Tibetan stray dog shelter and a Ya’an stray dog shelter. Human samples were collected from Linzhou County, Lhasa, Tibet, and Ya’an City, Sichuan Province (Table 1; Figure 1). Through our observation, we found that humans and dogs in Tibet eat a lot of high-protein food every day, such as butter, yak meat, and highland barley, compared with low altitudes in Ya’an City, Sichuan Province, the intake of plant fiber is relatively small. Meanwhile, in high altitude areas, due to the selection pressure of the extreme environments, the human lifestyle is mainly based on natural resources, such as cutting cordyceps under the forest, mushrooms, and grazing yaks, which increases the spatial and temporal overlap with wild animals. In addition, we collected human fecal samples from two different places at the same altitude, and there is no direct kinship between these humans. There is no direct kinship in the same species between high altitude and low altitude. Moreover, the two sampling sites are far away from each other, and there is no close contact between the sampling population. For rhesus macaques sample collection, we choose a continuous period of time in a day to follow the rhesus macaques and take samples through direct observation. Samples with a distance greater than 5 meters are recorded as samples from different individuals. We followed groups of rhesus macaques for as much fresh sample collection as possible in an hour, while guaranteeing that no fewer than 20 fresh samples were collected per population. For the downloaded macaques data, DNA extraction methods, sequencing methods and primers are consistent with this study. Therefore, we have not pursued a comparative discussion. Fecal samples were collected with sterile gloves, put into the sampling box at −20°C, and brought back to the laboratory for storage at −80°C.
## DNA extraction and PCR amplification
Microbial genomic DNA was extracted from fecal samples using a TIANamp Stool DNA kit (Tiangen, Beijing, China). The integrity of the extracted genomic DNA was verified by $1.0\%$ agarose gel electrophoresis. The V3-V4 regions of the bacterial 16S rRNA gene (from 341 to 806) were amplified from extracted DNA using the barcoded primers 341\u00B0F (5′- CCTACGGGNGGCWGCAG −3′) and 806 R (5′ GGACTACNVGGGTATCTAAT-3′) (Fadrosh et al., 2014), with a Biometra TOne 96 G PCR thermocycler (Germany). PCR amplification of the 16S rRNA gene was performed as previously described in Wu et al. [ 2020] (Wu et al., 2020). Specifically, the PCR was performed in a 50-μL reaction system containing 1.5 μL of each primer, 100 ng template DNA, 5 μL 10 × KOD Buffer, 5 μL 2.5 mM dNTPs, and 1 μL KOD polymerase. The PCR conditions consisted of a denaturation step at 95°C for 2 min, and amplification was carried out with 27 cycles at a melting temperature of 98°C for 10 s, an annealing temperature of 62°C for 30 s, and an extension temperature of 68°C for 30 s. The final extension step was performed at 68°C for 10 min. The barcoded PCR products were purified using a DNA gel extraction kit (Axygen, China) and quantified using Quantus™ Fluorometer (Promega, USA) (Wu et al., 2020). The purified amplicons were pooled in equimolar amounts and paired-end sequenced on an Illumina Hiseq PE250 platform, according to the standard protocols by Genedenovo Inc. (Guangzhou, China).
## Processing of sequencing data
Because the downloaded *Coyote data* were v4–v5 regions, we used USEARCH for tiling alignments after processing the sequences, and then used the plug-in “cutadapt” of QIIME2 to remove paired end reads from the primers and truncate the V4 region for subsequent analysis (Hall and Beiko, 2018). The plug-in “DADA2” was used to control sequence quality, correct amplicon errors, and generate ASVs (Callahan et al., 2016). Chimeras were filtered and the ASVs present in at least 2 samples were retained. Based on Silva_ 132 databases, trained a classification classifier against the bacterial V4 region of the 16S rRNA gene, and used this classifier to generate a classification map of out data. The resulting alignment was used for subsequent statistical analysis. Furthermore, the beta diversity distance matrices of the microbial community were calculated and performed by QIIME2.
## Statistical analysis
The alpha diversity Shannon index, ACE index, Simpson index, weighted and unweighted UniFrac distances were calculated by Qiime 2, and the Statistics significance test for each group in R statistical software (version 4.1.3). Linear Discriminant Analysis (LDA) Effect Size (LEfSe) was analyzed and visualized through Galaxy online platform. Venn (VN) map analysis and visualization were done via the online platform EVenn (Chen et al., 2021). Principal Co-ordinates Analysis (PCoA) and neutral community model (NCM) analysis were done by R (Chen et al., 2019), and part of the results visualization was done by the online platform ImageGP (Chen T. et al., 2022).
## Multivariate statistical analysis of gut microbiota diversity
After quality filtering, we obtained 14,331,096 raw reads across 152 fecal samples. The sequences were clustered at $100\%$ sequence identity and 4,320 Amplicon sequence variants (ASVs) were generated. After dilution flattening by the minimum number of sequences, the ACE index (mean ± SD, 445 ± 269), Shannon index (mean ± SD, 3.81 ± 0.97), and Simpson index (mean ± SD, 0.09 ± 0.07) were used to assess the gut microbiota alpha diversity (Figures 2A–C; Supplementary Table S1). The results showed that the gut microbiota diversity of wild macaques in the same altitude environments was significantly higher than that of other species ($p \leq 0.05$). There was no significant difference in the diversity between humans and dogs in the high altitude environments, and there was no significant difference in the ACE index between humans and dogs in the low altitude population, however, the Shannon and Simpson indexes showed that the diversity of humans was significantly higher than that of dogs. A comparison of high altitude populations with low altitude populations of the same species found that the in high altitude environments, the ACE index of three species is significantly higher, the shannon index of dogs and macaques is significantly higher, and the simpson index of dogs is significantly lower ($p \leq 0.05$).
**Figure 2:** *Microbiota alpha diversity analysis of fecal samples among wild macaques, humans and dogs in high altitude and low altitude environments. The alpha diversity among different groups (A) ACE index; (B) Shannon diversity; (C) Simpson diversity. The same letter indicates the difference is not significant (T-test, p > 0.05). HH stands for high altitude humans, HD stands for high altitude dogs, HM stands for high altitude wild macaques, LH stands for low altitude humans, LD stands for low altitude dogs, LM stands for low altitude wild macaques, LW stands for low Coyote.*
VN map analysis found 1,105 core shared ASVs (core microbiota) among wild macaques, humans, and dogs in high altitude environments, which accounted for $45.97\%$, $56.41\%$, and $56.23\%$ of the proportion in the total types in each species, respectively (Figure 3A, Supplementary Table S2). The relative abundances of these core microbiota in wild macaques, humans, and dogs gut microbiota in high altitude environments were $89.16\%$, $92.18\%$, and $95.97\%$, respectively (Figure 3B; Supplementary Table S2). The relative abundance of the core microbiota between humans and dogs is more than $97\%$, however, there were 207 core shared ASVs among wild macaques, humans, and dogs at low altitudes, which accounted for $10.03\%$, $25.71\%$, and $23.58\%$ of the proportion in the total types in each species, respectively (Figure 3C; Supplementary Table S2), and the relative abundances of these core microbiota in the gut microbiota of wild macaques, human, and dogs at low altitude were $27.65\%$, $73.39\%$, and $83.19\%$, respectively (Figure 3D; Supplementary Table S2). In these three subjects, the proportion and relative abundance of the core bacteria types in each subjects were significantly higher in high altitude environments than in low altitude environments ($p \leq 0.05$; Figures 3E–H), indicating that the gut microbiota composition of wild macaques and dogs was significantly more similar to that of humans at high altitude. At the same altitude, comparisons among different species also found that the number of core shared ASVs, the proportion and relative abundance of core bacterial types were higher in high altitude populations than in low altitude populations (Supplementary Table S3). In addition, it was found that the number of species-specific ASVs of wild macaques, humans, and dogs was also higher in high altitude environments than that at low altitude (HH: 1,281, HM: 1,229, HD: 1,409), but the abundance of these specific ASVs was lower, however, the relative abundance of core shared ASVs in high altitude environments of the same species was above $75\%$. The 578 core shared ASVs were identified in humans at high and low altitudes, and the relative abundance of these shared ASVs was $77.54\%$ at high altitudes and $94.95\%$ at low altitudes. There were 1,175 core shared ASVs in wild macaques at high and low altitudes, and the relative abundance of these core shared ASVs was $84.13\%$ at high altitudes and $81.87\%$ at low altitudes. There were 556 core shared ASVs in dogs at high and low altitudes, and the relative abundance of these core shared ASVs was $85.93\%$ at high altitudes and $95.57\%$ at low altitudes. This shows that the core microbiota in the same species is conservative (Supplementary Figure S1).
**Figure 3:** *Venn diagram and percentage histogram of core ASV between different groups. (A, C) Numbers in plots are marked for how many ASVs are in this part. (B, D) The histogram represents the relative abundance of core ASVs. (E) Differential analysis of the number of core shared microbial species as a percentage of the total number of species in the high and low altitude populations(T-test). (F–H) Differential analysis of relative abundances of core shared microbiota between the three species at high and low altitude (T-test). The same letter indicates the difference is not significant (p > 0.05), HH stands for high altitude humans, HD stands for high altitude dogs, HM stands for high altitude wild macaques, LH stands for low altitude humans, LD stands for low altitude dogs, LM stands for low altitude wild macaques.*
The distribution of beta diversity measures (weighted and unweighted UniFrac distances) was compared for the different geographical populations. PCoA was used to show the patterns of separation among different groups. PCoA analysis based on unweighted UniFrac distance shows that distinct clusters were clearly formed between the same species in high altitude and low altitude environments, and the distance between human and dog in the same altitude environments was significantly smaller than that between humans and macaques (Figure 4A). PCoA analysis based on weighted UniFrac distance shows that there is no obvious separation between different groups (Figure 4B). And the comparison between different species at the same altitude shows that the distance between dogs and wolves at low altitude is closer and that between humans and dogs at high altitude is closer (Figure 4B). The Wilcoxon rank sum test, based on weighted and unweighted UniFrac distance among different species at high and low altitudes, also found that the distance among wild macaques, humans, and dogs gut microbiota at high altitudes were significantly lower than that at low altitudes ($p \leq 0.05$, Figures 4C,D), which fully showed that the similarity of gut microbiota composition of wild macaques, humans, and dogs at high altitudes was significantly higher. Comparisons between different species also found that unweighted UniFrac distance between humans and dogs was significantly smaller than between humans and macaques in high altitude and low altitude environments ($p \leq 0.05$, Figures 4E,G). The weighted UniFrac distance between human and dog was significantly smaller than that between humans and macaques in high altitude environments ($p \leq 0.05$, Figure 4F), whereas the opposite was true in low altitude environments ($p \leq 0.05$, Figure 4H). In addition, our PCoA analysis of the gut microbiota compositional structures between dissimilar human sex and ages based on the weighted and unweighted UniFrac distances, in humans, the gut microbiota composition of individuals older than 30 years and younger than 30 years of age was not clearly separated, and adonis analysis also showed that there was no significant difference in gut microbiota composition between individuals older than 30 years and those younger than 30 years of age ($p \leq 0.05$, Supplementary Figures S2A,B). There were also no significant differences between the sexes ($p \leq 0.05$, Supplementary Figures S2C,D).
**Figure 4:** *Microbiota beta diversity analysis of fecal samples among wild macaques, humans, and dogs in high altitude and low altitude environments. (A) PCoA plot using unweighted UniFrac distances dissimilarity based on ASVs in different groups. (B) PCoA plot using weighted UniFrac distances dissimilarity based on ASVs in different groups. (C) Similarity analysis between different species at high and low altitude based on unweighted UniFrac distances in wild macaques, humans, and dogs (Wilcoxon rank sum test). (D) Similarity analysis between different species at high and low altitude based on weighted UniFrac distances in wild macaques, humans, and dogs (Wilcoxon rank sum test). (E,G) Similarity analysis based on unweighted UniFrac distances in between humans and dogs and between humans and macaques (Wilcoxon rank sum test). (F,H) Similarity analysis based on weighted UniFrac distances in between humans and dogs and between humans and macaques (Wilcoxon rank sum test). The same letter indicates the difference is not significant (p > 0.05). HH stands for high altitude humans, HD stands for high altitude dogs, HM stands for high altitude wild macaques, LH stands for low altitude humans, LD stands for low altitude dogs, LM stands for low altitude wild macaques.*
## Taxonomy-based comparisons of gut microbiota
Across all ASVs, the taxonomic analysis identified 29 known bacterial phyla, 184 families, and 478 genera. At the phylum level, the gut microbiota of wild macaques, humans, and dogs are dominated by Firmicutes, Bacteroidetes, Proteobacteria, Fusobacteria, and Actinobacteria (average relative abundance >$1\%$, Figure 5A). At the family level, the predominant bacterial families isolated were Prevotellaceae, Ruminococcaceae, Lachnospiraceae, Lactobacillaceae, and Veillonellaceae (mean relative abundance >$5\%$; Figure 5B). At the genus level, the predominant bacterial genera isolated were Prevotella 9, Lactobacillus, Fusobacterium, Bacteroides, *Clostridium sensu* stricto 1, and Faecalibacterium (mean relative abundance >$3\%$; Figure 5C).
**Figure 5:** *Gut microbiota taxonomic composition. Composition of gut microbiota among different groups at (A) phylum level, (B) family level and (C) genus level. The same letter indicates the difference is not significant (p > 0.05). HH stands for high altitude humans, HD stands for high altitude dogs, HM stands for high altitude wild macaques, LH stands for low altitude humans, LD stands for low altitude dogs, LM stands for low altitude wild macaques, LW stands for low Coyote.*
To further characterize the microbiota in the gut of different species, we performed LEfSe analysis (LDA > 2, $p \leq 0.05$) of the relative abundances at the genus level of the gut microbiota of different species in the high- and low altitude environments and found that the Prevotella 7, Roseburia, Agathobacter, Bacteroides, Faecalibacterium, Lachnoclostridium, Metagenome, Ruminococcaceae UCG 003, Parasutterella, Alistipes, Parabacteroides, and [Ruminococcus] torques group were significantly more abundant in the human gut microbiota than wild macaques and dogs, and were significantly high altitude indicative (Figures 6A,B). The abundance of Ruminococcaceae UCG 002, Ruminococcaceae UCG 010, Rikenellaceae RC9 gut group, Ruminococcaceae UCG 013, Treponema 2, Anaerovibrio, Ruminococcaceae UCG 005, Succinivibrio, Christensenellaceae R 7 group, Prevotellaceae NK3B31 group, Ruminococcaceae NK4A214 group, Ruminococcaceae UCG 014, [Eubacterium] coprostanoligenes group, and CAG 873 in the gut microbiota of wild macaques was significantly higher than that of humans and dogs and had a significant species indicator effect (Figures 6A,B). The Megasphaera, Megamonas, Turicibacter, Cetobacterium, Collinsella, Holdemanella, Sarcina, and [Ruminococcus] gnavus group are significantly more abundant in the gut microbiota of dogs than those of humans and wild macaques and have a significant indicator effect (Figures 6A,B). In addition, LEFSe analysis of the relative abundance of gut microbiota composition of humans, wild macaques, and dogs in high altitude and low altitude environments showed that the abundance of Actinobacillus, Alloprevotella, Anaerobiospirillum, Prevotella 2, Staphylococcus, Sutterella, and Veillonella in high altitude environments was significantly higher than that in low altitude environments and had significant high altitude environments indicators (Figures 6C–E), however, in the low altitude environments, no indicator microbiota with significantly higher relative abundance was found in the three species.
**Figure 6:** *Heatmap showing the genus level LEFSe test (LDA > 2, p < 0.05) of gut microbiota among different species in the same altitude environments and between different altitude environments in the same species. (A) Comparison among three species at high altitude, (B) Comparison among three species at low altitude, (C) Comparison between high altitude and low altitude of humans, (D) Comparison between high altitude and low altitude of macaques, (E) Comparison between high altitude and low altitude of dogs. HH stands for high altitude humans, HD stands for high altitude dogs, HM stands for high altitude wild macaques, LH stands for low altitude humans, LD stands for low altitude dogs, LM stands for low altitude wild macaques, LW stands for low Coyote.*
## Gut microbiota community assembly process measurement
The analysis of their gut microbiota community assembly structure by NCM showed that the wild macaques, humans, and dogs gut microbiota in both high- and low altitude environments showed moderate fit to the neutral model (Figure 7). The goodness of fit of the models across species was in the following order for high- and low altitude populations: wild macaques (HM: R2 = 0.766, LM: R2 = 0.64) > humans (HH: R2 = 0.673, LH: R2 = 0.63) > dogs (HD: R2 = 0.576, LD: R2 = 0.466). Meanwhile, the fit of wild macaques, humans, and dogs gut microbiota in high altitude environments was higher than that in low altitude environments. This illustrates that the gut microbiota of wild macaques mostly influenced by stochastic processes, whether in high- or low altitude environments. The wild macaques, humans, and dogs gut microbiota communities in high altitude environments are all more influenced by stochastic processes than in low altitude environments. In addition, the product of metacommunity size and migration rate (Nm) value related to the gut microbiota community diffusion coefficient shows that wild macaques (HH: 764, HM: 1,257, HD: 1,139; LH: 136, LM: 466, LD: 171) are the diffusion coefficient largest in high altitude environments, followed by dogs and humans. Meanwhile, wild macaques, humans, and dogs in high altitude environments are higher diffusion coefficients of gut microbiota than those in low altitude environments. The migration rate ‘m’ of wild macaques, humans, and dogs in high altitude environments was significantly higher than that in low altitude environments (Figure 7).
**Figure 7:** *Quantitative results of the random process of gut microbiota community assembly in different groupings based on NCM. The solid black line represents the best fit to the NCM, and the dashed black line represents the 95% confidence interval around the model prediction. ASVs that occur more frequently or less frequently than NCM predictions are shown in different colors. “Nm” indicates the diffusion coefficient, and “Rsqr” indicates the fit to the model. HH stands for high altitude humans, HD stands for high altitude dogs, HM stands for high altitude wild macaques, LH stands for low altitude humans, LD stands for low altitude dogs, LM stands for low altitude wild macaques.*
## Discussions
Host diet and phylogeny are two major factors that affect the composition and structure of gut microbiota (Groussin et al., 2017; Youngblut et al., 2019). In terms of phylogenetic relationship and morphology, the phylogenetic relationship between humans and wild macaques is closer, and the phylogenetic relationship between wolves and dogs is closer (dos Reis et al., 2012; Wang et al., 2013). Due to the early domestication of dogs by humans, the contact with humans is closer and the diet similarity is higher (Wang et al., 2013). In this study, the beta diversity analysis of different species at the same altitude found that the similarity between humans and dogs was greater than that between humans and wild macaques in gut microbiota compositional diversity, while low altitude dogs are more similar to wolves. Core shared microbiota analysis also found that the relative abundance of core shard microbiota was highest between humans and dogs, followed by between wolf and dogs, then finally between humans and wild macaques. Beta diversity analysis found that in high altitude environments, the weighted and unweighted UniFrac distance between humans and dogs is significantly smaller than that between humans and macaques ($p \leq 0.05$), and in low altitude environments, the unweighted UniFrac distance between humans and dogs is also significantly smaller than that between humans and macaques($p \leq 0.05$). This is consistent with the results obtained by Coelho et al. [ 2018] through metagenomics studies in humans, dogs, mice, and pigs (Coelho et al., 2018). These results show that in the same altitude environments, similar diets promote the convergence of gut microbiota of dogs and humans. Meanwhile, our results also showed that the influence of genetic relationships on the composition of gut microbiota among different species seemed weak. This also corroborates findings in vertebrates that gut microbiota compositional differences between species are positively correlated with host evolutionary divergence times and that gut microbiota composition is more similar within host species than between species (Moeller et al., 2017; Song et al., 2020; Dillard et al., 2022). In addition, in this study, alpha diversity analysis found that the gut microbiota diversity of wild macaques with a more complex diet is much higher than that of humans and dogs with simpler diets ($p \leq 0.05$), which indicates that the gut microbiota composition of wild macaques has a higher diversity. During the sampling period, we observed that the diets of humans and dogs were similar, consisting mainly of rice, noodles, meat, fruits, and vegetables, however, wild macaques mainly eat leaves and fruits with a higher content of cellulose and lignin, which may provide additional resources to increase the diversity of the gut microbiota. Wild macaques living in wild natural environments (e.g., soil, larger scale, seasonality, social interactions) are also exposed to a more diverse microbial community compared to humans and dogs (Raulo et al., 2018; Trosvik et al., 2018). Limited by neutral dispersal, the more environmental microbial species the host is in contact with, the more likely the microbial species remain in the host (Burns et al., 2016; Clayton et al., 2018; Ross et al., 2018), therefore, the gut microbiota of wild macaques is more alpha diverse than that of humans and dogs.
The mammalian gut microbiota is shaped by the dispersal of organisms into habitats, followed by natural selection (i.e., habitat filtration), drift, and in situ diversification (Vellend, 2010). A comparison of mammalian phylogenies suggests that differences in selective pressures between the intestinal environments of mammalian species contribute to the diversification of the gut microbiota (Moeller et al., 2017). Geographic proximity and predator–prey interactions enable gut microbiota to flow between distantly related host species, resulting in the convergence of gut microbiota belonging to carnivorous and herbivorous mammals of different taxonomic purposes (Moeller et al., 2017). Studies have also found that drift and selection in the environment will also affect the assemblage of gut microbiota that inhabit animals (Trosvik et al., 2018), however, the low temperature, low oxygen, and high ultraviolet intensity in a high altitude environments pose a great challenge to the survival of animals (Simonson et al., 2010; Yu et al., 2016; Zhu et al., 2021). Previous studies have shown that in the long-term hypoxic environment, the Tibetan genotype changes toward environmental adaptability (Beall, 2011), while also driving changes in the composition structure of the gut microbiota. The more compositionally diverse gut microbiota is also able to promote the stability of the gut micro-ecosystem, increase the rate of dietary fermentation of the host, and help the host adapt to the high altitude environments (Li and Zhao, 2015; Zhang et al., 2016). Similar results were obtained in our study. The alpha diversity showed that the ACE index of humans, wild macaques, and dogs gut microbiota in high altitude environments was significantly higher than that in low altitude environments. However, there is no significant difference between the Simpson index of human and macaque in the high altitude environments and the low altitude environments, and there is no significant difference between the Shannon index of human in the high altitude environments and the low altitude environments. These results show that there are more species of gut microbiota in humans, wild macaques, and dogs at high altitude, but some unique microbiota in humans and wild macaques only exist in a few individuals. Beta diversity results showed that both weighted and unweighted UniFrac distances of wild macaques, humans, and dogs were significantly smaller in high altitude environments than in low altitude populations ($p \leq 0.05$), and the similarity of gut microbiota composition was significantly higher than that in low altitude environments. Core shared microbiota analysis also found that the species ratio and relative abundance of the core microbiota of each subject were significantly higher in high altitude environments than in low altitude environments ($p \leq 0.05$). These results strongly indicate that the convergence and sharing of gut microbiota among wild macaques, humans, and dogs are more significant in the high altitude environments, which strongly drives the convergence and adaptation of gut microbiota among wild macaques, humans, and dogs. In addition, beta diversity analysis found that the clustering of gut microbiota among the three species at high and low altitudes was more obvious in unweighted UniFrac distance than in weighted UniFrac distance. At high altitude, the weighted and unweighted UniFrac distance between humans and dogs was significantly lower than that between humans and macaques ($p \leq 0.05$). Core shared microbiota analysis also found that the relative abundance of human and dog core microbiota reached more than $89\%$ in low altitude environments and $97\%$ in high altitude environments. These results suggest that the contribution of altitude to the convergent adaptation of the gut microbiota of wild macaques, humans, and dogs to high altitude environments is mainly reflected in the compositional diversity of the microbiota, which indirectly affects the core shared microbiota abundance among different species. And at high altitude environments, similar diets promote their further convergent in terms of diversity and abundance.
NCM analysis also found that the extent of gut microbiota dispersal and influence by stochastic factors were higher in the high altitude wild macaque, human, and dog populations. This may be due to the fact that the lifestyle of human beings in the high altitude areas we sampled is mainly based on grazing and under forest collection, and the grazing area and under forest collection area are also the main habitats of wild macaques. The dog samples are from the captive populations of local residents, which have a high degree of niche overlap. As a result, the contact among species is closer than that of low altitude populations, and the selection pressure of low temperature, low oxygen, and high-intensity ultraviolet rays in a high altitude environments jointly leads to the diffusion, migration, and fusion of gut microbiota among different species. At high altitude, the stress of extreme environments, and natural resource dependent life patterns of humans (grazing and understory harvesting), are closely related and are also important factors driving the convergence of gut microbiota from wild macaques, humans, and dogs at high altitude.
Taxonomic composition analysis found that Firmicutes and Bacteroidetes were dominant among the three mammalians’ gut microbiota, which was consistent with previous findings (Duncan et al., 2008; Fogel, 2015). The LEFSe analysis showed that the abundance of Actinobacillus, Alloprevotella, Anaerobiospirillum, Prevotella 2, Staphylococcus, Sutterella, and Veillonella in wild macaques, humans, and dogs in the high altitude environments is significantly higher than that in the low altitude environments and has a significant role in indicating high altitude environments, however, in the low altitude environments, no indicator microbiota with a significantly higher relative abundance was found in the three species. Previous studies have found that *Actinobacillus is* significantly positively correlated with systolic blood pressure in blood pressure regulation (Chen B. Y. et al., 2022). Alloprevotella can utilize carbohydrates and undergo fermentation to produce acetate and succinate, two major end metabolites (Xiao et al., 2013), while also having a cardiovascular risk-reducing effect (Kong et al., 2019). However, altitude has a positive linear relationship with systolic blood pressure (SBP) and an important effect on host blood pressure (Aryal et al., 2016). These results indicates that the high abundance of Actinobacillus and Alloprevotella can help the host regulate blood pressure and adapt to the high altitude hypoxic environment. Anaerobiospirillum is isolated from the feces of dogs and cats. It can use glucose metabolism to produce succinic and acetic acids but may also form trace lactic and formic acids (Davis et al., 1976). Prevotella is a probiotic widely distributed in the gut of animals, which helps to decompose protein and carbohydrates (Davis et al., 1976; Kovatcheva-Datchary et al., 2015). Sutterella was confirmed to be associated with obesity in mice (Liu et al., 2018). This indicates that these microbiotas were significantly more abundant in the gut of wild macaques, humans, and dogs at high altitudes, which will promote the host to digest and decompose food and produce energy substances, and help the host to adapt to the high energy demand in the high altitude environments. Veillonella is a kind of microbiota that can enhance performance, using lactic acid as a carbon source, it can quickly decompose lactic acid into propionic acid, thereby reducing the concentration of lactic acid and improving sports performance (Scheiman et al., 2019). The Veillonella in high abundance is able to improve host tolerance, prompting its adaptation to high altitude environments. Staphylococcus, were found in the Berry, typically causes surgical and skin infections, respiratory diseases, and food poisoning (Licitra, 2013), however, the reasoning as to why the abundance of *Staphylococcus in* the intestines of wild macaques, humans, and dogs at high altitudes is significantly higher than that at low altitudes needs to be revealed. These results indicate that these common characteristic bacteria play an important role in the adaptation of wild macaques, humans, and dogs to high altitude environments such as energy compensation and hypoxia adaptation.
In conclusion, our results show that diet and high altitude strongly drive convergent adaptation of gut microbiota in wild macaques, humans, and dogs to high altitude environments. Among them, the contribution of high altitude environments to the convergent adaptation of the gut microbiota of wild macaques, humans, and dogs are mainly reflected in the compositional diversity of the microbiota, which indirectly affects the core shared microbiota abundance among different species. And at high altitude environments, similar diets promote their further convergent in terms of diversity and abundance. Meanwhile, the convergence of intestinal microbiota in animals at high altitudes may be partly due to microbial diffusion between hosts. In addition, the microbiota is significantly enriched in wild macaques, humans, and dogs from high altitude environments and plays an important role in the hosts energy compensation and cardiovascular regulation and helping the host adapt to the high energy demand and low oxygen pressure of high altitude environments.
## Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, PRJNA760678.
## Author contributions
HuX, JZ, and YY designed the experiment and wrote the first draft. MD, HoX, SY, YX, DL, MX, QN, and MZ collected the fecal samples and performed preliminary preparation. All authors have helped in revision and approved the final manuscript.
## Funding
This work was supported by the National Natural Science Foundation of China under Grant [31870355].
## 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/fmicb.2023.1067240/full#supplementary-material
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|
---
title: 'Primary care providers'' knowledge, attitudes, and practices related to prediabetes
in China: A cross-sectional study'
authors:
- Linhua Pi
- Jianru Yan
- Dongxue Fei
- Ying Zheng
- Xiajie Shi
- Zhen Wang
- Zhiguang Zhou
journal: Frontiers in Public Health
year: 2023
pmcid: PMC9995854
doi: 10.3389/fpubh.2023.1086147
license: CC BY 4.0
---
# Primary care providers' knowledge, attitudes, and practices related to prediabetes in China: A cross-sectional study
## Abstract
### Background
The management of prediabetes has great clinical significance, and primary care providers (PCPs) play important roles in the management and prevention of diabetes in China. Nevertheless, little is known about PCPs' knowledge, attitudes, and practices (KAP) regarding prediabetes. This cross-sectional study aimed to assess the KAP regarding prediabetes among PCPs in the Central China region.
### Methods
This cross-sectional study was conducted using self-administered KAP questionnaires among PCPs from Central China region.
### Results
In total, 720 PCPs completed the survey. Most physicians ($85.8\%$) claimed to be aware of the adverse effects of prediabetes and reported positive attitudes toward prediabetes prevention, but the PCPs' knowledge of prediabetes and management practices showed substantial gaps. The prediabetes knowledge level and practice subscale scores of the PCPs were only $54.7\%$ and $32.6\%$, respectively, of the corresponding optimal scores. Female PCPs showed higher prediabetes knowledge level scores ($$p \leq 0.04$$) and better practice scores ($$p \leq 0.038$$). Knowledge and attitude scores were inversely correlated with participants' age and duration of practice ($p \leq 0.001$). The PCPs who served in township hospitals had significantly higher knowledge and attitude scores than those who served in village clinics ($p \leq 0.001$). Furthermore, knowledge and practice scores increased with increasing professional titles. Recent continuing medical education (CME) attendance had a significant positive influence on knowledge of prediabetes ($$p \leq 0.029$$), but more than four-fifths of the surveyed PCPs did not participate in diabetes-related CME in the past year.
### Conclusions
Substantial gaps were observed in PCPs' knowledge and practices regarding prediabetes in the Central China region. CME programmes were under-utilized by PCPs. Structured programmes are required to improve PCPs' prediabetes-related knowledge and practices in China.
## Introduction
Prediabetes is a borderline glycaemic status presenting with impaired glucose metabolism that does not meet the diagnostic criteria for diabetes. According to the recent guidelines from the American Diabetes Association (ADA), prediabetes is defined by fasting plasma glucose (PG) level of 5.6–6.9 mmol/l, a 2-h PG level of 7.8–11.0 mmol/l in a 75-g oral glucose tolerance test (OGTT), and a hemoglobin A1c level of $5.7\%$−$6.4\%$ [1]. A recent national survey indicated that the prevalence of prediabetes was $35.2\%$ among adults living in China; thus, approximately 357 million Chinese have prediabetes [2]. Furthermore, prediabetes is associated with a high risk of progression to overt type 2 diabetes [3] and may confer an increased risk of premature mortality [4, 5] and multiple chronic complications observed in established diabetes [6, 7]. Numerous studies have confirmed that lifestyle interventions and medications are effective in delaying or preventing the development of diabetes (8–10).
Despite the success of preventive interventions, nearly $90\%$ of individuals with prediabetes are not informed of their conditions by health providers [11] and most American patients with prediabetes do not receive appropriate interventions [12, 13]. The situation in China may be worse, especially in the rural regions. Primary care physicians (PCPs) from townships or village doctors play a vital role in diabetes prevention by screening for and managing prediabetes. Thus, PCPs' knowledge of prediabetes and their attitudes and practices related to this condition are essential elements of prediabetes management. Previous studies conducted in the US [14, 15] and Latin America [16] indicated significant gaps in the knowledge of prediabetes among PCPs as well as inadequate detection and treatment of this condition by PCPs. Thus, understanding PCPs' knowledge, attitudes, and practices (KAP) related to prediabetes can facilitate the development of tailored strategies to improve knowledge, change attitudes, and address poor practices. To the best of our knowledge, PCPs' KAP related to prediabetes in China has not been described to date. Therefore, this study aimed to assess PCPs' knowledge of prediabetes as well as their attitudes toward prediabetes and prediabetes care practices in the Central China region. We also aimed to evaluate the association between provider characteristics and prediabetes-related KAP.
## Methods
This cross-sectional study was conducted between May and August 2022. Letters of invitation were sent to PCPs from township hospitals or village doctors across Yueyang, Huaihua, and Yongzhou cities in Hunan Province, Central South China. Those who agreed to participate were provided with a questionnaire through an online platform in China, which provided functions equivalent to Amazon Mechanical Turk. This study was approved by the Ethics Committee of the Second Xiangya Hospital of Central South University, and written consent was obtained from each participant.
The self-administered questionnaire included multiple-choice and open-ended questions evaluating participants' prediabetes KAP and was developed in line with the concepts proposed by a Johns Hopkins University group [15]. A pre-test was conducted among 10 PCPs to test the reliability and improve the clarity and interpretability of the questionnaire. The questionnaire consisted of two sections. The first section focused on PCPs' sociodemographic characteristics (sex and age), practice setting (township hospital or village clinic), physician seniority level, time since graduation in general medicine, history of diabetes in a first-degree relative, and experience of continuing medical education (CME) programmes related to diabetes in the past year. The second section was developed according to the latest ADA criteria and the Chinese Diabetes Society guidelines released in 2020 [17]. It consisted of 16 questions on the participants' prediabetes-related knowledge (6 questions), attitudes (6 questions), and practices (4 questions). Questions related to knowledge and practice were evaluated using a two-point scale (1 = correct, 0 = false, and not sure). The questions related to attitude used a five-point Likert scale (1 = positive attitude, 0 = negative practice, or uncertain). Additionally, we asked providers to select what they considered significant challenges to lifestyle modification from a list of potential barriers drawn from prior studies regarding similar topics [14, 16] and asked them to list other possible potential barriers they encountered in daily clinical practice.
## Statistical analyses
Data were extracted from the questionnaires and analyzed using SPSS version 25.0 (IBM Corporation, Chicago, IL, USA). Descriptive data were presented as numbers, percentages, means, and standard deviations, depending on whether the variables were categorical or continuous. The association between PCPs' characteristics and respondents' KAP was evaluated by Chi-square tests and t-tests, as appropriate. Multiple linear regression models were used to examine the association between the predictor variables and the KAP scores. Statistical significance was set at $P \leq 0.05.$
## Sociodemographic and other characteristics of the participants
This study enrolled 720 PCPs from the three cities in Hunan Province, Central China. The characteristics of the study participants are summarized in Table 1. The mean age of the participants was 44.7 ± 9.8 years. Among the participants, $65.5\%$ were male, and 260 and 460 physicians worked in township hospitals and village clinics, respectively. The physicians involved in this study included 630 resident, 79 attending, and 11 senior physicians. Among them, 117 had practiced for < 10 years, 124 for 10–20 years, and 479 for >20 years. The majority ($73.1\%$) of respondents had not participated in CME programmes related to diabetes in the past year, and 113 respondents had a family history of diabetes.
**Table 1**
| Provider characteristics | Mean ±SD | Number (n) | Percentage (%) |
| --- | --- | --- | --- |
| Sex | Sex | Sex | Sex |
| Male | | 472 | 65.5% |
| Female | | 248 | 34.5% |
| Age (years) | 44.7 ± 9.8 | | |
| ≤ 40 | | 218 | 30.3% |
| >40 | | 502 | 69.7% |
| Practice setting | Practice setting | Practice setting | Practice setting |
| Township hospital | | 260 | 36.1% |
| Village clinic | | 460 | 63.9% |
| Professional titles | Professional titles | Professional titles | Professional titles |
| Resident physicians | | 630 | 87.5% |
| Attending physicians | | 79 | 11.0% |
| Senior physicians | | 11 | 1.5% |
| Duration of practice (years) | 22.7 ± 11.7 | | |
| < 10 years | | 117 | 16.3% |
| 10–20 years | | 124 | 17.2% |
| ≥20 years | | 479 | 66.5% |
| CME attendance during the past year | CME attendance during the past year | CME attendance during the past year | CME attendance during the past year |
| No | | 526 | 73.1% |
| Yes | | 194 | 26.9% |
| Positive family history of diabetes | Positive family history of diabetes | Positive family history of diabetes | Positive family history of diabetes |
| Yes | | 113 | 15.7% |
| No | | 607 | 84.3% |
## Physicians' knowledge regarding prediabetes
The participants were asked if they knew what prediabetes was and to identify the risk factors for diabetes. While $86.5\%$ had adequate knowledge of the definition of prediabetes, $71.9\%$ had sufficient knowledge of the risk factors for diabetes. However, the PCPs generally lacked knowledge of glycaemic cut-offs for the diagnosis of prediabetes, and only $50.4\%$, $56.4\%$, and $26.1\%$ could identify the correct glycaemic cut-offs for diagnosing prediabetes based on the fasting glucose level, 2-h PG level during OGTT, and hemoglobin A1c level, respectively. Overall, only 62 of the 720 PCPs ($8.6\%$) correctly identified all three glycaemic cut-off values for the diagnosis of prediabetes, while 116 PCPs ($16.1\%$) were unaware of all three cut-off values. Less than half of the PCPs ($37.5\%$) chose $5\%$−$7\%$ as the recommended minimum amount of weight loss (Table 2).
**Table 2**
| Item | Correct choice | Score [0–1] |
| --- | --- | --- |
| | n (%) | Mean ±SD |
| Is prediabetes an intermediate stage between normal glycemia and diabetes? | 623 (86.5%) | 0.87 ± 0.34 |
| Which one is not a risk factor in prediabetes screening? | 518 (71.9%) | 0.72 ± 0.45 |
| Which one is the prediabetes laboratory criteria for fasting glucose level? | 363 (50.4%) | 0.50 ± 0.50 |
| Which one is the prediabetes laboratory criteria for the 2-h PG level during OGTT? | 406 (56.4%) | 0.56 ± 0.50 |
| Which one is the prediabetes laboratory criteria for HbA1c level | 188 (26.1%) | 0.26 ± 0.44 |
| Which one is the correct body weight loss recommendation for individuals with prediabetes? | 270 (37.5%) | 0.37 ± 0.48 |
| Total | 2,361 (54.7%) | 3.29 ± 1.19 |
## Physicians' attitudes toward prediabetes
Regarding attitudes toward prediabetes, more than $90\%$ of the respondents deemed that prediabetes increased the risk of diabetes development and premature mortality, and more than $80\%$ reported that most participants with prediabetes were unaware of their condition. Meanwhile, the majority of participants showed a positive attitude toward prediabetes prevention; $78.9\%$ thought prediabetes was reversible through regular exercise and drugs (metformin) (Table 3).
**Table 3**
| Item | Positive attitude | Score [0–1] |
| --- | --- | --- |
| | n (%) | Mean ±SD |
| Prediabetes is associated with a high risk of progression to overt type 2 diabetes | 662 (91.9%) | 0.92 ± 0.27 |
| Prediabetes is associated with an increased risk of premature mortality | 654 (90.8%) | 0.91 ± 0.28 |
| Most individuals with prediabetes have not been diagnosed | 603 (83.8%) | 0.84 ± 0.37 |
| Prediabetes is reversible | 568 (78.9%) | 0.79 ± 0.41 |
| Regular exercise helps delay or prevent the transition from prediabetes to diabetes | 685 (95.1%) | 0.95 ± 0.22 |
| Metformin helps delay or prevent the transition from prediabetes to diabetes | 533 (74.0%) | 0.74 ± 0.44 |
| Total | 3,705 (85.8%) | 5.15 ± 1.12 |
## Physicians' prediabetes-related practices
The overall practice score was low. Diet changes and physical activity were the most frequently employed methods for prediabetes management ($90.1\%$), followed by repeat laboratory work and follow-up clinic visits for individuals with prediabetes. Fifty ($6.9\%$) and 177 ($24.6\%$) of the surveyed PCPs identified the right choices recommended by the ADA guidelines and Chinese expert consensus, respectively. Only 59 ($8.2\%$) PCPs prescribed drugs (metformin, acarbose, etc.) to patients who failed to respond to lifestyle modification (Table 4).
**Table 4**
| Item | Correct choice | Score [0–1] |
| --- | --- | --- |
| | n (%) | Mean ±SD |
| Which one is your initial suggestion for individuals with prediabetes? | 653 (90.1%) | 0.91 ± 0.29 |
| How long do you recommend individuals with prediabetes to repeat laboratory work? | 50 (6.9%) | 0.07 ± 0.25 |
| How long do you recommend individuals with prediabetes to return for follow-up clinic visit? | 177 (24.6%) | 0.25 ± 0.43 |
| Management of a patient who failure to respond to lifestyle modification | 59 (8.2%) | 0.08 ± 0.27 |
| Total | 939 (32.6%) | 1.30 ± 0.66 |
## Barriers to lifestyle modification
At the time of the survey, most PCPs selected lack of recognition of the harm posed by prediabetes ($94.0\%$), lack of diet and exercise guidance ($91.4\%$), uncertainty of the effectiveness of lifestyle modifications ($88.5\%$), and lack of motivation ($82.2\%$) as barriers to lifestyle modification. Additionally, 237 ($23.9\%$) PCPs listed a lack of resources or financial limitations as a potential barrier in the open-ended question.
## Correlations among KAP scores
The total mean scores for knowledge, attitude, and practice were 3.29, 5.15, and 1.30, respectively, out of possible scores of 6, 6, and 4, respectively. The knowledge and attitude scores showed a weak positive correlation ($$p \leq 0.001$$, $r = 0.127$), similar to the knowledge and practice scores ($$p \leq 0.004$$, $r = 0.1107$).
## Factors associated with the overall KAP scores
The associations between the KAP scores and provider characteristics are presented in Table 5. Female PCPs reported significantly more prediabetes-related knowledge and practices than males. They also reported high mean attitude scores; however, the differences were not statistically significant. The knowledge and attitude scores varied significantly according to age; the lower age group showed higher scores, but with the practice score, the trend was reversed even though the difference was not statistically significant. Working in township hospitals was a significant predictor of higher knowledge and attitude scores.
**Table 5**
| Provider characteristics | Unnamed: 1 | Knowledge score | Knowledge score.1 | Attitude score | Attitude score.1 | Practice score | Practice score.1 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | n | Mean (SD) | p | Mean (SD) | p | Mean (SD) | p |
| Sex | Sex | Sex | Sex | Sex | Sex | Sex | Sex |
| Male | 472 | 3.20 ± 1.16 | 0.04 | 5.10 ± 1.15 | 0.477 | 1.27 ± 0.65 | 0.038 |
| Female | 248 | 3.47 ± 1.16 | | 5.16 ± 1.10 | | 1.38 ± 0.69 | |
| Age (years) | Age (years) | Age (years) | Age (years) | Age (years) | Age (years) | Age (years) | Age (years) |
| ≤ 40 | 218 | 3.57 ± 1.16 | < 0.001 | 5.40 ± 1.10 | < 0.001 | 0.65 ± 0.44 | 0.288 |
| >40 | 502 | 3.17 ± 1.18 | | 5.03 ± 1.11 | | 0.67 ± 0.30 | |
| Practice setting | Practice setting | Practice setting | Practice setting | Practice setting | Practice setting | Practice setting | Practice setting |
| Township hospital | 260 | 3.55 ± 1.23 | < 0.001 | 5.45 ± 1.03 | < 0.001 | 1.35 ± 0.67 | 0.202 |
| Village clinic | 460 | 3.14 ± 1.14 | | 5.0 ± 1.13 | | 1.28 ± 0.66 | |
| Professional titles | Professional titles | Professional titles | Professional titles | Professional titles | Professional titles | Professional titles | Professional titles |
| Resident physicians | 630 | 3.23 ± 1.16 | 0.001 | 5.12 ± 3.21 | 0.473 | 1.31 ± 0.68 | 0.041 |
| Attending physicians | 79 | 3.72 ± 1.31 | | 5.26 ± 0.97 | | 1.20 ± 0.49 | |
| Senior physicians | 11 | 3.82 ± 1.08 | | 5.36 ± 0.67 | | 1.73 ± 0.90 | |
| Duration of practice (years) | Duration of practice (years) | Duration of practice (years) | Duration of practice (years) | Duration of practice (years) | Duration of practice (years) | Duration of practice (years) | Duration of practice (years) |
| < 10 years | 117 | 3.63 ± 1.12 | < 0.001 | 5.33 ± 1.14 | < 0.001 | 1.36 ± 0.65 | 0.265 |
| 10–20 years | 124 | 3.43 ± 1.20 | | 5.42 ± 0.92 | | 1.36 ± 0.65 | |
| ≥20 years | 479 | 3.17 ± 1.18 | | 5.02 ± 1.15 | | 1.28 ± 0.67 | |
| CME attendance | CME attendance | CME attendance | CME attendance | CME attendance | CME attendance | CME attendance | CME attendance |
| No | 526 | 3.23 ± 1.19 | 0.029 | 5.22 ± 1.09 | 0.274 | 1.29 ± 0.65 | 0.448 |
| Yes | 194 | 3.45 ± 1.17 | | 5.12 ± 1.13 | | 1.34 ± 0.71 | |
| Positive family history of diabetes | Positive family history of diabetes | Positive family history of diabetes | Positive family history of diabetes | Positive family history of diabetes | Positive family history of diabetes | Positive family history of diabetes | Positive family history of diabetes |
| Yes | 113 | 3.48 ± 1.20 | 0.066 | 5.16 ± 1.13 | 0.339 | 1.40 ± 0.66 | 0.101 |
| No | 607 | 3.25 ± 1.18 | | 5.05 ± 1.06 | | 1.29 ± 0.66 | |
Professional titles also showed statistically significant relationships with knowledge scores. The scores increased with increasing professional titles. The duration of practice was significantly associated with the knowledge and attitudes toward prediabetes. Respondents in the group with < 10 years of experience showed the highest knowledge score, whereas respondents in the group with 10–20 years of experience showed the highest attitude score. CME attendance during the past year had a significant positive influence on knowledge of prediabetes.
The joint effect of provider characteristics on KAP scores was evaluated using multiple regression analysis. Professional title was a significant provider characteristic that predicted the knowledge level of participants. Significant provider characteristics that predicted the attitude of participants were sex and the practice setting. However, none of the factors included in our study could predict the practice scores of the participants.
## Discussion
To improve the management of diabetes and other chronic diseases, the Chinese government implemented the National Basic Public Health Services (BPHS) programme in 2009 [18]. PCPs play a crucial role in this programme. The BPHS programme offers blood glucose tests, blood pressure measurements, lifestyle consultations, and medical instructions for people with diabetes. To provide a practical tool for doctors, especially for PCPs and general practitioners, The Chinese Diabetes Society released an expert consensus regarding prediabetes in 2020 [17]. However, little is known about the competence of PCPs to perform this role. We conducted the first KAP study on prediabetes among Chinese PCPs from the Central China region. In the present study, most physicians ($85.8\%$) claimed to be aware of the harmful effects of prediabetes and reported positive attitudes toward prediabetes prevention, but a substantial gap was noted in PCPs' knowledge of prediabetes and management practices. The PCPs' knowledge level for prediabetes was $54.7\%$ of the optimal level, while their practice subscale score was $32.6\%$ of the optimum score. Our findings showed that female PCPs had higher levels of prediabetes knowledge and better practices. The knowledge and attitude scores were inversely correlated with the participants' age and duration of practice. PCPs who served in township hospitals had significantly higher knowledge and attitude scores than those who served in village clinics. Furthermore, knowledge and practice scores increased with increasing professional titles. We also found that CME attendance in the past year had a significant positively influence on knowledge of prediabetes.
Our study revealed a suboptimal level of prediabetes knowledge regarding diagnostic criteria among PCPs, which may lead to low rates of identification of patients with prediabetes. Only 62 of the 720 PCPs ($8.6\%$) correctly identified all three glycaemic cut-off values for the diagnosis of prediabetes, and $16.1\%$ were not aware of any of the glycaemic cut-off values for prediabetes diagnosis. Our findings were comparable with those obtained by other researchers in America [14, 15] and Latin America [16] who also reported large gaps in prediabetes knowledge among PCPs. While ~$90\%$ of individuals with prediabetes in the US are not informed of their conditions by healthcare providers [11], the situation in *China is* expected to be much worse. The low awareness of prediabetes could be partly attributed to the lack of optimal knowledge of prediabetes among PCPs. We also found that PCPs lacked knowledge of evidence-based recommendations for prediabetes. Weight loss is the most important factor in reducing the risk of incident diabetes, and evidence suggests that $5\%$−$7\%$ weight loss is sufficient to achieve this goal (19–21). Knowledge of this weight-loss target is essential for providing lifestyle consultations for patients with prediabetes. Previous studies have demonstrated that most patients with prediabetes do not receive appropriate interventions [12, 13]. In our study, only $37.5\%$ of respondents correctly identified the guideline recommendations for weight loss; addressing this knowledge gap may improve the management of prediabetes. We also observed that PCPs with a younger age and a shorter duration of practice possessed a higher level of knowledge than their counterparts. A previous study also showed that the diabetes knowledge of Iranian internists was inversely correlated with the participants' age and duration of practice [22]. This could be attributed to the fact that younger PCPs are more likely to keep abreast with the current trends in prediabetes. An important finding of our study was that participation in CME regarding diabetes could significantly improve PCPs' prediabetes knowledge, which is in accordance with previous studies regarding diabetes knowledge among PCPs (23–25). However, more than four-fifths of the surveyed PCPs had not participated in CME regarding diabetes in the past year. Although many CME programs are available for PCPs, they are under-utilized. One possible reason might be the COVID pandemic preventing the in-person participation. As a newly emerging technology, telemedicine has demonstrated enormous potential to provide an effective intervention in health care [26, 27]. To this end, telemedicine may be a feasible approach for overcome part of barriers during the COVID-19 pandemic period.
The current study indicated that most of the respondents deemed that prediabetes increased the risk of development of diabetes and premature mortality, and more than $80\%$ believed that most patients with prediabetes were unaware of their condition. Meanwhile, the majority had a positive attitude toward prediabetes prevention: $78.9\%$ thought prediabetes was reversible through regular exercise and drugs (metformin). Similar to the findings for the knowledge subscale, the overall attitude scores were inversely correlated with the participants' age and duration of practice, which could also be due to the fact that younger PCPs are more likely to keep abreast with current trends in prediabetes. Moreover, PCPs who served in township hospitals had significantly higher attitude scores than those who served in village clinics, which could be because PCPs, who served in township hospitals had greater accessibility to the latest medical knowledge.
We found some poor practices related to prediabetes among PCPs. In our survey, only $24.6\%$ and $6.9\%$ of the PCPs reported familiarity with the Chinese expert consensus, which recommends the interval for clinical follow-up and repeat laboratory assessments [17]. Thus, very few PCPs were aware of the expert consensus, and addressing this problem may improve delivery of care for prediabetes. In a Diabetes Prevention Program study, lifestyle modification or metformin treatment was proven to be effective in reducing the incidence of diabetes and the risk of microvascular complications [9, 28]. However, consistent with the results of previous studies (14–16), very few PCPs considered metformin use for prediabetes. This may be due to skepticism regarding the effectiveness of metformin [29].
The PCPs identified a lack of diet and exercise guidance as well as resource or financial limitations as important system-level barriers, and patient-related factors such as lack of recognition of prediabetes, uncertainty regarding the effectiveness of lifestyle modification, and lack of motivation as barriers to lifestyle modification. Therefore, goal-oriented measurements to tackle these barriers will be key to cost-effective management of prediabetes.
## Limitations
The present study had several limitations. First, this study was conducted among PCPs from the Central China region; due to variations in geographical and sociocultural characteristics across China, the results may not be generalizable. Second, the results of the KAP survey were self-reported by the participants, which may have introduced recall and social desirability biases, with more respondents reporting positive attitudes toward prediabetes.
## Conclusion
This study highlighted the substantial gaps in knowledge and practices regarding prediabetes among PCPs in the Central China region. The results also indicated underutilisation of prediabetes CME programmes by PCPs. Since PCPs play a crucial role in the prevention and management of diabetes, efforts to address these gaps in prediabetes knowledge and practice are of urgent importance. Structured programmes should be planned to improve prediabetes-related knowledge and practices among PCPs in China. In view of this, we make the following suggestions:
## 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 the Ethics Committee of the Second Xiangya Hospital of Central South. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
XS and ZW designed the study. LP, DF, and YZ collected the data. LP conducted the data analysis and drafted the manuscript. XS, ZW, and ZZ revised the manuscript. JY carefully edited the revised manuscript. All authors read and approved the submitted version of the manuscript.
## 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.2023.1086147/full#supplementary-material
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---
title: A combination of HLA-DP α and β chain polymorphisms paired with a SNP in the
DPB1 3’ UTR region, denoting expression levels, are associated with atopic dermatitis
authors:
- David J. Margolis
- Jamie L. Duke
- Nandita Mitra
- Ronald A. Berna
- Ole J. Hoffstad
- Jenna R. Wasserman
- Amalia Dinou
- Georgios Damianos
- Ioanna Kotsopoulou
- Nikolaos Tairis
- Deborah A. Ferriola
- Timothy L. Mosbruger
- Tristan J. Hayeck
- Albert C. Yan
- Dimitri S. Monos
journal: Frontiers in Genetics
year: 2023
pmcid: PMC9995861
doi: 10.3389/fgene.2023.1004138
license: CC BY 4.0
---
# A combination of HLA-DP α and β chain polymorphisms paired with a SNP in the DPB1 3’ UTR region, denoting expression levels, are associated with atopic dermatitis
## Abstract
Introduction: Components of the immune response have previously been associated with the pathophysiology of atopic dermatitis (AD), specifically the Human Leukocyte Antigen (HLA) Class II region via genome-wide association studies, however the exact elements have not been identified.
Methods: This study examines the genetic variation of HLA Class II genes using next generation sequencing (NGS) and evaluates the resultant amino acids, with particular attention on binding site residues, for associations with AD. The Genetics of AD cohort was used to evaluate HLA Class II allelic variation on 464 subjects with AD and 384 controls.
Results: Statistically significant associations with HLA-DP α and β alleles and specific amino acids were found, some conferring susceptibility to AD and others with a protective effect. Evaluation of polymorphic residues in DP binding pockets revealed the critical role of P1 and P6 (P1: α31M + (β84G or β84V) [protection]; α31Q + β84D [susceptibility] and P6: α11A + β11G [protection]) and were replicated with a national cohort of children consisting of 424 AD subjects. Independently, AD susceptibility-associated residues were associated with the G polymorphism of SNP rs9277534 in the 3’ UTR of the HLA-DPB1 gene, denoting higher expression of these HLA-DP alleles, while protection-associated residues were associated with the A polymorphism, denoting lower expression.
Discussion: These findings lay the foundation for evaluating non-self-antigens suspected to be associated with AD as they potentially interact with particular HLA Class II subcomponents, forming a complex involved in the pathophysiology of AD. It is possible that a combination of structural HLA-DP components and levels of expression of these components contribute to AD pathophysiology.
## 1 Introduction
Atopic dermatitis (AD) is one of the most common dermatologic illnesses with a known genetic predisposition (Abuabara et al., 2019a; Abuabara et al., 2019b; Chiesa Fuxench et al., 2019; Silverberg et al., 2019). It is often hypothesized to be a disorder involving both skin barrier and immune system dysfunction, the latter of which is thought to be mediated by antigen and centered on the activation of the TH2 pathway (Gough and Simmonds, 2007).
The Human Leukocyte Antigen (HLA) region of chromosome 6 is frequently associated with immune mediated illnesses (Gough and Simmonds, 2007). Using next-generation sequencing technology (NGS), we recently evaluated associations between HLA Class I genetic variation and AD (Margolis et al., 2021a; Margolis et al., 2021b). HLA Class II molecules, as part of the adaptive immune response, play a major role in the presentation of antigens to CD4 T cells, so we extended our study to include the characterization by NGS of HLA Class II polymorphisms. Presentation of antigen through the skin occurs via antigen presenting cells that contain receptors formed by highly polymorphic molecules (epitopes) in HLA Class II as HLA-DR, HLA-DQ, and HLA-DP (Couture et al., 2019). *These* genes and the subsequent receptor epitopes from these genes create highly variable HLA Class II binding groves that interact with antigen(s) (Couture et al., 2019; Gong et al., 2020). The formed epitopes within these grooves have varying affinities for antigen(s) thereby playing an important role in antigen presentation (van Lith et al., 2010; Couture et al., 2019; Gong et al., 2020). The functioning of HLA Class II is, therefore, consistent with the hypothesized immunologic basis of AD.
In the past, large genome wide association studies (GWAS) relied on imputation protocols for HLA and evaluated HLA associations in cohorts of primarily Europeans and Asians with AD (Paternoster et al., 2011; Sun et al., 2011; Weidinger et al., 2013; Paternoster et al., 2015). These reports identified HLAs associated with AD or other allergic illnesses, such as asthma and food allergy, which are diseases commonly seen with AD. These HLA associations were most frequently in the HLA Class II region (Weidinger et al., 2010; Paternoster et al., 2011; Hirota et al., 2012; Weidinger et al., 2013; Paternoster et al., 2015). Given the critical role of HLA Class II genes in the adaptive immune response, prior identification of specific HLA polymorphisms relevant to AD, and the potential role that HLA Class II polymorphisms may play in AD susceptibility/protection, deeper exploration of HLA Class variation is warranted.
Several smaller studies using older immunogenotyping technology have explored the relationship between HLA allelic variation and AD as well as comorbid “atopic” illness like asthma, food allergies, and seasonal allergies with conflicting results (Paternoster et al., 2011; Sun et al., 2011; Weidinger et al., 2013; Margolis et al., 2015; Paternoster et al., 2015). For example, Aron et al. found a strong positive relationship between HLA Class II DR4 and DR7 alleles with atopy and asthma in a Finnish cohort (Aron et al., 1996). HLA-DRB1, -DQB1 and -DPB1 genotypes were shown to be correlated with peanut allergy in a British study (Howell et al., 1999). In a small Japanese study, HLA-DRB1 and -DQB1 alleles were associated with severe AD with high IgE levels (Saeki et al., 1995). In contrast, Affes et al. found no association between AD and HLA-B, -DR and -DQ, whereas HLA-A had a protective effect in a cohort of Tunisian patients (Affes et al., 2007). Park et al. showed an association of HLA-DRB1 with AD in Korean children with food allergy (Park et al., 2012). In a small study of African-Americans, HLA-DRB1 allelic variation was specifically associated with HLA-DR receptor binding-pocket changes that were associated with both the onset and persistence of AD; these associations were not found in Whites (Margolis et al., 2015). Madore et al. reported an association between HLA-DQB1 and peanut allergy (Madore et al., 2013).
Employing advances in sequencing technologies, we performed HLA gene targeted sequencing to fully characterize the HLA genes in an AD case-control cohort [Genetics of Atopic Dermatitis (GAD) cohort], where the goal of this study was to determine whether genetic variation of these genes is associated with the likelihood of susceptibility to or protection from AD. Next-generation sequencing (NGS)-based technologies is now common in clinical practice for HLA genotyping labs supporting transplant programs but is still rarely performed for large-scale disease association cohort studies. We conducted a comprehensive and detailed characterization of HLA genes (including exonic, intronic, and some 3’ UTR sequences). Previously, we utilized this cohort to study the Class I HLA genes and found one allele associated with AD, B*53:01, and four alleles (A*01:01, A*02:01, B*07:02 and C*07:02) and six HLA protein residues (HLA-A 9F, HLA-A 97I, HLA-A 152V, HLA-A 156R, HLA-B 163E, and HLA-C 116S) that were associated with protection from AD (Margolis et al., 2021b). Our current study undertook a different approach to study the HLA Class II genes, taking into account the nature of the Class II genes to form dimers of the alpha and beta genes on the cell surface to present peptides. First, we evaluated allelic variation in the GAD cohort for the HLA Class II genes HLA-DRB1, DQA1, DQB1, DPA1, and DPB1. Based on the allelic variation, we assessed the differential frequency of specific amino acid residues comprising the binding pockets of the -DR molecules (Zerva et al., 1996; Raychaudhuri et al., 2012; van Deutekom and Keşmir, 2015). Next, we evaluated the differential frequency of HLA Class II A1-B1 haplotypes formed for the DQ and DP dimers and the pocket residues, critical for peptide binding, within each of the DQ or DP dimers. HLA-DRA is not polymorphic so it is not necessary to characterize the HLA-DRA1/-DRB1 dimers (Matern et al., 2020). Additionally, we also assessed the polymorphisms of the other, non-antigen recognition domains, DQ or DP α2, DQ or DP β2 domains, the transmembrane and cytoplasmic regions, and a 3’ UTR of DPB1 single nucleotide polymorphism (SNP) rs9277534, known to be associated with DP expression (Thomas et al., 2012; Schöne et al., 2018). A replication cohort, The Pediatric Eczema Elective Registry (PEER) was also HLA characterized by NGS and used to confirm the associations identified with the GAD cohort. Our set of analyses, which relied on the detailed characterization of HLA genes via NGS, help to further our understanding of the genetic basis of AD. Moreover, this analytic framework can potentially serve as a model for future studies of HLA polymorphisms identified through NGS in a range of diseases.
## 2.1 Population
Our primary study was comprised of 849 subjects from the Genetics of Atopic Dermatitis (GAD) cohort that included 464 cases (AD) and 385 controls (who did not have AD) (Margolis et al., 2015; Margolis et al., 2021b; Margolis et al., 2021c). All subjects were examined by dermatologists with expertise in the diagnosis of AD from the following Dermatology practice locations: University of Pennsylvania Perelman School of Medicine, Children’s Hospital of Philadelphia, Pennsylvania State University/Hershey Medical Center, and Washington University School of Medicine in St Louis. All subjects had a history and an exam consistent with AD (cases) or no history of AD (controls). There was no age restriction for enrollment. HLA polymorphisms, as well as AD genetic variation, can be highly dependent on race, so we focused on White and Black Americans in our study (Margolis et al., 2019). All subject-related information was obtained using a standard case report form that was completed by the subject or by an investigator after subject interview and/or medical record review.
All subjects or legal guardians provided written informed consent or, if appropriate, assent approved by their appropriate Institutional Review Board.
After completion of the study, a replication cohort of 424 AD subjects, called the Pediatric Eczema Elective Registry (PEER) was HLA characterized and used to assess the findings in the GAD cohort. This registry is described in detail is previous publications and compared to the 385 GAD controls (Margolis et al., 2012).
## 2.2 HLA genotyping
DNA was collected using Oragene DNA collection kits (DNA Genotek, Ottawa Canada) as previously reported (Margolis et al., 2012). The five HLA Class II genes (DRB1, DPA1, DPB1, DQA1, and DQB1) for individuals in the GAD cohort were sequenced using targeted amplicon-based NGS with Omixon Holotype HLA™ V2 kits (Budapest, Hungary). HLA genes were amplified (Qiagen LR PCR kits, Valencia, CA) on a Veriti thermal cycler (ThermoFisher, Waltham, MA), then amplicons from each gene were pooled per sample with library preparation occurring according to the manufacturer’s protocol. The final library was sequenced on an Illumina MiSeq (San Diego, CA) using paired-end 2 × 150 V2 chemistry. Omixon Twin™ (7,000 pairs/locus, v, 2.5.1) and GenDx NGSengine® (Utrecht, Netherlands, 300,000 pairs/sample) analyzed each set of Fastq files. Genotyping was conducted in the Immunogenetics Laboratory of Children’s Hospital of Philadelphia, a CLIA and ASHI accredited clinical laboratory, using clinical protocols with appropriate quality controls and standards.
## 2.3 Data analysis
The NGS sequencing included full genomic characterization of HLA-DPA1, -DQA1, -DQB1 genes and partial characterization for HLA-DPB1 and -DRB1 (exon 2 to the 3’ UTR). All results were presented at two field resolution. Protein variations were determined using the IPD-IMGT/HLA database (Robinson et al., 2020). We focused on known binding pocket residues for -DR, - DQ, and -DP as being critical for peptide binding and therefore possibly involved in disease processes.
Allelic frequencies (AF) were based on the number of chromosomes with alleles that coded for the residue variant and were estimated along with $95\%$ confidence intervals (CI). Epitope or Residue frequencies (RF) were based on the number of chromosomes with alleles that coded for the residue variant. Frequencies were estimated separately for those with and without AD. Logistic regression was used to estimate the odds ratio (OR) of having AD, assuming an additive genetic model for the allele or residue. Additional analyses were conducted within racial subgroups. Amino acid residue analysis was restricted to polymorphic residues within the binding pocket of the HLA molecules. Binding pocket residues were identified based on published crystal structures for each HLA molecule, whereby the residues that were found to be within a 4-Å neighborhood of the presented peptide were included (Stern et al., 1994; Chicz et al., 1997; Henderson et al., 2007; Dai et al., 2010; Tollefsen et al., 2012; Kusano et al., 2014; Jiang et al., 2019; Ting et al., 2020).
The mature Class II molecules are composed of an α and a β chain, products of the respective A1 and B1 genes of each of the DQ and DP Class II genes. The α1 domain of the α chain and the β1 domain of the β chain form the binding site. Since both chains are polymorphic, at least for DQ and DP molecules, we explored the possible contributions generated upon formation of the dimer, and therefore the concept of haplotypic analysis. Haplotype analysis was conducted using the BIGDAWG package in R (version 3.6.2), which utilizes the R-routine haplo.stats and haplo.em to estimate haplotypes, to account for the eventual dimerization of particular pairs of α and β chains (Pappas et al., 2016). Significance was assessed using a chi-squared test. The results of using the BIGDAWG package was further confirmed by using an independent approach for haplotype generation. Haplotype analysis was only performed for the HLA-DPA1/-DPB1 and HLA-DQA1/-DQB1 pairs of genes. Note that the haplotype analysis refers to the A1 and B1 genes of a single isotype and does not refer to the extended DP ∼ DQ haplotype. Haplotype analysis was not performed for the HLA-DRA/DRB1 pair of genes since DRA is not polymorphic (Matern et al., 2020) and not characterized in our study. The DRB1 gene was therefore evaluated assessing all polymorphisms located on the DRβ chain and participating in the formation of the DR binding pockets. To identify relevant residues on the α/β chains of DP and DQ molecules influencing AD, we used alleles or haplotypes with significant p-values showing opposing directionality for their association with AD, and identified polymorphic residues, focusing on those that participate in pocket formation. The described approach has been previously used to identify residues of relevance for tuberculoid leprosy on the DR molecules (Zerva et al., 1996).
For completeness, all remaining polymorphic residues that were outside of exon 2 of each A1 and B1 genes (exons 1, 3, 4, 5, 6, depending on the particular gene), were analyzed and evaluated for association using logistic regression in R. Specifically, for the DPB1 gene, an additional polymorphism SNP rs9277534 at the 3’ UTR was inferred based on HLA-DPB1 genotyping (Schöne et al., 2018). SNP rs9277534 is previously reported to be associated with expression levels of DP molecules and was evaluated as high or low HLA-DPB1 expression using logistic regression for its contribution to protection or disease (Thomas et al., 2012).
To further explore the relationship and the possible independent effect of significant amino acid positions and residues found in different regions of the DPA1 and DPB1 gene, including the A/G polymorphism of rs9277534 SNP, we assessed linkage disequilibrium (LD) among these entities. LD was calculated in the whole GAD cohort using the LD coefficient D (D = pAB – pApB, where pAB is the frequency of the amino acid residue of interest and rs9277534 SNP of interest co-occuring on the same haplotype, pA = frequency of the amino acid residue of interest, pB = frequency of rs9277534 SNP of interest; generally ranging from −0.25 to 0.25 but is dependent upon allele frequency). Further, the correlation coefficient, R 2, was calculated using D to account for the frequency of the alleles in the population (R 2 = D/(pA (1-pA)pB (1-pB) (Slatkin, 2008). The same strategy was used to evaluate the LD between two amino acid residues in different DPA1 or DPB1 regions, replacing the polymorphisms of rs9277534 SNP with a second amino acid residue of interest in the above calculations.
An additional type of analysis was undertaken involving the possible relationship of T cell epitope (TCE) groups with AD. Each TCE group is defined by a distinct combination of amino acid residues that influence the peptides bound to the HLA-DP protein and presented to T cells. Originally, and in the context of hematopoietic stem cell transplantation, the HLA-DPB1 alleles were organized into TCE groups by experimental methods by Zino et al. ( Zino et al., 2007). The definitions of TCE groups was further extended to all alleles using a functional distance method by Crivello et al. ( Crivello et al., 2015). The TCE classification is based on exon 2 amino acid composition and an updated TCE classification is maintained for all alleles as part of the IPD-IMGT/HLA database (Robinson et al., 2020) using the Crivello functional distance method (Crivello et al., 2015). HLA-DPB1 alleles from both the GAD and PEER cohorts were assigned the TCE group that is defined by the Crivello functional distance method available through the IPD-IMGT/HLA database. For the novel DPB1 alleles that exist in either the GAD or PEER cohort, all novelties are outside of exon 2, and were assigned a TCE group that corresponds to the known allele that shares exact exon 2 sequence with the novel allele.
The odds ratios were not adjusted for other atopic illnesses like asthma, seasonal allergies, or food allergies because these illnesses are likely on the same causal pathway as noted in studies of the atopic March (Kapoor et al., 2008; Davidson et al., 2019). Our final analysis was at the amino acid level within the binding pocket of the HLA molecules (e.g., a phenotype). We report the Bonferroni correct threshold by number of variants or residues analyzed per HLA gene. We also report the uncorrected p-value. All statistical analyses were conducted using Stata Version 17.0 (College Station, TX) or R (R Foundation, version 3.6.2).
## 3 Results
The GAD cohort consisted of 849 total subjects. Genotyping was available for -DRBI from 803 subjects, for -DPA1 from 783 subjects, for -DPB1 from 784 subject, for -DQA1 from 782 subjects, and for -DQB1 from 774 subjects. As previously described (Margolis et al., 2021b), in the entire GAD cohort $59.2\%$ ($$n = 503$$) reported their race as White, $37.1\%$ ($$n = 315$$) reported their race as Black, $56.2\%$ ($$n = 477$$) were Female ($$n = 477$$) and $54.6\%$ ($$n = 464$$) had atopic dermatitis. For the 464 individuals in the GAD cohort with AD, $50.0\%$ ($$n = 203$$) were White, $43.8\%$ ($$n = 203$$) were Black, $63.5\%$ ($$n = 63$.5$%) were Female. For those with AD, the median age of onset of AD was 0.75 years (interquartile range (IQR): 0.25–11), however the median age when a sample was obtained for this study was 54.0 years (IQR: 38.4–63.7) for those with AD and 51.9 years (IQR: 35.0–65.9) for the controls. Overall, $45.1\%$ ($$n = 342$$) of GAD had seasonal allergies and $39.3\%$ ($$n = 290$$) had asthma. Within the AD sub-cohort, $64.4\%$ had seasonal allergies ($$n = 290$$) and $56.4\%$ ($$n = 254$$) had asthma. As expected and previously reported (Paternoster et al., 2011; Sun et al., 2011; Weidinger et al., 2013; Paternoster et al., 2015), seasonal allergies and asthma are significantly different in the case and control groups ($p \leq 0.001$). Furthermore, the female subjects are significantly increased among those with AD as compared to the control group ($p \leq 0.001$) (Chiesa Fuxench et al., 2019; Silverberg et al., 2019).
Sixteen HLA-DRB1, ten HLA-DQA1, ten HLA- DQB1, four HLA-DPA1, and seven HLA-DPB1 alleles had a frequency of ≥0.05 in the full GAD cohort, the White sub-cohort, or the Black sub-cohort (Table 1, all alleles in Supplemental Table 1). Allelic frequency did vary by race and presence or absence of disease (Table 1). After statistical correction no DRB1 alleles were associated with AD. No DQA1 alleles and one DQB1 allele was significantly associated with AD after correction; DQB1*03:19 (2.45 (1.35, 4.44); $$p \leq 0.003$$). This allele is more common in Blacks than Whites (frequency <0.01). The low AF in Whites likely resulted in an unstable effect estimate for Whites (Table 1; Table 2). Three DPA1 alleles were significantly associated after correction with AD. DPA1*01:03 (0.60 (0.49, 0.73); $$p \leq 5.92$$ × 10−07) was associated with a decreased risk of AD and DPA1*02:01 (1.49 (1.17, 1.91); $$p \leq 0.0013$$), and DPA1*02:02 (1.78 (1.22, 2.59); $$p \leq 0.0028$$)) were significantly associated with an increased risk of AD. Finally, DPB1*04:01 (0.57 (0.46, 0.71); $$p \leq 7.81$$ × 10–07) was significantly associated with a decreased risk of AD. Effect estimates did vary by race, however, the $95\%$ CI overlapped (Table 2).
Since the A1 and B1 genes of -DP and -DQ are both polymorphic and exist in particular haplotype combinations (i.e., DQA1∼DQB1 and DPA1∼DPB1) resulting in heterodimers of α and β chains, the binding sites formed from the α and β chains of a given combination are functionally meaningful. The relative frequencies and p-values of the different DQA1∼DQB1 and DPA1∼DPB1 haplotypes in our control and AD populations are shown in Table 3. No remarkable differences are observed in the distribution of DQA1∼DQB1 haplotypes. A number of DPA1∼DPB1 haplotypes appear to have significant differences between the two groups (Table 3). Of note is that one of the DPA1∼DPB1 haplotypes (DPA1*01:03∼DPB1*04:01) is associated with protection and highly significant ($$p \leq 4.76$$ × 10−07). The same DPA1*01:03 and DPB1*04:01 alleles were found to be associated with protection with significant differences when evaluated as independent alleles (Table 2). Additionally, we noticed (Table 3; Figure 1A) that there are several DPB1 alleles (DPB1*06:01, 18:01 and 104:01) found in a haplotype with the DPA1*01:03 allele that have opposing direction (OR>1), suggesting association with AD.
Adapting the same approach previously applied in a study of tuberculoid leprosy (Zerva et al., 1996), whereby alleles with opposite directionality of association can guide our search for the relevant residues that influence disease process, we identified 14 residues (8, 9, 11, 36, 55, 56, 57, 65, 69, 76, 84, 85, 86 and 87) in the β1 domain to be different between the DPB1*04:01 and the group of DPB1*06:01, 18:01 and 104:01 alleles (Figure 1A). Of those residues in the β chain, the residue at position 84 is part of pocket 1, residue at position 76 is part of pocket 2, residues 69 and 76 are part of pocket 4, residues 65 and 69 are part of pocket 7, residues 9, 36 and 55 are part of pocket 9 and residue 11 is part of pocket 6 (Table 4). In a similar fashion we identified the pocket residues that are different between the DPA1 alleles associated with disease and protection (Figure 1B). The DPA1*01:03 allele was associated with protection and the other 3 alleles, DPA1*02:01, *02:02 and *03:01, were associated with disease (Table 2). For the DPα chain we find that residues 11 and 66 are part of pocket 6, while residue 31 is part of pocket 1 (Table 4). Thereafter, and knowing the different residues of the pockets associated with AD or protection, we evaluated the distribution of these pocket residues in the whole population of control and AD subjects. In the analysis of pocket residue combinations shown in Table 5 we found that amino acid combinations of pocket residues of the DP dimer may be associated with protection or AD. In terms of AD association or protection, it appears that while there are pockets influenced by both DP α and β chain residues, like pocket 1 (α31, β84) and 6 (α11, β11), there are others that are influenced only by residues of the β chain, like pocket 4 (β69 and 76), pocket 7 (β65 and 69) and pocket 9 (β36 and 55). More specifically the combinations of α31M + (β84G or β84V) at pocket 1, α11A+ β11G at pocket 6, β36A+ β55A at pocket 9, and β69K + β76M at pocket 4, are associated with lower risk and therefore protective, while the combination of α31Q + β84D is associated with increased risk of disease (Table 5). The performed analysis was targeted and involved predetermined positions and amino acids generated from the described analytical approach, hence, the only correction factor here is the number of different estimates, that is 10, and shown in Table 5.
Regarding the analysis of the DRB1 gene, we focused on the polymorphic pocket residues of DRβ chain. The DRB1 polymorphisms specific to pocket residues are shown in Supplemental Table 2, none of these differences were statistically significant after p-value correction ($$n = 63$$, threshold p-value = 0.0008). Also considering that the DQA1∼DQB1 haplotyping did not reveal any significant associations, we evaluated the polymorphic pocket residues of the DQA1 and DQB1 genes independently (Supplemental Table 2). Accounting for the 25 DQA1 and 45 DQB1 comparisons, none of these residues demonstrated a statistically significant difference after correction.
Assessing polymorphisms in the non-exon 2 sequences of the DRB1, DQA1, and DQB1 alleles revealed no significant differences (Supplemental Table 2). However, DPα and β chains, did reveal other additional residues being of significance located in exon 3 and other segments of the molecule like transmembrane and cytoplasmic domains (Supplemental Table 2). These residues were the following: DPα111, 127, 160 and 228 and DPβ96 and 170. To assess whether any of these polymorphisms outside of the α1 and β1 domains were conferring independent risk from those identified within the α1 and β1 domains in the previous analysis we performed an assessment of LD. The amino acids in these positions of the non-α1 and β1 domains were found to be in variable LD with polymorphisms of the α1 and β1 domains (Supplemental Table 3). Since very strong LD is demonstrated between the non-α1 and β1 residues with at least one of the α1 or β1 residues it is unclear as to whether they confer an independent contribution to susceptibility or protection.
Additionally, the frequency of SNP rs9277534 polymorphisms, that denote relative expression of the DPB1 gene, were evaluated. The G polymorphism for rs9277534 SNP located in the 3’ UTR of the DPB1 gene, reflecting higher expression, was associated with AD (OR = 1.45, $$p \leq 4.71$$e-04), while the alternative, A, reflecting lower expression, was associated with protection (OR = 0.69, $$p \leq 4.71$$e-04) (Table 6). It was also noted that neither G nor A was associated with any of the two racial groups included in our study.
**TABLE 6**
| SNP | Full dataset | Full dataset.1 | White | White.1 | Black | Black.1 |
| --- | --- | --- | --- | --- | --- | --- |
| SNP | OR (95% CI) | p-value | OR (95% CI) | p-value | OR (95% CI) | p-value |
| rs9277534 A (Low) | 0.69 (0.56,0.85) | 4.71E-04 | 0.90 (0.68,1.19) | 0.444 | 0.75 (0.51,1.10) | 0.140 |
| rs9277534 G (High) | 1.45 (1.18,1.78) | 4.71E-04 | 1.12 (0.84,1.48) | 0.444 | 1.33 (0.91,1.95) | 0.140 |
Considering the strong linkage disequilibrium of these A/G polymorphisms with DPB1 alleles, the LD of the A/G SNP polymorphism with each of the positions from Table 5 and Supplemental Table 2 with at least one significant association, was assessed (Table 7) (Schöne et al., 2018). The underlying principle here is that, while DPB1 alleles may be in LD with the 3’ UTR SNP rs9277534 polymorphism, that does not mean that individual residues of the DP molecule will necessarily be in LD with either G or A of the rs9277534 SNP. A particular residue may be present on several alleles, some of which may be in LD with the G and some others with A SNP. Indeed, it was observed that while some of these residues are in LD with the A (DPβ84G and β76M) or G polymorphism (DPβ84D), not all residue polymorphisms of the binding pockets, with significance for either susceptibility or protection, are in LD with the rs9277534 SNP (DPβ36A, β55A, β69K) (Table 7). Upon assessing the LD between exon 3 polymorphisms and the rs9277534 SNP, it was found that there is very strong LD between the two (DPβ96, β170) (Table 7), suggesting interrelated functionalities between the expression of DPβ molecule and the β2 domain itself.
**TABLE 7**
| Position | Residue | rs9277534 ‘A’ | Unnamed: 3 | rs9277534 ‘G’ | Unnamed: 5 |
| --- | --- | --- | --- | --- | --- |
| Position | Residue | Alleles | R 2 | Alleles | R 2 |
| β11 | G (p) | 02:01, 02:02, 04:01, 04:02, 23:01, 39:01, 40:01, 49:01, 106:01, 126:01, 138:01, 535:01 | 0.257 | 01:01, 05:01, 15:01, 16:01, 18:01, 19:01, 90:01, 100:01, 350:01, 417:01 | 0.257 |
| β11 | L | 17:01, 30:01, 55:01, 133:01, 907:01 | 0.257 | 03:01, 06:01, 09:01, 10:01, 11:01, 13:01, 14:01, 20:01, 21:01, 29:01, 35:01, 36:01, 45:01, 85:01, 104:01, 131:01, 519:01 | 0.257 |
| β36 | A (p) | 04:01, 39:01, 40:01, 49:01, 126:01, 133:01, 907:01 | 0.000 | 01:01, 11:01, 13:01, 15:01, 85:01, 90:01, 350:01, 417:01, 519:01 | 0.000 |
| β36 | V | 02:01, 02:02, 04:02, 17:01, 23:01, 30:01, 55:01, 106:01, 138:01, 535:01 | 0.000 | 03:01, 05:01, 06:01, 09:01, 10:01, 14:01, 16:01, 18:01, 19:01, 20:01, 21:01, 29:01, 35:01, 36:01, 45:01, 100:01, 104:01, 131:01 | 0.000 |
| β55 | A (p) | 04:01, 23:01, 39:01, 40:01, 55:01, 126:01, 133:01, 138:01, 907:01 | 0.000 | 01:01, 11:01, 13:01, 15:01, 85:01, 90:01, 350:01, 417:01, 519:01 | 0.000 |
| β55 | D | 02:01, 04:02, 17:01, 49:01 | 0.003 | 03:01, 06:01, 09:01, 10:01, 14:01, 16:01, 18:01, 20:01, 29:01, 35:01, 45:01, 104:01, 131:01 | 0.003 |
| β55 | E | 02:02, 30:01, 106:01, 535:01 | 0.024 | 05:01, 19:01, 21:01, 36:01, 100:01 | 0.024 |
| β69 | E | 02:01, 02:02, 17:01, 30:01, 55:01, 106:01, 133:01, 535:01 | 0.014 | 06:01, 09:01, 10:01, 13:01, 16:01, 19:01, 21:01, 29:01, 131:01, 519:01 | 0.014 |
| β69 | K (p) | 04:01, 04:02, 23:01, 39:01, 40:01, 49:01, 126:01, 138:01 | 0.004 | 01:01, 03:01, 05:01, 14:01, 18:01, 20:01, 35:01, 36:01, 45:01, 85:01, 90:01, 100:01, 104:01, 350:01, 417:01 | 0.004 |
| β69 | R | 907:01 | 0.030 | 11:01, 15:01 | 0.030 |
| β76 | I | 106:01, 133:01, 535:01 | 0.044 | 13:01, 19:01, 519:01 | 0.044 |
| β76 | M (p) | 02:01, 02:02, 04:01, 04:02, 17:01, 23:01, 30:01, 39:01, 40:01, 49:01, 55:01, 126:01, 138:01, 907:01 | 0.639 | 05:01, 06:01, 11:01, 15:01, 16:01, 18:01, 20:01, 21:01, 36:01, 85:01, 100:01, 131:01, 350:01 | 0.639 |
| β76 | V | <No alleles> | 0.549 | 01:01, 03:01, 09:01, 10:01, 14:01, 29:01, 35:01, 45:01, 90:01, 104:01, 417:01 | 0.549 |
| β84 | G (p) | 02:01, 02:02, 04:01, 04:02, 23:01, 39:01, 49:01, 126:01, 138:01 | 0.835 | 100:01, 350:01 | 0.835 |
| β84 | V (p) | 40:01 | 0.028 | 15:01, 18:01 | 0.028 |
| β84 | D (s) | 17:01, 30:01, 55:01, 106:01, 133:01, 535:01, 907:01 | 0.753 | 01:01, 03:01, 05:01, 06:01, 09:01, 10:01, 11:01, 13:01, 14:01, 16:01, 19:01, 20:01, 21:01, 29:01, 35:01, 36:01, 45:01, 85:01, 90:01, 104:01, 131:01, 417:01, 519:01 | 0.753 |
| β96 | K (s) | <no alleles> | 1.0 | 01:01, 03:01, 05:01, 06:01, 09:01, 10:01, 11:01, 13:01, 14:01, 15:01, 16:01, 18:01,19:01, 20:01, 21:01, 29:01, 35:01, 36:01, 45:01, 85:01, 90:01, 100:01, 104:01, 131:01, 350:01, 417:01, 519:01 | 1.0 |
| β96 | R (p) | 02:01, 02:02, 04:01, 04:02, 17:01, 23:01, 30:01, 39:01, 40:01, 49:01, 55:01, 106:01, 126:01, 133:01, 138:01, 535:01, 907:01 | 1.0 | <no alleles> | 1.0 |
| β170 | I (s) | <no alleles> | 1.0 | 01:01, 03:01, 05:01, 06:01, 09:01, 10:01, 11:01, 13:01, 14:01, 15:01, 16:01, 18:01, 19:01, 20:01, 21:01, 29:01, 35:01, 36:01, 45:01, 85:01, 90:01, 100:01, 104:01, 131:01, 350:01, 417:01, 519:01 | 1.0 |
| β170 | T (p) | 02:01, 02:02, 04:01, 04:02, 17:01, 23:01, 30:01, 39:01, 40:01, 49:01, 55:01, 106:01, 126:01, 133:01, 138:01, 535:01, 907:01 | 1.0 | <no alleles> | 1.0 |
Regarding the analysis of our data using the TCE groups for the DP alleles we found moderate correlation between TCE group 3 and multiple pocket residues to be associated with protection from AD: P1 (α31M + (β84G or β84V); $r = 0.53$), P4 (β69K + β76M; $r = 0.44$), P6 (α11A + β55G; $r = 0.59$) and P9 (β 36A + β84V; $r = 0.43$), while we also found moderate correlation between TCE group 1 and P1 residues associated with AD (α31Q + β84D; $r = 0.40$). Table 8 shows the relative association of each one of the TCE groups for protection or susceptibility to AD, whereby TCE group 3 is associated with protection from AD (OR = 0.64, $$p \leq 0.00100$$) and TCE group 1 is associated with susceptibility to AD (OR 1.76, $$p \leq 0.0097$$).
**TABLE 8**
| DPB1 TCE group | Full dataset | Full dataset.1 | White | White.1 | Black | Black.1 |
| --- | --- | --- | --- | --- | --- | --- |
| DPB1 TCE group | OR (95% CI) | p-value | OR (95% CI) | p-value | OR (95% CI) | p-value |
| TCE Group 1 | 1.76 (1.16,2.74) | 0.00969 | 1.05 (0.56,1.97) | 0.869 | 2.03 (1.07,4.18) | 0.0401 |
| TCE Group 2 | 1.34 (0.99,1.82) | 0.0571 | 1.40 (0.98,2.01) | 0.0655 | 1.86 (0.97,3.83) | 0.0734 |
| TCE Group 3 | 0.64 (0.49,0.83) | 0.00100 | 0.74 (0.53,1.03) | 0.0729 | 0.49 (0.29,0.79) | 0.00467 |
In PEER, a cohort of children with AD used as a replication cohort, we confirmed that children with DPA1*01:03 and DPB1*04:01, and the DPA1*01:03∼DPB1*04:01 haplotype, were less likely to have AD as compared to the GAD controls. For this comparison we used controls from the GAD cohort because the PEER cohort was comprised of only AD cases which were used to study the progression of disease over time. As in many genetic association studies, such as GWAS, data from a standard set of controls are often used for different case comparisons because the underlying premise is that the “public” control group is representative of the underlying relevant non-diseased population (Mitchell et al., 2014). We also confirmed the susceptibility role of P1: α31Q + β84D and protective role of P1: α31M + (β84G or β84V) and P6: α11A + β11G. However, the DP β chain residues of pockets 4 and 9 were not confirmed. ( Table 9). Furthermore, it was confirmed that the distribution of the SNP rs9277534 polymorphisms A/G in the PEER cohort was such that the A remains to be associated with protection and ‘G’ with susceptibility. None of the associations between TCE groups and AD were replicated in the PEER cohort. No correction was applied to the p values, as this study was a replication confirming the findings of the GAD cohort.
**TABLE 9**
| Unnamed: 0 | Unnamed: 1 | Unnamed: 2 | All subjects | All subjects.1 | White | White.1 | Black | Black.1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Alleles | Alleles | Alleles | OR (95% CI) | p-value | OR (95% CI) | p-value | OR (95% CI) | p-value |
| DPA1*01:03 | DPA1*01:03 | DPA1*01:03 | 0.74 (0.61,0.91) | 0.00421 | 1.05 0.78,1.42) | 0.767 | 0.60 (0.41,0.87) | 0.00833 |
| DPB1*04:01 | DPB1*04:01 | DPB1*04:01 | 0.75 (0.61,0.93) | 0.00941 | 0.95 (0.73,1.22) | 0.673 | 0.53 (0.31,0.89) | 0.0167 |
| DQB1*03:19 | DQB1*03:19 | DQB1*03:19 | 2.59 (1.50,4.80) | 0.00124 | 0.54 (0.07,2.81) | 0.483 | 2.45 (1.29,5.11) | 0.0103 |
| DPA1∼DPB1 haplotype | DPA1∼DPB1 haplotype | DPA1∼DPB1 haplotype | DPA1∼DPB1 haplotype | DPA1∼DPB1 haplotype | DPA1∼DPB1 haplotype | DPA1∼DPB1 haplotype | DPA1∼DPB1 haplotype | DPA1∼DPB1 haplotype |
| DPA1*01:03∼DPB1*04:01 | DPA1*01:03∼DPB1*04:01 | DPA1*01:03∼DPB1*04:01 | 0.72 (0.58,0.91) | 0.00439 | 0.93 (0.71,1.20) | 0.555 | 0.52 (0.30,0.91) | 0.0134 |
| Pockets | Pockets | Pockets | Pockets | Pockets | Pockets | Pockets | Pockets | Pockets |
| Chain | Pocket | Residues | | | | | | |
| DPA1+DPB1 | P1 | α31M + (β84G or β84V) | 0.72 (0.59,0.90) | 0.00291 | 0.85 (0.65,1.10) | 0.223 | 0.68 (0.46,1.01) | 0.0636 |
| DPA1+DPB1 | P1 | α31Q + β84D | 1.29 (1.03,1.61) | 0.0245 | 0.95 (0.69,1.31) | 0.765 | 1.42 (0.98,2.09) | 0.0678 |
| DPA1+DPB1 | P6 | α11A+ β11G | 0.66 (0.53,0.82) | 0.000173 | 0.76 (0.58,1.00) | 0.0540 | 0.60 (0.40,0.88) | 0.00954 |
| DPB1 | P9 | β36A+ β55A | 0.94 (0.76,1.16) | 0.571 | 0.95 (0.74,1.22) | 0.707 | 0.85 (0.58,1.24) | 0.393 |
| DPB1 | P4 | β69K + β76M | 0.82 (0.67,1.01) | 0.0647 | 0.92 (0.72,1.19) | 0.540 | 0.85 (0.57,1.26) | 0.417 |
| DPB1 Expression SNP | DPB1 Expression SNP | DPB1 Expression SNP | DPB1 Expression SNP | DPB1 Expression SNP | DPB1 Expression SNP | DPB1 Expression SNP | DPB1 Expression SNP | DPB1 Expression SNP |
| rs9277534_A_Low | rs9277534_A_Low | rs9277534_A_Low | 0.78 (0.63,0.96) | 0.0176 | 0.87 (0.67,1.13) | 0.296 | 0.85 (0.58,1.24) | 0.399 |
| rs9277534_G_High | rs9277534_G_High | rs9277534_G_High | 1.25 (1.02,1.54) | 0.0338 | 1.12 (0.86,1.46) | 0.401 | 1.14 (0.78,1.67) | 0.493 |
| DPB1 TCE Groups | DPB1 TCE Groups | DPB1 TCE Groups | DPB1 TCE Groups | DPB1 TCE Groups | DPB1 TCE Groups | DPB1 TCE Groups | DPB1 TCE Groups | DPB1 TCE Groups |
| TCE Group 1 | TCE Group 1 | TCE Group 1 | 1.11 (0.70,1.77) | 0.658 | 0.76 (0.38,1.47) | 0.431 | 1.39 (0.71,2.87) | 0.351 |
| TCE Group 3 | TCE Group 3 | TCE Group 3 | 0.95 (0.72,1.25) | 0.705 | 0.96 (0.69,1.33) | 0.788 | 0.86 (0.49,1.45) | 0.569 |
## 4 Discussion
HLA Class II genes are involved in the formation of molecules integral to the presentation of antigens to CD4 T cells and as a result are part of immune mediated responses and illnesses. We conducted a case-control study of individuals with and without AD using high-resolution NGS sequencing of genes in the HLA Class II region. We also evaluated individuals by White or Black race. After statistical adjustment for multiple comparisons, we found no associations between HLA-DRB1, DQA1 or DQB1 alleles or any of their positional residues or amino acids with AD. Analysis of DPA1 and DPB1 alleles, DPA1/DPB1 dimers and pocket residues had significantly different distributions between the control and disease cases. This evaluation allows for assessing the role of positional residues and of specific amino acids in a way that is independent of specific -DP alleles. This evaluation is meaningful because individual alleles may not be significantly associated with AD, while specific positional amino acids that are shared among multiple alleles can demonstrate significant associations between control and disease cases. Considering that the operational entities influencing peptide binding are the pocket residues, this type of analysis allows for a better assessment of the sub-molecular components of an HLA molecule that contributes to disease or protection. The findings were replicated in a second group of AD cases from the PEER cohort for the P1 and P6 pockets and their respective residues. Replication did not confirm the involvement of the P4 and P9 pocket residues. More specifically, P1; α31Q + β84D are associated with a susceptibility role and P1: α31M + (β84G or β84V) and P6: α11A + β11G are associated with a protective role. In summary, -DPA1 and B1 genomic variation is associated with AD but no other Class II genes are associated with AD.
Accounting for the relative expression of the DPs on the cell surface, whereby the residue β84G, which was associated with protection, is in LD with the rs9277534 3’ UTR SNP polymorphism indicating lower expression (A), while the residue (β84D) associated with disease is in LD with the SNP reflecting higher expression (G), it becomes apparent that a combination of polymorphisms in the coding region, together with cell surface expression of the DP molecule and the presented non-self antigen, may set the stage for the ensuing immune response that results in protection or disease.
It should also be mentioned that the absolute LD identified between exon 3 – transmembrane region-3 UTR SNP rs9277534 (residue 96R-170T-SNP A or residue 96K-170I-SNP G) of DPB1 in our population (Table 7), denotes a possible interdependence between these polymorphisms and expression as reflected by SNP rs9277534 in the 3’ UTR of the DPB1 gene. It is unclear as to how these non-exon 2 polymorphisms may coordinate functionalities critical for the molecule, but this LD character contrasts to the somehow strong but not absolute LD between the rs9277534 SNP and exon 2 polymorphisms that comprise the part of the DP that serves as a receptor for peptides and T cell receptor interactions (Table 7). Furthermore, residue DPβ11G that is associated with protection is in rather weak LD with exon 3 polymorphisms (β96 and β170, Table 8) and also the rs9277534 SNP A (Table 7). It therefore appears that association with protection may or may not depend entirely on the low levels of expression of the DP molecules. If we assume that protection can be an active state of the immune response, like susceptibility is, then there may be T cell responses of regulatory nature that actively diminish the immune response; these mechanisms may be influenced not only by structural components but also expression patterns and the peptides presented by the DP molecule. Lower expression of these DP structural components can be compatible with the protective role; nevertheless, the details of the interactions between structure and expression are not entirely clear. It should be clarified that in our study we claim associations of either pocket residues or SNPs denoting expression with susceptibility or protection but we do not demonstrate that these residues or SNP polymorphisms are engaged directly in specific mechanisms resulting in disease susceptibility or protection.
In conclusion, the LD observed among polymorphisms in exonic sequences (particularly exon 3 and to lesser extent exon 2) and non-coding regions (intron 2 or 3’ UTR) is an indicator suggesting coordinated functionalities maintained through the evolutionary history of the DPB1, connected through the different segments of the DPB1 genomic sequences. A hypothesis, therefore, can be developed that those particular structural components of DPs, along with their increased or reduced expression, form the basis for the pathophysiology of AD. It remains, that because of the LD between the residues and expression components, it is unclear what the exact mechanistic role each may play and what their exact interactions may be. It should be noted that the HLA-DP loci are not in LD with the other HLA Class I loci or HLA Class II loci, as there are at least four recombination hotspots located between the HLA-DP locus and the next closest HLA locus studied in this project, HLA-DQB1 (Jeffreys et al., 2001; Cullen et al., 2002; Miretti et al., 2005). The DPB1 alleles, amino acid residues and the expression marker are not correlated with the HLA Class I alleles or residues (Margolis et al., 2021b) that were either protective of or associated with AD from this same dataset (data not shown). Therefore, the involvement/engagement of the Class I and the Class II associated or protective elements in AD pathophysiology are rather independent and do not reflect any linkage disequilibrium effect.
In our study, the HLA typing was done by NGS. Unlike previous studies of AD, we identified and fully characterized both the A and the B genes of DQ and DP loci, allowing a better identification of pocket residues. This combined evaluation is important because polymorphisms on both A and B genes contribute to the binding site and therefore influence peptide binding. In the past, in HLA and disease association studies the characterization of the DQA1 and DPA1 genes was not performed routinely, either because it was not feasible at the time or because there was a belief that their polymorphism was limited and therefore inconsequential. Since the majority of HLA and disease associated studies lack this DPA1, DQA1 gene characterization, our understanding and contribution of the possible role of α pocket residues is unclear for the different HLA and disease association studies performed thus far. In our study, the β84D is in combination with α31Q (Table 5) and therefore it is likely that, this combined structural features of the DP molecule, along with its high expression may contribute to disease susceptibility. Recent studies of Type 1 diabetes mellitus provide another example of the importance of characterizing both A and B gene polymorphisms which show associations with both A and B genes of DQ and DP loci (Erlich et al., 2008; Noble, 2015; Enczmann et al., 2021).
The DP pockets and residues associated, in our study, with AD P1β84 and P6β11, have been previously identified by Castelli et al as influencing the binding of synthetic peptides originating from allergens, viral and tumor antigens to DP molecules (Castelli et al., 2002). The P1 and P6 pockets accommodate the main anchor residues of foreign peptides interacting with DPs. Even though the Castelli et al study (Castelli et al., 2002) does not address the contributions of the α chain residues, considering that these pockets are composed of polymorphic α chain residues as well, it is not unlikely that whatever the effect of the pockets, is a resultant of influences originating from both chains. It therefore becomes likely that a combination of DP α and β pocket polymorphisms, as they interact with different antigens, form the complex that initiates T cell responses. Indeed, in our study we find that not only β chain residues are associated with protection or disease, but also α chain residues (α11A, α31Q, and α31M).
Furthermore, the different HLA-DPB1 alleles are organized into T cell epitope (TCE) functional groups (Zino et al., 2007; Crivello et al., 2015). Each one of these TCE groups of DPB1 alleles is characterized by distinct structural features that differentiate one from the other group and influence the peptides bound to the DP protein. Most recently, the immunopeptidome of the alleles that belong to each one of these groups has been characterized (van Balen et al., 2020; Meurer et al., 2021; Laghmouchi et al., 2022). Considering that in our study of AD we have identified HLA-DPA1 and DPB1 alleles and specific residues relevant to the disease, we were interested to investigate as to whether any of the structural elements (alleles or residues) of the DP molecules correlated with the TCE groups, therefore, establishing a relationship between the DP structural elements of our study with the functional grouping of DPs including the peptide repertoire that characterizes each one of the TCE groups. Indeed, we found in the GAD cohort that those particular alleles belonging to a specific TCE group, having common structural features and a unique immunopeptidome bound to these alleles, correlate with the structural elements we found through our independent approach in the same cohort. However, due to lack of reproducibility with the PEER group we do not expand on this observation and refrain from a final conclusion. Nevertheless, it is very likely that the structural elements, identified as being relevant with the disease, correlate to particular DP TCE groups. As such, the immunopeptidome characterizing the different TCE groups may be instructive in identifying peptides involved with atopic dermatitis; the exact structural details within a particular TCE group that may play a role in atopic dermatitis remain unclear.
Atopic illnesses, like atopic dermatitis, are associated with TH2 dominant cellular response and often IgE antibody production in response to the presence and presentation of specific allergens. The activation of the TH2 results in the production of IL-4 and IL-13 cytokines. These cytokines are associated with the acute phase of AD and can also result in epidermal barrier dysfunction (Gong et al., 2020). The physiologic effect of these cytokines are diminished by IL-4 blocking agents that are currently being used to treat moderate to severe AD (Beck et al., 2014). The strength of the immune response to an antigen is likely related to genetic and environmental factors as well as the type and intensity of allergen.
Allergen presentation is influenced by HLA Class II and, in this study, we demonstrate that HLA Class II genetic variation at the level of allele and receptor epitope is associated with both an increased risk and decreased risk of AD. We do not currently know which allergens bind and with what intensity to the epitopes effected by the described genetic variation. We did previously show in an in silico study that HLA Class II epitope variation could result in differential binding of auto allergens suspected of being associated with AD (Gong et al., 2020). However, when an allergen is suspected to be associated with AD, it should now be possible to determine how it might interact with the epitopes described in this study and more precisely determine if it is likely to be associated with AD. Furthermore, agents that influence -DP binding might have an impact on AD.
As with all epidemiologic studies, this study has limitations. Although this study is the largest study of its kind to use NGS to genotype HLA Class II genes and then evaluate their associations with AD, we were under-powered to evaluate less common HLA alleles. For this reason, we a priori limited our analyses to alleles with a frequency of ≥0.05. We believe that this strategy is important for understanding the effect of HLA on the population at large. However, it is possible that important information about how HLA Class II allelic variation affects immune response with respect to AD might be gleaned by evaluating less frequent alleles. To evaluate these alleles, large cohorts will be needed that are designed to focus on less common alleles. Study subjects were classified by self-described race and not by genetic ancestry. It is possible that genetic admixture is not fully accounted for by self-described race and could have added bias to our results. However, it is important to note that we have previously shown that in an evaluation of a similar population, self-described race was highly concordant with an assessment of genetic ancestry (Margolis et al., 2012; Abuabara et al., 2020; Biagini et al., 2022). In addition, many of our findings had similar effect estimates in both races. The origin of the GAD is primarily from academic dermatology offices and as such patients were more likely to have treatment resistant AD than patients seen in general practice. As a result, it is possible that our results may not generalize to all clinical sites and to individuals with less treatment resistant AD. Our genotyping did not phase the A/B genes of the DQ or DP loci, so we used a well-known algorithm, haplo.stat to form our DPA1∼DPB1 and DQA1∼DQB1 haplotypes. It is possible that there was some inaccuracy in the estimation of haplotypes. However, as a sensitivity analysis, we used an alternative approach for coding the haplotypes that does not rely on the EM algorithm and found very similar results (see Supplemental Table 4). Finally, it is possible that our findings are not primarily associated with AD but other co-morbid illnesses that are part of the atopic March like asthma and seasonal allergies (Zino et al., 2007). However, it is generally believed that AD occurs before these illnesses (Kapoor et al., 2008).
In summary, we conducted a case-control study of individuals with AD and controls using NGS for the characterization of the three classic HLA Class II genes. NGS allows for a more thorough interrogation of Class II genes and found that the DP molecule is relevant to AD and that specific pockets and residues within these pockets, along with their expression levels, play a critical role in both susceptibility and protection.
## Data availability statement
The HLA Class II genotypes for the GAD and PEER cohorts presented in the study are deposited in the Zenodo repository, https://zenodo.org/record/7565999 (DOI: 10.5281/zenodo.7565999).
## Ethics statement
The studies involving human participants were reviewed and approved by University of Pennsylvania Institutional Review Board. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.
## Author contributions
DJM and DSM designed and oversaw the study. DJM, OH, and AY oversaw the collection of samples. OH prepared the samples. JW, AD, NT, and DF performed the HLA genotyping. AD, TM, DF, IK, GD, and JD analyzed the sequencing data. DJM, JD, NM, RB, OH, and TH conducted the statistical analyses. DJM, JD, NM, and DSM wrote the paper. All authors reviewed the results and approved the final version of the manuscript.
## Conflict of interest
DJM is or recently has been a consultant for Pfizer, Leo, and Sanofi with respect to studies of atopic dermatitis and served on an advisory board for the National Eczema Association. DSM is Chair of the Scientific Advisory Board of Omixon and owns options in Omixon. DSM, DF, and JD receive royalties from Omixon. AY has recently been a consultant for Pfizer and Sanofi with respect to studies of atopic dermatitis.
The remaining 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
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## Supplementary Material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2023.1004138/full#supplementary-material
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|
---
title: Correlation between diffusion tensor indices and fascicular morphometric parameters
of peripheral nerve
authors:
- Luka Pušnik
- Igor Serša
- Nejc Umek
- Erika Cvetko
- Žiga Snoj
journal: Frontiers in Physiology
year: 2023
pmcid: PMC9995878
doi: 10.3389/fphys.2023.1070227
license: CC BY 4.0
---
# Correlation between diffusion tensor indices and fascicular morphometric parameters of peripheral nerve
## Abstract
Introduction: Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that measures the anisotropy of water diffusion. Clinical magnetic resonance imaging scanners enable visualization of the structural integrity of larger axonal bundles in the central nervous system and smaller structures like peripheral nerves; however, their resolution for the depiction of nerve fascicular morphology is limited. Accordingly, high-field strength MRI and strong magnetic field gradients are needed to depict the fascicular pattern. The study aimed to quantify diffusion tensor indices with high-field strength MRI within different anatomical compartments of the median nerve and determine if they correlate with nerve structure at the fascicular level.
Methods: Three-dimensional pulsed gradient spin-echo (PGSE) imaging sequence in 19 different gradient directions and b value 1,150 s/mm2 was performed on a 9.4T wide-bore vertical superconducting magnet. Nine-millimeter-long segments of five median nerve samples were obtained from fresh cadavers and acquired in sixteen 0.625 mm thick slices. Each nerve sample had the fascicles, perineurium, and interfascicular epineurium segmented. The diffusion tensor was calculated from the region-average diffusion-weighted signals for all diffusion gradient directions. Subsequently, correlations between diffusion tensor indices of segmentations and nerve structure at the fascicular level (number of fascicles, fascicular ratio, and cross-sectional area of fascicles or nerve) were assessed. The acquired diffusion tensor imaging data was employed for display with trajectories and diffusion ellipsoids.
Results: The nerve fascicles proved to be the most anisotropic nerve compartment with fractional anisotropy 0.44 ± 0.05. In the interfascicular epineurium, the diffusion was more prominent in orthogonal directions with fractional anisotropy 0.13 ± 0.02. Diffusion tensor indices within the fascicles and perineurium differed significantly between the subjects ($p \leq 0.0001$); however, there were no differences within the interfascicular epineurium (p ≥ 0.37). There were no correlations between diffusion tensor indices and nerve structure at the fascicular level (p ≥ 0.29).
Conclusion: High-field strength MRI enabled the depiction of the anisotropic diffusion within the fascicles and perineurium. Diffusion tensor indices of the peripheral nerve did not correlate with nerve structure at the fascicular level. Future studies should investigate the relationship between diffusion tensor indices at the fascicular level and axon- and myelin-related parameters.
## 1 Introduction
Certain medical conditions and penetrating injuries might cause individual nerve fascicles to be selectively more affected; therefore, accurate recognition of fascicle topography has the uttermost clinical importance (Härtig et al., 2018). As the pattern of fascicular involvement aid in the diagnostic workup of peripheral neuropathies, there is a great emphasis on its recognition. Clinical methods such as nerve conduction studies and electromyography are invasive, unpleasant, and give limited information. Accordingly, exploring available non-invasive radiologic modalities to extract information regarding nerve fascicular anatomy is imperative (Bilgen et al., 2005; Delgado-Martínez et al., 2016; Möller et al., 2018).
The application of clinical magnetic resonance imaging (MRI) for the depiction of peripheral nerves is increasing as it enables the assessment of peripheral neuropathies, nerve injuries or entrapments, and even tumors of the peripheral nerves (Chhabra et al., 2013; Bäumer et al., 2014). However, in trauma-related peripheral neuropathy, clinical MRI has limited accuracy in detecting pathologies, except in cases of severe nerve stretch or where the entire cross-section is affected (Eppenberger et al., 2014). Furthermore, when evaluating neoplasms of peripheral nerve with clinical MRI, differentiation of malignant from benign lesions can sometimes be difficult to achieve, even when there are characteristic signs of the malignancy (Chhabra et al., 2013). To surmount this obstacle, different strategies have been proposed. For example, the employment of advanced hardware, higher magnetic field magnets, and stronger gradients enabled the depiction of smaller structures as nerve fascicles; however, such studies are generally limited to ex vivo (Bilgen et al., 2005; Huang et al., 2015). Recently, specific MRI sequences such as fat-suppressed 3D fast low-angle shots have been proposed to improve the delineation of the nerve fascicles in healthy volunteers (Wang et al., 2023). Additionally, advanced techniques such as diffusion tensor imaging (DTI) have been exploited to further expand the options for depicting peripheral nerve pathologies (Khalil et al., 2008).
DTI enables measuring the effect of membranes on the apparent diffusion of water molecules. The specific arrangement of peripheral nerves results in diffusivity being predominantly directed along the axis of the nerve than in a perpendicular direction, thus being anisotropic. It is known that intact membranes are the primary determinant for anisotropic diffusion, with myelination having a modulating effect (Beaulieu, 2002). DTI measurements have been further utilized for deriving diffusion tensor (DT) indices that quantify the anisotropy (Pridmore et al., 2021). In rodent models, DTI has shown promise in distinguishing healthy, transected, and regenerating nerves (Lehmann et al., 2010). Moreover, even in macroscopic absent nerve discontinuity, DTI has been proven to detect minor nerve injuries (Boyer et al., 2015). DT indices also tend to correlate with behavioral changes and axonal density of rodents during the regeneration phases; therefore, they might serve as a promising tool in the future for recognizing unsuccessful nerve repair that requires further surgical intervention (Lehmann et al., 2010; Morisaki et al., 2011; Manzanera Esteve et al., 2019). In more recent research, DTI has also yielded a convenient tool for determining the severity of nerve injury in rats with the ability to distinguish different degrees of partial nerve transections (Manzanera Esteve et al., 2021).
As DT indices of peripheral nerve reflect the structural integrity of the nerve, they have attracted substantial attention for clinical application (Morisaki et al., 2011; Rangavajla et al., 2014). In clinical settings, it has been shown that demyelinating disease reflects in DT indices with the reduction of fractional anisotropy (FA), providing additional data regarding axonal degeneration in patients with peripheral neuropathies (Takagi et al., 2009; Kakuda et al., 2011). These data could complement clinical examination, electrophysiological evaluation, and conventional MRI for early recognition of patients with neuropathies who are eligible for neuroprotective therapies (Mathys et al., 2013). It has also been suggested that DTI could be an additional tool for assessing nerve compression syndromes, notably carpal tunnel syndrome (Khalil et al., 2008; Stein et al., 2009). In addition, DTI allows tractographic reconstructions; therefore, it can provide information on nerve integrity, predict the optimal approach of tumor resection, and predilect possible compromises in nerve impairment (Bruno et al., 2019).
Intra- and extra-fascicular structures of peripheral nerve likely possess different diffusion properties. It is unclear how this might affect clinical MRI scans, which provide an averaged diffusion-weighted signal. Notably, in vivo studies generally require image post-processing, which can affect the calculation of DT indices (Hiltunen et al., 2005; Stein et al., 2009). Accordingly, basic research is needed to further understand nerve DT indices obtained on clinical MRI. Depiction of the diffusion process at the fascicular level within different nerve compartments (fascicles, perineurium, and interfascicular epineurium) could enhance the understanding of nerve DT indices. High-field MRI is required to further depict such small structures. The present study aimed to obtain knowledge on the diffusion characteristics of different nerve compartments on high-field MRI and determine their relationship with nerve fascicular morphometric parameters.
## 2.1 Sample preparation
A segment of the median nerve was obtained from the proximal upper arm of five fresh cadavers, less than 24 h postmortem. The cadavers were donated for research and educational purposes to the Institute of Anatomy, Faculty of Medicine, University of Ljubljana, through a willed cadaver donation program. Each nerve was cut into a 9 mm long segment, had carefully removed the surrounding connective tissue, and inserted in a 10-mm-diameter glass tube. To prevent sample dehydration, the glass tube was filled with perfluorinated liquid, Galden SV90 from Solvay (Brussels, Belgium), which does not produce any detectable MRI signal (Awais et al., 2022). The study was approved by the National Medical Ethics Committee of the Republic of Slovenia (Permit No: 0120–$\frac{239}{2020}$/3).
## 2.1.1 Nerve donors
The donors were four females and one male, with a mean age of 75 years (range 70–80). The interval from death to nerve sampling ranged from 5 to 23 h. There was limited data regarding premortem medical conditions, but all subjects had an atherosclerotic disease and/or arterial hypertension with varying degrees of severity. None of the subjects had known peripheral nerve disease.
## 2.2 Magnetic Resonance Microscopy image acquisition
Magnetic Resonance Microscopy (MRM) was performed on a 9.4T (400 MHz proton frequency) wide-bore vertical superconducting magnet (Jastec Superconductor Technology, Tokyo, Japan) connected to an NMR/MRI spectrometer (Tecmag, Houston TX, United States). Before the imaging, the tube with the sample was inserted in a Micro 2.5 gradient system with a 10 mm RF probe (Bruker, Ettlingen, Germany) of the magnet.
DTI of the nerves was performed using a three-dimensional (3D) pulsed gradient spin-echo (PGSE) imaging sequence with diffusion gradients in 19 different directions; however, all with the same b value of 1,150 s/mm2. The selected b value was chosen given the preliminary results, whereas we have tested various b values up to 1,800 s/mm2. The selected value provided optimal conditions for measuring the leading eigenvalue within the nerve fascicles. The theory also supports the selected b value for the two-point experiment with b 1 = 0 and b 2 = b > 0, where the optimal b value is equal to $b = 1.1$/D (Xing et al., 1997). Acquisition of an additional reference T 2-weighted image with no diffusion weighting ($b = 0$) was needed for DTI calculation. The images were acquired with the following parameters: TE/TR = $\frac{36}{880}$ ms; δ = 3 ms; ∆ = 27 ms; G0 = 0.26 T/m; field of view 9 × 4.5 × 10 mm3; matrix size, 256 × 128 × 16; and 4 signal averages. The image resolution along the in-plane directions was 35 μm. Scanning was performed at room temperature of 21°C with a total acquisition time of 1 day 16 h.
## 2.3 Image analysis and nerve morphometry
The nerve segments of peripheral nerves, acquired in 16 continuous slices of 0.625 mm thickness, were identified on reference T 2-weighted images (Figure 1A). Quality assessment of slices was performed, and slices with artifacts and partial volume effect were excluded from further analysis. In each included slice, fascicles, interfascicular epineurium, perineurium, and nerve cross-sectional area (CSA) were segmented. The fascicles were defined as intraneural hypointense oval- or round-shaped tissue circumferentially surrounded by a markedly hyperintense line representing the perineurium. The latter served as a reliable segmentation border (Figure 1B). The perineurium was segmented with a single measurement by two parallel lines, as shown in Figure 1C. The hyperintense tissue between the fascicles was defined as interfascicular epineurium (Figure 1D). The nerve was segmented to include the entire nerve but a minimal proportion of the background (Figure 1E). Segmentations were performed manually with the image processing software ImageJ (National Institutes of Health, Bethesda, Maryland, United States). The area was recorded for each segmentation and expressed as CSA for the nerve and fascicles. The fascicular ratio (FR) was calculated as a net fascicular CSA/nerve CSA, the ratio of perineurium as a net perineurium/nerve CSA, and the ratio of interfascicular epineurium as net interfascicular epineurium/nerve CSA (Tagliafico and Tagliafico, 2014).
**FIGURE 1:** *T
2
-weighed images, using b-value 0 s/mm2, displaying 0.625 mm thick representative slices of five analyzed median nerves. Note that each figure represents one out of sixteen slices. In figure (A) fascicles are sharply demarcated with a hyperintense line representing the perineurium. Further images depict the segmentation of (B) eight nerve fascicles, (C) thin layer of perineurium with two parallel lines, (D) interfascicular epineurium, and (E) nerve cross-sectional area.*
The diffusion tensor was calculated from the acquired three-dimensional data as described previously (Basser et al., 1994; Awais et al., 2022). For each image voxel, the calculated diffusion tensor was diagonalized, which yielded maps of the tensor eigenvalues D 1, D 2, and D 3 and of the corresponding eigenvectors (ε⇀1,ε⇀2, ε⇀3). Diffusion tensor and its diagonalization were also calculated for every delineated compartment from the corresponding average diffusion weighted signals of the compartment for 19 different diffusion gradient directions. Regional signal averaging enabled the calculation of the fractional anisotropy (FA), mean diffusivity (MD), and D ||/D ⊥ using the equations Eqs. 1–3 with less noise for each of the segmented compartments. The calculations were made using the software written in the C programming language, which has been previously developed and specifically modified by the authors (Awais et al., 2022). MD=D1+D2+D33 [1] FA=32D1−MD2+D2−MD2+D3−MD2D12+D22+D32 [2] D‖/D⊥=D1D2+D32 [3]
## 2.4 Intra-observer agreement
A subset of 10 nerve fascicles, interfascicular epineurium areas, perineurial areas, and nerve CSA were randomly selected and segmented again by the same observer 30 days after the primary segmentation to assess intra-observer agreement. Intraclass correlation coefficient (ICC) was calculated from the FA (Koo and Li, 2016). FA was chosen for ICC calculation as this index is most commonly used DTI readout parameter in clinical environment reflecting the degree of cellular structure alignment (Kronlage et al., 2018).
## 2.5 Trajectory, diffusion ellipsoids, and color-coded ellipsoid/fiber orientation display
The DT data of the acquired nerve segments were displayed with trajectories and diffusion ellipsoids rendered with POV-Ray software (Persistence of Vision Pty. Ltd., version 3.7, Williamstown, Victoria, Australia) (Awais et al., 2022). The software generated a tractography display of the entire peripheral nerve length using the components of the first eigenvector. The subsequent slices were displayed with ellipsoids whose size and orientation correspond to the eigenvalues (size) and the eigenvectors (orientation).
## 2.6 Statistical analysis
Statistical analysis was performed using GraphPad Prism 9 (GraphPad Software Inc., San Diego, United States). The Shapiro-Wilk test was used to evaluate the groups for normality. Because normality and equal variance assumptions were met, the fascicular eigenvalues (D 1, D 2, and D 3), as well as their derived indices (MD, FA, and D ||/D ⊥), were compared by two-way analysis of variance (ANOVA) followed by Tukey’s posthoc test when appropriate. When comparing DT indices of the perineurium, interfascicular epineurium, and nerve CSA one-way ANOVA followed by Tukey’s posthoc test was employed. The fascicular variability of FA was assessed using the coefficient of variation and then compared between fascicles and within fascicles with two-way ANOVA followed by Tukey’s posthoc test when appropriate. To determine the correlations between the DT indices and parameters at the fascicular level, linear regression was performed for each nerve sample then coefficients were compared using a one-sample t-test. The change of nerve FA in subsequent slices was calculated with linear regression. For the assessment of an intra-observer agreement, one-way ICC was used (Koo and Li, 2016). Differences were deemed statistically significant at $p \leq 0.05.$ Data are given as means ± standard deviations, ranges, or percentages when appropriate.
## 3.1 Nerve morphometric characteristics
After quality assessment, 56 image slices were included in the study (range of slices per nerve, 8–13). There were 7.94 ± 4.33 fascicles per slice with a mean fascicle CSA of 0.57 ± 0.66 mm2. The CSA of the nerve was 12.34 ± 3.53 mm2, and the FR was 0.46 ± 0.07. The ratio between perineurium/nerve CSA was 0.08 ± 0.03, and the ratio between interfascicular epineurium/nerve CSA was 0.46 ± 0.09.
## 3.2 DTI characteristics of nerve compartments
In the nerve fascicles, the eigenvalue D 1, with an average of 0.81 ± 0.09·10−9 m2/s, was the highest and approximately 2-times higher than eigenvalues D 2 or D 3. The mean fascicular eigenvalue D 1 was 27-times higher than in the interfascicular epineurium but 1.32-times lower than the mean eigenvalue D 1 of the perineurium (Tables 1, 2, 3). There were significant differences between nerve samples regarding the fascicular and perineurium eigenvalues ($p \leq 0.0001$ and $p \leq 0.0001$, respectively), while there were no significant differences between nerve samples regarding the interfascicular epineurium eigenvalues.
The mean MD followed the same pattern as eigenvalues, with the highest values calculated in the perineurial compartment. Compared to the fascicles, the perineurium had a $50.60\%$ ± $20.72\%$ higher MD, and the interfascicular epineurium had a $94.22\%$ ± $0.80\%$ lower MD (Tables 1, 2, 3). There were significant differences between nerve samples regarding the fascicular and perineural MD ($p \leq 0.0001$ and $p \leq 0.0001$, respectively), while there were no significant differences between nerve samples regarding the MD of the interfascicular epineurium.
The fascicle was the most anisotropic peripheral nerve compartment, with a mean FA of 0.44 ± 0.05. The coefficients of variation of fascicular FA throughout the same fascicle on sequential slices and between fascicles on the same slice were 0.17 ± 0.06 and 0.16 ± 0.03, respectively, and showed no statistically significant difference. Compared to the fascicles, the mean FA was lower in the perineurium (−$23.95\%$ ± $4.05\%$) and even lower in the interfascicular epineurium (−$70.38\%$ ± $3.91\%$). D ||/D ⊥ had the highest and most anisotropic values calculated in the fascicular compartment, while the interfascicular epineurium was the most isotropic compartment with a mean D ||/D ⊥ of 1.15 ± 0.04. Nerve samples differed significantly in FA and D ||/D ⊥ within the fascicles ($p \leq 0.0001$ and $p \leq 0.0001$, respectively) and perineurium ($$p \leq 0.0001$$ and $p \leq 0.0001$ respectively), while there were no significant differences in FA and D ||/D ⊥ of interfascicular epineurium (Tables 1, 2, 3).
DTI maps of five nerve segments providing eigenvalues (D 1, D 2, and D 3), MD, and FA are included in the Supplementary Materials (Supplementary Figures 1–5).
## 3.3 DTI characteristics of nerve cross-section
The mean eigenvalues D 1, D 2, and D 3 of the nerve were 0.46 ± 0.23·10−9 m2/s, 0.30 ± 0.15·10−9 m2/s, and 0.28 ± 0.14·10−9 m2/s, respectively, and differed significantly between the samples ($p \leq 0.0001$). Compared to the fascicular eigenvalues, mean nerve eigenvalues D 1, D 2, and D 3 were $43.33\%$ ± $23.07\%$, $27.87\%$ ± $17.33\%$, and $24.45\%$ ± $19.24\%$ lower, respectively.
The MD of the nerve was 0.34 ± 0.17·10−9 m2/s. This was approximately 11-times higher than the MD of the interfascicular epineurium but 1.51 and 2.29-times lower than the nerve fascicles and perineurium, respectively. The MD differed significantly between the nerve samples ($p \leq 0.0001$) (Figure 2D).
**FIGURE 2:** *Diffusion tensor indices that are calculated from the cross-sectional area of different nerve samples. Figures compare (A) eigenvalue D
1, (B) eigenvalue D
2, (C) eigenvalue D
3, (D) mean diffusivity (MD), (E) fractional anisotropy (FA), and (F)
D
||/D
⊥. Data are presented as means and standard deviations. ****p < 0.0001 compared to nerves 1, 2, 3, and 5; ####
p < 0.0001 compared to nerves 1, 2, 3, and 4.*
The mean FA of the nerve was 0.28 ± 0.04. The highest FA was noted in nerve sample 5 (Figure 2E), which differed significantly from others ($p \leq 0.0001$). The mean coefficient of variation calculated from the FA of nerve samples was 0.12 ± 0.07. The nerve FA was approximately one-third and one-sixth lower than the FA of fascicles and perineurium, respectively, and 2-times higher than the FA of the interfascicular epineurium. The FA of the fascicles and perineurium measured together was $22.85\%$ ± $7.75\%$ higher than the nerve FA. Nerve samples 1 and 2 had no change of FA detected throughout the nerve segments; however, statistically significant correlations were observed in nerves 3–5 (Figure 3). D ||/D ⊥ showed the same pattern as FA with the highest values in nerve 5 (Figure 2F), which differed significantly from other samples ($p \leq 0.0001$).
**FIGURE 3:** *Change of fractional anisotropy throughout the nerve segments. The figure depicts the fractional anisotropy of the nerve cross-sectional area in consecutive slices. (A) Nerve sample 1 and (B) nerve sample 2 had no change of FA detected throughout the segment; however, statistically significant correlations were observed in (C) nerve 3, (D) nerve 4, and (E) nerve 5. Note that the orientation of the nerve (i.e., proximal/distal part) was not tracked during the sample preparation process.*
## 3.4 Correlations between DT indices and nerve structure at the fascicular level
When evaluating the correlation between the nerve FA and the average FA of all fascicles on the same slice, we noted a moderately strong correlation ($r = 0.74$, $$p \leq 0.001$$). Additionally, we found a correlation between nerve FA and FA of the largest fascicle within all five nerves (r ≥ 0.47, $p \leq 0.05$).
However, we found no correlations when evaluating correlations between DT indices and structures at the fascicular level. No correlation was found between the DT indices of fascicles and CSA of fascicles (p ≥ 0.29). There was also no correlation between the mean FA of all fascicles on the slice and the number of fascicles ($$p \leq 0.59$$). We observed no correlation between the DT indices of nerve and the number of fascicles ($$p \leq 0.88$$). Additionally, there was no correlation between the nerve FA and FR ($$p \leq 0.34$$), and no correlation between the nerve FA and CSA of all fascicles on the same slice ($$p \leq 0.68$$).
## 3.5 Intra-observer agreement
ICC calculated from FA of the fascicles, and the nerve CSA showed excellent intra-observer agreement, 0.98 and 0.99, respectively. Good intra-observer agreement was observed for the perineurium, 0.89, and interfascicular epineurium, 0.86.
## 3.6 Anisotropic diffusion in nerves presented with tractography and diffusion ellipsoids
DT data was demonstrated with graphic displays. Tractographic displays were generated using the fastest diffusion direction, which was oriented longitudinally along the course of the nerve. The three-dimensional representation of anisotropic nerve fibers of one nerve sample is shown in Figure 4A. As seen, nerve fascicles can be tracked along the entire nerve segment, their fiber density remains constant, and the fibers intermingle within the fascicles.
**FIGURE 4:** *DT tractography and diffusion ellipsoids of one nerve sample. (A) The figure displays a three-dimensional representation of nerve fibers in an approximately 9 mm long segment of the median nerve. By convention, red color represents the x-direction, green color the y-direction, and blue color the z-direction; in our case, the nerve was oriented along the z-axis. (B) Diffusion ellipsoid display of the subsequent slices. The dimensions and orientations for the ellipsoids correspond to the eigenvalues and eigenvectors, while their color scheme is orientation-dependent and determined by the same principle as the colors in the tractography image.*
The presentation with diffusion ellipsoids shows ellipsoids that are oriented in the direction of the fastest diffusion (along the first eigenvector). The main axis of the ellipsoid provides information about the main diffusion direction in the voxel, while the shape of the ellipsoid gives the information about the degree of anisotropy. The difference in the eigenvalues within the fascicles and perineurium enables both compartments to be depicted and separated (Figure 4B). Note different eigenvectors of perineurium and fascicles that differ in values and orientations. In contrast, the interfascicular epineurium with considerably slower and isotropic diffusion cannot be adequately visualized. This anatomical compartment can be partially visualized in the background, where it can be seen as small dots of different colors and shapes.
## 4 Discussion
In our study, DTI in the magnetic field strength of 9.4T was employed on fresh ex vivo human median nerves, and DT indices of all major nerve anatomical structures were quantified. The anisotropic diffusion was shown throughout the long axis of the nerve, and nerve fascicles proved to be the most anisotropic nerve compartment. The DT indices of fascicles and perineurium differed significantly among subjects, while the interfascicular epineurium with the slowest and practically isotropic diffusion had no inter-subject differences. The DT indices of the peripheral nerve and anatomical compartments of the nerve did not correlate with nerve structure at the fascicular level.
DTI of peripheral nerves in the upper and lower extremities has been previously performed (Eppenberger et al., 2014; Kronlage et al., 2018; Godel et al., 2019). In the upper extremity, most investigations have been performed on the median nerve as it is the most frequently affected nerve in upper extremity entrapment neuropathies (Snoj et al., 2022). Several studies have depicted tractographic images of healthy median nerves and showed that Wallerian degeneration reduces the attenuation of trackable nerve fibers (Hiltunen et al., 2005; Khalil et al., 2008; Takagi et al., 2009; Boyer et al., 2015). More recent studies have predominately focused on the calculation of DT indices of nerve, mainly the FA (Kronlage et al., 2018; Awais et al., 2022). The nerve FA might vary along the longer nerve segment. Yao and Gai reported no change in FA along the length of the median nerve in the carpal tunnel (Yao and Gai, 2009), while the few other researchers demonstrated a decreasing trend of FA from proximal to distal locations near the carpal tunnel (Hiltunen et al., 2005; Guggenberger et al., 2013). Stein et al. [ 2009] showed that FA could differ significantly in a few centimeters long nerve segment. In our study, the FA did not diminish or augment throughout the nerve segment in two subjects, while the other three subjects had a trend of changing FA in the subsequent slices.
The mean FA of healthy median nerve reported in more extensive meta-analysis was 0.58 (Rojoa et al., 2021). In the wrist of healthy individuals, the mean FA was found to be in a broader interval between 0.48–0.71 (Kabakci et al., 2007; Stein et al., 2009; Barcelo et al., 2013; Guggenberger et al., 2013). Only the scarcity of studies have measured the FA of nerves in the upper arm and reported values in a similar interval range (Kronlage et al., 2018; Godel et al., 2019). It has been previously shown that the age of the subject is an important determinant for nerve FA (Tanitame et al., 2012; Kronlage et al., 2018). When accounting for this factor, the FA of the median nerve in our study was approximately two times lower compared to the FA of healthy peripheral nerves of similarly aged subjects in a study by Kronlage et al. [ 2018]. Several factors should be accounted for when interpreting lower FA in our study. The most important factors are likely environmental. In our study, the MRM acquisition was performed at room temperature. This caused the diffusion process to be approximately $40\%$ lower than the diffusion process at body temperature and may partially explain the differences in the values of DT indices compared to indices measured in previous in vivo studies (Holz et al., 2000; Lehmann et al., 2010). In a few ex vivo studies, unfrozen or fixated nerves were used (Boyer et al., 2015; Awais et al., 2022). Sample preparation can have an important impact on its integrity. It has been shown that tissue fixation in formaldehyde can significantly decrease the FA of a heart muscle (Lohr et al., 2020). Conversely, Haga et al. [ 2019] did not observe a change in FA between fixed and non-fixed marmoset brains; however, the use of formaldehyde did significantly decrease eigenvalues and MD of the fixed brain. As there is a lack of data on how formaldehyde might affect DT indices of peripheral nerve, we have used a fresh nerve to exclude the effects of the fixative procedure or possible rupture of the nerve cells during the freezing/thawing cycle.
The outstanding FA in nerve 5 can be partly attributed to the low body mass index of this donor; nevertheless, such inter-individual differences can still be found between healthy individuals (Kronlage et al., 2018). As reported by Godel et al. [ 2019], increased radial diffusivity reflects damage to myelin integrity, whereas changes in axonal diffusivity might be more specific for axonal degeneration. In nerve 4, eigenvalues D 1, D 2, and D 3 were equally increased which probably supports the hypothesis of a major role of environmental factors.
Differences in MR hardware and imaging protocols can also lead to discrepancies between studies. Most existing studies were performed on conventional 3T MRI (Stein et al., 2009; Barcelo et al., 2013; Guggenberger et al., 2013; Kronlage et al., 2018); however, some researchers have also applied 7T whole-body MRI systems (Riegler et al., 2016; Yoon et al., 2018). Although a high-field-strength system was used in our study, it has been previously shown on the brain that field strength has little effect on the FA (Zhan et al., 2013). Importantly, studies use scanners from different manufacturers, and it has been shown that FA of the median nerve in healthy individuals significantly differs within the wrist between different MR scanners (Guggenberger et al., 2013). Post-processing within in vivo studies involves applying threshold values to distinguish nerve from muscle fibers or ligaments (Hiltunen et al., 2005). In these studies, FA is calculated from the fiber tractography images, which normally have a high threshold value (Hiltunen et al., 2005; Kabakci et al., 2007; Stein et al., 2009; Barcelo et al., 2013). Thus, the impact of isotropic nerve compartments, such as interfascicular epineurium, that decreases FA of nerve CSA is excluded from the calculation to a certain degree. In our study, no threshold was applied to obtain the most reliable data. Consequently, the FA of fascicles and perineurium combined was approximately a quarter higher than the FA of the nerve, which included interfascicular epineurium.
The estimated signal-to-noise ratio (SNR) in the fascicular region was approximately 14 ($b = 0$ s/mm2). In comparison to the study by Yoon et al. ( Yoon et al., 2018), this SNR was comparable to their in vivo 7T MRI system. However, our in-plane resolution was considerably better (35 μm vs. 120 μm), and slices were also thinner (0.625 mm vs. 2 mm). The resolution advantage in our experiment is due to the more sensitive receiver coil, which had only 10 mm in diameter; thus, the filling factor (nerve to coil diameter ratio) was high, and the signal reception was, therefore, considerably better than in the in vivo experiment at 7T where a surface coil was used. Some advantage was also due to a somewhat stronger magnetic field (9.4T vs. 7T). This comparison demonstrates that in vivo MR imagining at 7T is promising but still needs several improvements, especially in signal detection, to match MR microscopy results at higher fields and dedicated hardware (special gradient and signal receive coils).
The long scanning window of approximately 40 h posed a challenge concerning tissue desiccation. Immersion in formaldehyde would be optimal for preventing the autolytic process; however, it contains hydrogen atoms that produce a signal on MRI. Hence, each nerve was placed instantly after the excision into the fluorinated carbon liquid, which substantially reduced problems with stability and autolysis. In our previous pilot study (Awais et al., 2022), minor nerve shrinkage of one pixel was noted during the scanning of the nerve sample in this liquid. This issue was adequately addressed with an innovative scanning strategy by reordering the scanning loops, whereas all twenty images were acquired simultaneously, not sequentially. Thus, the influence of the sample volume change was evenly distributed among all images and did not importantly affect the calculations of DT indices.
In our study, fascicles proved to have the highest FA in the analysis of the nerve compartments. This result was expected due to the specific arrangement of nerve fibers in the peripheral nerve (Sunderland, 1978). It is well known that the fascicular pattern can change in submillimeter sections (Sunderland, 1945; Sunderland, 1978). When evaluating the FA within the same fascicle, we found no statistical difference between the fascicular coefficients of variation in sequential slices and the fascicles of the same slice. We hypothesize that the fibers crossing between the fascicles contributed to high fascicular coefficients of variation within the same fascicle and explain why no difference was observed (Figley et al., 2022).
The perineurium had only moderately lower FA than the fascicles. The latter provides a diffusion barrier made of concentrically flat perineural cells, which contributes to poorer membrane permeability for water molecules and could lead to higher FA (Hill and Williams, 2002). As the perineurium is thinner than the resolution of our MRM system (Reina et al., 2015), it is essential to consider the partial volume effect. The segmentation of perineurium might partially include highly anisotropic fascicles and isotropic interfascicular epineurium. The interfascicular epineurium, as a collagenous compartment of the extracellular matrix, does not form any non-permeable barrier. Hence, it had the lowest FA mean approaching isotropic diffusion when considering the influence of the SNR (Peltonen et al., 2013; Awais et al., 2022). Fascicle FA has an important impact on nerve FA; however, measured fascicular parameters did not affect the nerve FA. Previous studies have shown that higher FA correlates with increased axonal density, axonal diameter, and myelin density (Takagi et al., 2009). Thus, demyelinating disorders could contribute to differences in fascicular FA between subjects included in this study (Kakuda et al., 2011). As seen, the FA was not correlating with structures at the fascicular level; therefore, axon- and myelin-related parameters tend to have a more important role in diffusion (Takagi et al., 2009).
We acknowledge that this study had some limitations. First, we had limited clinical data on the subjects from whom we obtained nerve samples. Consequently, the differences in DT indices between the subjects might have been even greater than they would have been between healthy subjects. It would be imperative in future research to expand the sample size and provide more clinical data about pathologies that could affect nerve integrity. Another limitation of this study is the acquisition at room temperature. As the environmental temperature could not be strictly controlled, minor fluctuations in room temperature could partially impact the DT indices of individual samples and cause important differences between the samples. A third limitation is that the determination of individual compartments could occasionally be ambiguous; hence, individual segmentations of the perineurium or smaller fascicles were more challenging to implement. This could lead to partially overlapping regions. Another limitation is a relatively long scanning time resulting in the subjection of nerve samples to the autolytic process. Accordingly, this has to be considered when compared to in vivo studies or ex vivo studies with shorter acquisition time. The last limitation is the limited ability to translate our results directly into the partial trauma series; non-etheless, we believe basic knowledge is essential for future understanding of how partial nerve transection reflects in a change of DT indices within different nerve compartments. Moreover, the understanding of diffusion within the nerve compartments can be translated into the nerve entrapment syndromes (where oedema is present) or diabetic neuropathy and thus help evaluate how individual compartments contribute to the anisotropy change.
## 5 Conclusion
High-resolution DTI depicted highly anisotropic diffusion within the fascicles and perineurium. The interfascicular epineurium had more isotropic diffusion. As median nerve DT indices did not correlate with nerve structure at the fascicular level, future studies should investigate their relationship with axon- and myelin-related parameters.
## Data availability statement
The raw data supporting the conclusion 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 National Medical Ethics Committee of the Republic of Slovenia (Permit No: 0120–$\frac{239}{2020}$/3). The participants gave their written consent to be donated for research and educational purposes.
## Author contributions
Conceptualization: LP, IS, NU, EC, and ŽS; methodology: IS and ŽS; software: IS; formal analysis: LP; investigation: LP; resources: IS, NU, EC, and ŽS; writing—original draft preparation: LP; supervision: IS, NU, EC, and ŽS. All authors have read and agreed to the published version of the manuscript.
## 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/fphys.2023.1070227/full#supplementary-material
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|
---
title: Long-term cultures of human pancreatic islets in self-assembling peptides hydrogels
authors:
- Amanda Marchini
- Maria Gessica Ciulla
- Barbara Antonioli
- Alessandro Agnoli
- Umberto Bovio
- Virginia Visnoviz
- Federico Bertuzzi
- Fabrizio Gelain
journal: Frontiers in Bioengineering and Biotechnology
year: 2023
pmcid: PMC9995881
doi: 10.3389/fbioe.2023.1105157
license: CC BY 4.0
---
# Long-term cultures of human pancreatic islets in self-assembling peptides hydrogels
## Abstract
Human pancreatic islets transplantation is an experimental therapeutic treatment for Type I Diabetes. Limited islets lifespan in culture remains the main drawback, due to the absence of native extracellular matrix as mechanical support after their enzymatic and mechanical isolation procedure. Extending the limited islets lifespan by creating a long-term in vitro culture remains a challenge. In this study, three biomimetic self-assembling peptides were proposed as potential candidates to recreate in vitro a pancreatic extracellular matrix, with the aim to mechanically and biologically support human pancreatic islets, by creating a three-dimensional culture system. The embedded human islets were analyzed for morphology and functionality in long-term cultures (14-and 28-days), by evaluating β-cells content, endocrine component, and extracellular matrix constituents. The three-dimensional support provided by HYDROSAP scaffold, and cultured into MIAMI medium, displayed a preserved islets functionality, a maintained rounded islets morphology and an invariable islets diameter up to 4 weeks, with results analogues to freshly-isolated islets. In vivo efficacy studies of the in vitro 3D cell culture system are ongoing; however, preliminary data suggest that human pancreatic islets pre-cultured for 2 weeks in HYDROSAP hydrogels and transplanted under subrenal capsule may restore normoglycemia in diabetic mice. Therefore, engineered self-assembling peptide scaffolds may provide a useful platform for long-term maintenance and preservation of functional human pancreatic islets in vitro.
## 1 Introduction
Type I *Diabetes mellitus* is a severe autoimmune disease caused by disruption of insulin-producing pancreatic β-cells. Loss of β-cells alters metabolic glucose homeostasis, leading to high blood glucose levels and chronic hyperglycemia (Katsarou et al., 2017). Among currently available therapies and since the seminal publication of the Edmonton protocol (Shapiro et al., 2000), intraportal transplantation of human pancreatic islets (hPIs) from multiorgan donors remains a consolidated and efficacious procedure for the replacement of β-cells. Before transplantation, hPIs are isolated from donor healthy pancreas thanks to a combination of mechanical and chemical methods that involve collagenase digestion. Isolated and purified hPIs are then transplanted into the liver via portal vein. This procedure has been successfully performed on patients with type I Diabetes, providing exogenous insulin independence for several years. Liver is considered the preferred transplantation site because the procedure is minimally invasive, with ease access and with a low rate of bleeding and thrombosis (Cayabyab et al., 2021). The outcome after 3-year follow-up reported a proper glucose control with no need of exogenous insulin administration, confirming an optimal glycemic homeostasis in patients (Bertuzzi et al., 2018b). At 5-year follow-up, islet transplantation led to insulin independence in almost all patients, without progress of secondary diabetic complications and with a stable kidney functionality (Bachul et al., 2021). However, a key component in successfully islets graft survival is a rapid revascularization that provides oxygen and nutritional supply to engrafted hPIs. Despite liver remains the preferred transplantation site, recent studies revealed that absence of extensive vascularization limits the in viability of transplanted hPIs in the long-term. Researches are investigating alternative transplantation sites (as intramuscular, omentum and subcutaneous sites) (Pellicciaro et al., 2017; Bertuzzi et al., 2018a; Yu et al., 2020), or strategies to promote rapid revascularization via angiogenic biomaterials (Vlahos et al., 2017; Fernandez et al., 2022).
Thus, some limitations hindered the success of transplantation, including global shortage of suitable donor organs, poor amount of purified islets after isolation procedure for clinics, time-limited islet functionality, lack of vascularization for oxygen and nutritional supply and morphological integrity due to basement membrane and extracellular matrix (ECM) disruption during enzymatic and mechanical digestion (Brandhorst et al., 2022). Basement membrane, and in general ECM, plays a crucial structural role, mediating adhesion and intracellular chemical signaling pathways, as well as β-cells survival and insulin secretion. Consequentially, islets undergo apoptosis, and quickly lose their integrity, morphology, and functionality due to the absence of ECM support and signaling (Cross et al., 2017; Townsend and Gannon, 2019). To maximize the success of transplantation in clinics, hPIs must be transplanted within 48–72 h of isolation (Ricordi et al., 2016; Cayabyab et al., 2021). Others focused on hPIs banking from multiple donors, thus developing protocols for pancreatic islets cryopreservation (Zhan et al., 2022).
However, the quest for strategies enabling long-term culturing of hPIs (e.g., pancreatic islets seeding in ECM-like scaffolds) is still a major goal for researchers. Recently developed protocols have demonstrated an alternative source of β-cells, obtaining a large-scale production of functional islet-like clusters from human pluripotent stem cells (Parent et al., 2022). Moreover, in recent years, organoid technology has attracted lots of attention for its unprecedent potential to reproduce in vitro structural and functional aspects of in vivo tissue/organ and bridge the gap between cellular- and tissue/organ-level in biological models (Takebe and Wells, 2019). Human pancreatic organoids could have opportunity to produce functional β-cells in vitro after human pluripotent stem cells differentiation process and could acts as source of islets for transplantation (Jiang et al., 2022; Yin et al., 2022). A tissue engineering approach is required for culturing insulin-producing cells in a ECM-like scaffold in three-dimensional (3D) constructs with the purpose to restore cell-matrix attachment and to maintain islet viability and insulin secretion functionality for long-time (Kumar et al., 2018; Elizondo et al., 2020; Abadpour et al., 2022; Perugini et al., 2022). Islet encapsulation within bioscaffold may provide an immune-isolation strategy for islet transplantation and engraftment, avoiding immune-suppression procedure to prevent rejection. Biomaterials can be also engineered to accommodate other type of cells (e.g., endothelial cells) with the aim to create a vascularized microdevice, exhibiting pro-angiogenic properties and improving transplantation outcome (Vlahos et al., 2017). Natural, synthetic or hybrid biomaterials with different methods of fabrication are currently used to incorporate insulin-secreting cells (Salg et al., 2019). Ideally, a scaffold should be a 3D porous biocompatible ECM-like matrix with a controlled degradation rate (Perez-Basterrechea et al., 2018). Among synthetic biomaterials, self-assembling peptides (SAPs) can be considered as suitable substrates for long-term islets maintenance for their tunable biomimetic and biomechanical properties in tissue engineering applications (Chen and Zou, 2019; Gelain et al., 2021). SAPs are small molecules, made of amino acids, capable of spontaneously self-assemble into various nanostructures, as nanofibers, nanotubes or nanovesicles, upon exposure to shifts of pH, temperature and osmolarity (Matson and Stupp, 2012; Pugliese and Gelain, 2017). Multiple functional bioactive motifs can be conjoined to SAPs to achieve the desired biomimetic properties and enhance cell-matrix interactions (Pugliese et al., 2018b).
Moreover, the introduction of branched SAP sequences ameliorated the mechanical stiffness, with the chance to mimic different living tissues (Pugliese et al., 2018a). SAPs used in this work have previously demonstrated an excellent cell attachment and differentiation potential in neural stem cell cultures (Gelain et al., 2012; Caprini et al., 2013; Marchini et al., 2020), a remarkable neuroregenerative potential in vivo (Marchini et al., 2019), and promising biocompatibility (Raspa et al., 2016); for all these reasons, SAPs could be considered promising scaffolds for other cell cultures and/or the regeneration of other tissues. In this regard, it has been reported that insulin-producing cells and neuronal cells share many characteristics, suggesting that these 2 cell types could also share the same growth and differentiation factors, due to common precursor during development process (Alpert et al., 1988). These similarities suggest that SAPs may have protective and beneficial effects on pancreatic islets cultures (Yuan et al., 2008). In this work, we investigated three different SAPs, namely HYDROSAP, FAQ and CK1, to generate 3D matrices for long-term maintenance (14-and 28-days) of hPIs. Freshly-isolated islets in suspension were considered positive control, while islets in suspension cultured for 14 and 28 days were considered as negative controls. In addition, we tested two types of culture media: MIAMI medium, a CMRL-based medium commonly used to maintain the pancreatic islets for few days before transplantation (Bertuzzi et al., 2018b), and medium enriched with growth factors (namely GF medium), that is a medium normally used for neural stem cell cultures (Gelati et al., 2013), containing epidermal growth factor (EGF) and basic fibroblast growth factor (bFGF). Embedded and free-floating islets were assessed for their morphology, viability, endocrine cellular content, and ECM molecules composition. Optimal ECM-like matrix was reached by HYDROSAP scaffold cultured in MIAMI medium, yielding results that demonstrated better islets preservation for 14 days, and then for 28 days: this was the case for cellular population composition (β-cells producing insulin, endocrine cells, and proliferative cells), apoptotic cells, endothelial cells, and ECM components (laminin, collagen I and collagen IV), as well as preservation of islets morphological features overtime. Finally, densely 3D-seeded hPIs in HYDROSAP scaffold were characterized to simulate the protocol developed for subsequent in vivo transplantation. Lastly, a preliminary in vivo study was conducted on nude diabetic mice, and islets maintained in culture for 14 and 28 days inside HYDROSAP were transplanted under kidney capsule.
## 2.1 Isolation of human pancreatic islets
hPIs used in this study were isolated from six cadaveric multiorgan donors, in particular three women and three men, donor age 52.6 ± 3.6 years, BMI 25.5 ± 2.6 kg/m2, islets purity 82.5 ± $5.2\%$, according to the procedure previously described (Ricordi et al., 1988; Petrelli et al., 2011). The overall protocol has been approved by the Niguarda Cà Granda Ethics Board. Islets were isolated using the automated method previously described (Ricordi et al., 1988). Pancreata were obtained from multiorgan cadaveric donors utilizing cold perfusion. Exclusion and inclusion criteria were applied based on the Italian Guidelines. Briefly, pancreata were digested by a cold enzymatic blend solution of collagenase and thermolysin (Liberase MTF GMP Grade kit, Roche Diagnostics, Mannheim, Germany) reconstituted in Hank’s Balanced Salt Solution (HBSS, Euroclone, Italy) with 25 mM of HEPES. Subsequently, islets were purified with discontinuous polysucrose solutions at decreasing density 1.132, 1.108, 1.096, 1.060 and 1.037 g/L (Mediatech-Cellgro, VA, United States). Islets were counted by dithizone staining islet equivalent (IEQ) method (see “Dithizone staining” section) and they were cultured at 24°C, $20\%$ O2, $5\%$ CO2 in a humidified atmosphere in MIAMI Medium #1A (Mediatech-Cellgro, VA, USA) supplemented with Ciprofloxacin (Fresenius Kabi, Verona, Italy), or in serum-free medium in the presence of basic fibroblast growth factor (bFGF, PeproTech) and epidermal growth factor (EGF, PeproTech) at final concentrations of 10 ng/ml and 20 ng/ml, respectively.
## 2.2 Peptide synthesis and purification
All reagents and solvents used for the peptide synthesis were purchased in highest quality commercially available and used without further purification. All peptides were synthesized with solid-phase Fmoc-based chemistry on Rink amide resin (0.19–0.56 mmol/g, 100–200 mesh) using a Liberty Blue System synthesizer (CEM Corp, Matthews, NC, Canada). Peptides were cleaved from resin by addition of a freshly prepared mixture containing $92.5\%$ TFA, $2.5\%$ H2O, $2.5\%$ DODt, $2.5\%$ TIS. All synthesis were carried out on a 0.25 mmol in presence of a 0.2 M amino acid solution (in DMF), 1 M DIC (in DMF), and 1 M Oxyma (in DMF). The deprotection of Fmoc groups was determined by a $10\%$ v/v of piperazine in 9:1 NMP/EtOH. The N-terminal acetylation (for CK1 and pureHYDROSAP components) was performed using $20\%$ v/v solution of Ac2O (in DMF). The crude products were purified via reversed-phase chromatography by semi-preparative Waters binary HPLC (>$96\%$) using a c18 RestekTM column and then lyophilized (Labconco, Kansas City, MO, USA). Purified peptides powder was subsequently dissolved in 0.1 M HCl to remove the presence of possible TFA salts. Three different SAPs were used for this study: pureHYDROSAP (Marchini et al., 2019; Marchini et al., 2020), FAQ (NH2-FAQRVPP-GGG-LDLKLDLKLDLK-CONH2) (Gelain et al., 2012) and CK1 (Ac-CGGLKLKLKLKLKLKGGC-CONH2) (Pugliese et al., 2018c; Ciulla et al., 2022). As previously described, pureHYDROSAP is composed by linear SAPs Ac-(LDLK)3-CONH2, Ac-KLPGWSGGGG-(LDLK)3-CONH2 (Caprini et al., 2013) and Ac-SSLSVNDGGG-(LDLK)3-CONH2 (Gelain et al., 2012) and branched SAP tris(LDLK)3-CONH2 (Pugliese et al., 2018a). For the experiments, pureHYDROSAP (abbrev. HYDROSAP), FAQ and CK1 powders were dissolved respectively to a final concentration of $2\%$ (w/v), $5\%$ (w/v) and $5\%$ (w/v) in distilled water (Gibco).
## 2.3 Rheological studies
The rheological experiments were carried out on a rotational AR-2000 ex rheometer (TA Instruments, Waters Corp, Milford, CT, United States) equipped with an acrylic cone-plate geometry (diameter, 20 mm; angle, 1; truncation gap, 34 µm). All the tests were performed at 37°C using a Peltier plate in the lower plate of the instrument to assess a continuum control of the temperature during each test. All samples were dissolved in distilled water at the corresponding concentration and incubated overnight at 4°C. Thus, 584 mM sucrose solution were added to each peptide solution (ratio 1:1); then, MIAMI medium (instead of the volume of hPIs) were included to obtain a final concentration of $0.76\%$ (w/v), $1.9\%$ (w/v) and $1.9\%$ (w/v) for HYDROSAP, FAQ and CK1, respectively. Each sample (50 µL) was gently placed on the middle of the Peltier plate. A lid on the top serves to protect sample from the water evaporation. To evaluate the storage (G′) and loss (G″) moduli increments, frequency-sweep experiments were recorded as a function of angular frequency (0.1–100 Hz) at a fixed strain of $1\%$ after a 3 h time-sweep experiment (performed at 1 Hz constant angular frequency) in presence of D-PBS (1X). Stress–strain sweeps were performed in the range $0.01\%$–1,$000\%$ of strain. Thixotropy of peptides was performed with shear-thinning tests by a series of peak holds at constant shear rates. All data were performed in triplicate and the results were processed with OriginPro 2019 (OriginLab Corporation, Northampton, MA, United States). Graphs were reported in the Supplementary Figure S1).
## 2.4 Culture of human pancreatic islets inside nanostructured scaffold
Cell cultures were prepared within $\frac{24}{48}$ h after receiving islets. hPIs were incapsulated inside hydrogels or plated in suspension in 48-well with two different type of culture media: MIAMI medium #1A (Mediatech-Cellgro, VA, United States) (a CMRL-based culture medium commonly used in clinical trial for islet culture before transplantation) or GF medium (a serum-free medium with bFGF and EGF growth factors). 25 IEQ (low density) or 500 IEQ (high density) were incapsulated inside SAPs, previously dissolved in distilled water and diluted with 584 mM sucrose solution (ratio 1:1). A droplet of 25 µL was placed onto glass coverslip in 48-well, medium was added to start SAP gelation and to obtain free-floating samples. Same concentration of islets was used for samples in suspension in 48-well. In these conditions, the samples were maintained in culture up to 14-days (T14) and 28-days (T28) at 24°C, $20\%$ O2, $5\%$ CO2 in a humidified atmosphere. As positive control, suspensions of islets in MIAMI medium or GF medium were maintained in culture for 1 day (T1). hPIs incapsulated insides hydrogel were monitored individually during culture time and brightfield images from day 1 to day 28 were acquired weekly via Zeiss light microscope at ×5 magnification.
## 2.5 Dithizone staining
Dithizone (Sigma-Aldrich, St Louis, MO, United States) is a zinc chelating agent, well-known to selectively stain pancreatic islets in a brownish red color. During islets purification and after 1 (T1), 14 (T14) and 28 days (T28) post-isolation, an aliquot of suspension of hPIs was stained with dithizone to track the islet morphology during time and to observe the purity of preparation. 200 µL of Dithizone solution was added to 1 ml of islets suspension in culture media for 10 min at room temperature. Following incubation time, three washes in D-PBS 1X (Gibco) was performed and images were acquired using Zeiss light microscope at ×5 magnification.
## 2.6 Diameter analysis
Images acquired via Zeiss light microscope of hPIs encapsulated inside SAPs and hPIs in suspension after Dithizone staining were processed via NIH-ImageJ software and Axiovision software. Diameter of each islet at each timepoint were calculated and reported.
## 2.7 Immunofluorescence staining
hPIs in suspension and encapsulated in hydrogel maintained in culture for 1, 14 and 28 days were fixed in paraformaldehyde (PFA) $2\%$ and $4\%$ (w/v in PBS) and cryosectioned at 50 µm-thick via Cryostat (Histo-Line Laboratories). Sections were permeabilized with $0.3\%$ Triton X-100 for 10 min at 4°C and blocked with $10\%$ normal goat serum (NGS, Gibco) for 1 h at room temperature. The following primary antibodies were used: rabbit anti-insulin (1:300, ThermoFisher), mouse anti-chromogranin (1:100, ThermoFisher), mouse anti-glucagon (1:8000, Sigma-Aldrich), rabbit anti-Ki67 (1:750. Novus Biologicals), rabbit anti-vWF (1:500, DakoCytomation), mouse anti-fibroblast (1:200, Acris Antibodies), rabbit anti-collagen IV (1:100, Cedarlane), mouse anti-collagen I (1:2000, Sigma-Aldrich), rabbit anti-laminin (1:30, Sigma-Aldrich). To reveal primary antibodies, the following secondary antibodies were used: goat anti-rabbit Cy3 (1:1,000, Jackson), goat anti-mouse Cy3 (1:1,000, Jackson), goat anti-rabbit Alexa 488 (1:1,000, Invitrogen) and goat anti-mouse Alexa 488 (1:1,000, Invitrogen). Cell nuclei are stained with HOECHST 33342 (1:500, Molecular Probes).
## 2.8 Apoptosis
Tunel assay (In situ cell death detection kit fluorescein, Roche) was performed to detect and quantify apoptotic cells. The protocol was performed following the manufacturer’s instructions. Briefly, slices were permeabilized with $0.3\%$ Triton X-100 for 10 min at 4°C and incubated with Tunel reaction mixture (1:10 enzyme in label solution) for 1 h at 37°C. Cell nuclei are stained with HOECHST 33342 (1:500, Molecular Probes).
## 2.9 Images acquisition and data analysis
Whole hPIs were imaged in fluorescence at × 20 and × 40 magnification via Zeiss Microscope connected to Apotome System and image analysis was performed with Fiji-ImageJ NIH-software. A minimum of three independent experiments were performed for each experimental condition, timepoint and marker. For insulin, chromogranin, glucagon, Ki67 and Tunel Assay markers, quantitative analysis were performed by counting manually positive cells for each marker, compared to total cells contained in a single islet and stained with Hoechst. For vWF, Fibroblast, Laminin and Collagen I and IV, fluorescent images were converted into binary images and reactivity area were quantified by measuring the number of positive pixels for each marker, compared to the total area of hPIs.
## 2.10 Experimental design for pilot in vivo experiment
All the animal procedures and ethical revision were performed according to the current Italian Legislation (Legislative Decree 4 March 2014, n.26) enforcing the $\frac{2010}{63}$/UE Directive on protection of animals used for biomedical research. In vivo protocol was approved by Institutional Animal Care and Use Committee (IACUC number $\frac{63}{2022}$-PR). 18 CD-1 Nude Male Mice, weight $\frac{27}{30}$ g (approx. 7 weeks) were divided into three groups: 1) six animals received freshly-isolated hPIs (positive control); 2) six animals received hPIs pre-cultured in SAPs for 2 weeks; 3) six animals received hPIs pre-cultured in SAPs for 4 weeks hPIs used for in vivo experimentations came from two different donors. After diabetes induction, 1,500 IEQ hPIs were transplanted under kidney capsule of diabetic mice, by inspiring a well-known surgical technique (Szot et al., 2007). Mice were monitored for 50 days post-surgery by studying non-fasting blood glucose level (non-fasting BGL) and body weight every other day, and intraperitoneal glucose tolerance test (IPGTT) at two- and 4-week post-transplantation. Normoglycemia was achieved when blood glucose levels scored values < 200 mg/dl. At the end of in vivo experiment, mice were sacrificed by cervical dislocation and engrafted kidneys were excised, fixed, and cryo-sectioned to visualized with immunohistochemical studies the presence of engrafted hPIs via human insulin markers. See Supplementary Figure S1 and Methods for more details.
## 2.11 Statistical analysis
Results are reported as means ± SD in all graphs. Data were processed using GraphPad Prism seven software for in vitro and in vivo experiments. In vitro tests were performed in triplicate: three different islets preparations, derived from three different donors, are used for each condition (time-points, type of medium and scaffold) for each marker. Statistical analyses were evaluated by Two-way ANOVA followed by Tukey’s multiple comparison. Comparison between low density and high density of hPIs were performed by two-way ANOVA followed by Bonferroni’s multiple comparison test. Statistical significance was set at p-value < 0.05.
## 3.1 Fabrication of bioscaffolds for in vitro 3D hPIs cultures
Biomaterials are key structural elements in 3D cell cultures to tune cell attachment, growth, differentiation and function (Marchini and Gelain, 2022). The ECM composition and organization to be mimicked by the biomaterials can differ and depends on the tissue of origin. Pancreatic ECM, enriched in laminin, collagen IV and VI, and fibronectin, provides structural and biochemical support to β-cells (Townsend and Gannon, 2019). Islet-ECM interaction regulates survival, insulin secretion, β-cells proliferation, differentiation, and preservation of round islet morphology (Stendahl et al., 2009). Current islet isolation techniques disrupt the native islet ECM, leading to faster cell death and dysfunction. Biomaterials, and in particular SAPs, can act as efficient synthetic substitutes, since they can be tailored according to specific in vitro and/or in vivo applications by changing mechanical, physicochemical and biological parameters (Marchini and Gelain, 2022). SAPs used in this work were widely applied in the field of neural stem cells (Gelain et al., 2012; Pugliese et al., 2018c; Marchini et al., 2019; Marchini et al., 2020). It is well-known that brain tissue is very soft, with a stiffness ranging from 0.1kPa to 1 kPa (Ciulla et al., 2022): pancreas stiffness lays in the similar range (Guimarães et al., 2020), with healthy pancreas tissue ∼900 Pa and with increased ECM stiffness in pancreatic cancer (∼2,900 Pa) (LeSavage et al., 2022). Three different SAPs (HYDROSAP, FAQ and CK1) were evaluated in terms of rheological analysis to investigate their viscoelastic and mechanical properties, as well as to compare their characteristics to that of pancreatic tissue. In order to mimic viscoelastic properties of normal human pancreatic tissue, thixotropy (Supplementary Figure S1, on the left), and stiffness, in terms of storage modulus (G′) and loss modulus (G″) (Supplementary Figure S1, on the right) were evaluated. The rheological characterization revealed that storage moduli in HYDROSAP, FAQ and CK1 enclose G’ values ranging to 826 ± 75 Pa for FAQ, 1,583 ± 165 for HYDROSAP, and 5,440 ± 421 Pa (CK1), showing a range of stiffness close to the ECM surrounding of human pancreatic cells. In other words, the SAPs scaffolds used in this study were satisfactorily similar (in term of rigidity) to the healthy pancreas tissue (HYDROSAP, FAQ), or to tumorigenic pancreas (CK1). Lastly, thixotropy experiments described the capacity of these materials to recover their original behavior after reversible breaking and recovering of self-assembled network.
In vitro studies, standard hPIs cultures were maintained in free-floating condition as a control group (Figure 1A). In Figure 1B, hPIs morphology and integrity are monitored and captured in brightfield microscopy. At 1-day post-isolation (T1), hPIs appeared as distinct round clusters; after 14 (T14) and 28 (T28) days, hPIs clustered together, by losing their original morphology and integrity. At T28, the edge appeared completely jagged and islets loss their morphological integrity, due to ECM disruption during enzymatic isolation. Dithizone staining, routinely applied in free-floating condition for assessing islet quality, purity, and morphology, demonstrated a robust decrease of β-cells functionality (Khiatah et al., 2019), underlined by a steady switch of islets color from reddish to brown one (Supplementary Figure S2). To overcome these problems, functionalized SAPs were used as building blocks to mimic biofunctional and structural features of ECM (Marin and Marchesan, 2022) and support hPIs, by controlling numerous cellular processes, such as cell adhesion (Figure 1C). In this regard, islets morphology was monitored through the weeks via an inverted microscope. As an example, in Figure 1D are shown hPIs embedded in HYDROSAP, but similar observations also were found for FAQ and CK1 (Supplementary Figure S2). As depicted in Figure 1D, hPIs were not subjected to morphological alteration during weeks. SAPs prevented cluster formation and hPIs edge remained clearly defined. To corroborate the above-mentioned qualitative data, the alteration in hPIs diameter was monitored overtime up to 28-days post-isolation (Figure 1E). As expected, hPIs embedded inside HYDROSAP kept stable their dimension, starting from 258.29 ± 46.36 μm at T1, to 256.17 ± 47.03 μm at T14, and 250.95 ± 46.42 μm at T28. Conversely, hPIs in suspension, in absence of a structural support, completely lost their initial morphology and their diameter decreased significantly from 234.49 ± 89.94 μm at T1, to 212.08 ± 70.51 μm at T14, and 197.5 ± 57.11 μm at T28. Indeed, at 14 and 28 DIV (days in vitro) statistical analysis showed significant differences in islets diameter between suspension of hPIs and hPIs inside HYDROSAP (***$p \leq 0.001$). Diameter conservation was also assessed for islets embedded inside FAQ and CK1 scaffold, as depicted in Supplementary Figure S3 and Supplementary Table S1.
**FIGURE 1:** *In vitro experimental design and hPIs morphological evaluation. (A) In standard condition, hPIs were maintained in free-floating suspension in culture medium. (B) Time-course of hPIs integrity captured in brightfield microscopy: round hPIs cluster at 1-day post-isolation (T1) turned into hPIs clustered together losing their original morphology and integrity after 14 (T14) and 28 (T28) days. (C) hPIs embedded in SAP scaffolds mimicking structural features of extracellular matrix. (D) Morphology and integrity of embedded hPIs inside HYDROSAP was monitored through the weeks via inverted microscope, showing no morphological alteration over weeks. (E) Islets diameter quantification over time till 28-days post-isolation for free-floating hPIs and hPIs seeded in HYDROSAP hydrogels. Graph shows mean ± SD of triplicate samples per each time point and statistical difference between groups (two-way ANOVA followed by Tukey post-hoc test; ***p < 0.001). Scale bar, 100 µm.*
## 3.2 Long-term 3D cultures of hPIs (14-days)
As demonstrated in the previous section, HYDROSAP, FAQ and CK1 could act as ECM-like scaffolds, due to their mechanical properties similar to pancreatic tissue, and their ability to maintain overtime morphological conformation of hPIs. First, cellular content in all samples at 14 DIV was analyzed with immunofluorescence stainings against the main islet hormones (insulin and glucagon), endocrine cells, proliferating cells and apoptotic cells (Figure 2). Islets were 3D seeded in HYDROSAP, FAQ and CK1 scaffolds, or plated in suspension, with two different cell culture media: MIAMI medium and GF medium. As a positive control, freshly-isolated suspensions of islets were cultured in MIAMI media for 1 day (T1). Islets maintained in suspension for 14-days in MIAMI or GF media (without mechanical support) were chosen as negative controls. Percentage of cells producing insulin (Figure 2A and Supplementary Table S2) decreased drastically (***$p \leq 0.001$) in suspension in MIAMI medium at T14, compared to freshly-isolated islets T1 (45.84 ± $12.59\%$ and 12.18 ± $9.29\%$, respectively). Conversely, in free-floating condition, GF medium seems to maintain more efficiently insulin content for 14-days, thanks to bFGF and EGF growth factors that increases insulin secretion (Wang et al., 2001; Lee et al., 2008). The effect of 3D matrices was then investigated. Only HYDROSAP scaffold cultured in MIAMI medium was able to preserve percentage of insulin cells (32.35 ± $19.51\%$); indeed, HYDROSAP was the only hydrogel not showing significant differences with freshly-isolated islets at T1. Percentage of glucagon decreased in all conditions with ***$p \leq 0.001$ (Figure 2B; Supplementary Table S2) compared to freshly-isolated islets in MIAMI medium (28.76 ± $9.75\%$) and in GF medium (28.56 ± $5.34\%$). Glucagon component is not a crucial hormone for patients with Type 1 Diabetes: its decreased amount can be considered as less crucial to the success of islets transplantation. The percentage of chromogranin, a protein located in secretory vesicles of endocrine cells, was also performed (Figure 2C). Percentage of chromogranin-positive endocrine-cells is conserved in islets embedded in HYDROSAP in MIAMI medium with 46.22 ± $19.68\%$ and in GF medium with 45.33 ± $23.64\%$, if compared with freshly isolated samples (61.73 ± $13.67\%$ for MIAMI medium and 58.31 ± $12.40\%$ for GF medium). Conversely, islets maintained in FAQ and CK1 obtained results comparable to islets in suspension for 14-days (27.04 ± $17.99\%$) (Supplementary Table S2). The proliferative capacity of all endocrine cells in islets was also tested (Figure 2D). Compared to T1 (8.06 ± $4.49\%$), proliferation rate was preserved overtime in all substrates, without significant differences between samples (Supplementary Table S2). Quantitative analysis of apoptotic cells inside islets grown into different substrates was achieved (Figure 2E). The rate of cell apoptosis is relatively higher in all conditions overtime, compared to hPIs suspension at T1 (2.07 ± $0.86\%$), with the highest percentage of apoptotic cells reached by islets maintained in CK1 in MIAMI medium for 14-days (7.96 ± $5.53\%$, **$p \leq 0.01$, Supplementary Table S2). Despite some detected apoptosis, the obtained results denoted a negligible degree of cytotoxicity.
**FIGURE 2:** *Cellular components characterization of hPIs cultured for 14-days in HYDROSAP, FAQ and CK1 scaffolds, or plated in suspension in MIAMI medium or GF medium. Freshly-isolated hPIs in suspension cultured in MIAMI medium for 1 day (T1) were considered as positive control; conversely, hPIs maintained in suspension for 14-days in MIAMI or GF media were chosen as negative controls. Immunofluorescence analysis and related representative images for (A) β-cells producing insulin in red, (B) α-cells producing glucagon in red, (C) endocrine component in green, (D) cells in proliferation in red, and (E) apoptotic cells in green show the preservation for 14-days of β-cells content, endocrine cells, and proliferative component and a controlled percentage of apoptotic cells in scaffold-embedded hPIs, especially for HYDROSAP. No significant difference was highlighted between two culture media. Cell nuclei are stained in blue. Images illustrate islets in selected culture conditions, depicted in each graph with a red dot. Data are presented as mean ± SD, two-way ANOVA with Tukey post-hoc test: statistical differences were evaluated among all groups and specified in Supplementary Table S2 (n = 3; *p < 0.05; **p < 0.01; ***p < 0.001). Scale bar, 100 µm.*
In order to characterize different ECM components inside hPIs and to evaluate their preservation right after their isolation (T1) and at 14 DIV, immunofluorescence analyses were performed for the main ECM components of pancreas (laminin, collagen IV, and collagen I), for fibroblast cells and for endothelial cells (Figure 3). Isolation procedure disrupts internal vascularization, innervation of islets, intra-islet ECM and peripheral islet ECM, by interrupting fundamental cell-matrix signaling (Stendahl et al., 2009). Biomimetic scaffolds could act as supporting elements by mimicking ECM and preserve, or even induce production, of some ECM components. Pancreatic islets are extensively vascularized and vascular network is essential for their function and survival (Xiong et al., 2020). Moreover, islets re-vascularization is a key elements for graft success following islet transplantation in the treatment of Type 1 Diabetes (Brissova and Powers, 2008). Von Willebrand factor (vWF) is a key component of blood and is produced by endothelial cells (Figure 3A). At 14 DIV, vWF expression did not decrease if compared to freshly-isolated islets: on the contrary a significantly higher expression in HYDROSAP with GF media (3.75 ± $1.08\%$) was found, probably due to the presence of EGF, that plays an important role in angiogenesis and significantly influence differentiation and proliferation of vascular endothelial cells (Chen et al., 2020). The combination of the multi-functionalized microenvironments of HYDROSAP and the exposure to EGF probably induced this considerable increase. Indeed, percentage of reactivity area in hPIs embedded into HYDROSAP in GF medium is almost doubled compared to freshly-isolated islets (1.96 ± $0.48\%$, ***$p \leq 0.001$); in hPIs cultured in HYDROSAP and CK1 in MIAMI medium (2.44 ± $1.25\%$, 2.58 ± $1.00\%$, respectively), vWF values were still higher than negative control (1.23 ± $0.36\%$) with **$p \leq 0.01$ and ***$p \leq 0.001$, respectively (Supplementary Table S3). Human islets are surrounded by a peri-islet capsule consisting of a layer of fibroblast and collagen fibers, produced by fibroblast. This capsule is closely associated with basement membrane which provides physical stability and anchorage to the islets (Banerjee, 2020). Fibroblasts are a cell population that participate on ECM and connective tissue formation, maintain tissue homeostasis, and preserve structural components. It was demonstrated that in a subcutaneous islet transplantation model, fibroblasts is associated with a faster graft re-vascularization, a higher insulin-positive area and a lower cell death (Perez-Basterrechea et al., 2017). Fibroblasts too are degraded during the enzymatic and mechanical isolation process of hPIs. As expected, bFGF, enclosed in GF media, enhanced fibroblast content in all hPIs encapsulated inside scaffolds (Figure 3B; Supplementary Table S3). The effect of bFGF was also observed in the short time in suspension of hPIs at T1 (4.30 ± $1.94\%$), compared to the counterpart in MIAMI medium (2.74 ± $1.50\%$, **$p \leq 0.01$). HYDROSAP scaffold showed significantly higher percentages of fibroblast positive cells (3.83 ± $1.34\%$ in MIAMI medium, 4.63 ± $1.43\%$ in GF medium). On the other hand, FAQ and CK1 preserved fibroblast component just like in positive control.
**FIGURE 3:** *Characterization of main ECM components, fibroblast cells and endothelial cells in hPIs at 14 days in vitro in HYDROSAP, FAQ and CK1 scaffolds or in suspension in MIAMI or GF media. Fresh-isolated hPIs (T1) were considered as positive control, while hPIs in suspension for 14-days in MIAMI or GF media as negative controls. Graphs and related images show percentage of reactivity area for (A) vWF in red, (B) fibroblast in red, (C) laminin in red, (D) collagen IV in red, and (E) collagen I in green, showing a stable expression in HYDROSAP scaffold (with no significant differences between culture media) for all these markers. Cell nuclei are stained in blue. Images illustrate islets in selected culture conditions, depicted in each graph with a red dot. Data are presented as mean ± SD, two-way ANOVA with Tukey post hoc test: statistical differences were evaluated among all groups and specified in Supplementary Table S3 (n = 3; *p < 0.05; **p < 0.01; ***p < 0.001). Scale bar, 100 µm.*
Together with collagens, laminin plays an important role among the ECM components of hPIs. If collagens provide structural stiffness to tissue, laminins maintain integrity and conformation of tissue structure. In human islets, laminins are involved in cytoskeletal remodeling, contractility, and control β-cells differentiation and insulin secretion (Llacua et al., 2018). Laminin resulted well-conserved in hPIs seeded in all SAPs (Figure 3C). Lasty, it was evaluated the most abundant ECM molecule in hPIs, i.e., collagens. Collagen improves endocrine functions, survival, and proliferation, and it is also a principal target during digestion for islet isolation (Riopel and Wang, 2014; Sakata et al., 2020). Collagen IV is a major component of the peri- and intra-islet ECM, modulates ECM stiffness, promotes cell survival, and decreases insulin production at high concentrations (Kaido et al., 2006). hPIs cultured for 14-days inside CK1 samples in GF medium (3.95 ± $2.33\%$) obtained an unexpected higher value in reactivity area compared to initial situation at T1 (1.41 ± $0.91\%$, ***$p \leq 0.001$) (Figure 3D and Supplementary Table S3). For the reason mentioned before, probably this result justified the lower values of β-cells in CK1-seeded islets (Figure 1A). The other SAPs preserved Collagen IV very well, without significant differences compared to hPIs in suspension at T1. Similar results were obtained for Collagen I (Figure 3E), that is normally localized within and around islets such as collagen IV, promotes islet cellular survival and differentiation and also improves β cell function (Wieland et al., 2021). CK1 scaffold supplemented with GF medium significantly enhanced Collagen I content (reactivity area) compared to freshly isolated islets (2.66 ± $1.76\%$ and 1.01 ± $0.76\%$, respectively), while no decreased expression was observed for other SAPs (Supplementary Table S3). Some studies have demonstrated that abnormal collagen upregulation is associated with cancer cell proliferation. Such doubled production of Collagen I and IV in cells cultured in CK1 scaffold for 14-days could be due to the peculiar rigidity of CK1 that matches with tumorigenic pancreatic tissue. Additional analysis on collagen fibers orientation could be significant in predicting cancer (Angel and Zambrzycki, 2022). Tumor-ECM is more abundant, condensed, and stiffer than the ECM in the surrounding healthy tissue. Moreover, oriented collagen fibers around tumor cells, identification of specific collagen organization patterns and evaluation of collagen-associated biomarkers are indicators of tumor progression (Baldari et al., 2022; Song et al., 2022).
In summary, a stable expression of insulin, endocrine component, proliferative cells, apoptotic cells, vWF expression, fibroblast, laminin, collagen IV and I, between freshly-isolated islets (positive control) and islets embedded into HYDROSAP for 14-days was achieved. Moreover, if compared to islet in suspension for 14 days in MIAMI medium (negative control), a higher percentage of insulin, glucagon, proliferative cells, vWF, fibroblast and collagen IV and a lower percentage of apoptotic cells was observed. No relevant differences between the 2 cell culture media were detected, even if the GF medium looked like to increase the percentage of some ECM components. As previously described, FAQ and CK1 have not fulfilled all requirements. Still, CK1, with its high storage modulus (5,440 ± 421 Pa, Supplementary Figure S1), could be further investigated for application in the field of pancreatic tumors. Indeed, enhanced ECM stiffness and the upregulation of collagen production were demonstrated to be closely correlated to cancer progression (Zhang et al., 2021; Kpeglo et al., 2022). Alternatively, since changing SAPs concentration influence SAPs storage and loss moduli (Caprini et al., 2013; Gelain et al., 2021) it is very likely that a fine-tuning of CK concentration could allow to obtain a scaffold that better matches native pancreas biomechanics.
Encouraged by the obtained results, the culture time was extended till 28-days to evaluate the feasibility of long-term cultures of hPIs. Among the analyzed SAPs, HYDROSAP was chosen as the best substrate for 3D hPIs cultures and therefore additionally tested in this work.
## 3.3 Extended (28-days) hPIs in vitro cultures
All marker analyses were repeated at 28 DIV (T28) and previous data at T1 and T14 were reported in graphs as reference (Figure 4). Only HYDROSAP scaffold was chosen to encapsulate hPIs for 28-days, while FAQ and CK1 were excluded from the study because of their sub-optimal performances. Figure 4 shows the overall in vitro characterization: endocrine markers (insulin, glucagon, and chromogranin), cells in proliferation, apoptotic cells and markers related to endothelial cells, fibroblast, and physiological ECM molecules (laminin, collagen I and IV). β-cells content (Figure 4A; Supplementary Table S4) in hPIs suspension in MIAMI medium at T1 scored 45.83 ± $12.59\%$ of total cells; this amount decreased drastically in islets in suspension at T28 (23.25 ± $5.57\%$, ***$p \leq 0.001$), but it was better preserved in HYDROSAP-embedded hPIs in MIAMI medium (38.50 ± $12.01\%$), without significant differences with hPIs at T1. On the other hand, only 27.11 ± $11.41\%$ is reached by hPIs in HYDROSAP in GF medium with a significant difference compared to positive control (***$p \leq 0.001$). Similarly, glucagon content is preserved over weeks in vitro compared to fresh-isolated islets (28.76 ± $9.75\%$) with values between 25.33 ± $7.00\%$ for hPIs-embedded HYDROSAP in MIAMI medium and 25.18 ± $10.43\%$ for hPIs-embedded HYDROSAP in GF medium at T28 (Figure 4B; Supplementary Table S4).
**FIGURE 4:** *Long-term time-tracking of hPIs in suspension or encapsulated in HYDROSAP hydrogel. Islets were cultured for 1 day (T1), 14 days (T14) and 28 days (T28) and fed with MIAMI or GF media. Graphs and related images represent the percentage of positive cells for (A) insulin (red), (B) glucagon (red), (C) endocrine cells (green), (D) proliferative cells (red), displaying steady values (28-days) especially for hPIs cultured in MIAMI medium, with a stable percentage of apoptotic cells (E, in green) over time. In long-term cultures, also the percentage of reactivity area for (F) endothelial cells in red, (G) fibroblast in red, (H) laminin in red, (I) collagen IV in red and (L) collagen I in green were evaluated: vWF and collagen I remained stable in HYDROSAP with both MIAMI and GF media, while fibroblast, laminin, and collagen IV decreased drastically at 28-days in HYDROSAP with GF media, while it stabilized in HYDROSAP with MIAMI medium. Cell nuclei are stained in blue. Images illustrate islets in selected culture conditions, depicted in each graph with a red dot. Data are presented as mean ± SD, two-way ANOVA with Tukey post-hoc test: statistical differences were evaluated among all groups at different timepoints and specified in Supplementary Table S4 (n = 3; *p < 0.05; **p < 0.01; ***p < 0.001). Scale bar, 100 µm.*
A similar trend observed for endocrine cells: the percentage of chromogranin-positive endocrine-cells was conserved in long-term cultures of HYDROSAP-embedded hPIs in MIAMI medium (49.33 ± $10.36\%$) (Figure 4C; Supplementary Table S4). In GF medium chromogranin reached a percentage of 40.47 ± $9.20\%$, significantly lower than in positive control (61.73 ± $13.67\%$, **$p \leq 0.01$). Cellular proliferative rate underwent a slow reduction in all groups: the percentage of 11.97 ± $4.01\%$ (at T14 in HYDROSAP MIAMI) is halved at T28 in the same culture conditions (5.24 ± $2.73\%$, *$p \leq 0.05$); identically, hPIs cultured in HYDROSAP in GF medium decreased from 11.42 ± $5.47\%$ at T14 to 6.66 ± $4.13\%$ at T28 (Figure 4D; Supplementary Table S4).
On the other hand, the percentage of apoptotic cells (TUNEL assay) in all conditions remained stable (Figure 4E; Supplementary Table S4) if compared to results obtained at T14, suggesting a controlled apoptosis at 28 DIV (Figure 1E).
Immunofluorescence analysis of ECM components showed a vWF expression of HYDROSAP-embedded hPIs in MIAMI medium similar to freshly-isolated islets; conversely, it was found a significantly higher expression in HYDROSAP-embedded hPIs at T14 (3.75 ± $1.08\%$) and T28 (2.81 ± $0.86\%$) in GF medium if compared to hPIs in MIAMI medium at T1 (1.96 ± $0.48\%$) and T14 (1.23 ± $0.37\%$) and to hPIs in GF medium at T1 and T14 (1.80 ± $1.16\%$ and 1.63 ± $0.38\%$, respectively) (Figure 4F and Supplementary Table S4). As mentioned before, this result is probably due to the combination of HYDROSAP scaffold and the presence of EGF in the GF culture medium (Figure 1F).
On the other hand, the high fibroblast content reported in HYDROSAP in GF media at T14 (4.63 ± $1.43\%$), decreased drastically after 28-days of culture (1.77 ± $0.75\%$, ***$p \leq 0.001$) (Figure 4G; Supplementary Table S4). Moreover, reactivity area for fibroblast marker is reduced also inside HYDROSAP maintained in MIAMI medium at T28 (1.31 ± $0.43\%$), compared to its T14 counterpart (3.83 ± $1.34\%$, ***$p \leq 0.001$).
Finally, extracellular matrix component (laminin, collagen IV, and collagen I) (Figure 4H–L; Supplementary Table S4) are well-conserved in HYDROSAP-embedded hPIs maintained in MIAMI medium for 28-days: statistical analysis did not show significant difference with freshly isolated hPIs and with same condition at T14. Conversely, the percentage of laminin (Figure 4H) and Collagen IV (Figure 4I) decreased drastically at 28 days in HYDROSAP with GF media (0.51 ± $0.52\%$ and 0.61 ± $0.20\%$ respectively) compared to the same conditions at T14 (3.36 ± $2.15\%$ for laminin with **$p \leq 0.01$ and 2.93 ± $2.00\%$ for Collagen IV with ***$p \leq 0.001$, Supplementary Table S4) In conclusion, hPIs cultured in vitro have obtained excellent results in term of cellular components. When 3D cultured in HYDROSAP scaffold, all main cellular elements are preserved up to 28 DIV if compared to fresh-standard hPIs cultures. At 28-days vWF and collagen I remained stable inside HYDROSAP both in *Miami medium* and GF medium. The percentage of fibroblast, laminin, and collagen IV decreased drastically at 28-days in HYDROSAP with GF media and remained almost stable in HYDROSAP cultured in MIAMI medium. Accordingly, HYDROSAP in MIAMI medium significantly preserved functionality of α and β cells, controlled cell apoptosis, preserved ECM components, maintained a rounded hPIs morphology and islets diameter in vitro up to 4 weeks.
## 3.4 Long-term culture of densely seeded hPIs
Abovementioned results were conducted with 25IEQ inside each 3D scaffold. Now, islets concentration was boosted to 500 IEQ to evaluate islets viability and functionality at high concentrations used for the in vivo preparation protocol (see methods). High concentration of hPIs allows to evaluate if an increased interaction between hPIs ameliorates (or worsens) the quality of long-term hPIs cultures. To distinguish these concentrations, 25IEQ are named Low-Density (LD) of hPIs and 500IEQ as High-Density (HD) of hPIs. Low-Density and High-Density of hPIs in suspension in MIAMI medium, hPIs in suspension in GF medium, hPIs-embedded HYDROSAP in MIAMI medium and hPIs-embedded HYDROSAP in GF medium were compared at 28-days of culture. hPIs in suspension in MIAMI and GF media cultured for 28-days are considered as negative controls. All markers considered in previous analysis were studied in Low-Density and High-Density conditions (Figure 5).
**FIGURE 5:** *Comparison between Low-Density (25IEQ) and High-Density (500IEQ) cultures of hPIs in suspension in MIAMI medium, in suspension in GF medium, embedded in HYDROSAP with MIAMI medium and embedded in HYDROSAP with GF medium after 28-days of culture. hPIs in suspension in MIAMI and GF medium cultured for 28-days are considered as negative controls. Graph shows mean ± SEM of triplicate samples per conditions for percentage of (A) β-cells content, (B) α-cells content, (C) endocrine cells, (D) proliferative cells, (E) apoptotic cells, (F) endothelial cells, (G) fibroblast, (H) laminin, (I) collagen IV, and (L) collagen I, indicating an increased percentage of α-cells, β cells and endocrine fraction, followed by a lower concentration of ECM components. Experiments were performed in triplicate for each condition, and statistical analysis (two-way ANOVA with Bonferroni post-hoc test) showed significant differences between the two analyzed conditions (*p < 0.05; **p < 0.01; ***p < 0.001).*
Percentage of insulin positive β-cells increased (Figure 5A) in HYDROSAP in GF medium (41.82 ± $9.01\%$, HD of hPIs), compared to its LD counterpart in GF medium (26.45 ± $11.61\%$, ***$p \leq 0.001$), obtaining values similar to HYDROSAP in MIAMI medium at HD (45.29 ± $4.91\%$) and to freshly-isolated islets at T1 (45.84 ± $12.59\%$, Figure 2A). α-cells secerning glucagon (Figure 5B) significantly enhanced glucagon expression in HD condition in hPIs suspended in GF medium (32.98 ± $5.87\%$), in hPIs-embedded HYDROSAP in MIAMI medium (38.16 ± $7.68\%$) and in hPIs-embedded HYDROSAP in GF medium (36.98 ± $7.66\%$). As mentioned before, glucagon content is poorly conserved in HYDROSAP scaffold overtime. However, maintaining hPIs in HD for 28-days into HYDROSAP allowed to reach the value of freshly-isolated hPIs at T1 (28.76 ± $9.75\%$, Figure 2B). Similar results were achieved for endocrine cells (Figure 5C).
Thus, β-cells, α-cells and endocrine component showed increased values in HD cultures (especially for hPIs maintained in GF medium, hPIs-embedded HYDROSAP in MIAMI medium and also in GF medium). Despite the increased hPIs concentration, proliferative cells and apoptotic cells percentages inside islets remained unaffected (Figure 5D, E).
However, HD conditions negatively affected vWF and Fibroblast expression, that decreased drastically in most conditions with ***$p \leq 0.001$ (Figure 5F, G). On the contrary, the major components of ECM (laminin, collagen IV and collagen I) (Figure 5H–L) remained stable, except for collagen IV in HYDROSAP in GF medium that significantly increased its expression in HD condition (1.42 ± $0.87\%$, compared to 0.61 ± $0.20\%$ at LD, *$p \leq 0.05$), and for Collagen I in HYDROSAP in MIAMI medium that drastically reduced its expression (0.45 ± $0.23\%$ in HD compared to 1.21 ± $0.74\%$ at LD, **$p \leq 0.01$).
In conclusion, HD cultures of hPIs in HYDROSAP up to 28 days showed an increased percentage of α-cells and β-cells, a controlled apoptosis, and a lower content of ECM components.
Encouraged by excellent in vitro results in term of preservation cellular components, we tested the efficacy of transplants of hPIs previously cultured in vitro. Inside HYDROSAP scaffold, β-cells and endocrine component were well-preserved up to 14 DIV and 28 DIV, if compared to standard hPIs cultures (hPIs suspensions at 1 day).
## 3.5 In vivo efficacy of hPIs pre-cultured in 3D bioscaffolds: Preliminary data
Effective functionality of hPIs maintained in culture for 2 and 4 weeks was investigated in vivo in a pilot study: hPIs were transplanted into sub-renal capsule of diabetic mice. The subcapsular space is considered an ideal site for pancreatic islets implantation in diabetic mice: indeed, pancreatic islets engraftment in the subrenal capsule site is a well-established protocol (Szot et al., 2007). Researchers reported that human islets transplantation into mice kidney cured diabetes in $75\%$–$80\%$ of recipients; while transplantation of murine islets yielded $100\%$ success (Stokes et al., 2017). Animals in vivo experiments were divided into three groups: 1) six mice receiving freshly-isolated hPIs (control group), 2) six animals transplanted with hPIs pre-cultured in HYDROSAP for 2 weeks, and 3) six animals receiving hPIs pre-cultured in HYDROSAP for 4 weeks. In these experiments, only hPIs cultured in MIAMI medium were considered. Freshly-isolated hPIs were immediately transplanted into the sub-renal capsule; conversely, hPIs pre-cultured in HYDROSAP were maintained in culture for two or 4 weeks at HD of hPIs and mechanically dissociated from HYDROSAP immediately before transplantation. As best examples, here we report two mice of the control group and one animal implanted with hPIs (pre-cultured for 2 weeks) that responded positively to islets transplantation, approaching normoglycemic values (Supplementary Figure S4) and an optimal responsiveness to intraperitoneal glucose tolerance tests (Supplementary Figure S4), especially 15 days after transplantation (Supplementary Figure S4). Also, a progressive increase of body weight through the course of the study (Supplementary Figure S4) and the presence of transplanted functional hPIs at the implant site (Supplementary Figure S5) confirmed the success of engraftment in all three animals. The poor success of the overall in vivo transplants ($\frac{3}{18}$ mice) and, in particular, of positive controls ($\frac{2}{6}$ mice), led us to consider this study as preliminary. A refinement of implantation technique is required. Indeed, the injection technique could have partially destroyed cells, or the amount of engrafted hPIs was not enough to restore normoglycemia (Estil Les et al., 2018) and hPIs resulted unable to secrete a sufficient amount of insulin to reduce blood glucose concentration at physiological levels (Perteghella et al., 2017). Moreover, most animals developed a severe hyperglycemia after STZ injection with secondary complication, leading to a high mortality rate and a low success of engraftment (Supplementary Figure S6) (Deeds et al., 2011). For example, a decrease in weight in all mice receiving pre-cultured hPIs was detected (Supplementary Figure S6).
Being a pilot study, it is not possible to draw specific conclusions about the efficacy of our 3D treatment for hPIs for now, and further investigation is required. However, one animal recovered normoglycemia after transplantation with hPIs pre-cultured for 2 weeks, and such return to physiological glucose level is very likely related to the success of hPIs pre-culturing and engraftment.
## 4 Conclusion
In summary, we successfully developed a 3D culture system capable of preserving human pancreatic islet morphology and functionality up to 28-days in vitro. The scaffold formulation has been carefully selected to better recapitulate 3D issue-specific conditions. HYDROSAP scaffold favors long-term hPIs cultures, by supporting islets functionality, rounded hPIs morphology and islets diameter. In particular, hPIs embedded in HYDROSAP and cultured in MIAMI medium for 14-and 28-days have shown an excellent preservation of cellular content (β-cells producing insulin, endocrine cells, and proliferative cells), endothelial cells and ECM component (laminin, collagen I and collagen IV). Validation of efficacy of hPIs cultured in vitro was also tested in preliminary in vivo experiments, obtaining a partial restoration of physiological blood glucose level in a diabetic animal transplanted with hPIs maintained in culture for 14-days. Non-etheless, further pre-clinical studies need to follow, including the refinement of injection technique in the subrenal space and establishing the sufficient amount of hPIs to be injected for restoring normoglycemia. Still, our findings demonstrate the potential of a helpful approach for long-term cultures of hPIs, in terms of islets survival and functionality, and pave may the way for a significant improvement of the current clinical treatment of Type 1 Diabetes with hPIs transplants.
## 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 study involves the use of human tissue collected from multiorgan cadaveric donors in conformity with the ethical requirements approved by the Niguarda Cà Granda Ethics Board. The animal procedures were reviewed and approved by Institutional Animal Care and Use Committee (IACUC number $\frac{63}{2022}$-PR), according to the current Italian Legislation (Legislative Decree March 4th, 2014 n.26) enforcing the $\frac{2010}{63}$/UE Directive on protection of animals used for biomedical research.
## Author contributions
FG contributed to the design of the project, supervised the whole work, obtained funding acquisition, and wrote the manuscript. AM contributed to the design of the study, executed in vitro experiments, analyzed the data, and wrote the manuscript. MC performed chemical synthesis, rheological analysis, and data collection. BA performed islets isolation and purification and contributed to data analysis. AA, UB, and VV performed in vitro experiments. FB supervised the whole work and contributed to data analysis. All author reviewed and edited the manuscript.
## 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/fbioe.2023.1105157/full#supplementary-material
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|
---
title: Bioactive cellulose acetate nanofiber loaded with annatto support skeletal
muscle cell attachment and proliferation
authors:
- Ana Elisa Antunes dos Santos
- Tiago Cotta
- João Paulo Ferreira Santos
- Juliana Sofia Fonseca Camargos
- Ana Carolina Correia do Carmo
- Erika Gabriele Alves Alcântara
- Claudia Fleck
- Aline Gonçalves Lio Copola
- Júlia Meireles Nogueira
- Gerluza Aparecida Borges Silva
- Luciana de Oliveira Andrade
- Roberta Viana Ferreira
- Erika Cristina Jorge
journal: Frontiers in Bioengineering and Biotechnology
year: 2023
pmcid: PMC9995891
doi: 10.3389/fbioe.2023.1116917
license: CC BY 4.0
---
# Bioactive cellulose acetate nanofiber loaded with annatto support skeletal muscle cell attachment and proliferation
## Abstract
Electrospinning emerged as a promising technique to produce scaffolds for cultivated meat in function of its simplicity, versatility, cost-effectiveness, and scalability. Cellulose acetate (CA) is a biocompatible and low-cost material that support cell adhesion and proliferation. Here we investigated CA nanofibers, associated or not with a bioactive annatto extract (CA@A), a food-dye, as potential scaffolds for cultivated meat and muscle tissue engineering. The obtained CA nanofibers were evaluated concerning its physicochemical, morphological, mechanical and biological traits. UV-vis spectroscopy and contact angle measurements confirmed the annatto extract incorporation into the CA nanofibers and the surface wettability of both scaffolds, respectively. SEM images revealed that the scaffolds are porous, containing fibers with no specific alignment. Compared with the pure CA nanofibers, CA@A nanofibers showed increased fiber diameter (420 ± 212 nm vs. 284 ± 130 nm). Mechanical properties revealed that the annatto extract induces a reduction of the stiffness of the scaffold. Molecular analyses revealed that while CA scaffold favored C2C12 myoblast differentiation, the annatto-loaded CA scaffold favored a proliferative state of these cells. These results suggest that the combination of cellulose acetate fibers loaded with annatto extract may be an interesting economical alternative for support long-term muscle cells culture with potential application as scaffold for cultivated meat and muscle tissue engineering.
## Introduction
Cultivated meat is an alternative source of animal protein for the increasing meat demand, explored to overcome the known problems of the livestock production methods, such as environmental impact, animal welfare and public health (Post, 2012; Bomkamp et al., 2021). It is meat produced by tissue engineering technique, which corresponds to the in vitro cultivation of myogenic cell lineages or muscle stem cells in a scaffold, capable of mimicking the extracellular matrix of the tissue (Ben-Ayre and Levenberg, 2019; Choi et al., 2020; Ahmad et al., 2021).
Besides mimicking the extracellular matrix, scaffolds used to produce cultivated meat need to support the development, growth and differentiation of the myogenic lineage in a mature muscle tissue in vitro culture conditions (Howard et al., 2008; Xing et al., 2019). In order to develop efficient scaffolds for cultivated meat, it is essential to take into account the structure of naturally occurring muscle tissue. Muscle tissue is composed of multinucleated cells (myofibers) that are uniaxially aligned along the main axis of the tissue (Pardo et al., 2021). Myofibers are long cells, with ∼40 mm in length and 10–100 μm in diameter, composed of bundles of contractile filaments composed of long chains of actin and myosin (myofibrils), 1–3 μm in size (Jun et al., 2009). It is also known that skeletal muscle exhibits anisotropic mechanical and electrical responses. Therefore, creating anisotropic scaffolds with micro- or nanoscale properties has become a common strategy for building muscle tissue engineering constructs (Grasman et al., 2015; Smoak and Mikos, 2020; Pardo et al., 2021). Approaches using micropatterning techniques to regulate cell alignment have been found to be effective in mimicking muscle tissue structure, composition, and function (Nakamoto et al., 2014; Xiang et al., 2022). Such materials have demonstrated the ability to induce muscle cell alignment, promote myogenic differentiation at early stages for cell fusion, and develop long and thick myotubes due to their morphological and topographical characteristics (Liu et al., 2017; Vogt et al., 2017; Bloise et al., 2018; Narayanan et al., 2020).
However, muscle tissue is composed of both aligned extracellular matrix (ECM) fibrils and a random mesh of collagen fibrils of connective tissues. Connective tissues, such as endomysium, perimysium, and epimysium, provide force transmission and mechanical support to the muscle architecture and are composed of a strong collagen network (Uehara et al., 2020). Most of the load capacity of muscle arises from the dense ECM that forms these tissues rather than the aligned muscle fibers, revealing the importance of a strong support structure to sustain mature muscle cells. Therefore, recapitulating the mechanical properties of the muscle tissue by using scaffolds mechanically similar to the ECM is essential for cultured meat to achieve the texture of conventional meat. It is also extremely important that the scaffolds are easy to manipulate and can resist the forces applied during the processing (Bookamp et al., 2021). A hybrid combining aligned and random nanofibers were presented by Park et al. [ 2016]. While the aligned fibers provided uniaxial topographic guidance for muscle cell development, the random fibers enhanced mechanical stability, support, and adequate permeability, and were amenable to physical manipulation. Scaffolds can also be used in combination with bioactive cues, such as growth factors, ECM proteins, or cell-signaling peptides, to promote specific cell behaviors (Borselli et al., 2011; Cezar and Mooney, 2015). Additional strategies to influence skeletal muscle cell differentiation and growth in scaffolds include mechanical and electromagnetic stimuli. Mechanical passive, phasic, and gradual stretch stimuli have been applied in cell-laden gel constructs to promote myotubes alignment and growth (Nishiguchi et al., 2011; Simsa et al., 2019; Kang et al., 2021). Electrical stimulation also improved the maturation of bovine myotubes and C2C12 myoblasts cultured in aligned scaffolds (MacQueen et al., 2019; Orellana et al., 2020). In addition, cell culture on conductive biomaterials such as polyaniline (Jun et al., 2009), gold or titanium coatings (Yang et al., 2016), or in the presence of magnetic nanoparticles and under external magnetic field stimulation (Pardo et al., 2022), has been reported as a strategy to enhance myotube maturation.
Electrospun nanofiber scaffolds present an interesting alternative for muscle cell cultivation because they can better simulate typical muscle fibrous architecture. Its nanoscale structure mimics the extracellular matrix and induces great cellular attachment due its nanofiber high aspect ratio, porosity and surface-to-volume ratio (Hejazian et al., 2012). Previous studies have demonstrated the importance of the nanoscale structure and its anisotropy in synthetic polymers for the development of 3D matrices (Mitchell & Tojeira, 2013; Marzio et al., 2020). Nanofibers contribute to rapid diffusion of oxygen and nutrients, as well as cell infiltration, promoting better cell proliferation and biocompatibility (Perez-Peruvyan et al., 2021). In addition, nanofiber scaffolds have the ability to induce cell alignment along the fibers that might induce muscle fiber maturation (Baker and Mauck, 2007).
Cellulose-based biomaterials offer some important advantages over conventional synthetic materials and show great scientific promise (Hickey and Pelling, 2019). Several studies have demonstrated that the hydrophilic hydroxyl moieties of the cellulose and specialized cellulose binding domains provide sites that favor adhesion and proliferation (Elsayed et al., 2020; Marino et al., 2021). Cellulose acetate (CA) is a modified natural polymer that has good solubility and mechanical properties, demonstrates biodegradability and biocompatibility, and can be easily controlled morphologically (Liu and Hsieh, 2002; Bifari et al., 2016; Angel et al., 2020). In addition, CA shows good fiber-forming ability, or spinnability, using a variety of solvents (Konwarh et al., 2013; Sánchez-Cid et al., 2022).
CA nanofibers are very interesting in cultivated meat applications because, in addition to being a low-cost material, their fabrication by the electrospinning process is relatively easy (Angel et al., 2020; Marino et al., 2021). Besides, contrary to scaffolds composed of plant-based materials, they do not need to be coated with ECM proteins or chemical modification to improve cell adhesion (Hickey and Pelling, 2019; Xiang et al., 2022). Santos et al. [ 2021] demonstrated that it is possible to grow fibroblasts on CA nanofibers without the need for coating. Thus, the application of CA nanofibers in a cultured meat production process may be a more economical option compared to other synthetic polymers. CA nanofiber incorporated into chitosan/silk fibroin scaffold has improved the proliferation, infiltration, and contractility of smooth muscle cells (Zhao et al., 2022). Nevertheless, studies with CA nanofibers for applications in tissue engineering and cultured meat are still scarce.
Although various nanofiber scaffolds have been developed for biomedical applications, few investigations have been done for applications in cultivated meat (Allen et al., 2017; MacQueen et al., 2019; Zoldan and Allen, 2019). MacQueen et al. [ 2019] demonstrated the growth of rabbit and bovine smooth muscle cells on rotary jet spun gelatin as well as a histological comparison of the engineered constructs to rabbit muscle, bacon, and ground beef. PCL and PNIPAAm scaffolds have been used to produce aligned cell sheets via electrospinning for application in muscle cell cultivation (Allen et al., 2017). The technique was patented by the cultivated meat company BioBQ for the potential development of cultivated jerky and brisket beef (Zoldan and Allen, 2019). In addition, edible and biodegradable electrospun nanofiber has been developed by cultivated meat companies such as Matrix Meats and Gelatex (Bomkamp et al., 2021).
Another important point in food production is preservation, which nowadays is focused on the use of natural products (Hernández-Ochoa et al., 2014). Recently, essential oils extracted from plants have received a lot of attention due to their meat protection properties. Antimicrobial properties of plant essential oils are derived from some main bioactive components such as phenolic acids, terpenes, aldehydes, and flavonoids (Patra, 2012). Various mechanisms such as changing the fatty acid profile and structure of cell membranes and increasing the cell permeability as well as affecting membrane proteins and inhibition of functional properties of the cell wall are effective in antimicrobial activity of essential oils (Yousefi et al., 2020). Annatto is the fruit of the annatto (*Bixa orellanna* L.) native to South America. Annatto seeds are considered antibiotics of medicinal character, acting as an anti-inflammatory for bruises and wounds, also having been used in the cure of bronchitis and external burns. In addition, annatto has a long history of use in the food industry as a natural dye (Cardarelli et al., 2008; Rivera-Madrid et al., 2016; Shahid-ul-Islam et al., 2016). Our research group produced scaffolds from cellulose acetate nanofibers loaded with annatto extract and demonstrated that the scaffold maintained the viability of mouse fibroblasts after 48 h of culture, in addition to allow cell attachment, spreading and colonization of the nanofiber (Santos et al., 2021).
Here we investigated physicochemical, morphological, mechanical and biological features of bioactive cellulose acetate (CA) and cellulose acetate loaded with annatto extract (CA@A) nanofibers to evaluate their potential for application in cultivated meat. We found that cellulose acetate nanofibers loaded with annatto extract favored cell adhesion and improved cell viability and long-term cell proliferation. Furthermore, random CA nanofiber favored the myoblast differentiation profile.
## Cellulose acetate (CA) and cellulose acetate with annatto extract (CA@A) nanofibers physicochemical characterization
The cellulose acetate (CA) and cellulose acetate with annatto extract (CA@A) nanofibers were obtained by electrospinning, as previously described (Santos et al., 2021). Briefly, crude annatto extract was obtained using the solvent extraction method. Annatto seeds were washed with distilled water to remove any adhering powder, and then macerated using a ceramic mortar and pestle. Macerated seeds were soaked in 0.05 g/mL ethanol. The mixture was stirred magnetically at 50°C for 60 min, and then filtered through a Whatman filter. The polymer was impregnated with the crude extract by mixing 5 g of powdered cellulose acetate with 20 mL crude annatto extract. The homogeneous mixture was then placed under a fume hood at room temperature (RT) for the ethanol to evaporate, and subsequently kiln dried at 50°C for 30 min. The cellulose acetate nanofibers (CA) and cellulose acetate with annatto extract nanofibers (CA@A) were obtained by electrospinning as described below: the cellulose acetate and cellulose acetate with crude annatto extract were dissolved in acetone-dimethylformamide (3:1 v/v) to obtain 12 wt% (w/v) solution. The polymer solution was fed into a 10 mL standard syringe attached to a 0.3 mm (gauge 30) inner diameter stainless needle. The electrospinning process utilized electric voltage of 12 kV, 10 cm working distance, collector rotation at 200 rpm, and 0.8 mL/h solution feed rate at room temperature (NB-EN1, NanoBond). Physicochemical characterization of the nanofibers was performed using the following analysis: i) UV-vis spectroscopy; ii) contact angle analyzer; and iii) Nanoscale Dynamic Mechanical Analysis.
The UV-vis spectroscopy of annatto extract was performed in a Perkin Elmer Lambda 1,050 spectrometer (Waltham, USA), with wavelength range of 250–800 nm and scanning speed of 267 nm/min. The annatto extract used in this analysis was diluted in acetone 1:50 (v/v) and the measurements were obtained right after its preparation. CA and CA@A nanofibers were also evaluated to determine their surface wettability, which was measured using a contact angle analyzer (KRÜSS model DSA-100; KRÜSS Scientific, Hamburg, Germany). Deionized water was automatically dripped onto the nanofiber samples and five contact angle measurements were averaged to obtain a reliable value.
Nanoscale Dynamic Mechanical Analysis (Nano-DMA) was performed using the Hysitron TI950 TriboIndenter device (Bruker Corporation, Billerica, USA) equipped with a Berkovich tip. Nanofiber samples were glued to an epoxy holder to ensure stability during measurement. A grid with 100 measurement points (10 × 10) was created for oscillatory measurements to simultaneously obtain both the linear- and visco-elastic responses of the sample. Specimens were loaded with a sinusoidal force-time-function and a maximum load of 75 μN oscillating at eight different frequencies (10, 31, 25, 115, 136, 157, 178, and 201 Hz). Loss (E″) and storage modulus (E’) were calculated from the measured force-displacement hysteresis loops using the software provided with the Bruker nanoindenter. The indents are approximately twice as small as the fiber diameter. We assumed that if an indent reached a pore, it would measure the fiber directly below it.
## Morphological characterization of the nanofibers
CA and CA@A nanofibers were also morphologically characterized using the Phenom XL (Phenom-World, Eindhoven, Netherlands) scanning electron microscope (SEM), with medium vacuum (60 Pa) and auto focus on an accelerating voltage of 5 kV. Nanofiber samples were sputtered with gold for 20 min, using a sputter coater (Cressington 108 model, Cressington Scientific Instruments).
Next, SEM images were used to obtain the average of fiber diameter, using the ImageJ software. From three SEM images from each nanofiber sample, 200 randomly selected fibers were measured using the line tool of the ImageJ software.
## C2C12 cell culture
Immortalized mouse myoblasts from the C2C12 cell lineage (ATCC® CRL1772™) were used in this work. C2C12 cells were maintained in growth medium [GM: DMEM-high glucose (Gibco), supplemented with $10\%$ bovine fetal serum (Gibco) and $1\%$ anti-anti (Gibco)], at 37°C and $5\%$ CO2. Cells were used among the fourth and eighth passages. When applicable, cell differentiation was induced at low serum condition [DM: DMEM (Gibco), supplemented with $2\%$ Horse Serum (Gibco) and $1\%$ anti-anti (Gibco)].
## C2C12 cell seeding onto CA and CA@A nanofibers
CA and CA@A nanofibers were sterilized using gamma irradiation, at RT with a standard dose of 10 kGy. 60Co gamma-ray source was used. Gamma irradiation sterilization was carried out at Gamma Irradiation Laboratory installed at the Nuclear Technology Development Centre (CDTN, Belo Horizonte, Brazil).
Before cell seeding, CA and CA@A nanofibers were cut into 16 mm disks and fixed in the well of a 24-well plate. The disks were equilibrated using 200 μL of GM for 24 h. Then, 8 × 104 cells C2C12 cells were carefully seeded onto each nanofiber disk. After 2 h of incubation at 37°C and $5\%$ CO2, the volume of GM was completed to 500 μL/well. All experiments were performed in triplicate.
## Non-adherent cell counting
After 24 h of cell seeding, supernatants were carefully collected from the well and transferred to a falcon tube. The well was carefully washed with PBS, which was also transferred to the same falcon tube containing the supernatant. After centrifugation at 184 g for 5 min, the pellet was resuspended in 50 µL of fresh GM and the cells were counted using a Neubauer chamber.
## MTT assay for cell viability analysis
Cell viability was assessed using MTT assay (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide), according to the manufacturer’s instructions (Thermo Fisher Scientific). Briefly, C2C12 cells were seeded onto the nanofibers as previously described. After 2 and 7 days, GM was replaced with the MTT solution, and the samples were incubated for 2 h at 37°C and $5\%$ CO2. Formazan crystals were then dissolved in 1 mL/well of isopropanol-acid (100 mL isopropanol:134 ul of hydrochloric acid). The solution was transferred to a 96-well plate in triplicate and absorbances were measured at 595 nm using a microplate reader (ELX800 device; BioTek, Winooski, USA).
## Cell morphology determined by SEM and F-actin staining
The morphology of the C2C12 cells cultivated onto CA and CA@A nanofibers was determined by i) SEM and ii) F-actin staining.
For SEM analysis, C2C12 cells were seeded onto CA and CA@A nanofibers and cultivated at 37°C and $5\%$ CO2 in GM. After 2 and 7 days of culturing, samples were fixed in $2.5\%$ glutaraldehyde for 6 h at RT. Samples were then rinsed with distilled water and gradually dehydrated in two increasing series of ethyl alcohol ($35\%$, $50\%$, $70\%$, $85\%$, $95\%$ and $100\%$ for 15 min/bath). Samples were metalized with gold and visualized using a Quanta 200 FEG SEM (FEI, Hillsboro, USA).
For F-actin staining, C2C12 cells were seeded onto CA and CA@A nanofibers and cultivated at 37°C and $5\%$ CO2 for 7 days in GM. After washing in PBS, cells were fixed in $3.7\%$ formaldehyde for 15 min at RT. Samples were permeabilized in $0.1\%$ Triton-X100 in PBS for 10 min at RT, washed with PBS, and incubated with 0.2 μg/mL Alexa Fluor 546 Phalloidin (Thermo Fisher) in PBS, for 30 min at RT. Next, cell nuclei were counterstained with DAPI (diluted to 1:1,000 in PBS) for 20 min at RT. Images were obtained in a Zeiss fluorescence microscope.
## RT-qPCR
1Cells were seeded onto each nanofiber in triplicate and cultivated for 7 days in GM only, or for 7 days in GM followed by an additional 7 days in DM. Both GM and DM were replaced by fresh medium every 2 days. All cells from the triplicate were then harvested in 1 mL TriReagent (Sigma-Aldrich) and the total RNA was isolated according to the manufacturer’s instructions. Next, 1 μg of each total RNA sample was converted into cDNA, following the instructions in the RevertAid H minus first strand cDNA synthesis kit (Thermo Fischer Scientific). GAPDH was used as a reference gene (AGGTCGGTGTGAACGGATTTG and TGTAGACCATGTAGTTGAGGTCA). MyoD, Myf5, MyoG and Desmin were used as target genes with the following primers: for MyoD (GTGGCAGCGAGCACTACA and GACACAGCCGCACTCTTC), for Myf5 (GCAAAGACCCGTGACTTCAC and GCATGTGGAAAAGTGATA), for MyoG (TGAGAGAGAAGGGGGAGGAG and CGGTATCATCAGCACAGGAG) and for Desmin (GTGGAGCGTGACAACCTGAT and ATGTTCTTAGCCGCGATGGT). RT-qPCR was performed using a Corbett 3,000 device (Qiagen, Helden, Germany), using 0.4–0.8 μM of each primer, 1 μL (diluted 1:10) of each cDNA, and 5 μL of iTaq universal SYBR Green Supermix (Bio-Rad, Hercules, USA), in a final volume of 10 μL. Reactions were performed as follows: 50°C for 2 min, 95°C for 2 min, followed by 45 cycles of 94°C for 15 s, 60°C–62°C for 15 s, and 72°C for 20 s. The dissociation step was performed at the end of the amplification step. *Relative* gene expression was determined using REST2009 software (based on the model by Pfaffl et al., 2002).
## Statistical analysis
All quantitative data are presented as means ± standard devi ations, and three repeated experiments were given. Statistical analysi s was performed using Student’s t-test or one-way analysis of variance followed by Fisher’s post hoc least-significant difference test for multiple comparisons. Differences were deemed significant at $p \leq 0.05.$
## Physicochemical, mechanical and morphological properties of the CA and CA@A nanofibers
In this work, the physicochemical and mechanical properties of the CA and CA@A nanofibers were performed using: i) the UV-vis spectroscopy, used specifically to confirm the annatto extract purity and its presence in the CA@A nanofiber; ii) the contact angle measurements, to determine the wettability properties of the nanofibers; and iii) the nanoscale dynamic mechanical analysis, to determine their mechanical properties.
Nanofibers components were assessed by UV-vis spectroscopy (Figure 1A). The annatto extract spectrum revealed a strong and exclusive absorption band at 410 nm. No bands could be observed using CA nanofiber, while the CA@A sample revealed a low-intensity absorption band centered at 410 nm (Figure 1A).
**FIGURE 1:** *UV-absorption and water contact angle measurements. (A) UV-vis spectra of annatto extract, cellulose acetate nanofibers (CA), and cellulose acetate nanofibers with annatto extract (CA@A). Water contact angles of (B) cellulose acetate nanofiber (CA) and (C) cellulose acetate nanofiber impregnated with annatto extract (CA@A).*
Wettability property was assessed for CA and CA@A nanofibers by analyzing the contact angle (Figures 1B,C). The contact angle for the CA nanofiber was approximately 77° ± 3° (Figure 1B), while the addition of annatto to the CA nanofiber decreased the contact angle to 50° ± 3° (Figure 1C). Contact angles below 90° are characteristic of a hydrophilic property of the nanofibers.
Mechanical properties of nanofibers at nanoscale were measured using Nano-DMA tests. Storage (E′) and loss (E″) modulus measurements for different frequencies are shown in Figure 2. Our results showed that the addition of annatto to the CA nanofibers decreased both E’ (Figure 2A) and E” (Figure 2B). At the frequency 10 Hz, E′ was 0.32277 GPa for the CA sample and 0.21148 GPa for the CA@A sample (Figure 2A), meaning that a reduction of $34\%$ in terms of storage modules was achieved by adding annatto. For the loss modulus E” was 0.00952 GPa for the CA sample and 0.00826 GPa for the CA@A sample (Figure 2B), i.e., a reduction of $13.26\%$.
**FIGURE 2:** *Dynamic mechanical analysis (DMA) of CA and CA@A nanofibers. (A) E′ (⌉) and (B) E” (⌉) curves for cellulose acetate (CA) and cellulose acetate nanofibers with annatto extract (CA@A) samples.*
We also obtained SEM images for both CA and CA@A nanofibers to allow their characterization based on the porous presence and the fiber diameters size (Figure 3). We found that both nanofibers presented smooth and relatively homogeneous porous mats and exhibited porous interconnectivity (Figures 3A,B for CA; Figures 3D,E for CA@A). We also analyzed fiber diameters and found that CA scaffolds present an average size of 284 ± 130 nm (Figure 3C), while the average size for CA@A was 420 ± 212 (Figure 3F).
**FIGURE 3:** *Scanning electron microscope (SEM) images of cellulose acetate (CA) and cellulose acetate annatto (CA@A) nanofibers at different magnifications and their size distribution. (A, B) Morphology of CA nanofibers with different magnifications. (C) Diameter distribution of CA nanofibers. (D, E) Morphology of CA@A nanofibers with different magnifications. (F) Diameter distribution of CA@A nanofibers.*
## Both nanofibers allowed adherence and induce the viability of skeletal muscle cells
In this work, C2C12 myoblasts were used to evaluate the potential use of CA and CA@A nanofibers as scaffolds for the production of cultivated meat.
We first evaluated the capacities of these cells to attach to the CA and CA@A nanofibers, by counting non-adherent cells present in the medium after 24 h of cell culture (Figure 4A). From the ∼80,000 cells that were seeded onto each scaffold, ∼1,000 cells/well were unable to adhere to any of the substrates (Figure 4A). The rates of adherent cells were approximately $97.5\%$ and $98\%$ for cells cultivated onto CA and CA@A nanofibers, respectively. No significant difference was found between the cell number in the CA and CA@A nanofibers.
**FIGURE 4:** *Initial analysis of cell-biomaterial adhesion and cell viability index. (A) C2C12 cell attachment to cellulose acetate (CA) and cellulose acetate nanofibers with annatto extract (CA@A) determined by cell supernatant counting after 24 h. (B) Graph representing % cell viability through the MTT assay of C2C12 myoblast cells incubated onto cellulose acetate (CA) and cellulose acetate nanofibers with annatto extract (CA@A) over 2 days and 7 days. The dotted line represents control, C2C12 plated on a monolayer for 2 and 7 days. Different letters demonstrate significant differences determined by a Student’s test (p < 0.05).*
We also evaluated the viability index of C2C12 cells cultivated during 2 and 7 days onto CA and CA@A nanofibers, using MTT assay (Figure 4B).
After 2 days of culture, no difference in the viability indexes could be observed between CA and CA@A (Figure 4B). After 7 days of culture, an increase in the viability index could be observed for the cells cultivated onto both nanofibers, compared to the index observed after 2 days. Besides, the viability index of the cells cultivated onto the CA@A nanofiber was also found to be higher than for those cultivated onto the CA nanofiber, after 7 days of culture (Figure 4B).
Altogether these results suggest that both nanofibers allow great cellular attachment and also induce an increase in cell viability index over time. The presence of annatto in the nanofiber seems to confer an additional positive effect in muscle cell viability, compared to the ones cultivated onto the pure nanofiber.
## C2C12 cell morphology when cultivated onto both nanofibers
The morphology of C2C12 cells cultivated onto CA and CA@A nanofibers was analyzed using SEM images, after 2 and 7 days of culture in GM (Figure 5).
**FIGURE 5:** *SEM images of C2C12 cells growth on CA and CA@A nanofibers. SEM images at different magnifications of C2C12 cells cultivated onto cellulose acetate nanofiber (A, B) and cellulose acetate nanofiber with annatto extract (C, D) after 2 days. SEM images at different magnifications of C2C12 cells cultivated onto cellulose acetate nanofiber (E, F) and cellulose acetate nanofiber with annatto extract (G, H) after 7 days. Scale bars indicate (A,C,E, G) 200 µm and (B,D,F, H) 50 µm. White arrows indicate cell-nanofiber adhesion points.*
After 2 days of culture, myoblasts could colonize the surface of both CA (Figures 5A,B) and CA@A nanofibers (Figures 5C,D), but more cell groups could be observed in the CA@A (Figure 5C compared to 5A). The magnified image revealed that cells could already establish the first cell-cell contacts between them (Figures 5B,D). Cells were also found to produce extensions to establish links with both CA (Figure 5B, arrow) and CA@A (Figure 5D, arrow) nanofibers and showed a spindle-shaped morphology, like mononucleated myoblasts.
An exponential increase in cell density could be observed after 7 days of culture, allowing the covering of both nanofiber surfaces (Figures 5E–H). Almost all the cells stretched along the nanofibers and exhibited elongated morphology on both CA (Figure 5E) and CA@A nanofibers (Figure 5F). We could also observe myoblasts covered by nanofibers (Figures 5G,H), suggesting cell migration through the pores of the nanofibers.
The morphology of C2C12 cells cultivated on CA and CA@A nanofibers was also assessed using fluorescence images from F-actin staining, a component of the cell cytoskeleton (Figure 6). Here, C2C12 cells were cultivated onto the nanofibers for 7 days, since the actin cytoskeleton is more easily resolved in higher density samples. The results showed that C2C12 cells cultivated onto the CA nanofiber were found to be more aligned and elongated (Figures 6A–C), compared to those cultivated onto the CA@A ones, which were found to be thinner and randomly distributed (Figures 6D–F).
**FIGURE 6:** *Phalloidin labelled F-actin (orange), DAPI nuclear staining (blue) and overlaid fluorescent image of C2C12 cellular components (merged) for CA (A, B, C) and CA@A (D, E, F). Scale bar = 50 µm.*
## C2C12 cells differentiate when cultivated onto CA and CA@A nanofibers
We have also assessed whether C2C12 cells could reach differentiation when cultivated onto CA and CA@A nanofibers, by RT-qPCR (Figure 7). C2C12 cells were cultivated in GM during seven or in GM for 7 days followed by additional 7 days in DM (14 days), onto both nanofibers.
**FIGURE 7:** *Relative expression levels of MyoD, Myf5, MyoG and Desmin in C2C12 cells cultured onto CA and CA@A nanofibers. (A) Relative gene expression analysis after 7 days of culture, comparing CA x CA@A. (B) Relative gene expression analysis after 14 days of culture, comparing CA x CA@A (C) Relative gene expression analysis in cells cultivated onto the CA nanofiber over time (7 days in GM x 7 days in GM followed by 7 days in DM). (D) Relative gene expression analysis in cells cultivated onto the CA@A nanofiber over time. Significative data obtained by REST2009 software, using p < 0.05.*
We first evaluated the relative gene expression by comparing the expression of the myogenic markers per nanofibers (CA x CA@A), at each analyzed day (7 and 14 days) (Figures 7A,B, respectively). After 7 days in culture, all myogenic markers were found to be upregulated in the cells cultivated onto the CA@A nanofiber, compared to the expression observed in the cells onto CA one (Figure 7A). After 14 days, however, which included 7 days of the cells cultivated in differentiation medium, relative gene expression analysis revealed that Myf5, MyoD and Desmin were downregulated in the cells cultivated onto the CA@A nanofiber, while MyoG was upregulated, all compared to the expression obtained on cells cultivated onto the CA nanofiber (Figure 7B).
We also evaluated the relative gene expression during cultivation (7 × 14 days) in each of the nanofibers (Figures 7C,D). Cells cultivated onto the AC nanofiber upregulated all myogenic markers at 14 days, compared to the expression at 7 days of culture (Figure 7C). When the same comparison was performed using the expression data of cells cultivated onto the AC@A nanofiber, however, again Myf5, MyoD and Desmin were found to be downregulated, while MyoG was upregulated at 14 days, all compared to the data obtained at 7 days of culture (Figure 7D).
## Discussion
In this work, we characterized the physicochemical and mechanical properties of nanofibers prepared by electrospun of cellulose acetate, as pure or containing annatto extract as a bioactive component, and the potential of applying these nanofibers as scaffolds to allow skeletal muscle cell growth and differentiation.
We first characterized the physicochemical, mechanical and morphological properties of both CA and CA@A nanofibers.
The UV spectrum of the pure annatto extract revealed a strong absorption band at 410 nm, which can be attributed to bixin and norbixin components of the annatto (Scotter, 2009; Giridhar, 2014; Rahmalia et al., 2015). No peaks attributed to additional annatto compounds were observed (Calogero et al., 2015; Pinzón-Garcia et al., 2016). The presence of annatto extract in the CA@A nanofiber was confirmed by a low-intensity absorption band centered at 410 nm.
Contact angle measurements allowed us to evaluate the wettability properties of the CA and CA@A nanofibers. Surface wettability is extremely important for cell adhesion, as hydrophilicity is the intrinsic property of the natural extracellular matrix (Menzies and Jones, 2010). Our results showed that both nanofibers exhibit hydrophilic properties. The enhancement in the wettability observed for the CA@A nanofiber compared to the CA nanofiber is associated with the hydrophilic nature of the bixin and norbixin molecules present in the annatto extract.
Mechanical properties play an important role in cell adhesion, differentiation, morphology and migration. In order to evaluate the effect of annatto on the stiffness of the nanofibers, we performed the Nano-DMA test. Our results revealed that the addition of annatto to cellulose acetate decreased the stiffness of the obtained nanofibers. Similar results were reported in previous studies showing a reduction in polymer stiffness as a result of adding eugenol, ginger, cinnamon, guarana, and rosemary extract (Bonilla et al., 2018; Ke et al., 2019; Moeini et al., 2022). Furthermore, the nanofiber stiffnesses obtained in our work (323 MPa and 211 MPa) presented comparable values to the electrospun matrices developed for muscle tissue engineering (Cooper et al., 2010; Riccotti et al., 2012; Luo et al., 2018; Jekins and Little, 2019).
Skeletal muscle ECM is a complex meshwork consisting of collagens, glycoproteins, proteoglycans, and elastin. Collagen fibrils in the skeletal muscle ECM vary in diameter from 30 to 300 nm (Ushiko, 2002), while elastin fibers are about 100 nm thick (Gasser, 2017). Morphological analysis revealed by SEM images showed that both CA and CA@A nanofibers present a smooth and homogeneous porous mat, exhibiting porous interconnectivity. This is considered an important property of a material to be used as scaffold for cell growth, since the porous presence allows cell migration and the colonization of the interior of the scaffold (Post et al., 2020). Porous nature might also allow vascularization as well as the formation of multiple layers of cells, both crucial processes for establishing a tissue-like construct (Gurdon et al., 1993). Besides that, Csapo et al. [ 2020] investigated the manner in which myoblasts detect and respond to fiber diameter differences and found that increased fiber diameters (from 335 ± 154 nm to 3,013 ± 531 nm) were able to induce myoblast proliferation and differentiation, as well as fusion into mature myotubes, indicating the ability of cells to respond to fiber topography. Our morphological analysis also showed that there was an increase in the CA@A diameter fibers when compared to CA nanofibers.
We next evaluated the biocompatibility of these nanofibers to support skeletal muscle cell growth and differentiation. Myoblasts from the C2C12 immortalized cell lineage were used in this work since they are easy to manipulate and are an excellent model to test the possibility of use of these nanofibers as scaffolds in muscle tissue engineering. In the presence of serum, C2C12 myoblasts are induced to proliferate. When these cells start making contact with each other, or when serum is removed from the medium, C2C12 cells initiate the differentiation program, meaning that these cells suffer growth arrest, elongate and fuse to each order to form a multinucleated myofiber (Bruyère et al., 2019).
Both nanofibers revealed significant capacity of cell attachment, since we found only ∼$2\%$ of the cells free in the medium after 24 h of plating. Besides attachment, cell viability analysis revealed that C2C12 cells were similarly viable after 2 days in culture onto both nanofibers. SEM images corroborated with this finding and allowed the observation of the first cell-cell and cell-nanofibers contacts. Cells tend to connect to each other and to sense the environment in which they were placed, showing the ability to recognize and interact with that milieu (Wijnhoven et al., 2020) and long-term behavior is highly dependent of the cell shape and cytoskeletal organization that are often initiated during the minutes to hours following adhesion (Cretel et al., 2008).
Both nanofibers induced an increase in cell viability index after 7 days of culture, suggesting that their large surface area is beneficial for long-duration cell culture. Again, SEM images corroborated with this data. However, the presence of annatto improved the viability index of C2C12 cells after 7 days in culture compared to pure CA nanofibers. The improvement in cell viability in CA@A nanofibers might occur due to i) the increased hydrophilicity of the nanofiber due to the addition of annatto (Golizadeh et al., 2019; Jenkins and Little, 2019; Zan et al., 2020) and ii) the presence of the antioxidant components in the annatto extract (Naranjo-Durán et al., 2021).
We have also investigated whether cell seeding and culturing onto CA and CA@A nanofibers would interfere with the progression of the differentiation process of these cells during time. We first evaluated cell shape by staining the F-actin component of the cytoskeleton after 7 days of culture. C2C12 cells plated onto CA nanofiber were found to be more aligned and elongated, while the same cells showed to be thinner and randomly distributed when cultivated onto CA@A.
To better assess the cell differentiation stage, we evaluated the gene expression pattern of the main myogenic markers in C2C12 cells cultivated onto CA and CA@A nanofibers. Here we investigated the expression of MyoD and Myf5, which are markers expressed first during the myogenesis process, being more related to the proliferation stage of these cells; and MyoG and Desmin, which are related to the cell fusion stage and is necessary to form multinucleated myotubes (Chal & Pourquié, 2017).
We found that cells cultivated onto CA@A nanofibers upregulate the expression of myogenic markers already after 7 days in culture, even cultivating these cells only with GM, suggesting that the annatto has an impact in inducing C2C12 cell differentiation. After 14 days of culture in GM, we could only observe the induction of MyoG expression, being all the other markers found as downregulated. This result suggested that, despite showing an important effect on cell differentiation at the beginning, the annatto might be interfering with the phenotype of these cells during time, since we could not observe an impact in all late myogenic markers. These results were corroborated with the analysis performed using the data obtained during time (7 × 14 days): the differentiation progress could be observed on cells cultivated onto the CA nanofiber over time, since all myogenic markers were found to be upregulated at 14 days, compared to their expression at 7 days, while AC@A allowed the upregulation of only MyoG over time. Altogether, these results suggested that the expression of myogenic markers are favored in cells cultivated onto CA nanofibers, while the annatto interfere with the myogenic differentiation of these cells.
The greater differentiation of C2C12 in pure nanofibers compared to nanofibers containing annatto can be associated with scaffold mechanical and morphological properties. In the present work we have shown that pure nanofiber has higher stiffness and smaller fiber diameter, which greatly contributed to C2C12 cellular differentiation. Our result is compatible with previous works showing more differentiated myoblasts on nanofibers with a smaller diameter and a higher stiffness (Choi et al., 2008; Ku et al., 2012; Luo et al., 2018). Altogether, these results suggested that the expression of myogenic markers are favored in cells cultivated onto CA nanofibers, while the annatto interfere with the myogenic differentiation of these cells.
Here, we demonstrated the adhesion, proliferation, and differentiation of muscle cells in the cellulose acetate nanofiber as a preliminary stage towards its application in cultured meat production. To obtain cultured meat, however, it is still crucial to study the interaction between the CA scaffolds and cells from agriculturally relevant species such as beef, pork, poultry and seafood.
## 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
TC and AEAS: investigation, formal analysis, writing. JSFC, ACCC, AGLC, and JMN: investigation and formal analysis. EGAA: supervision, methodology, formal analysis and reviewing. JPFS, LOA, CF, and GABS: formal analysis, supervision and reviewing. ECJ and RVF: conceptualization, methodology, supervision, writing, reviewing and editing. All authors listed have made a substantial, direct and intelectual contribution to the worked and approved it 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.
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|
---
title: A bibliometric and visual analysis of low carbohydrate diet
authors:
- Gang Lu
- Xin Huang
- Chun Lin
- Lijuan Zou
- Huashan Pan
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC9995895
doi: 10.3389/fnut.2023.1085623
license: CC BY 4.0
---
# A bibliometric and visual analysis of low carbohydrate diet
## Abstract
### Introduction
Numerous studies have confirmed the effects of low carbohydrate diet (LChD) on metabolism and chronic diseases. However, there were no bibliometric studies on LChD. This study was conducted through a bibliometric analysis to investigate the current status, hotspots and frontiers trends.
### Methods
We searched all research publications related to LChD from 2002 to 2021 on the Web of Scientific Core Collection (WoSCC). CiteSpace and VOSviewer software was used to analyze countries/regions, institutions, journals, authors, references, and keywords.
### Results
A total of 6938 papers were included, with an increasing trend of annual publication. LChD categories mainly included nutrition, endocrinology, and neurosciences which reflected the interdisciplinary characteristics. USA was with the largest number and the world science center in LChD field. Universities were main research institutions and five of the top 10 institutions were from USA. Eric Heath Kossoff had 101 publications and ranked first. Nutrients was the leading journal. “ A randomized trial of a low-carbohydrate diet for obesity” and “Obesity” were considered to be the most co-cited and cited reference respectively. The hotspots of LChD are four aspects, “ketogenic diet”, “metabolism disease”, “cardiovascular disease” and “cancer”. We summarized that “oxidative stress”, “gut microbiota”, and “inflammation factors” are becoming frontiers trends of LChD research in the future and deserve further study.
### Discussion
Over the past 20 years research on LChD has gained great attention. To better explore LChD field, multilevel mechanism studies will be required in the future.
## Introduction
Obesity-related complications affect almost all body systems and are significant risk factors for coronary heart disease, type 2 diabetes, cancers such as endometrial, breast, prostate, and skin cancers, as well as several other chronic non-communicable diseases. Diet therapy methods, theories, and applications are constantly updated as a result of ongoing research on the metabolism of the organism in normal and disease states [1]. It has been demonstrated that consuming a diet high in carbohydrate increases the risk of developing metabolic and chronic diseases, and that lowering carbohydrate intake decreases the incidence of morbidity [2]. The effects of LChD on health have garnered a lot of attention recently. There are many types of low carbon diet prescriptions according on the carbohydrate intake ratio. The American Diabetes Association recommended a conventional 2,000 calorie daily diet with < 130 g of carbohydrates [3]. The other study suggested consuming < $40\%$ of one's daily calories from carbohydrates [4]. Anyway, the two prescriptions LChD above are powered by glucose first and then switch to ketone bodies after fasting. Moreover, the ketogenic diet (KD) is another LChD that calls for a very low carbohydrate intake (< $10\%$). KD, a sort of LChD, was initially used to cure epilepsy [5]. Atkins, an American, wrote about an LChD in his 1972 book “Dr. Atkins' New Diet Revolution,” in which the intake of carbohydrate was rigorously limited while the intake of protein and fat is raised [6]. Currently modified Atkins diet, a easier KD, has showed very similar effects with KD [7]. A LChD can lower excess body weight [8, 9], as well as the risk of diabetes, cancer, cardiovascular disease, and internal inflammatory responses brought on by obesity [10, 11]. Of fact, some research has indicated that LChD can also produce negative health effects, such as gastric dysfunction [12], atherosclerosis [13], physical fatigue [14], etc.
Studies on LChD are becoming increasingly popular in recent years as a response of the academic community's intense interest in the disease's favorable health effects (15–17). Most of studies, nevertheless, have concentrated on how LChD affects certain disease locations. We require a thorough understanding of the development process and research trends in this subject given the rapid proliferation of research on LChD. However, there are no bibliometric and visual analysis article on LChD.
Bibliometrics, a mathematical and statistical tool for quantitatively analyzing all knowledge [18], has been used to assess distributions, collaboration, citation, keywords, hotspots, and frontiers trends [19]. CiteSpace and VOSviewer are software for visualization for bibliometrics analysis [20, 21]. These two software generate network maps that allow researchers to intuitively analyze the current status within the field, and determine the research hotspots and frontiers trends [22]. Therefore, this study employs CiteSpace and VOSviewer software to analyze the publications on LChD from 2002 to 2021, to evaluate and analysis the research hotspots and frontiers trends. This has been the first study to use bibliometric strategies in the field of LChD. The study is expected to help researchers extract potential information for further research in the field of LChD research and offer them helpful advice in choosing ground-breaking subject matter by answering the following questions:
## Data acquisition and search strategy
In this study, WoSCC was selected as the data source. As a high-quality digital literature resource database, WoSCC has been accepted by many researchers, and considered as the most suitable database for literature analysis [23]. All publications were retrieved from the Science Citation Index Expanded (SCI-E) of the WoSCC database on November 12, 2022. We completed the search within the same day to avoid any bias caused by database updates. The following methods were conducted for search publications: topic words = (“low carbohydrate” OR “low-carbohydrate” OR “low carb” OR “low-carb” OR “ketogenic” OR “carbohydrate-restricted” OR “carbohydrate restricted” OR “restricted carbohydrate” OR “restricting carbohydrate” OR “carbohydrate restriction” OR “South Beach diet” OR “Atkins diet”). In order to more accurately analyze the current status, hotspots and frontiers trends of LChD, the publications from 2002 to 2021 were selected. Time span = January 1, 2002–December 31, 2021. To ensure the representativeness of the included studies, the types of publications were limited to “articles” and “reviews” [24]. No languages limitation to avoid bias in the geographical distribution of publications. The content of literature records were “full records and cited references,” downloaded and saved in plain text document format.
## Statistical analysis
We used the CiteSpace (6.1.R3) and VOSviewer (1.6.18) for a bibliometric analysis of 6,938 publications on LChD from 2002 to 2021. The java-based program CiteSpace does bibliometric analysis of publications using distribution network maps, co-citation network maps, dual maps of journal overlay, and keyword burst citation maps [25]. Nodes and links are included in the visual network diagram produced by CiteSpace. Every node is a factor, such as an author, an institution, or a country [26]. Links between different nodes show a network of relationships involving co-operation, co-citation, or co-occurrence [27] A wider line indicates a more effective collaboration. The higher the centrality, the larger the circle is in terms of centrality. When a node has a purple circle around it, it has a high centrality score and is therefore an important node in the field [22]. VOSviewer was used to form keyword co-occurrence of overlay visualization. The colors represent the years [28]. The size of the node is proportional to the frequency of keyword occurrences [29]. Data was managed, charts were made, and all data tables were created using Microsoft Excel 2021 software.
## Annual output and categories
A total of 6,938 publications including 5,350 articles and 1,588 reviews, related to LChD from 2002 to 2021 were retrieved by searching the WoSCC database. The flowchart was shown in Figure 1. The annual publications reflected the activities in the field and the attention given to certain areas of research [30]. As seen in Figure 2, the number of annual publications on LChD showed an overall upward trend in spite of fluctuation slightly in some years over the past 20 years. It indicates that LChD research is becoming a research of great interest to scholars and has attracted great interest from scholars in recent years.
**Figure 1:** *The flowchart searching papers in databases.* **Figure 2:** *The annual number of publications on LChD from 2002 to 2021.*
LChD publications in the past 20 years can be divided into 2 stages. The initial stage (2002–2010) was a steady growth period. The average number of publications was 188 publications every year, with the lowest number of publications being 72 publications in 2002 and the highest number being 273 publications in 2009. In 1927, a low carbohydrate ketogenic diet had been reported for epilepsy [31]. As an early study in 1948, LChD was used to control of dental caries [32]. Since 2002, LChD was contributed to a variety of areas, including obesity [33], diabetes [34], and cardiovascular disease [35]. Although the number of papers varied at this stage, the overall trend was one of consistent growth. The second stage (2011–2021) was a sustained growth period. The average number of publications annually was 476 publications. The number of publications reached 872 in 2021. Nutrition has a significant role in daily life, and it is crucial for the advancement of social development to support research on diet and health. LChD research has gained popularity as a nutritional approach and is rapidly developing into a research hotspot.
The categories refer to the disciplines covered by the dissertation research. At top 10 categories (Table 1), Nutrition Dietetics had 1,621 publications and ranked first, followed by Clinical Neurology (1,269 publications), Endocrinology Metabolism (960 publications), Neurosciences (734 publications) and Pediatrics (472 publications). LChD research mainly covered the fields of nutrition, endocrinology, and neurosciences, reflecting the multidisciplinary nature and comprehensive knowledge.
**Table 1**
| Rank | Category | Publications |
| --- | --- | --- |
| 1 | Nutrition dietetics | 1621 |
| 2 | Clinical neurology | 1269 |
| 3 | Endocrinology metabolism | 960 |
| 4 | Neurosciences | 734 |
| 5 | Pediatrics | 472 |
| 6 | Biochemistry molecular biology | 417 |
| 7 | Medicine general internal | 314 |
| 8 | Medicine research experimental | 282 |
| 9 | Pharmacology pharmacy | 269 |
| 10 | Multidisciplinary sciences | 227 |
## Analysis of countries/regions
In total, 112 countries/regions participated in 6,938 publications on LChD from 2002 to 2021. CiteSpace generated the countries/regions distribution map, and 112 nodes and 880 links were shown in the map (Figure 3). Table 2 presented the top 10 countries/regions published in LChD research field. USA had the highest number of publications, 2,862 papers, accounting for $41.25\%$. The Yuasa phenomenon states that the nation whose research output accounts for more than $25\%$ of all scientific output at any given moment can be referred to as the world center of science during that time [36]. As the leader in LChD research, USA published far more than a quarter of the total publications and was the world science center in the field of LChD. England (543 publications), Italy (472 publications), China (449 publications), and Germany (441 publications) followed closely behind. In terms of centrality, Canada (0.16) ranked first, followed by, Spain (0.14), Australia (0.13), England (0.13), France (0.11) and, which maintain close cooperation relationships. Countries/regions with centrality played an important role in LChD research. Germany, Canada, Australia and France each had < 450 publications, but their research roles were important. In terms of publications, China had 449 papers, but the centrality was only 0.01. It demonstrated that despite having a high publications number, China had few connections and little influence over the network map. The level of LChD research in China therefore was raised effectively by deepening the field's research, advancing cross-disciplinary and cross-field collaboration, and enhancing researchers' capacity for creative thinking and global communication.
**Figure 3:** *Map of countries/regions on LChD from 2002 to 2021.* TABLE_PLACEHOLDER:Table 2
## Analysis of institutions
A total of 604 institutions provided research in the field of LChD. CiteSpace generated the institutions distribution map with 604 nodes and 2,103 links (Figure 4). The institutions with large numbers of publications have been identified as influential institutions [37]. Table 3 listed the top 10 institutions in publications, and they were the most influential institutions in LChD research. Universities were major institutions for LChD research. Harvard University ranking first, had 451 papers, followed by University of California System (258 publications), Johns Hopkins University (216 publications), Udice French research universities (191 publications), and University of London (174 publications). Five of the top 10 institutions were from USA, which further confirmed US predominance in the field of LChD research. Duke University, Harvard University, Johns Hopkins University and University of Toronto had close collaboration relationships.
**Figure 4:** *Map of institutions on LChD from 2002 to 2021.* TABLE_PLACEHOLDER:Table 3
## Analysis of authors
In total of 890 authors participated in 6,938 publications on LChD from 2002 to 2021. CiteSpace generated the institutions distribution map with 890 nodes and 1965 links (Figure 5). The top 10 authors participating in the LChD research are shown in Table 4. The most productive authors were Eric Heath Kossoff (101 publications), Jeff Scott Volek (69 publications), Jong M. Rho (62 publications), William S. Yancy (45 publications), and Maria Luz Fernandez (43 publications). Eric Heath Kossoff ranked first in the number of publications devoted to the study of the effects of a high-fat, low-carb ketogenic diet on neurological disorders. He demonstrated that a high-fat, low-carb ketogenic diet reduced the number of seizures in refractory epilepsy and reported no cardiovascular or cerebrovascular events [38, 39]. In addition ketogenic diets are being applied to a range of neurological disorders from autism to Alzheimer's disease [40]. Jeff Scott Volek was the second position of papers. He reported that in individuals with atherosclerotic dyslipidemia, a 12-week carbohydrate restriction diet improved postprandial vascular function more than a low-fat diet [41]. An study revealed that LChD ($10\%$) not only decreased lipid deposition but avoided the buildup of plasma and aortic oxidation, decreased inflammatory cytokines within the artery wall, and prevented atherosclerosis [42]. Jong M. Rho was in the third place in terms of number of publications. In addition to a high-fat, low-carbon-water ketogenic diet that improves epilepsy [43], he emphasized that a ketogenic diet enhances mitochondrial function and reduces autistic behavior in humans and rodent models of autism spectrum disorder [44, 45]. The authors' collaboration displayed a geographical concentration and general decentralization.
**Figure 5:** *Map of authors on LChD from 2002 to 2021.* TABLE_PLACEHOLDER:Table 4
## Analysis of journals
Researchers can accurately understand the core journals in a topic by analyzing its source journals, which also serves as a reliable resource for further field research [46]. A total of 1,545 academic journals published 6,938 publications in the field of research on LChD from 2002 to 2021. As shown in Table 5, the top 10 journals accounted for $17.93\%$ of the total publications. The most productive journals were Nutrients (292 publications), Epilepsia (203 publications), Epilepsy Research (134 publications), PLoS One (116 publications), and American Journal of Clinical Nutrition (105 publications). Of the top 10 journals, eight journals' IF more than 3.0. With a maximum of 8.472, the top 2 journals had an IF >6.0. This shows that high IF journals are open to publishing LChD research.
**Table 5**
| Rank | Journal | Publications | IF (2021) |
| --- | --- | --- | --- |
| 1 | Nutrients | 292 | 6.706 |
| 2 | Epilepsia | 203 | 6.74 |
| 3 | Epilepsy Research | 134 | 2.991 |
| 4 | PLoS One | 116 | 3.752 |
| 5 | American Journal of Clinical Nutrition | 105 | 8.472 |
| 6 | Epilepsy and Behavior | 96 | 3.337 |
| 7 | Journal of Child Neurology | 78 | 2.363 |
| 8 | British Journal of Nutrition | 76 | 4.125 |
| 9 | Seizure European Journal of Epilepsy | 74 | 3.414 |
| 10 | Nutrition | 73 | 4.893 |
Figure 6 illustrated the dual- map overlay of journals that produced literature linked to the topic of LChD. On the map, the right labels represented the disciplines of the journals that published the cited papers, while the left labels represented the fields of the citing journals. Citation links can show the in and out of the citation dataset. Figure 6 showed 5 reference pathways. Three yellow pathways indicate articles published in molecular/biological/immunology journals mainly citing journals in the molecular/biological/genetics field. Two green pathways suggest that articles published in medicine/clinical journals mainly cite journals in the molecular/biology/genetics/health/nursing/medicine fields. One red pathway shows that the publications from neurology/sports/ophthalmology mainly cite journals in the in molecular/biology/genetics field.
**Figure 6:** *Dual-map overlay of academic journals from 2002 to 2021.*
## Analysis of co-cited references
The co-cited reference analysis is one of the important indicators in bibliometric research and is usually used to explore research priorities in specific academic fields [47]. CiteSpace generated the co-cited reference map, and 1,693 nodes and 9,455 links were shown in the map (Figure 7). The top 5 co-cited references in terms of frequency were in Table 6. Analysis of co-cited references provided basic data for LChD research. Noteworthy were three publications from the New England Journal of Medicine and two from the Annals of Internal Medicine, both of which have significant academic influence. The five references were all clinical trials. In most co-cited reference, obese people were given Atkins diet, and lost more weight in the first 6 months [48]. Additionally, high density lipoprotein cholesterol levels increased and triglyceride levels decreased more in Atkins diet participants than in control group, indicating that Atkins diet had a higher impact on the risk factors for coronary heart disease. The second-most co-cited reference reported that patients who received a carbohydrate-restricted diet with 30 g per day or less, lost more weight than control group did and had relative improvements in their insulin sensitivity and triglyceride levels [49]. The third most co-cited reference of 132 obese people on who were restricted carbohydrate intake to < 30 g per day showed more beneficial effects than those on conventional diets at 1 year; the effects of restricted carbohydrate on atherogenic dyslipidemia and glycemic control remained more favorable [34]. Diet therapies were given to moderately obese subjects, and a low-carbohydrate diet and a Mediterranean diet were found to have beneficial effects on lipids and blood glucose, respectively [50]. Individualized dietary regimens tailored to individual preferences and metabolism are recommended. A low-carbohydrate, ketogenic diet exhibited higher participant retention and more weight loss compared to low-fat diets in the literature with the sixth greatest co-citation frequency [51].
**Figure 7:** *The map of co-cited reference on LChD from 2002 to 2021.* TABLE_PLACEHOLDER:Table 6
## References analysis
High cited references lay the foundation and accelerate the development of research in the field [23]. The top 10 cited references were listed in Table 7. Of the top 10 references, 7 references were articles and 3 were reviews. Three references were published in the New England Journal of Medicine and two were published in Lancet. “ Obesity” published by Haslam et al. in 2005, was cited 3,136 times, and ranked first. Shai et al. published in 2008 in New England Journal of Medicine of “Weight loss with a low-carbohydrate, Mediterranean, or low-fat diet” was cited 1,250 times, and ranked second. Foster et al. published in 2003 in “A randomized trial of a low-carbohydrate diet for obesity” in New England Journal of Medicinewas cited 1,124 times, and ranked third.
**Table 7**
| Rank | Title | Author | Type | Journal | Year | Citations |
| --- | --- | --- | --- | --- | --- | --- |
| 1 | Obesity | Haslam DW, et al. | Review | Lancet | 2005 | 3136 |
| 2 | Weight loss with a low-carbohydrate, Mediterranean, or low-fat diet | Shai I, et al. | Article | New England Journal of Medicine | 2008 | 1250 |
| 3 | Hepatic fibroblast growth factor 21 is regulated by PPAR alpha and is a key mediator of hepatic lipid metabolism in ketotic states | Michael K Badman, et al. | Article | Cell Metabolism | 2007 | 1125 |
| 4 | A randomized trial of a low-carbohydrate diet for obesity | Foster GD, et al. | Article | New England Journal of Medicine | 2003 | 1124 |
| 5 | Comparison of the Atkins, Ornish, Weight watchers, and Zone diets for weight loss and heart disease risk reduction | Dansinger ML, et al. | Article | JAMA | 2005 | 1100 |
| 6 | Nutrition recommendations and interventions for diabetes—a position statement of the American Diabetes Association | American Diabetes Association, et al. | Article | Diabetes Care | 2008 | 1074 |
| 7 | Childhood obesity | Han JC, et al. | Review | Lancet | 2010 | 1010 |
| 8 | Weight-loss outcomes: A systematic review and meta-analysis of weight-loss clinical trials with a minimum 1-year follow-up | Franz MJ, et al. | Review | Journal of the American Dietetic Association | 2007 | 953 |
| 9 | The ketone metabolite beta-hydroxybutyrate blocks NLRP3 inflammasome-mediated inflammatory disease | Youm YH, et al. | Article | Nature Medicine | 2015 | 935 |
| 10 | A low-carbohydrate as compared with a low-fat diet in severe obesity | Samaha FF, et al. | Article | New England Journal of Medicine | 2003 | 844 |
## Analysis of keywords
The map of keywords can present the main research objects and the hot topics and frontiers trends. In this study, VOSviewer software performed the keyword co-occurrence of overlay visualization (Figure 8). A total of 9,750 keywords, 172 keywords met the thresholds when the minimum number of occurrences of a keywords was 20. From Figure 8, we found that the keywords research hotspots were categorized into “ketogenic diet,” “metabolism disease,” “cardiovascular disease” and “cancer.” Bursts keywords were frequently used at a period time, reflecting the frontiers trends. We used CiteSpace software to map the top 32 keywords with the strongest citation bursts from 2002 to 2021 (Figure 9). We summarized that “oxidative stress,” “gut microbiota,” and “inflammation factors” are becoming frontiers trends of LChD research in the future.
**Figure 8:** *Map of keyword co-occurrence of overlay visualization on LChD from 2002 to 2021.* **Figure 9:** *Map of keyword with the strongest citation bursts on LChD from 2002 to 2021.*
## Discussion
We performed a bibliometric analysis of the publications from WoSCC on LChD from 2002 to 2021 using CiteSpace and VOSviewer software. We then summarized the current status, hotspots and frontiers trends in this field.
A total of 6,938 publications including 5,350 articles and 1,588 reviews, related to LChD from 2002 to 2021 were retrieved by searching WOSCC database. The number of annual publications on LChD showed an overall upward trend in spite of fluctuation slightly in some years. LChD research mainly involved the categories of nutrition, endocrinology, and neurosciences, reflecting the multidisciplinary nature and comprehensive knowledge about LChD research. USA was with the largest number and the world science center in LChD field, and Australia, Canada, England, France and Germany maintained close cooperation relationships. Universities were major institutions for LChD research. Five of the top 10 institutions were from USA, which further confirmed US predominance in the field of LChD research. Duke University, Harvard University, Johns Hopkins University and University of Toronto had close collaboration relationships. The most productive authors were Eric Heath Kossoff, Jeff Scott Volek, Jong M. Rho, William S. Yancy, and Maria Luz Fernandez. The authors' collaboration showed a geographical concentration and general decentralization. The most productive journals were Nutrients, Epilepsia, Epilepsy Research, PLoS One, and American Journal of Clinical Nutrition. “ A randomized trial of a low-carbohydrate diet for obesity” and “Obesity” were considered to be the most co-cited and cited reference respectively.
Based on the keywords the keyword co-occurrence of overlay visualization, we can explore the hotspots. From Figure 8, we summarized and analyzed four hotspots in LChD field. Here, we further analyzed the following aspects according to the application field of LChD: ketogenic diet, metabolism disease, cardiovascular disease and cancer.
Burst keywords can explore the future development trends. Therefore, we summarized the burst keywords into three aspects, and considered them to be frontiers trends of LChD field and anticipated to occur frequently in the future years.
## Limitations
To the best of our knowledge, the present study is the first bibliometric analysis to assess LChD. However, it has many limitations. First, considering that the data difference and incompleteness of other database data, we only analyzed publications from the WoSCC. Next, to better present the analysis result and to ensure the quality of the included literature, we included only articles and reviews published in English. This may lead to some screening bias.
## Conclusion
We searched all research publications related to LChD on the Web of Scientific Core Collection (WoSCC). CiteSpace software was used to analyze countries/regions, institutions, journals, authors, references, and keywords. LChD is a popular diet, attracting attention from scholars. The hotspots of LChD are three aspects, “metabolism disease,” “cardiovascular disease,” and “risk factor.” We summarize that “research on prevention and treatment,” “research on diet,” and “research on molecular level” are becoming frontiers trends of LChD research in the future directions and deserve further study.
## 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
HP and GL: conceptualization. XH: methodology, writing-original draft preparation, and writing-review and editing. CL and LZ: software. LZ: investigation, data curation, and supervision. GL and XH: resources. GL and CL: visualization. All authors have read and agreed to the published version of the manuscript.
## 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.
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|
---
title: 'Macular vessel density in the superficial plexus is not associated to cerebrospinal
fluid core biomarkers for Alzheimer’s disease in individuals with mild cognitive
impairment: The NORFACE cohort'
authors:
- Marta Marquié
- Ainhoa García-Sánchez
- Emilio Alarcón-Martín
- Joan Martínez
- Miguel Castilla-Martí
- Luis Castilla-Martí
- Adelina Orellana
- Laura Montrreal
- Itziar de Rojas
- Pablo García-González
- Raquel Puerta
- Clàudia Olivé
- Amanda Cano
- Isabel Hernández
- Maitée Rosende-Roca
- Liliana Vargas
- Juan Pablo Tartari
- Ester Esteban-De Antonio
- Urszula Bojaryn
- Mario Ricciardi
- Diana M. Ariton
- Vanesa Pytel
- Montserrat Alegret
- Gemma Ortega
- Ana Espinosa
- Alba Pérez-Cordón
- Ángela Sanabria
- Nathalia Muñoz
- Núria Lleonart
- Núria Aguilera
- Lluís Tárraga
- Sergi Valero
- Agustín Ruiz
- Mercè Boada
journal: Frontiers in Neuroscience
year: 2023
pmcid: PMC9995931
doi: 10.3389/fnins.2023.1076177
license: CC BY 4.0
---
# Macular vessel density in the superficial plexus is not associated to cerebrospinal fluid core biomarkers for Alzheimer’s disease in individuals with mild cognitive impairment: The NORFACE cohort
## Abstract
### Background
Optical coherence tomography angiography (OCT-A) is a novel method in the dementia field that allows the detection of retinal vascular changes. The comparison of OCT-A measures with established Alzheimer’s disease (AD)-related biomarkers is essential to validate the former as a marker of cerebrovascular impairment in the AD continuum. We aimed to investigate the association of macular vessel density (VD) in the superficial plexus quantified by OCT-A with the AT(N) classification based on cerebrospinal fluid (CSF) Aβ1-42, p181-tau and t-tau measurements in individuals with mild cognitive impairment (MCI).
### Materials and methods
Clinical, demographic, ophthalmological, OCT-A and CSF core biomarkers for AD data from the Neuro-ophthalmology Research at Fundació ACE (NORFACE) project were analyzed. Differences in macular VD in four quadrants (superior, nasal, inferior, and temporal) among three AT(N) groups [Normal, Alzheimer and Suspected non-Alzheimer pathology (SNAP)] were assessed in a multivariate regression model, adjusted for age, APOE ε4 status, hypertension, diabetes mellitus, dyslipidemia, heart disease, chronic obstructive pulmonary disease and smoking habit, using the Normal AT(N) group as the reference category.
### Results
The study cohort comprised 144 MCI participants: 66 Normal AT(N), 45 Alzheimer AT(N) and 33 SNAP AT(N). Regression analysis showed no significant association of the AT(N) groups with any of the regional macular VD measures (all, $p \leq 0.16$). The interaction between sex and AT(N) groups had no effect on differentiating VD. Lastly, CSF Aβ1-42, p181-tau and t-tau measures were not correlated to VD (all r < 0.13; $p \leq 0.13$).
### Discussion
Our study showed that macular VD measures were not associated with the AT(N) classification based on CSF biomarkers in patients with MCI, and did not differ between AD and other underlying causes of cognitive decline in our cohort.
## Introduction
Alzheimer’s disease (AD) is a slowly progressive neurodegenerative condition with long preclinical and prodromal stages that precede the onset of dementia. In this regard, the National Institute on Aging-Alzheimer’s Association (NIA-AA) research criteria for AD were reviewed in 2018 to introduce Aβ (A), tau (T) and neurodegeneration (N) biomarkers, resulting in the AT(N) framework (Jack et al., 2018). In these new criteria, the biological definition of AD, independently of the clinical syndrome, was established according to the underlying pathological processes occurring in the brain (amyloid plaque and neurofibrillary tangle deposition), which can be detected in vivo using fluid and/or neuroimaging biomarkers. Thus, individuals who are not yet demented [cognitively unimpaired or with mild cognitive impairment (MCI) (Albert et al., 2011)] can be then classified as presenting an underlying AD pathology if they show abnormal A and T biomarkers.
On this matter, the search for and validation of novel sensitive and specific biomarkers for the early detection of AD is currently a main research focus in the field, and includes plasma (Simrén et al., 2021), genomic (de Rojas et al., 2021), and retinal (Ngolab et al., 2019) parameters, among others. In particular, the retina is considered a “window into the brain” and can be accessed non-invasively through optical coherence tomography (OCT), which is a fast, inexpensive, non-invasive and widely accessible tool used for the diagnosis and monitoring of common ocular pathologies (Jaffe and Caprioli, 2004). The study of retinal biomarkers for AD started with the analysis of structural parameters (e.g., thickness and volume of the different retinal layers in the peripapillary and macular regions) and more recently, investigators have focused on the study of the retinal microvasculature [e.g., vessel density (VD) and size of the foveal avascular zone, mainly] (Jiang et al., 2018; Querques et al., 2019; Wu et al., 2020; Biscetti et al., 2021). Besides, the retinal microvascular network can be directly assessed in vivo using high-resolution OCT-angiography (OCT-A), while this cannot be done for cerebrovascular disease with brain imaging techniques. Cerebrovascular changes are a very common concomitant pathology to AD in elderly individuals with cognitive decline, as shown in autopsy studies (Gorelick et al., 2011; Attems and Jellinger, 2014), and are also involved in the pathophysiology of AD (Sweeney et al., 2019). It is hypothesized that the presence of retinal microvascular damage in patients with cognitive impairment could reflect microvascular damage occurring in the brain. Thus, OCT-A measures in the retina could be used as a proxy of brain vascular changes. Several publications have demonstrated abnormalities in retinal vascular parameters in AD and MCI patients compared to healthy controls, such as increases in the foveal avascular zone (FAZ) and decreases in VD (Cheung et al., 2014; Grewal et al., 2018; Jiang et al., 2018; Querques et al., 2019; Zabel et al., 2019; Zhang et al., 2019; Wu et al., 2020; Biscetti et al., 2021; Chalkias et al., 2021; Rifai et al., 2021), pointing to retinal vascular loss. The majority of cohorts reported in the literature, though, were relatively small and the results have not been validated in larger studies. A recent publication from our group, using data from a large clinical cohort, detected higher macular VD in the superficial plexus in MCI due to AD patients compared to cognitively healthy individuals (Marquié et al., 2022), contrary to previous literature results. Importantly, the inclusion of AD biomarkers in OCT-A studies in order to further validate this technique is warranted for the development of the field.
In the present study, we analyzed data from 144 individuals with MCI from the Neuro Ophthalmology Research at Fundació ACE (NORFACE) cohort with the goal to validate novel retinal vascular parameters against established AD core biomarkers. In particular, we investigated the association of macular VD in the superficial plexus quantified by OCT-A with the AT(N) classification based on cerebrospinal fluid (CSF) Aβ1-42, p181-tau and t-tau measurements. Our hypothesis was that MCI participants with an AD CSF profile (reduced Aβ and elevated tau) would show higher macular VD measures than those participants with a normal CSF profile, in line with the results of a recent publication from our group (Marquié et al., 2022).
## Study subjects
This study is embedded in the NORFACE project, which was founded in 2014 with the goal investigate retinal biomarkers of AD and analyze the relationship between retinal changes and different types of neurodegenerative disorders (Sánchez et al., 2018). Between February 2018 and March 2019, consecutive patients with a diagnosis of MCI (Petersen, 2004) evaluated at Ace Alzheimer Center Barcelona and who underwent, within 12 months, a lumbar puncture (LP) for the quantification of CSF core biomarkers for AD and an ophthalmological exam/OCT scan, were enrolled in the present study. Participants were recruited from three different sources: [1] the Memory Clinic, an outpatient diagnostic unit for individuals with cognitive decline referred by physicians from the Catalan Public Health System (Boada et al., 2014), [2] Fundació ACE’s Open House Initiative (Rodríguez-Gómez et al., 2015), a social program that assesses for free the cognitive status of individuals from the community without the need for a physician’s referral, and [3] the BIOFACE project, a research study of novel biomarkers in early onset MCI (Esteban-De Antonio et al., 2021). Inclusion criteria were: consensus-based clinical diagnosis of MCI (Petersen, 2004), age ≥50 years old, availability of APOE ε4 status, ability to complete the full ophthalmological exam and OCT scan and CSF core biomarkers for AD performed within 12 months of the OCT-A scan.
Further, a group of participants with subjective cognitive decline (SCD) from the Fundació ACE Healthy Brain Initiative (FACEHBI) cohort (Rodriguez-Gomez et al., 2017) with no objective impairment on formal cognitive testing and absence of brain amyloid uptake in a Florbetaben-PET scan (SCD Aβ-) were included as the control group ($$n = 83$$) in additional analyses. These SCD participants underwent the same cognitive testing, ophthalmological exam and OCT scan protocol than the MCI participants from the NORFACE cohort included in the main analyses.
## Clinical evaluation
Neurological, neuropsychological and social evaluations of the study participants were performed at Ace Alzheimer Center Barcelona. A consensus-based diagnosis of cognitive status was reached for each participant at the time of the study recruitment by a multidisciplinary team of professionals (Boada et al., 2014). Cognitive assessment consisted of the Spanish version of the Mini-Mental State Examination (MMSE) (Folstein et al., 1975; Blesa et al., 2001), the memory test of the Spanish version of the 7 min screening neurocognitive battery (del Ser Quijano et al., 2004), the Spanish version of the Neuropsychiatric Inventory Questionnaire (NPI-Q) (Cummings et al., 1994; Boada et al., 2005), the Geriatric Dementia Scale (GDS) (Reisberg et al., 1982), the Clinical Dementia Rating Score (CDR) (Morris, 1993), the Blessed Dementia Scale (Blessed et al., 1968), and a comprehensive neuropsychological battery of Fundació ACE (NBACE) (Alegret et al., 2012, 2013). Demographic information collected included age, gender and years of formal education. Past medical history collected included smoking habit (former or current), hypertension, diabetes mellitus, dyslipidemia, heart disease, and chronic obstructive pulmonary disease (COPD). This clinical information was obtained from participants’ medical records. MCI was defined using Petersen’s (Petersen, 2004) and the Cardiovascular health and cognition study criteria (Lopez et al., 2003).
## APOE genotyping
Genomic DNA was extracted from peripheral blood using the commercially available Chemagic system (Perkin Elmer). APOE genotypes were extracted from the Axiom SP array (Thermo Fisher Scientific) (Moreno-Grau et al., 2019; de Rojas et al., 2021). Ace Alzheimer Center Barcelona has this variable as a standard in its assessment protocols. Alternatively, the APOE genotypes were determined using fluorogenic allele-specific oligonucleotide probes (TaqMan assay; Life Technologies, Spain) for rs7412 (Test ID: C____904973_10) and rs429358 (Test ID: C___3084793_20). For the TaqMan assays, PCR and real-time fluorescence measurements were carried out on a QuantStudio3 real-time PCR system (Thermo Fisher Scientific, Spain) using the TaqMan Universal Master Mix (ref: 4364341, Life Technologies, Spain) methodology according to manufacturer’s instructions. The polymerase chain reaction was performed as follows: first, a pre-read step for 30 s at 60°C, denaturation for 10 min at 95°C, followed by 40 cycles at 95°C for 15 s and 60°C for 1 min, and a post read stage for 30 s at 60°C. The genotype was determined using the Genotyping App for Thermo Fisher Scientific Cloud by clustering analysis. The laboratory technicians were blinded to other study variables. APOE ε4 status (presence of at least one ε4 allele) was reported as a covariate in the analyses.
## Lumbar puncture and quantification of cerebrospinal fluid core biomarkers for Alzheimer’s disease
Lumbar punctures were performed at Ace Alzheimer Center Barcelona by an experienced neurologist under fasting conditions. CSF was collected passively in two 10-ml polypropylene tubes (Sarstedt Ref 62.610.018) centrifuged (2000 × g 10 min at 4°C), aliquoted and stored in polypropylene tubes (Sarstedt Ref 72.694.007) at −80°C until its use. Time delay between CSF collection and storage was less than 2 h. The collection protocol follows the recommendations of the Alzheimer’s Biomarkers Standardization Initiative (Vanderstichele et al., 2012). The day of the analysis, one aliquot of 0.5 mL was thawed and used for the determination of Aβ1-42, Total Tau (T-tau) and p181-tau. Aβ and tau proteins were quantified by either the commercially available enzyme linked immunosorbent assays (ELISA) (Innotest, Fujirebio Europe) ($$n = 114$$) or the chemiluminescence enzyme immunoassay (CLEIA) using the Lumipulse G 600 II automatic platform (Fujirebio Inc.) (Leitão et al., 2019) ($$n = 30$$).
Using CSF biomarkers, participants were classified into three categories according to the AT(N) scheme (Jack et al., 2018): Normal AD biomarkers (A-T-N-), Alzheimer’s continuum (including A+T-N-, A+T+N-, A+T+N+ and A+T-N+) and Non-AD pathologic changes [Suspected non-Alzheimer pathology (SNAP), including A-T+N-, A-T-N+ and A-T+N+], where A refers to aggregated Aβ, T to aggregated tau and N to neurodegeneration or neuronal injury (Supplementary Table 1). Cut-offs from the Ace Alzheimer Center Barcelona CSF program were used to dichotomize each CSF biomarker into ± as follows: for ELISA, Aβ1-42 < 676 pg/ml for A, p181-tau > 58 pg/ml for T and t-tau > 367 pg/ml for N; for CLEIA, Aβ1-42 < 796 pg/ml for A; p181-tau > 54 pg/ml for T and t-tau > 412 pg/ml for N (Orellana et al., 2022).
## Neuro-ophthalmological evaluation
In parallel to the cognitive assessment, study participants underwent a complete neuro-ophthalmological evaluation, which lasted about 20 min and was performed by an optometrist. The evaluation comprised: [1] a review of past ophthalmological diseases, treatments and surgeries, [2] monocular visual acuity assessment with the participants wearing their habitual correction for refractive error using a pinhole occluder and the Early Treatment of Diabetic Retinopathy Study (ETDRS) chart (Chew et al., 2009; Bokinni et al., 2015), [3] intraocular pressure (IOP) measurement by Icare tonometry (Pakrou et al., 2008), and [4] swept source (SS) OCT scan. More details can be found elsewhere (Marquié et al., 2022). The ophthalmologist and neurologists were blind to each other’s diagnosis.
## Optical coherence tomography and angiography
Participants were imaged with a DRI OCT Triton–Swept Source (SS) OCT (Topcon Co. Tokyo, Japan). The OCT exam was completed in about 5–10 min, no pupil dilation was required, and both eyes were scanned separately. The OCT Angiography Ratio Analysis (OCTARA) processing software was used to analyzed the data. An automatic segmentation method was employed to obtain measures of the superficial vascular plexus and the quantification of VD, expressed as the % of area covered by vessels. VD measures were obtained in a 6 mm × 6 mm area centered in the fovea, and the central area (1 mm circle) was excluded from the analysis. The parafoveal area, defined by two concentrical rings measuring 1 and 3 mm in diameter, respectively, was subdivided into four quadrants: nasal, superior, temporal and inferior. More details can be found in a recent publication from the NORFACE cohort (Marquié et al., 2022). The retinal vascular network consists of two separate beds: the superficial vascular plexus, at the level of the ganglion cell layer, and the deep vascular plexus, at the level of the outer plexiform layer (Toussaint et al., 1961). The FAZ is the region in the center of the macula within the fovea that is devoid of vasculature. In our analysis we used data from macular VD in the superficial vascular plexus.
Only VD measures from the right eye were used for the analysis, as in previous papers from our group (Sánchez et al., 2018, 2020; Marquié et al., 2020). OCT-related exclusion criteria were the following: lack of collaboration in the neuro-ophthalmological exam or OCT scan, OCT data obtained only from the left eye, presence of OCT artifacts and diseases that could affect retinal measurements [e.g., open-angle glaucoma and other neuropathies maculopathies, prior retinal surgery, intraocular pressure (IOP) ≥24 mmHg, high myopia (< −6 Dp) or hyperopia (> + 6 Dp) and optic nerve congenital abnormalities].
## Ethics statement
This study and its informed consent were approved by the ethics committees of the Hospital Clínic i Provincial de Barcelona in accordance with Spanish biomedical laws (Law $\frac{14}{2007}$, July 3rd, about biomedical research; Royal Decree $\frac{1716}{2011}$, November 18th) and followed the recommendations of the Declaration of Helsinki. All participants signed an additional informed consent for the lumbar puncture procedure.
This study and its informed consent were approved by the Ethics Committees of the Hospital Clínic i Provincial de Barcelona in accordance with Spanish biomedical laws (Law $\frac{14}{2007}$, July 3rd, about biomedical research; Royal Decree $\frac{1716}{2011}$, November 18th) and followed the recommendations of the Declaration of Helsinki. All participants signed an additional informed consent for the Lumbar puncture procedure. The patients/participants provided their written informed consent to participate in this study.
## Statistical analysis
Data processing and analysis were carried out using R version 4.1.2 (R Core Team, n.d.).1 *All data* were examined for normality, skew and restriction of range. All quantitative variables were normally distributed. Frequency analysis and measures of central tendency and dispersion were used to describe the demographic (age, sex, education) and clinical (hypertension, diabetes mellitus, dyslipidemia, heart disease, chronic obstructive pulmonary disease (COPD) and smoking) variables among the three AT(N) groups (Normal, Alzheimer, and SNAP). To summarize the distribution of these demographic and clinical variables among the three AT(N) groups, bivariate Analysis of Variance (ANOVA) and Pearson’s chi-squared tests were executed.
For the final multivariate model, we included all clinical variables (hypertension, diabetes mellitus, dyslipidemia, heart disease, COPD, and smoking) as adjusting factors. To identify which demographic variables should be additionally included as adjusting factors in the final model, a multinomial regression analysis was executed to determine their differential distribution among the three AT(N) groups. An analysis was performed for the demographic variables (age, sex, education, and APOE ε4 status) including all the clinical variables (hypertension, diabetes mellitus, dyslipidemia, heart disease, COPD and smoking habit) as adjusting factors. The Normal AT(N) group was considered the reference category. For these analyses, alpha level was set at $p \leq 0.05.$
The main analyses consisted of four multivariate regression analyses, one for every macular VD measure (nasal, superior, temporal, and inferior quadrants), including the three AT(N) groups (Normal, Alzheimer and SNAP) as discriminant factors and adjusting their effect by all six clinical variables and those demographic factors that showed any significant effect in the former multinomial regression analysis. The Normal AT(N) group was considered the reference category. Regression coefficients (the mean change in the outcome variable for one unit of change in the predictor variable while holding other predictors in the model constant), betas (the degree of change in the outcome variable for every one unit of change in the predictor variable) and t (assessing whether the beta coefficient is significantly different from zero) are reported.
The former four multivariate regression analysis were rerun without including the A+T-N- participants within the AT(N) Alzheimer group (amyloidosis alone without tauopathy or neurodegeneration, $$n = 9$$).
Additionally, the former four multivariate regression analyses were repeated including a group of participants with subjective cognitive decline (SCD) and absent brain amyloid uptake in a FBB-PET scan (SCD Aβ-) from the Fundació ACE Healthy Brain Initiative (FACEHBI) cohort (Rodriguez-Gomez et al., 2017) as the reference category ($$n = 83$$).
For the former multivariate regression analyses, alpha level was set up at $p \leq 0.004$ ($\frac{0.05}{12}$) after Bonferroni’s correction for multiple comparisons.
The association between individual CSF biomarkers (Aβ1-42, p181-tau and t-tau) and each of the four macular VD measurements (nasal, superior, temporal and inferior quadrants) was explored using separate partial correlations, including the same covariates. These analyses were performed separately for the ELISA and CLEIA groups and also for the whole cohort after a log-transformation of each CSF biomarker value. For these correlation analyses, alpha level was set at $p \leq 0.004$ ($\frac{0.05}{12}$), after Bonferroni’s correction for multiple comparisons.
To investigate whether a differential effect could be detected when considering sex, the previous four multivariate regression analyses were executed again, including now the interaction between AT(N) group and sex as the main factor of interest and the same covariates. For these analyses, alpha level was set at $p \leq 0.0125$ ($\frac{0.05}{4}$), after Bonferroni’s correction for multiple comparisons.
## Demographic and clinical characteristics of the cohort
Data from 1,648 individuals with available clinical information and an OCT-A scan performed between January 2018 and March 2019 were initially reviewed (Figure 1). Several exclusion criteria were applied: lack of CSF AD core biomarkers data within 12 months of the OCT-A scan ($$n = 1363$$), not fulfilling the diagnostic criteria for MCI ($$n = 115$$), ophthalmological conditions that could interfere with OCT-A measurements ($$n = 23$$; $$n = 8$$ due to retinal surgery, $$n = 6$$ due to retinopathy, $$n = 5$$ due to open angle glaucoma, $$n = 3$$ due to IOP > 24 mmHg, $$n = 1$$ due to other reasons) and finally, lack of information on APOE ε4 status ($$n = 3$$).
**FIGURE 1:** *Participants’ selection algorithm. AD, Alzheimer’s disease; APOE, apolipoprotein E; OCT-A, optical coherence tomography-angiography; CSF, cerebrospinal fluid; IOP, intraocular pressure; MCI, mild cognitive impairment; SNAP, suspected non-Alzheimer pathology; VD, vessel density.*
The final sample consisted of 144 MCI individuals with available OCT-A scan and CSF AD core biomarkers performed within 12 months. Participants were classified into three groups according to the CSF AT(N) scheme (Jack et al., 2018): 66 Normal AT(N), 45 Alzheimer AT(N) and 33 SNAP AT(N). The demographic characteristics and past medical history of the cohort are displayed in Table 1.
**TABLE 1**
| Unnamed: 0 | Total (n = 144) | Normal AT(N) (n = 66) | Alzheimer AT(N) (n = 45) | SNAP AT(N) (n = 33) | Intergroup significance |
| --- | --- | --- | --- | --- | --- |
| Age (years, mean ± SD) | 70.10 ± 8.01 | 66.82 ± 8.52 | 73.02 ± 6.00 | 72.70 ± 6.98 | <0.001a |
| Sex (% women) | 57.64% | 60.61% | 46.67% | 66.67% | 0.169b |
| Education (years, mean ± SD) | 8.17 ± 3.95 | 8.26 ± 3.23 | 8.64 ± 4.95 | 7.36 ± 3.73 | 0.395a |
| APOE ε4 status (% positive) | 31.25% | 18.18% | 44.44% | 39.39% | 0.007b |
| Hypertension (%) | 48.61% | 42.42% | 48.89% | 60.61% | 0.233b |
| Diabetes mellitus (%) | 19.44% | 18.18% | 15.56% | 27.27% | 0.408b |
| Dyslipidemia (%) | 38.89% | 31.82% | 40.00% | 51.52% | 0.163b |
| Heart disease (%) | 9.72% | 10.61% | 6.67% | 12.12% | 0.728b |
| COPD (%) | 14.58% | 15.15% | 11.11% | 18.18% | 0.642b |
| Smoking habit (%) | 9.72% | 12.12% | 6.67% | 9.09% | 0.679b |
## Multinomial regression analysis of demographic characteristics among AT(N) groups
The multinomial regression analysis exploring the distribution of age, sex, education and the APOE ε4 status among the AT(N) groups, using the clinical variables (hypertension, diabetes mellitus, dyslipidemia, heart disease, COPD, smoking habit) as adjusting factors, showed that age and the APOE ε4 status had a significant effect, so both were included as adjusting factors in the final analysis (Supplementary Table 2).
## Multivariate regression analysis of macular VD differences among AT(N) groups
Table 2 depicts the contribution of age, the APOE ε4 status, hypertension, diabetes mellitus, dyslipidemia, heart disease, COPD and smoking habit as adjusting factors and the AT(N) groups to macular VD variance in each quadrant. Regression models revealed no significant effect of AT(N) groups on VD values (all, p ≥ 0.162) (Figure 2). Similarly, APOE ε4 status, hypertension, diabetes mellitus, heart disease, COPD and smoking habit did not show any significant effect on VD (all, p ≥ 0.124). Age showed an inverse relationship with VD in the superior quadrant ($$p \leq 0.041$$) and dyslipidemia an inverse relationship with VD in the nasal quadrant ($$p \leq 0.043$$), but both were no longer significant after correction for multiple comparisons ($p \leq 0.004$).
Raw and adjusted sector-specific macular VD measures across diagnostic groups are displayed in Supplementary Table 3.
Representative macular VD images from the superficial plexus for each AT(N) group are shown in Figure 3.
**FIGURE 3:** *Representative macular VD images from the superficial vascular plexus for each AT(N) group: Normal (A), Alzheimer (B), and SNAP (C). SNAP, suspected non-Alzheimer pathology; VD, vessel density.*
A sensitivity analysis was executed with the former multivariate regression analysis without including the A+T-N- participants within the Alzheimer AT(N) group ($$n = 9$$), showing similar results than the former, with no significant effect of AT(N) groups on VD values (Supplementary Table 4).
The former multivariate regression analysis were repeated using a cohort of 83 individuals with SCD with no evidence of brain amyloid deposition (negative FBB-PET scan) from the FACEHBI cohort as the reference category (SCD Aβ-), including age, sex, years of education, APOEε4 status, hypertension, diabetes mellitus, dyslipidemia, heart disease, COPD and smoking habit as covariates. In this case, regression models revealed significant differences in VD in several macular regions between groups (see Supplementary Tables 5, 6). The Alzheimer AT(N) group showed higher VD measures in the temporal quadrant ($$p \leq 0.005$$) compared to the SCD Aβ- group, but that was no longer significant after correction for multiple comparisons ($p \leq 0.004$). The SNAP AT(N) group showed higher VD measures in the nasal ($$p \leq 0.008$$), temporal ($$p \leq 0.002$$) and inferior ($$p \leq 0.004$$) quadrants compared to the SCD Aβ- group, but only the temporal and the inferior quadrants remained significant after correction for multiple comparisons ($p \leq 0.004$). Additionally, age was negatively associated with VD in the superior quadrant ($$p \leq 0.004$$).
## Sex effect in macular VD across AT(N) groups
The interaction between sex and AT(N) groups had no effect in differentiating VD measurements, adjusted by age, age, APOE ε4 status, hypertension, diabetes mellitus, dyslipidemia, heart disease, COPD and smoking habit (Supplementary Table 7). Thus, differences in macular VD among AT(N) groups were not significantly influenced by sex.
## Association of macular VD with CSF Aβ, p-tau and t-tau measurements
Cerebrospinal fluid Aβ1-42, p-tau and t-tau values from the whole cohort ($$n = 144$$) were log-transformed and correlated separately with the four macular VD measures. None of the three CSF biomarkers showed a significant correlation with any macular VD measurements, adjusted by age, APOE ε4 status, hypertension, diabetes mellitus, dyslipidemia, heart disease, COPD, smoking habit and CSF technique (all r < 0.13; p ≥ 0.133) (see Table 3). Additionally, the analyses were repeated using raw CSF data for the ELISA ($$n = 114$$) and CLEIA ($$n = 30$$) groups separately, showing again a lack of correlation of each CSF biomarker with macular VD measures (see Supplementary Tables 8, 9).
**TABLE 3**
| CSF measurements (n = 144) | Variable | r | Significance |
| --- | --- | --- | --- |
| CSF Aβ1-42 | VD nasal | 0.02 | 0.808 |
| CSF Aβ1-42 | VD superior | -0.13 | 0.133 |
| CSF Aβ1-42 | VD temporal | -0.02 | 0.832 |
| CSF Aβ1-42 | VD inferior | 0.06 | 0.499 |
| CSF p181-tau | VD nasal | 0.02 | 0.791 |
| CSF p181-tau | VD superior | -0.06 | 0.516 |
| CSF p181-tau | VD temporal | 0.04 | 0.651 |
| CSF p181-tau | VD inferior | 0.12 | 0.154 |
| CSF t-tau | VD nasal | 0.04 | 0.676 |
| CSF t-tau | VD superior | -0.05 | 0.562 |
| CSF t-tau | VD temporal | 0.03 | 0.767 |
| CSF t-tau | VD inferior | 0.09 | 0.306 |
## Discussion
In this study from the NORFACE cohort, we analyzed the relationship of macular VD in the superficial plexus and CSF AD core biomarkers in a cohort of 144 individuals with MCI. Our data showed that macular VD did not significantly differ among Normal, Alzheimer and SNAP AT(N) groups in individuals with MCI after adjustment for age, APOE ε4 status, hypertension, diabetes mellitus, dyslipidemia, heart disease, COPD and smoking habit. Additionally, macular VD was not associated to Aβ, p-tau and t-tau measures in CSF.
In a recent publication by our group, also analyzing macular VD measures from the NORFACE cohort ($$n = 672$$), patients clinically defined as MCI-Alzheimer (MCI-AD) and MCI-Vascular (MCI-Va) presented significant differences in macular VD, in opposite directions, in the temporal and inferior quadrants, respectively, compared to cognitively unimpaired (CU) individuals (Marquié et al., 2022). These results suggested that macular VD might be able to differentiate two pathogenic pathways (AD- and cerebrovascular-related) in the early stages of cognitive decline. In particular, MCI-AD participants showed higher VD in the temporal quadrant, while MCI-Va participants showed lower VD in the inferior quadrant compared to CU individuals. In the present work, we did not replicate these findings using a biological definition of AD in MCI patients [the Alzheimer AT(N) group], although it is worth noting that our reference category for comparison here was the Normal AT(N) MCI group, and not a CU group as in our previous article, and we did not include a clinical diagnosis of MCI-Vascular. We did, though, perform additional analysis using a cohort of 83 SCD Aβ- as the reference category, similarly to our previous publication (Marquié et al., 2022), and found that two of the AT(N) groups of MCI participants (Alzheimer and SNAP) showed significantly higher VD measured in several macular regions compared to the SCD Aβ- group, although only VD in the temporal and inferior quadrants from the SNAP group remained significant after correction for multiple comparisons. This result actually agrees with those of our previous publication, in which the clinically defined MCI-AD group showed higher VD in the temporal quadrant compared to CU individuals (Marquié et al., 2022). One potential explanation for the finding of higher macular VD in early stages of neurodegeneration [MCI individuals with Alzheimer and SNAP AT(N) profiles] is a temporary retinal vessel dilation and increased blood flow related to neuroinflammation, which takes place as a compensatory mechanism in response to the early vascular dysregulation (hypoxia) occurring in these diseases (Serrano-Pozo et al., 2011; Iturria-Medina et al., 2016; Sousa et al., 2018). These dilated retinal vessels would then become visible and cause a stronger vascular signal (higher VD) in OCT-A compared to controls.
Related to the latter and relevant to our results, the AT(N) classification (Jack et al., 2018) is restricted to Aβ (A), tau (T) and neurodegeneration (N) biomarkers, but does not take into account the presence of cerebrovascular pathology. As brain vascular lesions are very common in the elderly (Gorelick et al., 2011; Attems and Jellinger, 2014) and an important contributor to cognitive decline (Sweeney et al., 2019), potentially the three AT(N) groups of MCI patients depicted in our study (Normal, Alzheimer, and SNAP) could all have a similar burden of cerebrovascular lesions, and macular VD measures as quantified by OCT-A could be reflecting this. Unfortunately, in the present study, we did not have access to measures of brain vascular pathology from magnetic resonance imaging to compare them with VD.
Several other publications have investigated the association between retinal vascular parameters assessed by OCT-A and Alzheimer’s biomarkers quantified by either CSF or PET imaging in different stages of the AD continuum and found interesting results. O’Bryhim et al. [ 2018] reported increase in the size of the FAZ in preclinical AD participants (as defined by positive amyloid in CSF or PET) compared to cognitively normal controls. Frost et al. [ 2013] showed that cognitively healthy individuals from the AIBL study with PET-positive amyloid presented higher burden of retinal microvascular damage (larger venular branching asymmetry factor and arteriolar length-to-diameter ratio assessed in retinal photographs) compared to those with negative scans. Lee et al. [ 2020] showed decreased peripapillary capillary density in patients with subcortical vascular cognitive impairment compared to cognitively normal individuals and patients with PET-Aβ + cognitive impairment. In this study, capillary density was negatively associated with white matter volume quantified by brain MRI. In the other direction, van de Kreeke et al. [ 2019] reported a significantly higher VD in PET-Aβ + compared to PET-Aβ- healthy controls, but no differences in the FAZ. The authors attributed these results to increased retinal blood flow due to inflammation secondary to hypoxia in the preclinical AD stage, which allows microvessels normally not detected on OCT-A to become visible, thus resulting in a higher VD (van de Kreeke et al., 2019). Biscetti et al. [ 2021] reported a significant reduction in both vascular perfusion density and vessel length density in a group of 24 MCI with a CSF AD-profile compared to a group of 13 controls, while measures of fractal dimensions were higher in the MCI group. The authors claimed that vascular perfusion density might reflect amyloid angiopathy-related chronic injury, while fractal dimensions could show early vessel recruitment as a compensatory mechanism (Biscetti et al., 2021). Lastly, Elahi et al. [ 2021] investigated a cohort of 75 cognitively normal adults, showing significantly reduced macular capillary density in APOE ε4 carriers compared to non-carriers, while no differences were detected between PET-Aβ + vs. PET-Aβ–individuals.
An additional finding of our study was a negative correlation between age and macular VD in the superior quadrant (although no longer significant after correcting for multiple comparisons), meaning that the elder individuals in our cohort showed lower VD in this region. Similarly, previous publications from the NORFACE cohort showed an association of age with several retinal measures (Sánchez et al., 2018, 2020). Other studies have also demonstrated a decrease in retinal vascular measures, including VD, with aging (Barteselli et al., 2012; Yu et al., 2015; Iafe et al., 2016; Wei et al., 2017). Related to this issue, the concept of “retinal age” has recently emerged. Several articles have detected, by analyzing retinal fundus images from large cohorts using deep learning algorithms, that the retinal age gap (the difference between retinal age and chronological age) is a robust biomarker for aging and closely related to the risk of mortality (Nusinovici et al., 2022; Zhu et al., 2022b). Moreover, it is associated with arterial stiffness and future cardiovascular disease events (Zhu et al., 2022a) and is able to identify individuals at high risk of developing future Parkinson’s disease (Hu et al., 2022). These findings suggest that retinal measures could potentially be used as a proxy of biological aging, but further research in this area is needed.
Lastly, our data showed no effects of the interaction between sex and the AT(N) group in predicting macular VD. Although sex is usually added as a covariate in analyses, few publications investigated sex as an independent factor potentially involved in OCT-A measures. Hashmani et al., showed that males had greater VD than females in the foveal region (Hashmani et al., 2019), while Su et al. [ 2022] also described sex-dependent differences in the effect of aging on retinal VD changes. The effect of sex on FAZ size remains uncertain, with discrepant results among studies (Samara et al., 2015; Yu et al., 2015; Tan et al., 2016).
We acknowledge that our study had several limitations. First, the lack of significant results could be related to the small sample size of the AT(N) groups. Second, the results were cross-sectional, and thus not able to show longitudinal changes in diagnosis, macular VD and CSF Aβ and tau measures. Third, we did not use a cognitively healthy cohort as a control group for the comparisons, but instead MCI patients with a Normal AT(N) profile in the CSF, who could potentially harbor a certain burden of cerebrovascular pathology. Fourth, no MRI measures of brain vascular pathology were available to compare with the retinal vascular pathology. Lastly, the VD measurements were limited to the macular region and the superficial vascular plexus, and we lacked information about changes in the FAZ and VD in the deep plexus.
We also consider that our study had several strengths compared to previous works. Our cohort consisted of a large and single-site sample of MCI patients with OCT-A and CSF AD core biomarkers performed within 12 months. We used age, APOE ε4 status and cardiovascular conditions as covariates in all our analyses. Lastly, the neurologist and optometrist were blinded to each other’s diagnosis.
In summary, our study detected no significant differences on a retinal vascular measure (macular VD in the superficial vascular plexus) among Normal, Alzheimer and SNAP AT(N) groups in a cohort of MCI patients, and a lack of association with Aβ and tau measures in CSF.
## Data availability statement
According Ace Alzheimer Center Barcelona’s policies and following the Spanish Law $\frac{14}{2007}$ of 3 July on Biomedical Research, clinical data will not be uploaded to servers or public repositories. The datasets generated and/or analyzed for this study will be made available by the corresponding author on reasonable request, after internal Data Accessing Committee evaluation, Ethics Committee clearance and MDTA signature between institutions.
## Author contributions
MM, LT, AR, and MB were involved in the study design and conduct. MM, AG-S, SV, MC-M, and LC-M were in charge of the data analysis. MM wrote the manuscript. MM, AG-S, and SV performed the statistical analysis. EA-M, AG-S, and IR prepared the databases. MM, JM, IH, MR-R, LV, JPT, EE-D, UB, MA, GO, AE, AP-C, ÁS, NA, AO, and LM were involved in the acquisition of data. All authors contributed to the interpretation of findings, critical review of the manuscript, approval of the final manuscript, and agreement to be accountable for all aspects of the work.
## Conflict of interest
MB has consulted for Araclon, Avid, Grifols, Lilly, Nutricia, Roche, Eisai and Servier. She received fees from lectures and funds for research from Araclon, Biogen, Grifols, Nutricia, Roche and Servier. She reports grants/research funding from Abbvie, Araclon, Biogen Research Limited, Bioiberica, Grifols, Lilly, SA, Merck Sharp & Dohme, Kyowa Hakko Kirin, Laboratorios Servier, Nutricia SRL, Oryzon Genomics, Piramal Imaging Limited, Roche Pharma SA, and Schwabe Farma Iberica SLU, all outside the submitted work. She has not received personal compensation from these organizations. AR was member of the scientific advisory board of Landsteiner Genmed and Grifols SA. AR holds stocks in Landsteiner Genmed. The remaining 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
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## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnins.2023.1076177/full#supplementary-material
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---
title: 'The reasons for not returning to work and health-related quality of life among
young and middle-aged patients with stroke: A cross-sectional study'
authors:
- Xi Pan
- Zhi Wang
- Lin Yao
- Lan Xu
journal: Frontiers in Neurology
year: 2023
pmcid: PMC9995965
doi: 10.3389/fneur.2023.1078251
license: CC BY 4.0
---
# The reasons for not returning to work and health-related quality of life among young and middle-aged patients with stroke: A cross-sectional study
## Abstract
### Objectives
This study aimed to explore the reasons and influencing factors for non-return to work (non-RTW) within 1 year among young and middle-aged patients with stroke and to assess their health-related quality of life (HRQoL) at 1 year across different reasons.
### Methods
The study was conducted as a telephone-based cross-sectional survey. Seven hundred eighty-nine young and middle-aged patients with stroke aged between 18 and 54 years for men and 18 and 49 years for women in the electronic medical system were included. Data collection included demographic characteristics, socioeconomic status, behavioral habits, history of chronic diseases, work status, reasons for non-RTW, and HRQoL.
### Results
Of 789 patients, 435 ($55.1\%$) (mean [SD] age, 47.7 [7.8] years) did not return to work within 1 year after stroke. Among the patients who did not RTW, $58.9\%$ were unable to work, $9.7\%$ retired early, $11.03\%$ became full-time homemakers or were unemployed, and $20.5\%$ were reluctant to work. The disordered multiclass logistic regression model showed that the factors influencing the reasons for non-RTW included age, gender, education, income, health insurance, diabetes comorbidity, ability to perform activities of daily living, and mobility of the right upper extremity. Furthermore, patients who were unable to work had significantly lower HRQoL compared to those who had RTW, followed by those who retired early.
### Conclusions
More than half did not RTW within 1 year in our study. The results will help inform future research to identify interventions to promote RTW and improve HRQoL for young and middle-aged patients with stroke.
## 1. Introduction
Recent data show that the incidence of stroke is increasing among young and middle-aged people and is highest in Asians compared to that in other ethnic groups [1, 2]. According to reports, nearly $40\%$ of patients with stroke are of working age, an age group whose specific social characteristics dictate a higher willingness to return to work (RTW) after a stroke [3]. RTW is the primary goal of the rehabilitation process for most working-age patients [4], and it is closely related to the patient's quality of life, physical and mental health, subjective wellbeing, and life satisfaction [5].
Unfortunately, it can be challenging for stroke sufferers to return to work [6]. Several studies have demonstrated that with proper rehabilitation, most young and middle-aged post-stroke survivors can achieve functional independence and high activity levels [1, 7]. Nevertheless, the proportion of patients with stroke who do not return to work ranges from 25 to $50\%$ (8–10). Exploring the reasons for non-RTW among young and middle-aged patients with stroke and the associated factors require clinical practice by identifying the types of non-RTW that may occur in different patients and that can be improved through rehabilitation [4, 11, 12]. Although previous research has explored the factors impacting non-RTW after stroke, such as gender and advanced age (8–10), most studies have evaluated non-RTW as a whole and cannot differentiate between various non-RTW types and their associated factors. However, some qualitative studies have been conducted to explore the related causes and influencing factors [4, 11], but the researchers' opinions and thoughts may introduce bias in interpreting the results, resulting in a lack of objectivity and the inability to identify relevant influencing factors.
To the best of our knowledge, no specific study has been conducted that quantitatively describes the reason for non-RTW following stroke, and its associated factors are mainly unclear. In addition, it is uncertain whether the reported reasons for non-RTW are related to health-related quality of life (HRQoL). Therefore, the aims of this study were to [1] quantify reasons for non-RTW among young and middle-aged patients with stroke; [2] identify factors predicting different reasons for non-RTW, focusing mainly on sociodemographic and clinical characteristics factors; and [3] investigate the impact of different reasons for non-RTW on HRQoL.
## 2. Methods
The study was conducted as a telephone-based cross-sectional survey. The central review committee of the First Affiliated Hospital of Soochow University approved the study protocol (No. 2022025).
## 2.1. Participants
All patients were admitted to our neurology department between 1 July 2020 and 1 July 2021, with a diagnosis of a first-time stroke. From July 2021 to July 2022, young and middle-aged patients with stroke who had been discharged from the electronic medical system for 1 year were eligible to be surveyed by telephone. Of the 1,136 patients recorded in the electronic medical record system, 789 patients were included in this study based on the following criteria: (i) first stroke, (ii) the diagnosis of stroke (hemorrhage stroke, ischemic stroke, or hemorrhagic stroke combined with ischemic stroke), (iii) working age (18–59 years for men and 18–54 years for women) at the stroke onset, and (iv) active employment status (full-time or part-time competitive employment, or self-employment) at the stroke onset. We excluded patients who had stopped working before the onset and those with other critical illnesses, such as heart failure, respiratory failure, malignant tumors, severe trauma, and other acute diseases.
## 2.2. Data collection
A trained research assistant administered the telephone survey to participants over the phone. After obtaining verbal consent, a 20-min telephone survey was conducted.
Patient characteristics included baseline demographic characteristics (age, gender, and marital status), socioeconomic status (per capita monthly household income and education level), behavioral habits (smoking and alcohol consumption), history of comorbid chronic diseases (hypertension, diabetes, dyslipidemia, coronary heart disease, and atrial fibrillation), the stroke type, the degree of functional dependence at discharge, limb muscle strength, stroke complications (dysarthria, visual deficiency, swallowing disturbances, reduced bladder control, and sensory disturbances), and the occupational type before the onset. Among them, demographic characteristics, the history of comorbid chronic diseases, the stroke type, the degree of functional dependence at discharge, limb muscle strength, and stroke complications were obtained from the electronic medical record. Moreover, socioeconomic status, behavioral habits, and occupational type before the onset was obtained during the 20-min interview.
Age was categorized into 25–34, 35–44, and 45–55. The education level was categorized into primary school (Elementary school and below), junior high school, secondary school, or college and above. Family per capita monthly income (income level), which is equal to family income divided by the number of family members, was categorized as “ <1000,” “1001–3000,” “3001–5000,” and “>5000.” The degree of functional dependence is scored according to the activities of daily living (ADL) scale: 100 points mean no dependence, 60–99 points mean mild dependence, 40–60 points mean moderate dependence, and <40 points mean severe dependence. The muscle strength of the left upper limb, the right upper limb, the left lower limb, and the right lower limb was evaluated with a clinical examination (levels 0–5). If the muscle strength of the limb is below grade 4, the limb is considered dysfunctional.
## 2.3. Outcome
The outcome of this study was RTW after stroke, defined as active employment at the former or new occupation (full-time or part-time competitive job, or self-employment) based on these follow-up questions: (i) “Have you been able to return to work?”; ( ii) “Have you changed work?”; ( iii) “*What is* the reason for changing work?”; and (iv) “*What is* your reason for not returning to work?” Patients who did not return to work within 12 months were classified as non-RTW for the following reasons: [1] unable to work (if the patient reports being unable to work due to physical dysfunction), [2] early retirement (if a patient reported retiring after stroke but had not yet reached retirement age), [3] full-time homemaker or unemployed (if a patient reported becoming a full-time homemaker or being laid off after stroke), and [4] reluctance to work (if a patient reported being unable to return to work due to other reasons such as work stress, the new crown epidemic, their children's demands, or for unspecified reasons).
An additional outcome variable was health-related quality of life (HRQoL), which was measured using the health utility value of EQ-5D-5L [13], which is comprised of a five-level descriptive health classifier questionnaire and a visual analog scale (EQ-VAS). The descriptive questionnaire evaluates five dimensions (5D): mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. There are five response levels (5L) for each size, ranging from no problems to extreme problems. Using the latest EQ-5D-5L health utility value conversion table based on the Chinese population, the EQ-5D-5L health status was converted into health utility values to describe the respondents' HRQoL. The health utility values range from −0.391 to 1, with zero denoting death, one representing perfect health, and negative values indicating that the current health state is worse than death. The health dimensions of the EQ-5D-5L were dichotomized into “no limitations” (“no problems”) and “limitations” (from “slight problems” to “unable”). The Cronbach's coefficient was 0.761 in the study.
## 2.4. Statistical analysis
This study used descriptive statistics such as mean, standard deviation, and frequency to describe the demographic characteristics and reasons for RTW and HRQoL variables. The chi-square test was used to compare patient characteristics between the RTW and four non-RTW groups. We fitted a disordered multiclass logistic regression model to evaluate the association between patient characteristics and the four reasons for non-RTW, with RTW as the reference. Furthermore, descriptive statistics for the EQ-5D dimensions, EQ-5D index, and EQ-VAS were calculated. Differences in the distribution of continuous variables over different categorical groups were evaluated using the Kruskal–Wallis test, and where differences were detected, Dunn's test was used for pairwise comparisons. For nominal variables, a chi-square test was used as applicable. All statistical tests were performed using a two-sided α value of 0.05. Analyses were conducted using SPSS, version 22.0.
## 3.1. Baseline patient characteristics
The final study sample included 1,136 patients in the electronic medical record system (Figure 1). Among the 789 patients, 576 ($73.0\%$) were men, and 740 ($93.8\%$) were patients with ischemic stroke, 153 ($19.4\%$) had a college degree or higher, 169 ($21.4\%$) were physical workers. The mean (SD) age of these patients was 47.68 (7.8) years; 647 ($82.0\%$) were aged 40 years or older. The six common chronic diseases in the population were hypertension (456 [$57.8\%$]), diabetes (160 [$20.3\%$]), dyslipidemia (18 [$2.3\%$]), atrial fibrillation (19 [$2.4\%$]), coronary heart disease (17 [$2.2\%$]), and kidney disease (13 [$1.7\%$]). The five common dysfunctions owing to stroke were dysarthria (155 [$19.7\%$]), visual deficiency (19 [$2.4\%$]), dysphagia (45 [$5.7\%$]), reduced bladder control (24 [$3.0\%$]), and sensory disturbance (108 [$13.7\%$]) (Table 1).
**Figure 1:** *Flowchart of patient enrollment.* TABLE_PLACEHOLDER:Table 1
## 3.2. Non-return to work
In total, 354 patients ($44.9\%$) returned to work within 1 year after discharge from a stroke. Among them, 291 patients ($36.9\%$) returned to their original work, 37 patients ($4.7\%$) changed work owing to stroke, and 26 patients ($3.3\%$) changed work for other reasons. Among the 435 patients who did not RTW, 256 ($32.5\%$) were unable to work owing to stroke, 42 ($5.2\%$) retired early owing to stroke, 48 ($6.1\%$) became full-time homemakers or were unemployed, and 89 ($11.3\%$) showed reluctance to work (Table 2).
**Table 2**
| Work status | n = 789 |
| --- | --- |
| RTW | 354 (44.9) |
| Returned to their original work | 291 (36.9) |
| Changed work owing to stroke | 37 (4.7) |
| Changed work owing to other reasons | 26 (3.3) |
| Non-RTW | 435 (55.1) |
| Unable to work | 256 (32.5) |
| Early retirement | 42 (5.2) |
| Full-time homemakers or were unemployed | 48 (6.1) |
| Reluctance to work | 89 (11.3) |
## 3.3. Factors for non-return to work
Baseline data showed that factors influencing the reason for non-RTW included age at onset, gender, education level, per capita monthly household income, medical insurance, hypertension, diabetes mellitus, daily life dependence, the muscle strength of the four limbs, and pre-stroke occupation (Table 1). Furthermore, with different reasons for non-RTW as dependent variables (with RTW as the control) and variables with statistical significance in the univariate analysis as independent variables, an unordered multiclass logistic regression analysis was performed. The multinomial logistic regression modeling results are presented in Table 3. Younger patients are less likely to be unable to work and retire earlier than older patients. Patients aged 40–50 years were less likely than those aged 50 years or older to be reluctant to work (odds ratio [OR], 0.371; $95\%$ CI, 0.142–0.966). Female patients were more likely than male patients to be at home full time (OR, 2.793; $95\%$ CI, 1.054–7.403) and to be reluctant to work (OR, 2.433; $95\%$ CI, 1.037–5.710). The likelihood of being unable to work decreases as education increases (OR, 0.687; $95\%$ CI, 0.514, 0.919). As monthly per capita household income increases, the possibility of being unable to work (OR, 0.684; $95\%$ CI, 0.505–0.926) and being at home full time (OR, 0.433; $95\%$ CI, 0.244–0.767) decreases. Patients with medical insurance were less likely to be unable to work (OR, 0.511; $95\%$ CI, 0.296–0.882), to retire early (OR, 0.249; $95\%$ CI, 0.079–0.787), and to be reluctant to work (OR, 0.284; $95\%$ CI, 0.125–0.646) than those without medical insurance. Patients with diabetes were more likely to choose early retirement than those without diabetes (OR, 4.585; $95\%$ CI, 1.459–14.404). The likelihood of being unable to work increases as the dependence on daily life increases (OR, 1.630; $95\%$ CI, 1.273–2.087). Patients who cannot lift their right upper limb are more likely to be unable to work (OR, 8.174; $95\%$ CI, 2.409–27.733) and to retire early (OR, 26.894; $95\%$ CI, 2.853–253.551) than those who can lift their right upper limb. Dysphagia, dysarthria, dysuria, and sensory disorder after a stroke had no significant effect on the reasons for non-RTW (Table 3). Figure 2 summarizes Table 3, a visualization of the statistically significantly associated variables with at least one of the four non-RTW reasons.
## 3.4. Health-related quality of life in the non-RTW groups
The most prominent problem in the “unable to work” group was the usual activities ($38.94\%$). The most significant problem in the “early retirement” group was mobility ($31.71\%$). In the other three groups, including those who had returned to work, the most prominent problem was pain/discomfort (13.38, 17.50, and $14.49\%$), as shown in Figure 3. Compared to patients who had RTW, those who were unable to work reported higher rates of health problems in all dimensions of the EQ-5D-5L; those who retired early reported higher rates of health problems in the mobility, self-care, and usual activities dimensions (30.95, 16.67, and $19.05\%$); and those who were reluctant to work reported the higher rates of health problems in the self-care dimension ($6.74\%$). When stratified by gender, male patients had similar rates of health problems as the overall population, while female patients who were unable to work had higher rates of health problems in the mobility, self-care, and usual activities dimensions (36.84, 28.95, and $36.84\%$). Female patients who retired early had higher rates of health problems in their usual activities ($14.29\%$). Female patients who were reluctant to work had an increased proportion of self-care health problems ($16.13\%$) (Table 4). Furthermore, compared to patients who had RTW, patients who were unable to work had significantly lower EQ-5D index and EQ-5D VAS ($P \leq 0.05$), male patients who retired early had significantly lower EQ-5D index and EQ-5D VAS ($P \leq 0.05$), and female patients who retired early had significantly lower EQ-5D VAS ($P \leq 0.05$). There was no significant difference between the female patients who were reluctant to work and those who were unable to work in terms of the EQ-5D score. Male patients who were full-time homemakers or unemployed had the second-lowest EQ-5D VAS, behind those who were unable to work and those who retired early, although the difference was not statistically significant (Table 5; Figures 4, 5).
**Figure 3:** *Limitations (%) per health domain of the EQ-5D-5L among patients who have returned to work and the four groups of patients who have not returned to work.* TABLE_PLACEHOLDER:Table 4 TABLE_PLACEHOLDER:Table 5 **Figure 4:** *Distribution of EQ-5D index among patients who have returned to work and the four groups of patients who have not returned to work, stratified by gender.* **Figure 5:** *Distribution of EQ-5D VAS among patients who have returned to work and the four groups of patients who have not returned to work, stratified by gender.*
## 4. Discussion
We found that more than half of previously employed individuals did not return to work within 1 year of being hospitalized for a stroke. Among those who were non-RTW, $32.45\%$ were unable to work due to health reasons, $5.23\%$ retired early, $6.08\%$ were full-time homemakers or were unemployed, and $11.28\%$ were reluctant to work. Moreover, our study explored various demographic, socioeconomic, and clinical factors associated with reasons for non-RTW, which the association may be informative when planning interventions for recovery after stroke. Furthermore, the HRQoL of patients who were unable to work was significantly lower than those who had RTW, followed by those who retired early. In addition, female patients who were reluctant to work had a lower EQ-5D index second only to those who were unable to work, which may be related to a higher rate of limitations with self-care. Similarly, male patients who were unable to work, retired early, and stayed at home full time had lower EQ-5D VAS.
In the present study, <$50\%$ of patients with stroke returned to work within 1 year after discharge from the hospital. This rate is relatively lower compared to other countries, where rates have ranged between 50 and $75\%$ over the past two decades (8–10). Several factors may explain this observation. First, the accessibility of post-stroke rehabilitation services in *China is* poor, and the vocational rehabilitation system is not well developed [14]. Vocational rehabilitation can effectively facilitate the RTW of patients with stroke, improving their mood, physical function, participation, health-related quality of life, work self-efficacy, and confidence [15, 16]. Second, the age-based retirement policy implemented in the country could have a role. Currently, men retire at 60 and women at 55. Given that $50.44\%$ of the study participants were 50 and over, pension policies hampered RTW motivation, especially for women whose retirement age was 5 years younger. Previous studies have shown that the rate of RTW after stroke varies within and between countries. For example, the rate is $59\%$ to $68\%$ in the United States [10, 17], $65\%$ to $74\%$ in Sweden [8, 18, 19], $70\%$ in Israel [20], $75\%$ in Germany [21], $75\%$ in Finland [22], $72\%$ in the Netherlands [23], $50\%$ in Denmark [9], and $55\%$ in Japan [24]. Across countries, there may be differences in sampling practices, current unemployment rates, sickness benefits, insurance assistance, social assistance programs, or employment protection laws.
We identified several sociodemographic and clinical characteristics associated with reasons for non-RTW. Many studies have reported that daily life dependence and right upper limb paralysis after stroke adversely affect RTW [23, 25]. In particular, the right upper extremity hand function is essential in early rehab, as it directly affects the ability to work. However, the corresponding confidence intervals are wide, making it impossible to determine the true effect. Similar results suggest that socioeconomic levels, such as age, education level, income, and medical insurance, may be an additional important factor in determining RTW. This result is consistent with earlier Swedish and international studies [8]. Meanwhile, patients aged 40–50 years were 0.629 times less likely to be reluctant to work than those aged 50 or older. This could be because middle-aged patients in this age group bear the financial burden of supporting their parents and children simultaneously, and traditional Chinese culture dictates that they are less likely to be unwilling to work when they are able to do so. Furthermore, people with diabetes were more likely to choose early retirement. Patients with diabetes have to consistently consider their diet, exercise, medication, and blood glucose monitoring in their job routines, which can have a detrimental effect on their treatment and make managing the disease even more complicated, potentially leading to early retirement.
More importantly, we report the HRQoL associated with non-RTW attributed to different reasons. Patients unable to work had the lowest 1-year health-related quality of life, which was related to the effects of stroke. Moreover, patients who were unable to work had the highest rates of health problems in all five dimensions of mobility, self-care, usual activities, pain/discomfort, and anxiety/depression at 1-year post-stroke, and this category accounted for $32.45\%$ of all the young and middle-aged stroke population in this study. This indicates that stroke has a significant impact on physical functioning and that boosting recovery from the condition is the most effective approach to increasing RTW rates within 1 year. However, it is worth noting that female patients who were reluctant to work had an EQ-5D index second only to those who were unable to work, which may be related to a higher rate ($16.13\%$) of limitations with self-care. Similarly, male patients who were unable to work retired early and stayed at home full time had lower EQ-5D VAS. This may be due to the fact that male patients are typically the primary breadwinners in their families, their eagerness to RTW is greater, and their self-reported quality of life is lower when they are unable to return to work. Life satisfaction studies indicate that RTW improves health and wellbeing after stroke and is more important than non-RTW for overall life satisfaction. This difference was pronounced for male patients [26, 27].
This research is subject to certain limitations. First, the researcher's classification of the reasons for not returning to work may be subjective, and some patients may report multiple reasons for non-RTW, and for such patients, we ask for the main reason for non-RTW. Second, although 89 of the 435 patients ($20.5\%$) in our study who failed to RTW declared that they left the workforce for reasons of being reluctant to work or to give a reason, we did not have detailed explanations for these decisions. We did not collect information about patient-reported work conditions or job quality, including job stress, job satisfaction, and job safety. Information about patient-reported work conditions, in addition to health and socioeconomic characteristics, is important. This information may help determine patient-centered interventions supporting RTW. Finally, our study included only patients with stroke from a single center, which may caution us from generalizing to a larger population. Furthermore, the sample size available for the study resulted in wide $95\%$ confidence intervals. Larger sample sizes should be considered in future studies to increase the precision of effect estimates.
## 5. Conclusion
More than half of young and middle-aged patients with stroke did not RTW within 1 year. Our study highlights the most frequently cited reasons for non-RTW, how they vary across sociodemographic and clinical profile factors, and their impact on HRQoL at 1 year. In vocational rehabilitation, more focus should be directed to female patients who were reluctant to work and male patients who were full-time homemakers or unemployed.
## 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.
## Ethics statement
The studies involving human participants were reviewed and approved by the Central Review Committee of the First Affiliated Hospital of Soochow University. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
XP: paper writing and data analysis. ZW: data collection and data organization. LY: data compilation and data analysis. LX: project preparation and thesis revision. 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.
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|
---
title: An elevated parametric thyroid feedback quantile-based index is associated
with atrial fibrillation
authors:
- Vanesa Alonso-Ventura
- Patricia Campos-Magallon
- Belen Moreno-Franco
- Pilar Calmarza
- Fernando Calvo-Gracia
- Jose Manuel Lou-Bonafonte
- Patricia de Diego-Garcia
- Jose Antonio Casasnovas
- Victoria Marco-Benedi
- Fernando Civeira
- Martin Laclaustra
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC9995977
doi: 10.3389/fendo.2023.1087958
license: CC BY 4.0
---
# An elevated parametric thyroid feedback quantile-based index is associated with atrial fibrillation
## Abstract
### Introduction
Atrial fibrillation is associated with hyperthyroidism. Within the euthyroid range, it is also associated with high thyroxine (fT4), but not with thyrotropin (TSH). We aim to describe differences in thyroid regulation, measured by the Parametric Thyroid Feedback Quantile-Based Index (PTFQI), between patients with atrial fibrillation and the general population.
### Materials and methods
Thyroid parameters (PTFQI, TSH, and fT4) of a sample of 84 euthyroid subjects with atrial fibrillation (cases) were compared to a reference sample of euthyroid healthcare patients (controls). We calculated age and sex adjusted ORs for atrial fibrillation across tertiles of these parameters. Also, within cases, we studied thyroid parameters association with clinical characteristics of the atrial fibrillation.
### Results
After adjusting for age and sex, fT4 and PTFQI were higher in subjects with atrial fibrillation when compared to the general sample ($p \leq 0.01$ and $$p \leq 0.01$$, respectively). Atrial fibrillation ORs of the third versus the first PTFQI tertile was 1.88($95\%$CI 1.07,3.42), and there was a gradient across tertiles (p trend=0.02). Among atrial fibrillation patients, we observed that higher PTFQI was associated with sleep apnea/hypopnea syndrome (OSAS) ($$p \leq 0.03$$), higher fT4 was associated with the presence of an arrhythmogenic trigger ($$p \leq 0.02$$) and with heart failure ($p \leq 0.01$), and higher TSH was also associated with OSAS ($p \leq 0.01$).
### Conclusions
Euthyroid subjects with atrial fibrillation have an elevation of the pituitary TSH-inhibition threshold, measured by PTFQI, with respect to the general population. Within atrial fibrillation patients, high PTFQI was associated with OSAS, and high fT4 with heart failure. These results hint of the existence of a relationship between thyroid regulation and atrial fibrillation.
## Introduction
Atrial fibrillation is the most common cardiac arrhythmia in our setting [1, 2]. Thyroid disorders and, in particular, hyperthyroidism are a recognized risk factor for atrial arrhythmias (2–5), which have to be considered among other conditions that pose a risk for developing atrial fibrillation (smoking, diabetes mellitus, hypertension, coronary heart disease, heart failure, left atrial enlargement, or obesity) [6].
Similarly to patients with thyroid disease, in euthyroid subjects (normal values of thyrotropin -TSH- and free thyroxine -fT4-), fT4 levels in the higher normal range are associated with the development of atrial fibrillation (7–9). However, TSH levels are not so clearly associated with the development of atrial fibrillation: on the one hand, there are studies that conclude that there is no association [7, 8, 10] and, on the other hand, there are studies that suggest a higher risk of developing atrial fibrillation with TSH values close to the lower normal limit [9, 11, 12]. High fT4 is most often present in the context of primary thyroid disease [13], where TSH tends to be suppressed. The controversy in previous studies suggests that primary thyroid disease justify only part of the cases in which high fT4 is associated with atrial fibrillation. Thus, investigating whether altered thyroid regulation is related to atrial fibrillation, beyond the already known effects of primary hyperthyroidism, becomes relevant. Indeed, our previous results [14] encourage researching this association. Thyroid regulation can be studied with biochemical indexes. In particular, the Parametric Thyroid Feedback Quantile-Based Index (PTFQI) [14, 15] quantifies the deviations from the physiological population-average pituitary response to thyroid hormone and ranges from -1 to 1: negative values indicate an abnormally low TSH for fT4 values (a down-regulated TSH-inhibition threshold) and positive values indicate an abnormally high TSH given fT4 levels (an up-regulated TSH-inhibition threshold).
The main objective of this work is to describe differences in thyroid regulation and thyroid hormone levels between euthyroid subjects with atrial fibrillation and the general population, as well as to investigate the association of these parameters with clinical conditions that favor it or worsen its prognosis.
## Design and subjects
Thyroid parameters (PTFQI, TSH, and fT4) of a sample of patients with atrial fibrillation were compared to a reference sample of healthcare patients (case-control study). Patients with atrial fibrillation (paroxistic or persistent) who required, at least, one hospital admission were included as cases. The control sample PTFQI distribution tertiles were used as cut-offs for dividing cases and controls. We compared atrial fibrillation odds across the tertiles.
Within the cases sample, additionally, a cross-sectional study of association of the thyroid parameters with clinical characteristics was conducted (cross-sectional study).
We reviewed the 165 clinical records of the patients with atrial fibrillation who were admitted to the Miguel Servet University Hospital in Zaragoza (Spain) from July 2017 to June 2019. Exclusion criteria included previous thyroid disease or abnormal TSH and fT4 (which was the main cause of exclusion, $72\%$) and diseases or pharmacological treatments that interfere with the thyroid axis, among which amiodarone use accounted for the $35\%$ of excluded pacients (Supplementary Material Table 1). Only patients who had TSH and fT4 values available before medical intervention (electrical cardioversion, catheter ablation, or treatment with amiodarone) were included. The number of patients identified for the analysis was 84 (Supplementary Material Figure 1).
Thyroid parameters of healthcare patients older than 18 years who underwent a thyroid hormone measurement across three months of 2018, in the central laboratory of the Miguel Servet University Hospital (6051 patients) were used to calculate the reference parameters for the PTFQI formula [14]. Euthyroid participants of this sample constituted the control group ($$n = 5256$$).
This study protocol was approved by the Ethical Committee of Clinical Research of Aragón (CEICA) (expedient numbers 19-041 and 19-519) who authorized review of clinical records.
## Laboratory measurements and methods
TSH and fT4 were measured in an automated analyzer by Unicel DXi Beckman’s System®. Their normal ranges were 0.38-5.33 µUI/mL for TSH and 7.46-21.11 pmol/L for fT4.
## Parametric thyroid feedback quantile-based index
The PTFQI [14, 15] is calculated with a formula: ϕ ((fT4 - µfT4)/σfT4) - (1-ϕ ((ln TSH - µln TSH)/σln TSH))), where µfT4 = 11.49 pmol/L, σfT4 = 2.46 pmol/L, µln TSH = 0.55, and σln TSH = 1.00 in our reference sample (see above).
## Clinical data of atrial fibrillation patients
Demographics (sex, age), clinical diagnoses (atrial fibrillation triggers, heart failure, obstructive sleep apnea/hypopnea syndrome -OSAS-) and echocardiographic findings (atrial enlargement, heart valve disease) were retrieved from clinical records. Obesity was defined as BMI ≥ 30 kg/m2.
## Statistical analysis
Continuous variables were described as mean and SD, and categorical variables as percentage and count. Linear and logistic regression models were fitted to estimate differences and ORs.
The control sample PTFQI distribution tertiles were used to divide cases and controls into three groups. We compared case and control groups, after adjusting for age and sex.
Within the cases sample, the association of the three thyroid parameters (PTFQI, TSH, and fT4) with dichotomous characteristics of their atrial fibrillation was also studied. These models were additionally adjusted for type 2 diabetes and obesity.
All analyses were performed with statistical computing software R version 4.1.
## Results
Among the 165 clinical records of atrial fibrillation reviewed, after applying the exclusion criteria, $51\%$ were included for further analysis (Supplementary Material Table 1). Clinical records of those 84 euthyroid subjects ($47.6\%$ men) with a mean age of 70.3 (14.3) years were analyzed. The mean age of their first atrial fibrillation episode was 69.1 (14.6) years (Supplementary Material Tables 2, 4).
## Thyroid parameters comparison between subjects with atrial fibrillation and the general sample
The mean age of cases was higher than that of controls (70.3 vs. 58.5 years, $p \leq 0.01$) and the proportion of men in the general sample was lower ($47.6\%$ vs. $36.1\%$, $$p \leq 0.03$$). In the adjusted model, we found that fT4 and PTFQI were higher in subjects with atrial fibrillation when compared to the general sample ($p \leq 0.01$ and $$p \leq 0.01$$, respectively) (Table 1). However, despite not significantly, TSH was higher in the atrial fibrillation sample, contrary to the expected lower TSH in primary thyroid disorders with fT4 elevation.
**Table 1**
| Variable | General Sample | Atrial Fibrillation Sample | Difference (95% CI) | p |
| --- | --- | --- | --- | --- |
| PTFQI | 0.04 | 0.12 | 0.08 (0.02,0.14) | 0.01 * |
| TSH geometric mean | 0.62 | 0.65 | 0.03 (-0.09,0.15) | 0.61 |
| fT4 | 11.74 | 12.33 | 0.59 (0.17,1.01) | < 0.01 * |
Atrial fibrillation cases count grew across higher PTFQI tertiles (Figure 1). Adjusted ORs of the third versus the first PTFQI tertile for atrial fibrillation was 1.88 ($95\%$ CI 1.07,3.42) (p trend=0.02) (Table 2). Conversely, there was not a clear atrial fibrillation adjusted OR gradient across TSH tertiles nor fT4 tertiles (Supplementary Material Table 5).
**Figure 1:** *Atrial fibrillation plotted over the general sample in the thyroid regulation space. Gray dots represent all thyroid analyses between September and November of 2018 (6434 samples). The rectangle defines the normal values. Black dots represent atrial fibrillation subjects (cases). Black dashed curves represent the 33rd and 66th percentiles of the control group PTFQI dividing it in 3 equal parts. fT4 – thyroxine; TSH – thyrotropin; PTFQI-Parametric Thyroid Feedback Quantile-based Index; P number- Percentile number.* TABLE_PLACEHOLDER:Table 2
## Thyroid parameters among atrial fibrillation patients
After adjusting for age, sex, diabetes, and obesity, we observed a relationship between thyroid parameters (PTFQI, TSH, and fT4) and some clinical features. Among atrial fibrillation subjects, those who presented any of the clinical characteristics studied (atrial enlargement, heart valve disease, arrhythmogenic trigger, heart failure, OSAS) had a numerically higher PTFQI, although statistical significance was only reached in OSAS group ($$p \leq 0.03$$) (Table 3).
**Table 3**
| Variable | Variable.1 | Absent | Present | p |
| --- | --- | --- | --- | --- |
| Atrial enlargement | n/N | 33/84 | 51/84 | |
| PTFQI | Mean(SD) | 0.09 (0.29) | 0.15 (0.28) | 0.44 |
| TSH | Geometric mean | 1.76 | 2.10 | 0.11 |
| fT4 | Mean(SD) | 0.97 (0.17) | 0.95 (0.19) | 0.33 |
| Heart valve disease | n/N | 43/84 | 41/84 | |
| PTFQI | Mean(SD) | 0.09 (0.27) | 0.16 (0.30) | 0.45 |
| TSH | Geometric mean | 1.91 | 2.00 | 0.37 |
| fT4 | Mean(SD) | 0.94 (0.14) | 0.98 (0.21) | 0.99 |
| Arrhythmogenic trigger | n/N | 80/84 | 4/84 | |
| PTFQI | Mean(SD) | 0.11 (0.28) | 0.35 (0.21) | 0.12 |
| TSH | Geometric mean | 1.97 | 1.68 | 0.66 |
| fT4 | Mean(SD) | 0.95 (0.17) | 1.16 (0.17) | 0.02 * |
| Heart failure | n/N | 67/84 | 17/84 | |
| PTFQI | Mean(SD) | 0.10 (0.28) | 0.24 (0.27) | 0.10 |
| TSH | Geometric mean | 2.03 | 1.70 | 0.77 |
| fT4 | Mean(SD) | 0.93 (0.16) | 1.09 (0.20) | < 0.01 * |
| Obstructive sleep apnea/hypopnea syndrome | n/N | 79/84 | 5/84 | |
| PTFQI | Mean(SD) | 0.11 (0.28) | 0.39 (0.32) | 0.03 * |
| TSH | Geometric mean | 1.81 | 4.04 | < 0.01 * |
| fT4 | Mean(SD) | 0.96 (0.17) | 0.99 (0.26) | 0.75 |
Significant differences in fT4 were found in the presence of an arrhythmogenic trigger ($$p \leq 0.02$$) and heart failure ($p \leq 0.01$). Patients with these characteristics had slightly lower, although not statistically significant, TSH values. In the remainder characteristics considered, TSH was numerically higher, reaching statistical significance for subjects with OSAS ($p \leq 0.01$) (Table 3).
## Discussion
The major finding in this study was that patients with atrial fibrillation had a significantly higher PTFQI when compared to the general population. While high-normal fT4 levels had been previously associated with atrial fibrillation development [11], this is the first time that thyroid central regulation measured by PTFQI in atrial fibrillation patients is compared to that in the general population. Recognizing the importance of thyroid regulation on atrial fibrillation may provide tools for atrial fibrillation prevention strategies.
Among atrial fibrillation subjects, those with OSAS had higher PTFQI and TSH, while those with an arrhythmogenic trigger and heart failure had higher fT4 levels.
## TSH and fT4 and atrial fibrillation odds
The association between TSH within its normal range and atrial fibrillation is controversial in both new-onset and recurrent atrial fibrillation [11, 16]. In our study, we found similar TSH concentrations in cases and controls, although they were slightly higher, despite non-significant, in atrial fibrillation cases. If most triggering effects would depend on raised fT4 due to primary thyroid disease, a lower TSH would be expected among atrial fibrillation patients.
The evidence regarding fT4 and its relationship with atrial fibrillation is more consistent, and it is present even without hyperthyroidism. In fact, higher fT4 levels within the reference range were associated with an increased prevalence and incidence of atrial fibrillation, which could represent a challenge to the diagnostic paradigm of this entity [7]. The mechanism of fT4 impact on atrial fibrillation risk might be explained by the increase in vascular resistance, cardiac contractility, heart rate, and atrial automaticity that thyroid signaling stimulates [7].
Thus, our separate analyses of TSH and fT4 suggest that levels of both variables are higher in subjects with atrial fibrillation when compared to the general population, although the most important effect is that of fT4. The absence of TSH inhibition leads to delve deeper in the analysis of regulation.
## Thyroid regulation and atrial fibrillation odds
Here, we described for the first time that those patients with atrial fibrillation had higher PTFQI (meaning a concomitant elevation of TSH and fT4, which indicates a higher pituitary TSH-inhibition threshold) despite being euthyroid. Assuming that fT4 and TSH were at equilibrium for all participants [17], the observed deviation from the average equilibrium in the population may mean, either a normal or high-normal thyroid secretory capacity together with a decreased hypothalamus-pituitary sensitivity to fT4, or an elevation of TSH secretion accompanied by a decreased thyroid secretory capacity [18], both implying that TSH inhibition is decreased with respect to the population average. There is a known association of atrial fibrillation with high normal fT4, which must stem from a high functioning thyroid, a higher secretion of TSH, or both. Given that we found higher PTFQI, the latter mechanism seems more probably responsible. Therefore, central up-regulation of the thyroid axis might be relevant in this atrial arrhythmia.
Attending to the different distribution of thyroid hormone receptors [19] (beta-receptor predominant in thyrotrophic cells and alpha-receptor predominant in myocytes), a reduced pituitary sensitivity to fT4 might be involved consistently in the development of atrial fibrillation: while fT4 levels are not enough to inhibit TSH release at the pituitary level, they are enough to exert possible deleterious effects on the heart.
Although this hypothesis could explain the relation of higher PTFQI with atrial fibrillation development, a cause-effect relationship cannot be established with the present study and the mechanism that relate this pituitary TSH-inhibition threshold elevation and atrial fibrillation should be elucidated in future research.
## Thyroid parameters and atrial fibrillation clinical characteristics
We found higher fT4 among atrial fibrillation patients with heart failure, an association that to our knowledge had not been previously described. A general association of high fT4 and heart failure was already known [20], and one of the mechanisms that explains it is that fT4 favors atrial fibrillation. Given that all the patients had atrial fibrillation, the association described must depend on other factors beyond fT4 arrhythmogenicity. Higher thyroxine initially stimulates contractility and cardiac output but longstanding thyrotoxicosis leads to a decrease of these parameters and to heart failure [21, 22]. Although our study includes only euthyroid patients, similar mechanisms could be at stake.
Within atrial fibrillation patients, PTFQI does not seem to be particularly associated with atrial enlargement, heart valve disease, arrhythmogenic trigger, or heart failure, but we found association with OSAS, where the pituitary TSH-inhibition threshold was substantially higher. Previous studies have shown that thyroid dysfunction is associated with sleep disorders [23]. Hypothyroidism may play a role in the development of OSAS, although the specific pathophysiologic mechanism remains elusive [23]. Despite high PTFQI does not resemble a hypothyroid condition, insufficient action of thyroid hormone (peripheral resistance) might occur in some peripheral tissues, mimicking hypothyroid-like effects. Also, OSAS, or conditions that appear in the OSAS context, like obesity, may induce changes in the thyroid regulation. However, the small number of OSAS patients in our study dictates prudence in interpreting this result. It will be of interest to investigate in the future if this is one mechanism that connects both diseases.
## Future prevention strategies
Adequate control of hyperthyroidism helps preventing atrial fibrillation [2]. In addition, thyroid treatment in atrial fibrillation patients with overt hyperthyroidism has been proposed in several studies not only for better control but also for avoiding recurrences [2, 24]. In the specific case of euthyroid subjects, given the association of high levels of fT4 with the development of atrial fibrillation, the need for the redefinition of normal limits has been proposed [7]. However, beyond this, according to our results, the thyroid regulation measured by PTFQI could help to detect subjects at risk of developing atrial fibrillation. Also, current cost-saving strategies that promote measuring only TSH to evaluate thyroid function may need to be reconsidered, at least in the arrhythmology field. Furthermore, adjusting treatments to normalize PTFQI might be a new opportunity to be addressed in future research.
## Limitations
This study has several limitations. First, since the study included a general sample of healthcare patients who, for different reasons underwent a biochemical analysis, all results must be carefully interpreted because this sample is not fully representative of the general population, but of healthcare patients. In addition, patients with atrial fibrillation were not removed from the general sample (as that information was not available) which leads to a bias towards the null of our results due to misclassification error. Nonetheless, this has a limited impact, as we assume that only a very small fraction of the reference population had atrial fibrillation. Also, considering the direction of the bias, we still were able to demonstrate several associations with statistical significance, so true differences can be assumed to be larger than those described here. Second, the modest sample size implies that statistical significance may not have been reached in some analyses in spite of an apparent tendency in the associations. Third, fT3 levels were not available, so its effects on thyroid regulation and cardiomyocytes could not be studied. Finally, only thyroid parameters and not detailed clinical characteristics were available for the general sample limiting study of other different conditions between both groups.
## Conclusions
In conclusion, euthyroid subjects with atrial fibrillation have an elevation of the pituitary TSH-inhibition threshold, measured by PTFQI, with respect to the general population. Although higher fT4 levels were observed among them, a TSH inhibition consistent with primary thyroid disorders was not present. Within atrial fibrillation patients, high PTFQI was associated with OSAS, and high fT4 with heart failure. These results hint of the existence of a relationship between the thyroid regulation and atrial fibrillation.
## 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.
## Ethics statement
This study protocol was approved by the Ethical Committee of Clinical Research of Aragón: CEICA, Instituto Aragonés de Ciencias de la Salud, Avda, San Juan Bosco, 13. 50009, Zaragoza, España (expedient numbers 19-041 and 19-519) who authorized review of clinical records.
## Author contributions
VA-V and ML conceptualized the research question and designed the study. VA-V and ML performed the statistical data analysis. VA-V, BM-F, JML-B and ML, interpreted the results and VA-V and ML wrote the manuscript. PC-M, PC, FC-G, JML-B, PD-G, JAC, VM-B, and FC revised the manuscript for important intellectual content. 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/fendo.2023.1087958/full#supplementary-material
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|
---
title: 'Diet was less significant than physical activity in the prognosis of people
with sarcopenia and metabolic dysfunction-associated fatty liver diseases: Analysis
of the National Health and Nutrition Examination Survey III'
authors:
- Yun Yi
- Chun Wang
- Yang Ding
- JiangHua He
- YuQing Lv
- Ying Chang
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC9995978
doi: 10.3389/fendo.2023.1101892
license: CC BY 4.0
---
# Diet was less significant than physical activity in the prognosis of people with sarcopenia and metabolic dysfunction-associated fatty liver diseases: Analysis of the National Health and Nutrition Examination Survey III
## Abstract
### Background
Sarcopenia is prevalent in metabolic dysfunction-associated fatty liver diseases (MAFLD), and the primary treatment for both diseases is lifestyle modification. We studied how dietary components and physical activity affect individuals with sarcopenia and MAFLD.
### Materials and methods
We conducted a study utilizing National Health and Nutrition Examination Survey (NHANES) III (1988–1994) data with Linked Mortality file (through 2019). The diagnosis of fatty liver disease (FLD) was based on ultrasound images revealing moderate and severe steatosis. Using bioelectrical measures, sarcopenia was assessed. Using self-report data, dietary intake and physical activity levels were evaluated.
### Results
Among 12,259 participants, 2,473 presented with MAFLD, and 290 of whom had sarcopenia. Higher levels of physical activity (odds ratio [OR] = 0.51 [0.36–0.95]) and calorie (OR = 0.58 [0.41–0.83]) intake reduced the likelihood of sarcopenia in MAFLD patients. During a median follow-up period of 15.3 years, 1,164 MAFLD and 181 MAFLD patients with sarcopenia perished. Increased activity levels improved the prognosis of patients with sarcopenia (Insufficiently active, HR = 0.75 [0.58–0.97]; Active, HR = 0.64 [0.48–0.86]), which was particularly pronounced in older patients.
### Conclusion
In the general population, hyperglycemia was highly related to MAFLD prognosis. Physical inactivity and a protein-restricted diet corresponded to sarcopenia, with physical inactivity being connected to poor outcomes. Adding protein supplements would be beneficial for older people with sarcopenia who are unable to exercise due to frailty, while the survival benefits were negligible.
## Introduction
Non-alcoholic fatty liver disease (NAFLD), initially defined as fatty liver disease in the absence of significant alcohol intake and other causes of steatosis, is a common liver disorder that is strongly associated with features of the metabolic syndrome [1]. With a prevalence of approximately $25\%$ in the general population, NAFLD has emerged as a leading cause of advanced liver disorders, posing an underestimated global healthcare burden [2]. However, the term “non-alcoholic” overemphasized the absence of alcohol consumption while underemphasizing the significance of metabolic factors, which are the primary drivers of the course of the disease [3]. It has been suggested that metabolic (dysfunction)-associated fatty liver disease (MAFLD), which endorsed a list of positive diagnostic criteria and offered a more comprehensive description of its metabolic-related natural courses, may represent the importance of metabolic risk factors and improve the detection of the disease [4, 5]. Despite the rising prevalence and increasing impact of MAFLD [5, 6], there is an absence of approved pharmacotherapy for this significant condition, whose treatment remains limited to lifestyle modification [7, 8].
Sarcopenia is a geriatric syndrome characterized by generalized loss of muscle mass and its function, and is associated with adverse outcomes [9, 10]. Since age-related sarcopenia is inevitable, inactivity and poor diet can accelerate the process. Physical inactivity may contribute to the development of sarcopenia [11, 12], and an increase in moderate-to-vigorous physical activity levels could potentially prevent sarcopenia from developing [13]. A cohort study demonstrated that malnutrition is related to a fourfold increased risk of developing sarcopenia over a four-year follow-up period [14].Moreover, lean muscle mass in older individuals is positively associated with protein consumption [15], where insufficient protein intake and a lack of amino acid availability contribute to deficits in muscle protein synthesis [16]. Physical exercise has a protective effect on muscle mass and function maintenance, in comparison, the effect of supplemental nutrition on muscle function is uncertain (17–19). A number of studies have revealed that dietary supplements may enhance the benefit of exercise training despite the relatively low quality of the evidence [20]; however, the existing evidence for nutrition interventions is based on groups with varying ages, frailties, and nutritional conditions, and the findings are inconsistent [21, 22]. Currently, large scale clinical trials are addressing the role of exercise and nutritional interventions in the treatment of sarcopenia, such as the European SPRINTT trial (NCT02582138) [23]. In addition to aforementioned variables, sarcopenia is secondary with chronic illness, such as liver diseases, renal diseases, inflammatory diseases, and malignancies [24]. Recent studies have observed a significantly higher prevalence of sarcopenia among obese and NAFLD patients (25–27). Multiple potential mechanisms evolved in the link between sarcopenia and NAFLD, including insulin resistance, elevated inflammation, myokines secreted by skeletal muscle, vitamin D deficiency and physical inactivity, but the specific mechanism is yet unclear [28].
Lifestyle modification remains the first-line intervention for fatty liver diseases (FLD), and a standard approach consists of a $7\%$–$10\%$ weight loss from baseline. Similarly, there are no approved pharmacological treatment for sarcopenia. In liver cirrhosis, the severity of sarcopenia increased as the liver disease progress [29], which was primarily regarded as a sign of malnutrition and required nutritional supplementation. However, these treatments had minimal benefits for survival improvement [30].
The Third National Health and Nutrition Examination Survey (NHANES III) was a well-designed population-based program, collecting data from US adults from 1988 to 1994. In this context, we aim to analyze the associations between diet, physical activity and sarcopenic MAFLD using the population-based survey data.
## Data source and population
National Health and Nutrition Examination Survey (NHANES) is a population-based survey program carried out by the National Center for Health Statistics (NCHS), which aims to evaluate the health and nutritional status of civilian, non-institutionalized members in the US population [31]. Our work is predicated on the database of NHANES III (1988–1994) [32], which is the only survey that recorded liver ultrasonography data using a Toshiba Sonolayer SSA-90A and Toshiba video recorders [33]. The steatosis severity of participants was reevaluated and graded by experts between 2009 and 2010, and FLD was defined as moderate or severe hepatic steatosis based on hepatic ultrasound imaging. Household interviews were conducted by qualified health technicians utilizing a computer-assisted personal interview system to collect data on demographic variables and health history. Body mass index (BMI) was computed by dividing weight in kilograms by height in meters squared, rounding to the nearest decimal. The Linked Mortality Files (LMF) have been updated with mortality follow-up data through December 31, 2019 [34]. During the follow-up phase, respondents without matched death records were presumed alive. Survival time was counted from a subject who participated in the survey to death or December 31, 2019. Informed consent was obtained from all participants, and ethical approval was obtained from the NCHS Ethics Review Board.
A total of 20,050 subjects were included in the NHANES III survey. Among these subjects, 7,791 were excluded based on the following criteria [1]: missing data of BIA ($$n = 4186$$); [2] missing data of height or weight ($$n = 25$$); [3] positive serologic markers for hepatitis B ($$n = 73$$) or C ($$n = 348$$) virus; [4] patients with missing data of liver ultrasounds ($$n = 3159$$); After applying the above exclusion criteria, we included 12,259 subjects aged 18 to 75 years, of which 2,473 were MAFLD patients, and 9,786 were non-MAFLD patients (Figure 1).
**Figure 1:** *Flow Chart of Participants for the Study.*
## Definition of MAFLD
MAFLD was diagnosed in individuals with FLD and any of the following three medical conditions: overweight/obesity (body mass index [BMI] ≥ 25 kg/m2), type 2 diabetes mellitus (T2DM), or the existence of metabolic dysregulation [5]. Metabolic dysregulation was defined by the presence of at least two metabolic risk abnormalities: (a) waist circumference ≥ 102 cm in men and ≥ 88 cm in women; (b) blood pressure ≥ $\frac{130}{85}$ mmHg or specific drug treatment; (c) TG ≥ 150 mg/dL or specific drug treatment; (d) HDL-C < 40 mg/dL for men and < 50 mg/dL for women; (e) prediabetes (FPG = 100–125 mg/dL or HbA1c = $5.7\%$–$6.4\%$); (f) homeostasis model assessment of insulin resistance score (HOMA-IR) ≥ 2.5; and/or (g) CRP > 2 mg/L. The classification of individuals into MAFLD and non-MAFLD categories was based on their diagnoses.
## Definition of sarcopenia
Following the recommendation of 2nd edition of European Working Group on Sarcopenia in Older People (EWGSOP2), this study employs bioelectrical impedance analysis (BIA) to diagnose sarcopenia based on the existence of decreased muscle quantity or quality [9]. For the NHANES III database, BIA was measured as the resistance at 50 kHz between the right wrist and ankle of a supine participant using A Valhalla 1990B Bio-Resistance Body Composition Analyzer (Valhalla Medical, San Diego, California, USA).
Here, Skeletal muscle mass (SMM) was calculated by BIA from NHANES III database using Janssen’s equation: SMM (kg)= (height in cm)2/BIA-resistance × 0.401 + (sex × 3.825) + (age in years × −0.071) + 5.102, where BIA-resistance is measured in ohms, and sex is encoded as 1 for male and 0 for female [35]. Using the following formula, skeletal muscle mass index (SMI) was calculated: SMI = skeletal muscle mass in kg/body weight in kg × 100. Participants were considered to have sarcopenia if their SMI was more than two standard deviation below the sex-specific mean for young adults aged 18 to 39 [9, 35].
## Physical activity level
Physical activity questionnaires were given at a home interview for all participants, inquiring about the frequency of leisure time activities (walking, running or jogging, riding, swimming, aerobics, dancing, etc.) in the previous month. The intensity of each activity was evaluated by metabolic equivalent (MET) based on the criteria from the Compendium of Physical Activities [36], which defines one MET as the energy expended at resting metabolic rate.
The NHANES III datasets collected information on the intensity rating and frequency of each individual’s daily physical activity. The activities are classified into moderate (METs ranging from 3 to 6) and vigorous (METs above 6) categories based on their intensity rates. Active group was characterized as those who engaged in moderate or vigorous activity at least five or three times per week. The inactive group was defined as those who participate in no physical activity during their leisure time. The insufficiently active group fell in the middle between active and inactive levels of physical activity [37, 38].
## Ascertainment of nutrient components intake
A nutritional interview comprising a 24-hour recall of dietary intake was conducted, with participants providing information on specific foods and quantities. Following the instruction of the Nutrient Composition Data Bank, the grams of nutrient components (carbohydrate, protein, fat, cholesterol, saturated fatty acids, monounsaturated fatty acid, and polyunsaturated fatty acid) were recorded and calculated.
In our study, the absolute quantity and percentage of energy intake from each macronutrient were categorized into gender-specific quartiles (Q1, Q2, Q3, and Q4). Additionally, the contribution of carbohydrates, proteins, and fatty acids to the overall amount of energy intake (% of total energy consumed) was calculated. The quartile variables were modeled as dummy variables, comparing each quartile to the lowest one (Q1).
In accordance with the American Gastroenterological Association’s (AGA) guidelines for lifestyle modification for NAFLD management, we further grouped individuals based on their calorie and protein intake [7]. The definition of a hypocaloric diet was < 1200 kcal/day for women and < 1500 kcal/day for men. In addition, the relative daily protein intake of participants was graded as low (< 1.2 g/kg), adequate (1.2–1.5 g/kg), and high (> 1.5 g/kg) based upon recommendations for patients with sarcopenia.
## Other definitions
Household interviews were conducted by skilled interviewers utilizing a computer-assisted personal interview system to collect data on demographic variables and health history. The data on body measurements were gathered by qualified health technicians. Body mass index (BMI) was computed by dividing weight in kilograms by height in meters squared, and then rounding to the nearest decimal. Participants were asked to fast for 9 hours before the blood sample was collected. Serum insulin and plasma glucose concentrations were measured by radioimmunoassay and a hexokinase enzymatic array from fasting blood samples. The HOMA-IR score was determined by the following formula: HOMA-IR = (Fasting insulin in μIU/mL) × (Fasting glucose in mg/dL)/405 [39]. In addition, concentrations of alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine, gamma-glutamyl transferase (GGT), total bilirubin, albumin, glycated hemoglobin (HbA1c), low-density lipoprotein cholesterol (LDL-C), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and C-reactive protein (CRP) were measured. Details of measurements are available at http://www.cdc.gov/nchs/nhanes/index.htm.
T2DM was defined by a self-reported diabetic medical history, an FPG ≥ 126 mg/dL, or an HbA1c of ≥ $6.5\%$. Hypertension (HTN) was defined by self-reported medical history of HTN, systolic blood pressure readings above 130 mmHg, or diastolic blood pressure measures above 80 mmHg from an average of 3 measurements. Hyperlipidemia (HL) was defined by a reported history of HL, cholesterol ≥ 200 mg/dL, LDL-C ≥ 130 mg/dL, or HDL-C ≤ 40 mg/dL for men and ≤ 50 mg/dL for women.
NAFLD Fibrosis Score (NFS) score is a non-invasive method to separate NAFLD patients with and without advanced fibrosis, calculated as: NFS = -1.675 + (0.037 × Age in years) + (0.094 × BMI in kg/m2) + (1.13 × Impaired fasting glucose or diabetes) + (0.99 × AST in U/L/ALT in U/L) – (0.013 × Platelets in ×109/L) – (0.66 × Albumin in g/dL), where impaired fasting glucose/diabetes is encoded as 1 and 0 for participants with or without abnormal fasting glucose [40]. Fibrosis-4 (FIB-4) index was designed to predict significant fibrosis in a simple equation: (Age in years × AST in U/L)/(Platelets in ×109/L × ALT0.5 in U/L) [41]. Advanced fibrosis was determined by a NFS > 0.675 [40] or Fibrosis-4 (FIB-4) index > 2.67 [41].
## Statistical analysis
We compared the baseline characteristics of MAFLD and non-MAFLD participants using data from NHANES III. Continuous variables were expressed as means ± standard deviation (SD), while categorical variables were expressed as percentages. The Student t-test was utilized for normally distributed variables, the Chi-squared test for categorical variables, and the Mann-Whitney U-test for non-normally distributed variables. Multivariate logistic regression models adjusted for confounders were used to evaluate the association between sarcopenia and other clinical covariates. In tests of interaction, age (dichotomized into < 60 years and ≥ 60 years) modified the effect of sarcopenia, whereas gender did not interact significantly with sarcopenia. Cox proportional hazards models were developed to estimate hazard ratios (HR) and $95\%$ confidence intervals (CI) of risk factors for all-cause mortality in participants with sarcopenia or MAFLD. Model 1 was adjusted for age, sex, race, and BMI levels. Model 2 was adjusted for age, sex, race, BMI levels, and the existence of advanced fibrosis. Model 3 was adjusted for variables mentioned in model 2 with T2DM. Model 4 was adjusted for all variables in model 3 with other medical histories (HTN, HL, smoking). No evident interactions between MAFLD and sarcopenia were found ($p \leq 0.05$). All tests were two-tailed, and a p value less than 0.05 was considered statistically significant. R 4.2.0 (https://www.r-project.org/) was used to conduct all analyses.
## Data characteristics
A total of 12,259 participants from NHANES III data sets were included in this analysis, of whom 2,473 ($20.2\%$) were diagnosed with MAFLD (Figure 1). The included participants contained 5,862 ($47.8\%$) males aged 43.8 ± 15.9 years. Individuals with MAFLD had a higher prevalence of sarcopenia than those without MAFLD ($11.7\%$ vs. $3.0\%$), and this tendency persisted regardless of age, sex, ethnicity, levels of physical activity, calorie consumption, and liver fibrosis (Figure 2). The statistical differences between MAFLD and non-MAFLD groups were listed in Table S3.
**Figure 2:** *Prevalence of Sarcopenia among Participants with and without MAFLD. MAFLD, metabolic-associated fatty liver diseases..*
The demographic, laboratory, and lifestyle characteristics of participants were demonstrated in Tables S1–2, categorized by the presence of MAFLD and sarcopenia. Sarcopenia, with or without MAFLD, was characterized by female gender, advanced age, and central obesity. Moreover, self-report data demonstrated that those with sarcopenia consumed fewer calories and engaged in less physical activity than those without the condition.
## Identify risk factors for sarcopenia among MAFLD participants
The fully-adjusted logistic regression model showed that the presence of MAFLD was associated with an increased risk of sarcopenia (odds ratio [OR] = 1.38 [$95\%$ CI 1.11–1.73]) (Table 1). We then generated multivariate Logistic regression models (adjusted for age, sex, and race) to identify sarcopenia-related factors by calculating their ORs amongst the MAFLD population. As shown in Table 2, sarcopenia was associated with physical activity levels (active vs. inactive, OR=0.51 [$95\%$ CI 0.36–0.95]), calorie intake (Q2 vs. Q1, OR = 0.58 [$95\%$ CI 0.41–0.83]), carbohydrates (Q2 vs. Q1, OR = 0.54 [$95\%$ CI 0.37–0.76]), and fatty acids (Q2 vs. Q1, OR = 0.62 [$95\%$ CI 0.44–0.89]) intake.
Ordinal logistic regressions were performed to further reveal the relationship between sarcopenia and lifestyle factors. Sarcopenia was significantly and negatively associated with higher levels of physical activity (OR = 0.74 [$95\%$ CI 0.62–0.87]) (Table S4) and appropriate relative protein intake (OR = 0.48 [$95\%$ CI 0.35–0.65]) (Table S5). In contrast, there was no connection between sarcopenia and absolute calorie, carbohydrates, protein, or fat consumption (Tables S6–7).
## All-cause mortality
Of the overall NHANES III cohort (1988–1994), 290 ($2.37\%$) patients presented with MAFLD and sarcopenia, of whom 181 ($62.41\%$) individuals died after a median follow-up of 15.3 years.
Analyses of the relationships between MAFLD and sarcopenia and all-cause mortality were conducted using Models 1 through 4, which included age, sex, race, health behavior, and medical history as adjustments (Table 3). The presence of sarcopenia was associated with a poorer prognosis after modifications, whereas the presence of MAFLD was unable to predict survival when a history of T2DM was added to the model (Models 3–4).
**Table 3**
| Covariate | Model 1 | Model 1.1 | Model 2 | Model 2.1 | Model 3 | Model 3.1 | Model 4 | Model 4.1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Covariate | HR (95% CI) | P-value | HR (95% CI) | P-value | HR (95% CI) | P-value | HR (95% CI) | P-value |
| MAFLD | 1.11 (1.03–1.19) | 0.007 | 1.11 (1.04–1.20) | 0.003 | 1.05 (0.97–1.13) | 0.209 | 1.03 (0.96–1.11) | 0.427 |
| Sarcopenia | 1.18 (1.06–1.34) | 0.003 | 1.17 (1.04–1.30) | 0.009 | 1.16 (1.03–1.30) | 0.013 | 1.14 (1.02–1.28) | 0.025 |
| Age | 1.09 (1.09–1.09) | < 0.001 | 1.09 (1.09–1.09) | < 0.001 | 1.09 (1.08–1.09) | < 0.001 | 1.09 (1.08–1.09) | < 0.001 |
| Male | 1.46 (1.37–1.55) | < 0.001 | 1.43 (1.35–1.52) | < 0.001 | 1.46 (1.37–1.55) | < 0.001 | 1.28 (1.20–1.37) | < 0.001 |
| Race | Race | Race | Race | Race | Race | Race | Race | Race |
| Black | Reference | | Reference | | Reference | | Reference | |
| Hispanic | 0.69 (0.63–0.75) | < 0.001 | 0.73 (0.67–0.80) | < 0.001 | 0.70 (0.64–0.76) | < 0.001 | 0.74 (0.67–0.80) | < 0.001 |
| White | 0.81 (0.75–0.87) | < 0.001 | 0.79 (0.74–0.86) | < 0.001 | 0.82 (0.76–0.88) | 0.002 | 0.83 (0.78–0.90) | < 0.001 |
| Others | 0.55 (0.45–0.66) | < 0.001 | 0.55 (0.46–0.67) | < 0.001 | 0.55 (0.46–0.66) | < 0.001 | 0.61 (0.50–0.73) | < 0.001 |
| Obesity | Obesity | Obesity | Obesity | Obesity | Obesity | Obesity | Obesity | Obesity |
| Normal | Reference | | Reference | | Reference | | Reference | |
| Obese | 0.87 (0.86–0.93) | < 0.001 | 0.90 (0.84–0.97) | 0.005 | 0.87 (0.81–0.94) | < 0.001 | 0.86 (0.80–0.93) | <0.001 |
| Overweight | 1.03 (0.13–1.12) | 0.409 | 1.10 (1.02–1.19) | 0.014 | 1.03 (0.95–1.10) | 0.503 | 0.99 (0.91–1.07) | 0.767 |
| Advanced fibrosis | | | 1.33 (1.21–1.46) | < 0.001 | 1.21 (1.10–1.33) | < 0.001 | 1.22 (1.11–1.35) | < 0.001 |
| T2DM | | | | | 2.00 (1.83–2.18) | < 0.001 | 1.94 (1.77–2.11) | < 0.001 |
| HTN | | | | | | | 1.30 (1.22–1.39) | < 0.001 |
| HL | | | | | | | 0.92 (0.85–0.98) | 0.017 |
| Smoking | | | | | | | 1.58 (1.48–1.69) | < 0.001 |
For the purpose of modifying the interaction between age and sarcopenia, adjusted HRs calculated for individuals with sarcopenia were split into two age groups (Table 4). Higher levels of activity improved the survival of sarcopenia (Insufficiently active, HR = 0.75 [$95\%$ CI 0.58–0.97]; Active, HR=0.64 [$95\%$ CI 0.48–0.86), which was more prominent in older patients. In both age categories, adequate protein intake was not significantly associated with long-term outcomes.
**Table 4**
| Covariate | Overall | Overall.1 | < 60 years | < 60 years.1 | ≥ 60 years | ≥ 60 years.1 |
| --- | --- | --- | --- | --- | --- | --- |
| Covariate | HR (95% CI) | P-value | HR (95% CI) | P-value | HR (95% CI) | P-value |
| Age | 1.07 (1.06–1.08) | < 0.001 | 1.07 (1.05–1.09) | < 0.001 | 1.10 (1.06–1.13) | < 0.001 |
| Male | 1.47 (1.15–1.88) | 0.002 | 1.92 (1.28–2.88) | 0.002 | 1.21 (0.87–1.67) | 0.251 |
| Race | Race | Race | Race | Race | Race | Race |
| Black | Reference | | Reference | | Reference | |
| Hispanic | 0.87 (0.65–1.17) | 0.353 | 0.87 (0.53–1.43) | 0.581 | 0.85 (0.58–1.23) | 0.383 |
| White | 0.98 (0.76–1.27) | 0.896 | 0.78 (0.50–1.22) | 0.276 | 1.14 (0.83–1.56) | 0.435 |
| Others | 1.06 (0.46–2.48) | 0.884 | 1.20 (0.35-4.08) | 0.768 | 0.92 (0.28–3.08) | 0.896 |
| Physical activity | Physical activity | Physical activity | Physical activity | Physical activity | Physical activity | Physical activity |
| Inactive | Reference | | Reference | | Reference | |
| Insufficiently active | 0.75 (0.58–0.97) | 0.027 | 0.74 (0.45–1.02) | 0.176 | 0.78 (0.57–1.08) | 0.132 |
| Active | 0.64 (0.48–0.86) | 0.003 | 0.80 (0.51–1.30) | 0.392 | 0.60 (0.42–0.86) | 0.006 |
| Relative protein intake | Relative protein intake | Relative protein intake | Relative protein intake | Relative protein intake | Relative protein intake | Relative protein intake |
| < 1.2 g/kg | Reference | | Reference | | Reference | |
| 1.2–1.5 g/kg | 0.93 (0.43–1.99) | 0.851 | 0.72 (0.26–2.00) | 0.534 | 1.09 (0.34–3.51) | 0.886 |
| > 1.5g/kg | 0.90 (0.56–1.45) | 0.667 | 1.14 (0.57–2.29) | 0.715 | 0.77 (0.40–1.49) | 0.434 |
| Cirrhosis | 1.12 (0.86–1.45) | 0.395 | 0.96 (0.60–1.56) | 0.883 | 1.15 (0.84–1.59) | 0.383 |
| T2DM | 1.39 (1.05–1.82) | 0.02 | 1.76 (1.08–2.85) | 0.023 | 1.26 (0.90–1.78) | 0.182 |
| HTN | 1.12 (0.90–1.40) | 0.311 | 1.31 (0.90–1.91) | 0.165 | 1.00 (0.76–1.32) | 0.983 |
| HL | 1.02 (0.80–1.31) | 0.862 | 0.82 (0.52–1.31) | 0.409 | 1.08 (0.79–1.47) | 0.64 |
| Smoking | 1.40 (1.11–1.77) | 0.004 | 1.36 (0.92–2.01) | 0.123 | 1.54 (1.14–2.07) | 0.005 |
Diabetes had the greatest impact on the prognosis of persons with MAFLD (HR = 1.84 [$95\%$ CI 1.59–2.12]), and increasing activity levels also improved the survival (Insufficiently active, HR = 0.85 [$95\%$ CI 0.73–0.99]; Active, HR = 0.64 [$95\%$ CI 0.67–0.93]). A daily protein intake of greater than 1.5 g/kg protein was associated with a better prognosis in older MAFLD patients, but had no significant effect on younger individuals (Table S8).
## Discussion
In this study, we used data sets from NHANES III (1988–1994) to investigate the clinical impact of dietary components and physical activity on patients with sarcopenia and MAFLD, revealing an increased incidence of sarcopenia in patients with MAFLD. Decreased physical activity levels and insufficient protein consumption may contribute to sarcopenia, with reduced physical activity being related to unfavorable outcomes.
Sarcopenia is strongly age-related and primarily observed in older people, while chronic diseases may induce sarcopenia in younger individuals [42]. Consistent with earlier studies that demonstrated a positive correlation between NAFLD and sarcopenia [43, 44], sarcopenia was more prevalent in MAFLD than non-MAFLD participants ($11.7\%$ vs. $3.0\%$) in our study and related to a higher mortality. Insulin resistance may function as the main pathologic mechanism of MAFLD and sarcopenia. Insulin could activate the mammalian target of rapamycin (mTOR) and enhance its downstream effectors, 4E-binding protein 1 and ribosomal S6 kinase 1, mediating skeletal muscle anabolism and maintaining muscle mass [45]. Impaired insulin sensitivity may interrupt the glucose metabolism and result in excess glucose conversion to triacylglycerol in the liver, which also leads to hepatic insulin resistance. Other factors, such as chronic inflammation, hyperammonemia, alterations in sex hormones, and insulin-like growth factor-1 signaling may also interfere with the glucose disposal in skeletal muscles and lead to muscle loss [42, 46, 47], which helps to explain the co-existence of sarcopenia with MAFLD. The impact of sarcopenia on the long-term prognosis of MAFLD is anticipated to be substantial, since both sarcopenia and liver fibrosis caused by MAFLD are independently associated with increased risk of death from all causes [47].
Prior studies showed a strong interest in elucidating how sarcopenia contributes to adverse outcomes in patients with chronic liver diseases, particularly those with cirrhosis. Molecular studies have shown that cirrhotic patients had an increase in muscle cell autophagy [48] and a higher expression of myostatin that inhibited mTOR signaling and suppressed protein synthesis [49]. Besides, hyperammonia, a common abnormality caused by liver dysfunction and portosystemic shunting, may contribute to both myostatin upregulation and autophagy processes (48–50). In addition to the detrimental impact of sarcopenia on cirrhosis, additional investigation is needed to understand how sarcopenia affects the prognosis of MAFLD. Moreover, we confirmed that sarcopenia was an independent predictor of survival in individuals either with or without MAFLD. Our research further revealed a strong correlation between MAFLD and T2DM rather than severe fibrosis, indicating that metabolic dysregulation was mainly responsible for the unfavorable prognosis of MAFLD patients in the general community.
Despite compelling evidence that sarcopenia is associated with negative outcomes, no viable methods to reverse muscle mass loss have been identified [51]. Previous studies supported the hypothesis that physical activity can enhance the functional capacity of skeletal muscle, but its effect on gaining muscle mass remained uncertain [52]. Here, we revealed that in patients diagnosed with sarcopenia, increasing the intensity and frequency of exercises is linked to a better prognosis, especially in the older population. Exercise may boost the muscle accumulation by increasing hormone levels such as testosterone [53] and insulin-like growth factor-1 (IGF-1) [54], and it may promote mitochondrial biogenesis by inhibiting TNF-α and various other molecular mechanisms. Exercise also upregulated PGC-1α and Toll-like receptors downregulation that enhanced the anti-inflammatory and anti-atrophy effects [55, 56]. Autophagy contributes to decreased synthesis and increased proteolysis of skeletal muscle in patients with chronic liver diseases. Physical exercise may rescue the impaired mTORC1 signaling by stimulating phosphatidic acid [57], therefore maintaining muscle mass by activating protein synthesis and inhibiting autophagy. As resistance exercises were more effective at stimulating skeletal muscle protein synthesis [58], the effect of different types of exercise on preventing sarcopenia and improving survival required more validation.
Given that physical activity was rather compromised in older people by their frailty or diseases, a protein supplement was considered a practical choice for preserving muscle mass [59, 60]. Although older and younger individuals had similar rates of protein turnover [61], elderly people have a more muted response to administered amino acids than young people [62]. Lower mTOR and p70S6K concentrations [63], along with a concurrent decline in positive regulators (such as IGF-1) and an increase in negative regulators (such as AMPK) in older skeletal muscle, may explain their resistance to amino acid feedings [64]. Some observational and cohort studies demonstrate that adequate protein consumption is well tolerated without major adverse events and can prevent muscle loss [65, 66], but there is insufficient evidence to support the hypothesis that protein intake can improve the long-term outcomes. Several randomized controlled trials were performed in cirrhotic individuals with sarcopenia; nevertheless, nutrition supplementation through multiple routes had little influence on sarcopenia or survival [30]. Supplemental hormone therapy and mechanistic targeted treatments were produced as more precise treatments for sarcopenia, necessitating a clearer knowledge for its pathophysiological process [51, 67].
This research has a few limitations. First, the diagnosis of FLD was established by ultrasound images from NHANES III, but fibrosis data were not available with ultrasound. In the absence of liver stiffness measurement (LSM) results, advanced fibrosis was determined by NFS and FIB-4 scores. The relationship between MAFLD-related fibrosis and sarcopenia should be evaluated further. Moreover, the NHANES III database was relatively outdated in comparison to other NHANES survey cycles. Second, we calculated the skeletal muscle mass using BIA measurements, whereas dual-energy X-ray absorptiometry (DXA) is the primary method for measuring body composition. Since sarcopenia is defined as loss of both muscle mass and function, the NHANES database does not contain muscle function measurements, such as contractile strength, maintenance of contraction, and muscle fatigue in response to persistent and repetitive contraction [68]. Finally, the mortality data came from a separate national database that matched the NHANES III data, where the data on liver-associated mortality was not available. Given the cross-sectional nature of the NHANES database, the progression of liver diseases cannot be determined.
In summary, our data demonstrate that sarcopenia is more prevalent and is associated with an increased risk of all-cause death among MAFLD participants. MAFLD patients who suffer from sarcopenia may benefit from physical activity and a proper intake of proteins. Therefore, clinicians should recognize and manage sarcopenia in patients with MAFLD in order to improve their life quality and overall survival outcome.
## 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.
## Ethics statement
The studies involving human participants were reviewed and approved by NCHS Ethics Review Board. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
YY and YC: conception and design. YY, CW, YD, JH, and YL: data collection, data management, and formal statistical analysis. YY and CW: manuscript writing. YC: manuscript revising. All authors involved in writing and approved the final manuscript.
## 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.2023.1101892/full#supplementary-material
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|
---
title: 'Association between serum carcinoembryonic antigen and cardiometabolic risks:
Implication for cardiometabolic prevention'
authors:
- Chia-Hao Chang
- Hsu-Huei Weng
- Yu-Chih Lin
- Chia-Ni Lin
- Tung-Jung Huang
- Mei-Yen Chen
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC9995979
doi: 10.3389/fendo.2023.1113178
license: CC BY 4.0
---
# Association between serum carcinoembryonic antigen and cardiometabolic risks: Implication for cardiometabolic prevention
## Abstract
### Background
Serum carcinoembryonic antigen (CEA) is a biomarker commonly used to detect colorectal cancer. CEA levels are affected by many factors, including cardiometabolic diseases, such as cardiovascular diseases (CVDs) and diabetes. Cardiometabolic diseases and cancer share a similar pathological inflammatory pathway, which correlates with an unhealthy lifestyle. Hence, establishing an adequate CEA cut-off value might be a valuable reference for developing precision healthcare programs for cardiometabolic disease prevention. This study aimed to investigate the association between cardiometabolic risks and serum CEA and the underlying factors.
### Methods
A community-based, cross-sectional study was conducted between March and December 2021 on the western coast of Taiwan. Lifestyle data were assessed using a structured questionnaire. The cardiometabolic biomarkers, serum CEA, urine malondialdehyde, and 1-hydroxypyrene were quantified by the central laboratory of the collaborating hospital. Chi-square and binary multivariable logistic regression implemented in R version 4.0.2 were used to identify factors defining the risk of high serum CEA levels.
### Results
A total of 6,295 adult residents without cancer-related diseases completed the study. The mean age was 48.6 (SD = 16.4) years, $56\%$ were female, $32\%$ had metabolic syndrome, and $23\%$ and $10\%$ had CVDs and diabetes, respectively. Multivariate logistic regression showed that age ≥ 65 years, male sex, alcohol consumption, smoking, infrequent use of dental floss, fewer remaining teeth, CVDs, diabetes, and oxidative stress were significantly associated with serum CEA ≥ 3 ng/mL. The discriminatory performance of the area under the receiver operating characteristic curve was 0.75 (0.73–0.76), showing that this model was suitable for distinguishing high CEA levels.
### Conclusion
Our findings highlight the importance of understanding cardiometabolic diseases, unhealthy lifestyles, and oxidative stress, which contribute to high serum CEA. This study demonstrates that CEA, a well-known tumor marker, can help the early detection and prevention of cardiometabolic diseases via personalized lifestyle modification.
## Introduction
Recently, researchers studying the interplay between carcinogenesis and cardiometabolic diseases (CMDs) have focused on reactive oxygen species (ROS) and redox imbalance (1–3). ROS, such as oxygen, nitrogen, and sulfur, are highly reactive derivatives of oxygen metabolism and are considered normal cellular metabolites. ROS can be used as a biomarker for oxidative stress. ROS plays a double-edged role in cellular damage and protection [4, 5]. Levels of oxidative stress biomarkers, such as urinary 1-hydroxypyrene (1-OHP) and malondialdehyde (MDA), were significantly higher in patients with colorectal cancer (CRC) and correlated with aging, smoking, liver diseases, and CMDs [2, 6]. ROS activate the pro-inflammatory signaling pathway and cytokines that induce endothelial cell dysfunction and cause vascular smooth muscle migration and hyperplasia. Serial reactions result in atheroma formation and further CMDs such as hypertension, heart disease, stroke, and type 2 diabetes [2, 7, 8]. Serum carcinoembryonic antigen (CEA) is an inflammatory biomarker commonly used to detect colorectal cancer. CEA levels are affected by many factors, including CMDs.
CMDs are recognized as systemic diseases induced by dysregulation of systemic inflammation, immunity, and metabolism and have been shown to have direct effects on atherosclerotic plaques, insulin resistance, and diabetes [9, 10]. Furthermore, CMDs and cancer are the leading causes of morbidity and mortality worldwide [11, 12] and in Taiwan [13, 14], with similar biological mechanisms related to the inflammation process, as well as many modifiable risk factors, such as smoking, low vegetable, and fruit intake, obesity, physical inactivity, hypertension, dyslipidemia, and non-modifiable aging, as well as genetic factors [9, 15, 16]. According to the literature, most CMDs and cancers can be prevented through modifiable risk factors, such as reduced tobacco and alcohol consumption, changes in an unhealthy diet, and physical activity [9, 11, 15]. Additionally, cardiometabolic risks can be detected early, before progression to CMDs, via primary health examination. Cardiometabolic risks are a cluster of risk factors such as abdominal obesity, impaired glucose tolerance, elevated blood pressure, triglycerides, and low high-density lipoprotein cholesterol, which increases the risk of CMDs. Furthermore, the presence of three or more of these risk factors is known as metabolic syndrome (MetS) [9, 14, 17].
Except for fecal occult blood tests, serum carcinoembryonic antigen (CEA) is commonly used for the early detection of CRC in many annual health examination settings. CEA, a surface glycoprotein mainly found in epithelial and mucus-secreting cells of the colon, participates in cancer invasion and metastasis [18, 19]. CEA is a malignant transformation and chronic inflammation marker and was first identified as a colon cancer antigen; it was previously used as a prognostic marker in CRC and monitoring response to therapy [20, 21]. Previous studies showed increased serum CEA levels in CRC and chronic diseases, such as hyperglycemia, CVDs, and type 2 diabetes [17, 19, 22]. European cardiologists recently reported that CEA was associated with the severity of heart failure outcomes, including cardiovascular morbidity and mortality [3, 18, 23]. The underlying mechanism might be due to imbalanced oxidative damage and endoplasmic reticulum stress production, triggering redox imbalance and increasing oxidative damage to proteins, lipids, and DNA [24] However, no cut-off reference value is available to distinguish high serum CEA levels in clinical practice. Traditionally, clinicians used serum CEA for tumor detection in CRC and to monitor the response to further treatment. Few studies have linked CEA levels and cardiometabolic risks in primary prevention to mitigate CMDs pathogenesis and progression. Hence, we aimed to investigate the possible modifiable factors associated with high serum CEA levels and establish a cut-off value of serum CEA levels for the prevention of CMDs among adults in rural communities.
## Design and population
This study was part of a series of health promotion programs designed to explore health needs and provide tailored health care for adults in rural areas. Community-based annual health screening was conducted in collaboration with a local hospital between March and December 2021 in western coastal Yunlin County, Taiwan. Participants were selected using convenience sampling. The inclusion criteria were as follows: [1] age ≥ 20 years, [2] the ability to complete questionnaires in a Mandarin or Taiwanese dialect via a face-to-face interview, and [3] agreement to participate in the study after providing informed consent. The exclusion criteria were as follows: [1] inability to complete the questionnaires, [2] inability to perform self-care or walk independently, [3] diagnosis of cancer-related diseases, and [4] incomplete health surveys or laboratory data.
## Procedure and ethical considerations
This study conformed to the principles outlined in the Declaration of Helsinki and was approved by the Institutional Review Board of the Research Ethics Committee (IRB no: 202000109B0C101). All participants were informed about the study’s purpose, procedures, benefits, and potential risks agreed to participate, and signed an informed consent form. Five registered nurses were recruited as research assistants and trained by the investigators. The one-on-one questionnaire interview included health-related lifestyle behaviors and was established from a previous study [25]. The questionnaire designed was based on the relationships between a healthy lifestyle and anti-inflammatory reactions, such as adequate diets, regular exercise, and oral hygiene are benefits for cardiometabolic health [15, 25]. Blood and urine samples were drawn and stored according to the standard procedure by the central laboratory of the collaborating hospital.
## Demographic and health history
Demographic and health history included age, sex, level of education (years of education received), and self-reported comorbidities diagnosed by a physician (diabetes, hypertension, heart disease, and stroke).
## Substance use was assessed
Substance use was assessed: (a) regular alcohol consumption at least three times per week and (b) cigarette smoking, with responses categorized as “never” vs. “yes: former or current user.”
## Healthy diet
Healthy diet was assessed using the frequency of at least three portions of vegetables and two portions of fruit per day, with responses categorized as “less: never or seldom” and “often: usually or always.”
## Regular exercise
Regular exercise was based on whether the participants usually or always (often) exercised for > 30 min, at least three times per week, or seldom or never (less) engaged in exercise.
## Oral health
Oral health was measured as follows: (a) the number of natural teeth and fixed dentures were self-reported, and (b) frequency of using dental floss before bed with responses of “less: never or seldom” or “often: usually or always.”
## Cardiometabolic risk factors
Cardiometabolic risk factors were based on the national standard [14], including the presence of five physiological biomarkers: (a) elevated central obesity (waist circumference) in males and females > 90 and 80 cm, respectively, (b) elevated systolic/diastolic blood pressure > $\frac{130}{85}$ mmHg, (c) low serum high-density lipoprotein-cholesterol (HDL-C) in males and females < 40 and 50 mg/dL, respectively, (d) elevated serum fasting blood glucose (FBG) > 100 mg/dL, and (e) elevated serum triglyceride (TG) levels > 150 mg/dL. MetS were defined by the presence of three or more risk factors.
## Carcinoembryonic antigen
Carcinoembryonic antigen (CEA, ng/mL) was measured by electrochemiluminescence immunoassay (ECLIA) on Roche Cobas e801 analyzer. Instead of using reference intervals published in manufacturers’ package inserts, we used CEA ≥ 3.0 ng/mL as the cut-off value for the high serum level group based on previous studies, considering age, sex, and smoking habits [18, 22, 26].
## Urine 1-hydroxypyrene and malondialdehyde
Urine 1-hydroxypyrene (1-OHP) and malondialdehyde (MDA) (μg/g CRE): Spot urine samples were collected and sent to the central laboratory of the collaborating hospital for analysis. Urinary 1-OHP was analyzed using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and urinary MDA was quantified using standard thiobarbituric acid reactive substances (TBARS) assay. The urinary creatinine concentration was used for urinary 1-OHP and MDA adjustments [5, 6].
## Statistical analysis
This study used the R version 4.0.2 software (The R Foundation for Statistical Computing, Vienna, Austria) for data analysis, including [1] Chi-square and t-tests performed to confirm the differences according to the CEA category (CEA < 3 or CEA ≥ 3 ng/mL); [2] binary multivariable logistic regression used to identify the factors affecting the risk of CEA ≥ 3; [3] to measured effects of data discrepancies. The dataset was randomly divided into two subsets using the Caret R package, with $80\%$ of the data ($$n = 5036$$) in the training subset and the remaining $20\%$ ($$n = 1259$$) in the validation set. In the training cohort, significant variables ($p \leq 0.05$) were selected for binary multivariable logistic regression analysis in the univariate analysis. The model of the training cohort used backward elimination processes to predict the risk of CEA levels ≥ 3 ng/mL. The fitted model was applied to the training and validation subsets. The probability of CEA levels ≥ 3 was calculated based on the beta coefficients of the training subset. The area under the receiver operating characteristic (ROC) curve (AUC) values of the training and validation datasets were calculated using the pROC R package, and [4] To evaluate overfitting, the logistic regression model was fitted to the 1000 bootstrap samples, and the corresponding values for the AUC were calculated. The results were averaged to provide a final bootstrap estimate for AUC optimism. The differences in the values for the averaged AUC and training subset AUC provided an estimate of optimism.
## Demographic characteristics
A total of 6,295 participants aged ≥ 20 years who completed the community annual health examination were included, of whom 3507 ($56\%$) were female and 1204 ($19.1\%$) were classified as having a CEA ≥ 3 ng/mL (Table 1). The mean age of the participants was 48.6 years (SD = 16.4, range 20–90 years), with more than three-quarters of those aged < 65 years.
**Table 1**
| Variables | Unnamed: 1 | Total | CEA12 <3 | CEA≥3 | χ2/t | p-value |
| --- | --- | --- | --- | --- | --- | --- |
| | | n (%) | n (%) / M±SD | n (%) / M±SD | | |
| Age (years) | <65 | 4952 (79) | 4197 (82) | 755 (63) | 225.9 | <0.001 |
| | ≥65 | 1343 (21) | 894 (18) | 449 (37) | | |
| Gender | Female | 3507 (56) | 3033 (60) | 474 (39) | 161.1 | <0.001 |
| | Male | 2788 (44) | 2058 (40) | 730 (61) | | |
| Healthy diet | Often1 | 3361 (53) | 2765 (54) | 596 (50) | 9.1 | <0.01 |
| | Less | 2934 (47) | 2326 (46) | 608 (50) | | |
| Alcohol | Never | 5277 (84) | 4398 (86) | 879 (73) | 128.6 | <0.001 |
| | Yes2 | 1018 (16) | 693 (14) | 325 (27) | | |
| Smoking | Never | 5462 (87) | 4597 (90) | 865 (72) | 288.8 | <0.001 |
| | Yes | 833 (13) | 494 (10) | 339 (28) | | |
| Exercise | Often1 | 3414 (54) | 2775 (55) | 639 (53) | 0.8 | 0.37 |
| | Less | 2881 (46) | 2316 (45) | 565 (47) | | |
| Dental floss | Often1 | 3203 (51) | 2746 (54) | 457 (38) | 99.5 | <0.001 |
| | Less | 3092 (49) | 2345 (46) | 747 (62) | | |
| Remaining teeth | ≥20 | 5143 (82) | 4300 (84) | 843 (70) | 135.9 | <0.001 |
| | <20 | 1152 (18) | 791 (16) | 361 (30) | | |
| WC (cm)3 | < 80/90 | 3735 (59) | 3060 (60) | 675 (56) | 6.6 | 0.01 |
| | ≥ 80/90 | 2560 (41) | 2031 (40) | 529 (44) | | |
| BP (mmHg)4 | < 130/85 | 3505(56) | 2966(58) | 539(45) | 71.8 | <0.001 |
| | ≥ 130/85 | 2790(44) | 2125(42) | 665(55) | | |
| FBG (mg/dL)5 | < 100 | 3886 (62) | 3312 (65) | 574 (48) | 124.5 | <0.001 |
| | ≥ 100 | 2409 (38) | 1779 (35) | 630 (52) | | |
| HDL-C (mg/dL)6 | > 40/50 | 5111 (81) | 4164 (82) | 947 (79) | 6.3 | 0.01 |
| | ≤ 40/50 | 1184 (19) | 927 (18) | 257 (21) | | |
| TG (mg/dL)7 | < 150 | 5083 (81) | 4187 (82) | 896 (74) | 38.4 | <0.001 |
| | ≥ 150 | 1212 (19) | 904 (18) | 308 (26) | | |
| MetS8 | < 3 risk factors | 4309 (68) | 3611 (71) | 698 (58) | 75.7 | <0.001 |
| | ≥ 3 risk factors | 1986 (32) | 1480 (29) | 506 (42) | | |
| CVD9 | No | 4838 (77) | 4084 (80) | 754 (63) | 169.5 | <0.001 |
| | Yes | 1457 (23) | 1007 (20) | 450 (37) | | |
| Diabetes | No | 5677 (90) | 4709 (92) | 968 (80) | 161.0 | <0.001 |
| | Yes | 618 (10) | 382 (8) | 236 (20) | | |
| MDA (μg/g CRE)10 | | 0.47 (0.71) | 0.45 (0.70) | 0.57 (0.72) | 4.9 | <0.001 |
| 1-OHP (μg/g CRE)11 | | 0.09 (0.17) | 0.08(0.16) | 0.12 (0.18) | 7.2 | <0.001 |
## Factors associated with high serum CEA level
Univariate analysis showed that male sex ($p \leq 0.001$), age ≥ 65 years ($p \leq 0.001$), alcohol consumption ($p \leq 0.001$), cigarette smoking ($p \leq 0.001$), less consumption of vegetables and fruits ($p \leq 0.01$), reduced use of dental floss ($p \leq 0.001$), and fewer than 20 natural teeth ($p \leq 0.001$), were significantly associated with high serum CEA levels (Table 1). To compare participants with or without cardiometabolic risks, those with increased abdominal obesity ($p \leq 0.01$), elevated systolic/diastolic blood pressure ($p \leq 0.001$), elevated serum FBG ($p \leq 0.001$), low HDL-C ($p \leq 0.01$), elevated TG ($p \leq 0.001$), and MetS ($p \leq 0.001$) were significantly associated with higher serum CEA level. Participants who reported having been diagnosed with CVDs (hypertension or heart disease, $p \leq 0.001$) and diabetes ($p \leq 0.001$) by a physician were classified as having high serum CEA levels. Owing to the lack of reference values for urine 1-OHP and MDA concentration levels, we further compared the mean differences and found that higher levels of urine MDA ($p \leq 0.001$) and 1-OHP ($p \leq 0.001$) were significantly associated with high serum CEA (Table 1).
The multivariable logistic regression model shows that the estimated odds of participants with ages ≥ 65 [odds ratio (OR) = 2.25, $95\%$ confidence interval (CI) 1.88–2.70], male sex (OR = 1.71, $95\%$ CI 1.47–1.98), alcohol consumption (OR = 1.25, $95\%$ CI 1.04–1.5), cigarette smoking (OR = 3.11, $95\%$ CI 2.56–3.77), less using dental floss (OR = 1.32, $95\%$ CI 1.14–1.53), fewer remaining teeth (OR = 1.32, $95\%$ CI 1.11–1.57), CVDs (OR = 1.41, $95\%$ CI 1.20–1.67), diabetes (OR = 1.81, $95\%$ CI 1.48–2.21), urine MDA (OR = 1.14, $95\%$ CI 1.04–1.24), and 1-OHP (OR = 1.90, $95\%$ CI 1.33–2.73) were significantly associated with higher serum CEA levels (Table 2). Overall, the discriminatory performance of the full model revealed an AUC of 0.747 (0.733–0.762) (Figure 1A), indicating the suitability of this model in identifying participants with high serum CEA levels.
## Training and validation
From the dataset of the 6295 participants, we used $80\%$ of the entries as training data ($$n = 5036$$) and $20\%$ for testing ($$n = 1259$$). To clarify the potential confounding variables in the training set, backward elimination by binary multivariable logistic regression was used to assess the association between CEA ≥ 3 and various factors. A comparison of the ROC curves for the training and validation data indicated an area difference of 0.003 (0.749–0.746, $$p \leq 0.87$$), reflecting a small disparity between the two curves and suggesting a small decay in the model performance in prospective testing (Figure 1B).
The partial area under the ROC curve (pAUC) allows us to focus on the area of interest on the left/right side of the ROC plot (Figures 1C, D), i.e., average sensitivity, between 80–$100\%$ specificity values, and average specificity, between 80–$100\%$ sensitivity values. Figure 1C shows a slight disparity ($$p \leq 0.4$$) in the model’s performance in prospective testing under a high valid negative rate. However, Figure 1D shows a slight disparity ($$p \leq 0.3$$) in the model performance in prospective testing under a high true positive rate. To validate this difference, bootstrap processes were repeated 1000 times, and the results were averaged to provide an optimum correction for an AUC of 0.003 (AUC range = 0.758–0.79), indicating the lack of overfitting.
## Discussion
To the best of our knowledge, this study is the first to investigate the relationship between serum CEA ≥ 3 (ng/mL), oxidative stress biomarkers, unhealthy lifestyle factors (such as poor oral hygiene, smoking, and fewer remaining teeth), and cardiometabolic risks. The present study provides valuable findings for further interventional studies and evidence-based lifestyle modifications for the early detection and prevention of cardiometabolic risks. Three crucial findings were obtained from this study. First, a high prevalence of cardiometabolic risks was observed, which was significantly associated with high serum CEA levels. Second, an unhealthy lifestyle was significantly associated with a high serum CEA level. Third, the oxidant stress biomarkers 1-OHP and MDA were also positively associated with high serum CEA levels.
## Serum CEA can be used for the early detection of cardiometabolic risks
The present study demonstrated that a high prevalence of cardiometabolic risk is significantly associated with high serum CEA levels. For instance, $44\%$, $41\%$, $38\%$, $32\%$, $23\%$, and $10\%$ of participants had elevated blood pressure, central obesity, elevated FBG level, MetS, CVDs, and diabetes, respectively. In addition, almost all cardiometabolic risk factors were significantly associated with high serum CEA levels. Similar to previous studies, CEA levels did not only increase in CRC but were also higher in some chronic diseases, especially CVDs, MetS, and diabetes [17, 19, 22]. This finding implies that clinicians can use serum CEA as a useful biomarker for the early detection of cardiometabolic risks and unhealthy lifestyles rather than solely as a tumor marker for CRC. Furthermore, if participants had smoked and suffered from CRC and cardiometabolic risks, it is important to clarify which factor primarily contributed to their high serum CEA levels. Huang et al. [ 19] demonstrated that postoperative serum CEA levels could not predict survival in CRC patients with type 2 diabetes. Type 2 diabetes and cigarette smoking influence serum CEA levels, which may cause a prognostic bias.
The commonly used clinical threshold value for tumor detection is serum CEA ≥ 5 (ng/mL) [19, 26, 27]. However, based on previous studies [18,22] and considering smoking, age, and sex, we used CEA ≥ 3 (ng/mL) as a cut-off value and used this model to distinguish high serum CEA levels based on the area under the ROC curve (AUC). Considering that nearly one-third of the total death rate is caused by CMDs, the increase was higher than ever of cancer in Taiwan [13, 14]. The findings presented herein could guide further studies for the early detection and prevention of cardiometabolic risks using serum CEA levels as a useful biomarker.
## Serum CEA levels can be reduced by adopting a healthier lifestyle
Despite male sex and aging factors, the present study revealed that an unhealthy diet (e.g., inadequate amounts of vegetables and fruits), alcohol consumption, and smoking significantly increased serum CEA levels. Furthermore, the present study indicated that urinary 1-OHP and MDA levels correlated with higher serum CEA levels. A possible mechanism might be due to ROS and redox imbalance. Evidence supports that ROS activates the inflammation process and induces endothelial cell dysfunction, causing vascular smooth muscle migration and hyperplasia [10, 15, 16]. These findings agreed with previous studies showing that aging, substance use, and an unhealthy diet correlated with elevated CEA levels [24, 28, 29]. On the other hand, some dietary compounds and metabolites, such as components of the Mediterranean diet pattern (rich in whole grains, fish, fruits, and vegetables), directly affect HDL-C composition and enhance anti-inflammatory and vasoprotective properties [9, 15, 30].
Increased oxidative stress plays a significant role in cardiometabolic risk as well as the initiation and progression of atherosclerosis. However, adopting a healthy diet and engaging in regular exercise are associated with the prevention of CMDs by reducing the inflammatory process [10, 31, 32]. Therefore, the American Heart Association (AHA) guidelines suggest that all adults consume a healthy diet that emphasizes the intake of vegetables and fruits, in addition to exercising for at least 150 min per week [9]. However, the present study did not accurately account for the effect of regular exercise on serum CEA levels, which could have been insufficiently characterized, as our questionnaire only asked whether the participants exercised for > 30 min, at least three times per week. This criterion does not meet the AHA recommendation of 150 mins per week. Further studies should use more precise tools to gauge exercise behavior.
Moreover, the present study showed that infrequent use of dental floss before bed and tooth loss with < 20 remaining teeth were associated with high serum CEA levels. Several studies have shown that lifestyle modifications, including oral hygiene, regular exercise, a healthy diet, and weight control, are important in managing cardiometabolic risks [9, 33]. This finding echoed those of previous studies reporting that poor oral hygiene facilitates infections by *Helicobacter pylori* and other bacteria that increase the inflammatory reaction via dental plaque, which in turn increases the possibility of periodontal disease, type 2 diabetes, and CVDs (33–35). Hence, it is worth initiating further interventional studies for adults with high serum CEA through personalized healthcare, including smoking and alcohol cessation, maintaining an adequate number of natural teeth through good oral hygiene, and following a Mediterranean diet.
## Strengths and limitations
This is the first study involving large-scale reporting of the relationship between the traditional use of serum CEA as a tumor marker and cardiometabolic diseases, identifying determinant factors associated with higher serum CEA levels. Moreover, we used R version statistical analysis to identify CEA levels ≥ 3 as a reasonable cut-off value to distinguish factors associated with high serum CEA, which can be applied to clinical and community settings for early detection of unhealthy lifestyles and providing personalized health promotion programs. However, this study had some limitations. First, it was conducted in only one county, which may limit the generalizability of the findings. Second, the health-related behavior questions were mostly self-reported, which might generate measurement bias and affect the study findings. For instance, the frequencies relative to vegetable consumption and exercise might be inaccurate. In addition, owing to the coronavirus disease pandemic, the number of remaining teeth was self-reported and not counted by the research assistants. Furthermore, our study lacks deep probing into the history of cardiometabolic diseases, such as prescribed medications for hypertension, heart disease, and diabetes. Hence, the prevalence of cardiometabolic risks may be underestimated.
## Conclusion
A high prevalence of cardiometabolic risk factors was associated with high serum CEA levels. Furthermore, unhealthy lifestyles and oxidative stress biomarkers contributed to high serum CEA levels. CEA ≥ 3 ng/mL was a meaningful threshold value for classifying significant risk factors. Therefore, in addition to being a tumor marker for CRC, CEA could be used in clinical and community settings for the early detection and prevention of CMDs through individualized lifestyle modifications.
## 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.
## Ethics statement
The studies involving human participants were reviewed and approved by the institutional review board of the Chang Gung Memorial Hospital Foundation (IRB no: 202000109B0C101). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
C-HC and H-HW contributed to statistical analysis. M-YC and C-HC conceived and designed the study and interpreted the data. C-NL, H-HW, Y-CL, and T-JH collected the data and contributed to the study direction. 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
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|
---
title: RAB23 regulates musculoskeletal development and patterning
authors:
- Md. Rakibul Hasan
- Anna Koskenranta
- Kirsi Alakurtti
- Maarit Takatalo
- David P. Rice
journal: Frontiers in Cell and Developmental Biology
year: 2023
pmcid: PMC9995984
doi: 10.3389/fcell.2023.1049131
license: CC BY 4.0
---
# RAB23 regulates musculoskeletal development and patterning
## Abstract
RAB23 is a small GTPase which functions at the plasma membrane to regulate growth factor signaling. Mutations in RAB23 cause Carpenter syndrome, a condition that affects normal organogenesis and patterning. In this study, we investigate the role of RAB23 in musculoskeletal development and show that it is required for patella bone formation and for the maintenance of tendon progenitors. The patella is the largest sesamoid bone in mammals and plays a critical role during movement by providing structural and mechanical support to the knee. Rab23 −/− mice fail to form a patella and normal knee joint. The patella is formed from Sox9 and scleraxis (Scx) double-positive chondroprogenitor cells. We show that RAB23 is required for the specification of SOX9 and scleraxis double-positive patella chondroprogenitors during the formation of patella anlagen and the subsequent establishment of patellofemoral joint. We find that scleraxis and SOX9 expression are disrupted in Rab23 −/− mice, and as a result, development of the quadriceps tendons, cruciate ligaments, patella tendons, and entheses is either abnormal or lost. TGFβ-BMP signaling is known to regulate patella initiation and patella progenitor differentiation and growth. We find that the expression of TGFβR2, BMPR1, BMP4, and pSmad are barely detectable in the future patella site and in the rudimentary tendons and ligaments around the patellofemoral joint in Rab23 −/− mice. Also, we show that GLI1, SOX9, and scleraxis, which regulate entheses establishment and maturation, are weakly expressed in Rab23 −/− mice. Further analysis of the skeletal phenotype of Rab23 −/− mice showed a close resemblance to that of Tgfβ2 −/− mice, highlighting a possible role for RAB23 in regulating TGFβ superfamily signaling.
## Introduction
Ras-associated protein 23 (RAB23) belongs to the large family of RAB GTPase proteins, many of which are key regulators of intracellular membrane trafficking events (Stenmark, 2009). They control intercellular signaling by restricting ligand secretion and by cargo (ligand-bound receptor) internalization (Entchev et al., 2000; Lanzetti et al., 2000). RAB23 is localized at the plasma membrane and is proposed to be involved in the endocytic pathway (Evans et al., 2003).
RAB23 regulates growth factor signaling (Eggenschwiler et al., 2001; Fuller et al., 2014; Hasan et al., 2020), and mutations in RAB23 affect the development and patterning of multiple organs and give rise to a diverse complex congenital anomaly, collectively known as Carpenter syndrome (CS, MIM# 201000) or acrocephalopolysyndactyly type II (Carpenter, 1909; Jenkins et al., 2007). CS causes a wide spectrum of defects, such as craniosynostosis, congenital heart defects, obesity, polydactyly, and multiple skeletal abnormalities including decreased hip mobility, spina bifida occulta, kyphoscoliosis, short muscular neck, genu valgum, and lateral displacement of the patella and mandibular defects (Cohen et al., 1987; Jenkins et al., 2007). Rab23 open brain mutant mice exhibit abnormalities in the neural tube, vertebral column, and axial skeleton, such as the ribs, limbs, and skull (Gunther et al., 1994; Eggenschwiler and Anderson, 2000; Hasan et al., 2020).
RAB23 acts as an inhibitor of the Hedgehog (Hh) pathway with RAB23 possibly restricting the entry of Smo (smoothened) to the cilium (Eggenschwiler et al., 2001; Boehlke et al., 2010). However, the direct ciliary function appears unlikely as endogenous RAB23 has not been localized to the primary cilium but to the plasma membrane and the endocytic pathway, and additionally, cilium length and function are reported to be normal in Rab23 opb2 nodes (Evans et al., 2003; Fuller et al., 2014). RAB23 is also reported to have Hh pathway-independent functions in left–right asymmetry (Fuller et al., 2014) and in the limb, neural tube, and cranial facial development (Eggenschwiler et al., 2006). Rab23 opb2 mutations caused lateralization defects linked to defective nodal signal production in the lateral plate mesoderm (Fuller et al., 2014). We have previously demonstrated that RAB23 regulates early calvarial osteogenesis in mice through canonical FGFR signaling and non-canonical Hh signaling (Hasan et al., 2020).
The patella, commonly known as the kneecap, is the largest sesamoid bone in mammals. Like other sesamoid bones, the patella bone is flat, located close to a joint, and embedded within tendons (Samuels et al., 2017; Eyal et al., 2019). The patella plays an important role during movement, providing structural support and stability to the knee joint (Lennox et al., 1994; Sarin et al., 1999). Relatively little is known about the development of the patella bone. Mouse studies showed that the patella initially develops as a part of the femur from scleraxis (Scx) and Sox9 double-positive chondroprogenitor cells (Blitz et al., 2013; Eyal et al., 2015). TGFβ2 signaling is required for the specification of the patella chondroprogenitor cells, and subsequently BMP signaling is required for the differentiation and growth of the patella progenitors to make the patella anlagen. The patella anlagen separate from the femur by making the patellofemoral synovial joint (Eyal et al., 2015; Eyal et al., 2019). Even though RAB23 is known to regulate aspects of musculoskeletal patterning and development, how RAB23 contributes to patella development has not been studied.
Here, we show that RAB23-deficient mice failed to develop patella and a loss of Scx expression in patella progenitor cells. SOX9 was initially expressed in the patella progenitors, but this expression was transient and lost at later developmental stages. Concomitant to Scx and SOX9 disruption, TGFβ-BMP signaling, as well as phospho-pErk$\frac{44}{42}$ and GLI1, also affected the patella and surrounding musculoskeletal structures. These anomalies collectively rendered a lack of patella anlagen and disruption of the tendons, ligaments, and entheses of the knee joint. We further analyzed the skeletal phenotype of Rab23 −/− mice and found that the overall phenotype closely resembles that of TGFβ2-null mice, which may indicate a role for RAB23 in regulating TGFβ superfamily signaling.
## Mice
The null allele mice with Rab23 −/− mutation was originally in C3H/Hej background backcrossed to the C57Bl/6J mouse strain (Charles River). The genotype of all mice and embryos were verified with PCR-based genotyping as previously described (Kasarskis et al., 1998; Eggenschwiler and Anderson, 2000). Rab23 −/− mice survive until embryonic days E18.5 and die neonatally. The details of the Rab23 −/− mouse generation could be found as explained (Hasan et al., 2020).
## Antibodies and reagents
Rabbit polyclonal anti-RAB23 antibody (Cat#11101-1-AP) was purchased from Proteintech. Rabbit polyclonal anti-SOX9 antibody (Cat#AB5535) was purchased from Sigma-Aldrich. Rabbit polyclonal anti-BMPR1A antibody (Cat#38-6000) was purchased from Invitrogen. Rabbit polyclonal anti-pSmad$\frac{1}{5}$/8 (Cat#AB3848) was purchased from Millipore. Rabbit monoclonal anti-phospho-p$\frac{44}{42}$ (Cat#9101) was purchased from Cell Signaling Technology. Mouse monoclonal anti-BMP4 (SC-12721), monoclonal anti-Tenascin-C (E-9, SC-25328), monoclonal anti-GDF-5 (A-10, SC-373744), monoclonal anti-GLI-1 (C-1, SC-515751), monoclonal anti-TGFβR2 (D2, SC-17799), monoclonal anti-COL1A1 (3G3, SC-293182), monoclonal anti-COL2A1 (M2138, SC-52658), and monoclonal anti-Scleraxis (A-7, SC-518082) antibodies were purchased from Santa Cruz Biotechnology. Goat anti-rabbit IgG (H + L) Alexa 488 (Cat#A-11008), goat anti-mouse IgG (H + L) Alexa 546 (Cat#A-110003), goat anti-mouse IgG (H + L) Alexa 488 (Cat#A-11001), and goat anti-rabbit IgG (H + L) Alexa 546 (Cat#A11010) were purchased from Thermo Fisher Scientific. Anti-Digoxigenin-AP, Fab fragments (Cat#11093274910), and BM purple AP substrate precipitating (Cat#11442074001) were purchased from Roche. $4\%$ PFA in PBS (Cat#15424389) and Hoechst 33342 (Cat# H3570) were purchased from Thermo Fisher Scientific. BSA (Cat#A3059) and Triton X-100 (Cat#T8787) were purchased from Sigma-Aldrich. EdU (5-ethynyl-2′-deoxyuridine) (reference number: A10044) and the Click-iT EdU Alexa Fluor 594 Imaging kit (reference number: C10339) were purchased from Invitrogen.
## In situ hybridization
For in situ hybridization, Wt and Rab23 −/− embryos were, respectively, collected at E14.0 and E15.5, dissected and fixed in $4\%$ paraformaldehyde (PFA) overnight, followed by dehydration up to $100\%$ ethanol, and then, embedded in paraffin. The knee samples were sagittally sectioned (7 μm) for digoxigenin label in situ hybridization. In situ hybridization was performed according to a standard protocol. For the detection of digoxigenin-labeled RNA probes, BMP purple AP substrate precipitating solution (Roche) was used. The following probes were used: Sox9, Scx, and Gli1 (Thomas et al., 1997; Schweitzer et al., 2001; Veistinen et al., 2017). All probes were transcribed from plasmids.
## Immunohistochemistry
Immunohistochemistry on the sagittal sections (7 μm) of the knee was performed using RAB23, SOX9, Scleraxis, Tenascin-C, GDF5, GLI1, TGFβR2, BMPR1, BMP4, pSmad$\frac{1}{5}$/8, phospho-p$\frac{44}{42}$, COL1A1, and COL2A1 primary antibodies. Alexa fluor 488 and Alexa fluor 546 secondary antibodies were used to detect the primary antibodies. The samples were permeabilized by triton X-100 and antigen retrieval was performed by citrate buffer. All the other procedures were followed according to the protocol described (Veistinen et al., 2017; Hasan et al., 2020).
## Immunofluorescence microscopy
Fluorescence microscopy was performed at the Biomedicum imaging unit, University of Helsinki. All the immunohistochemical samples were imaged using a Zeiss Axio Imager Z2 microscope. Excitation was achieved using $\frac{350}{488}$/594 nm wavelength. All images were taken at RT and analyzed with Fiji ImageJ 1.51b (64-bit) software.
## Skeletal staining
The Alcian blue–Alizarin red staining of E18.5 whole body was performed as previously described (Rice et al., 2003). Sectional tissue samples were stained with Alcian blue and nuclear fast red. For the analysis of bone, cartilage and sectional tissue images were captured using Analysis software (Soft Imaging System) and Olympus BX41 microscope and analyzed using Image J.
## EdU pulsing and staining
Cell proliferation was assessed by prior EdU incorporation followed by the EdU click reaction. The EdU was detected with the Click-iT EdU imaging kit, Alexa fluor 594 (Molecular probes, Invitrogen), according to the manufacturer’s instruction.
The mice were pulsed with 0.05 mg/g (i.p.) EdU in PBS for 2 h. After sacrificing E15.5 and E17.5, embryonic hind limbs were collected and fixed o/n with $4\%$ paraformaldehyde and processed for paraffin sections. Paraffin-embedded Wt and Rab23 −/− embryonic hind limbs were sectioned sagittally with 7 μm size. The sections were deparaffinized and stained with a Click-iT kit. During the EdU click reaction procedure, in brief, sectional explants were deparaffinized by xylene and rehydrated by a gradient of ethanol series. After washing with 2 mg/mL glycine, limb sections were permeabilized with $0.5\%$ Triton X-100. After washing several times in PBS, 10 μM EdU cocktail was used for click reaction for 30 min in the dark room. After PBS washing several times, 5 μg/mL Hoechst was used for counterstaining. The Vectashield mounting medium was used to mount the slides. Slides were then imaged with a fluorescence microscope with an emission wavelength of 615 nm.
Cell counting: The number of EdU-positive cells and total cells was counted from the end of femur head (289 × 96 pixels), *Wt patella* tendon vs. Rab23 −/− patella tendon-like structure, and Wt quadriceps tendon vs. Rab23 −/− tendon-like structure (143 × 144 pixels) at the embryonic stage E15.5 and E17.5. The total cell number was determined with Hoechst 33342 staining of nuclei.
## In situ apoptosis detection
In situ apoptosis detection assay on the hind limb sagittal sections at E14.0 and E15.5 in Wt and Rab23 −/− mice was performed according to the manufacturer’s instruction (Abcam, ab206386).
## X-ray micro-computed tomography
E18.5 WT and Rab23 −/− whole-body and limb samples were collected and fixed with $4\%$ paraformaldehyde overnight. The whole body, excluding limb samples, was dehydrated by gradient ethanol series to $70\%$ (v/v), and limb samples were additionally stained with $0.3\%$ phosphotungstic acid (PTA) in $70\%$ ethanol followed by both samples being processed for x-ray microtomography (μCT) imaging with Bruker SkyScan1272 (desktop micro-CT system, Bruker microCT N.V., Kontich, Belgium). Tomography 3D reconstructions were obtained using the program NRecon (desktop micro-CT system, Bruker microCT N.V., Kontich, Belgium).
## Statistical analysis
Paired Student’s t-test has been applied to perform the statistics of all the data obtained from EdU experiment and skeletal measurements. p value less than 0.05 is considered statistically significant.
## RAB23-deficient mice fail to develop patella
Skeletal analysis by Alcian blue and Alizarin red and also by μ-CT of hind limbs in Wt and Rab23 −/− mice at E18.5 showed that Rab23 −/− mice failed to develop the patella (Figure 1A). We further analyzed this developmental defect in sagittal tissue sections stained with Alcian blue staining of the patellofemoral sagittal sectional tissues at E18.5 (Figure 1A, inset, upper panel). To understand if the failure of patella formation in Rab23 −/− mice was due to the programmed cell death of patella chondroprogenitors, we performed in situ apoptosis detection assay on the hind limb sagittal sections at E14.0 and E15.5 in Wt and Rab23 −/− mice. The results show that the failure of patella formation in Rab23 −/− mice were not due to cell death (Supplementary Figure S1).
**FIGURE 1:** *Rab23
−/−
mice failed to develop patella and analysis of RAB23 protein expression in the developing patella. (A) Analysis of patella formation in Wt and Rab23
−/−
mice at E18.5 by Alcian blue and Alizarin red staining (upper panel) and by µ-CT imaging (lower panel). Both analysis methods show that while Wt mice developed patella, Rab23
−/−
mice failed to form patella. The absence of patella development in Rab23
−/−
mice was further confirmed by Alcian blue and fast red staining of the sagittal-sectional tissue from the hind limbs (inset, upper panel) at E18.5. (n = 10 samples for each genotype). p: patella, f: femur, t: tibia, and fi: fibula. (B) Immunohistochemical expression analysis of RAB23 in the Wt sagittal-sectional tissues of the hind limbs obtained from E14.0 and E15.5 embryos (lower panel). Expression analysis shows that RAB23 is expressed in the developing patella, patella tendon, quadriceps tendon, rectus femoris tendon, and vastus medialis tendon. RAB23 expression is also observed in the meniscus and articular cartilage. To highlight the cartilage cells of the patella, sagittal sections from E14.0 and E15.5 hind limbs are stained with Alcian blue and fast red (upper panel). (n = 3 samples for each age). p: patella, f: femur, and t: tibia. Scale bar: 200 μm.*
## RAB23 expression analysis in the developing patella
To understand the expression of RAB23 in the developing patella, we performed immunohistochemical (IHC) staining on the sagittal sections of hind limbs obtained from E14.0 and E15.5 WT mice. We found that RAB23 was highly expressed in the future patella region at E14.0 (Figure 1B). At both E14.0 and E15.5 developmental stages, RAB23 is widely expressed in the patellar tendon, cruciate ligaments, in the meniscus, articular cartilage, quadriceps tendon and muscles, rectus femoris tendon, and vastus medialis tendon (Figure 1B). We have validated the specificity of RAB23 antibody by IHC staining on the sagittal sections of hind limbs obtained from E14.0 and E15.5 Rab23 −/− mice (Supplementary Figure S2).
## Scleraxis and SOX9 expression are affected in the patella musculoskeletal structure in Rab23
−/−
mice
To understand how RAB23 influences patella development, we examined the expression and co-expression of SOX9 and Scx in chondroprogenitor cells in Wt and Rab23 −/− mice (Figure 2). Co-expression analysis by IHC staining revealed that RAB23 deficiency caused the ablation of patella progenitor cells (Figure 2A). Wt controls showed SOX9 and Scx double-positive patella progenitor cells at E14.0, and the patella already developed at E15.5 were about to separate from the femur (Figure 2A).
**FIGURE 2:** *RAB23 deficiency failed to specify Scx and SOX9 double-positive patella progenitor cells. (A, B) Immunohistochemical (A) and in situ
(B) expression analysis of Scleraxis (Scx) and SOX9 in the Wt and Rab23
−/−
mice sagittal-sections of the hind limbs obtained from E14.0 and E15.5 embryos. Wt samples show the expression of Scx and SOX9 double-positive patella progenitor cells, while Rab23
−/−
samples fail to express Scx and SOX9 double-positive progenitor cells (A). In Wt samples, Scx shows robust expression in the future patella site (E14.0) and shows expression in the patella tendon, quadriceps tendon, rectus femoris, and vastus medialis tendon (A, B). Scx expression in Rab23
−/−
samples is missing in the future patella site at E14.0 (blue arrow) and is barely detectable in the malformed patella tendon-like structure and quadriceps tendon (A, B). At E15.5 in Rab23
−/−
samples, Scx remained undetectable at the patella site (blue arrow and black arrow) and shows rudimentary expression in the patella tendon and in the quadriceps tendon (A, B). Analysis of SOX9 expression shows that in Wt samples, SOX9 was expressed in the future patella site at E14.0 and in the patella at E15.5 (A, B). In Rab23
−/−
samples, SOX9 was initially barely expressed in the future patella site at E14.0 (white arrow and black arrow) but was abolished at E15.5 samples (white arrow) (A). While SOX9 showed uniform expression throughout the articular cartilage in the Wt femur toward the patella and tibia, Rab23
−/−
samples showed more SOX9 expressions in the femur head toward the imaginary patellofemoral site (A, B). To highlight the cartilage cells of the patella, sagittal sections from E14.0 and E15.5 hind limbs were stained with Alcian blue and fast red (A, upper panel). Black dotted oval shape indicates the future patella site (A). Black dotted marking indicates the end of the femur toward the patella (B) (n = 3 samples for each age and genotype). p: patella and f: femur. Scale bar: 200 μm.*
Detailed analysis by IHC and by in situ hybridization showed that Scx expression in the future patella site was lost and was poorly detectable in the rectus femoris tendon and vastus medialis tendon and in the patellar tendon in Rab23 −/− samples at E14.0 (Figures 2A, B). At E15.5, Scx was not detectable in the future patella site but showed disorganized expression in the patella tendon-like structure and rectus femoris tendon and vastus medialis tendon in Rab23 −/− samples (Figures 2A, B). However, in Wt samples, Scx showed robust expression in the patella site, in the patella tendon, in the rectus femoris tendon, and vastus medialis tendon (Figures 2A, B). In addition, we stained Wt and Rab23 −/− samples with COL1A1 at E15.5 and E17.5 to understand whether the disrupted expression of Scx in Rab23 −/− samples could show an effect on subsequent maturation of rectus femoris tendon and vastus medialis tendon. The results showed that COL1A1 in Rab23 −/− samples were expressed poorly and failed to mark these tendon structures at both the embryonic stage, while Wt samples showed a well-formed structure of these tendons (Supplementary Figure S3). Interestingly, the chondroprogenitor marker SOX9 very well marked the future patella and the boundary of articular cartilage of the femur in Wt samples, and in Rab23 −/− samples, SOX9 was poorly expressed in the patella site at E14.0 and lost at E15.5 (Figure 2). SOX9 was misexpressed in these samples and heterogeneously marked the boundary of the articular cartilage of the femur (Figure 2). The expression of SOX9 was more prominent toward the femur head in Rab23 −/− samples (Figure 2). We further stained E15.5 and E17.5 Wt and Rab23 −/− samples with COL2A1, a marker for chondrocytes. We found that consistent with SOX9 expression, COL2A1 showed an expanded expression in the articular cartilage toward the femur and tibia heads in Rab23 −/− samples (Supplementary Figure S4). Collectively, these data indicated that RAB23 is required for the specification of patella progenitor cells where RAB23 regulates the co-expression of scleraxis and SOX9 in the patella chondroprogenitor for patella anlagen formation. As Scx along with COL1A1 is the earliest marker for tendon and ligament formation, RAB23 regulation of Scx and COL1A1 suggests a role for RAB23 in knee joint tendonogenesis and ligamentogenesis.
## Cell proliferation in the developing patella in Wt and Rab23
−/−
mice
To understand cell proliferation during the development of patella, EdU pulsed Wt and Rab23 −/− mice knee samples were analyzed at E15.5 and E17.5, respectively. The results showed that the cells in the patella, in the quadriceps tendon, and in the boundary cell in Wt samples were highly proliferative (Figure 3A). On the contrary, Rab23 −/− samples failed to develop a patella and it showed more proliferating cells toward the femur head where SOX9 and COL2A1 expression have been expanded (Figure 3A and Supplementary Figure S4). Sequential tissue sections of Wt knee samples revealed that during patella development, the joint-forming cells initially undergo robust proliferation at the interzone (data not shown) and later when the patellofemoral joint has been established remained proliferative at the vicinity of the patellofemoral joint (Figure 3A). No such patellofemoral joint was found in Rab23 −/− samples and instead of the quadriceps tendon, a malformed tendon-like structure was observed which represented reduced and aberrant EdU pulsed proliferative cells (Figure 3A). Here, we observed that the nuclear morphology of the interzone or boundary cells was flat and elongated in the Wt sample at E15.5. As Rab23 −/− mice failed to develop patella, no such interzone or boundary was observed. Therefore, the cells on the tip of the femur were morphologically irregular and showed different shapes (Figure 3B).
**FIGURE 3:** *Analysis of cell proliferation and the expression of GDF5 in the Wt and Rab23
−/−
patellofemoral samples. (A) EdU pulsed cell proliferation analysis in the patellofemoral Wt and Rab23
−/−
samples at E15.5 and E17.5. Images show during and after the separation of the patella from the femur, the boundary cells undergo robust cell proliferation in Wt samples at E15.5 and E17.5, (white arrows). On the contrary, no patella and patellofemoral joint were established in Rab23
−/−
samples, but showed an expansion of the femur head toward the presumptive patella site. We, therefore, compared cell proliferation at the end of the femur (A) in Wt and Rab23
−/−
samples and also compared the cell proliferation of quadriceps tendon (B) in Wt vs. tendon-like structure (b´) in Rab23
−/−
samples at E15.5 and E17.5. Counting of all cells (blue + EdU
+
cells) and the ratio of EdU
+
cells (EdU
+
cells/all cells) show that proliferative cells in the femur head increased non-significantly in Rab23
−/−
compared to Wt at E15.5 and E17.5. Counting of all cells (blue + EdU
+
cells) and the ratio of EdU
+
cells (EdU
+
cells/all cells) show that proliferative cells in the tendon-like structure decreased significantly in Rab23
−/−
compared to Wt at E15.5 and E17.5 (n = 3 samples for each genotype). p value ˂ 0.05 (*); p value ˂ 0.02 (**). Nuclei were stained with Hoechst (blue), EdU
+
cells (red), Qt: quadriceps tendon, p: patella, and f: femur. Scale bar: 100 μm. (B) Morphological observation of boundary cells of the femur toward the patella in Wt vs. boundary cells of the femur in Rab23
−/−
at E15.5. Images show the cell nuclear shape in the boundary cells is flat and elongated in Wt samples (inset), whereas Rab23
−/−
samples show the cell nuclear shape is morphologically irregular (inset). Nuclei (blue), bc: boundary cell. (C) Immunohistochemical analysis of GDF5 and SOX9 in Wt and Rab23
−/−
mice sagittal-sectional tissues of the hind limbs at E14.0 and E15.5. Wt samples at E14.0 show GDF5 expression in the interzone cells between the future patella and femur junction where GDF5 mostly co-localizes with SOX9. At E15.5, GDF5 is expressed at the femur and patella periphery closer to the patellofemoral joint. GDF5 is not expressed in Rab23
−/−
samples at E14.0 and shows poor expression in the femur head at E15.5 Rab23
−/−
samples. To highlight the cartilage cells of the patella, femur and interzone cell sagittal sections from E14.0 and E15.5 hind limbs were stained with Alcian blue and fast red (upper panel). (n = 3 samples for each age and genotype). p: patella, f: femur, and t: tibia. Scale bar: 200 μm.*
## Analysis of GDF5 expression in Wt and Rab23
−/−
boundary cells during patellofemoral joint formation
While the patella anlagen tend to separate from the femur by joint formation, joint-forming interzone (zone between the femur and patella anlagen) cells gradually lose their chondrocyte appearance and start the expression of Gdf5, Tapp3, Wnt9a, and Wnt4 as boundary marking genes (Eyal et al., 2015). To understand whether RAB23 influenced the expression of GDF5 that marked interzone cells during the establishment of patellofemoral joint, immunostaining analysis of the knee samples showed that at E14.0, GDF5 was expressed in the interzone cells in Wt samples where GDF5 mostly co-expressed with SOX9-positive cells (Figure 3C). At E15.5, when the patellofemoral joint has been established in Wt samples, the expression of GDF5 was found in the vicinity of the patellofemoral joint where robust proliferating cells have been observed (Figures 3A–C). At both embryonic stages, the shape of the interzone cells was found flat and elongated (Figure 3C). On the contrary, Rab23 −/− samples at E14.0–E15.5 expressed barely detectable GDF5, which failed to mark interzone cells at E14.0, and subsequently failed to develop a patellofemoral joint at E15.5 (Figure 3C).
## RAB23 regulates the co-expression of SOX9 and Scx in the fibrocartilaginous entheses
Our results show that RAB23 regulates the co-expression of SOX9 and Scx in the patella progenitors during their specification. SOX9 and Scx double-positive cells contribute to the establishment of a bridge between cartilages and tendons/ligaments, known as entheses (Sugimoto et al., 2013). A study showed GDF5-expressing cells giving rise to the fibrocartilaginous cells under the regulation of Hh signaling which later forms the enthesis (Dyment et al., 2015). To understand whether RAB23 regulates the co-expression of SOX9 and Scx in the entheses, immunohistochemical expression analysis of SOX9 and Scx in Wt and Rab23 −/− samples at E14.0 and E15.5 revealed that Wt samples predominantly co-expressed SOX9 and Scx in the enthesis between the quadriceps tendon and the patella and also the enthesis that formed between the patella tendon and the tibia at both the embryonic stages (Figure 4A). In Rab23 −/− samples, the co-expression of SOX9 and Scx was missing at both the embryonic stages except at the junction that formed between the patella tendon-like structure and tibia, which gave rise to malformed enthesis at E15.5 (Figure 4A). We have further examined the expression of Sox9 and Scx in the fibrocartilaginous entheses by in situ hybridization. Our result reaffirmed the robust expression of Sox9 and Scx in these structures in Wt samples at E14.0 and E15.5, whereas Rab23 −/− samples showed a reduced expression of these markers at both embryonic stages (Figure 4B).
**FIGURE 4:** *Scleraxis, SOX9, and GLI1 expression analysis during enthesis formation. (A, B) Immunohistochemical and in situ expression analysis of SOX9 and Scx in the Wt and Rab23
−/−
mice sagittal sections of the hind limbs obtained from E14.0 and E15.5 embryos. In Wt samples, both these markers are predominantly co-expressed in the junction between the quadriceps tendon and the patella (yellow arrow) (A) and also the patella tendon and the tibia (yellow and black arrows) at both the embryonic stages (A, B). In Rab23
−/−
samples, since no patella was formed, co-expression of SOX9 and Scx is missing (white arrows) (A) except that the junction between the patella tendon-like structure and tibia gives rise to malformed entheses at E15.5 (white and black arrows) (A, B). To highlight the cartilage cells of the patella, sagittal sections from E14.0 and E15.5 hind limbs are stained with Alcian blue and fast red (upper panel) (n = 3 samples for each age and genotype). p: patella, pt: patellar tendon, f: femur, and t: tibia. Scale bar: 200 μm. (C, D) Immunohistochemical and in situ expression analysis of Hh (Hedgehog) component Gli1 in the Wt and Rab23
−/−
mice sagittal-sectional tissues of hind limbs are obtained from E14.0 and E15.5 embryos. In Wt samples, Gli1 is predominantly expressed in the future patella, patella tendon, quadriceps tendon, rectus femoris, and vastus medialis tendon and in the meniscus at both the embryonic stages (C, D). In Rab23
−/−
samples, Gli1 is weakly expressed throughout the E14.0 and E15.5 tissues and gives rise to the malformed entheses (black arrows) (C, D). Black dotted marking indicates the end of the femur toward the patella (D) (n = 3 samples for each age and genotype). p: patella, pt: patellar tendon, f: femur, and t: tibia. Scale bar: 200 μm.*
## Analysis of GLI1 expression in Wt and Rab23
−/−
entheses
We have previously reported that RAB23 regulates GLI1 during early calvarial bone and suture development (Hasan et al., 2020). Other studies on knee and synovial joint formation revealed that GLI1-positive cells contribute to the formation of entheses and ablation of Hh signaling in the tenocyte impairs matrix organization in the enthesis (Liu et al., 2012; Liu et al., 2013; Breidenbach et al., 2015; Schwartz et al., 2017). To understand whether RAB23 could show an effect on GLI1 expression during the fibrocartilaginous entheses formation around the knee, we analyzed GLI1 expression by IHC (Figure 4C) and by in situ hybridization (Figure 4D) in the Wt and Rab23 −/− samples at E14.0 and E15.5, respectively. The result showed that GLI1 expression marked the fibrocartilaginous entheses that formed between the quadriceps tendon and patella and tibia and patella tendon in Wt samples. However, Rab23 −/− samples developed only one enthesis between the patellar tendon-like structure and the tibia at both embryonic stages (Figures 4C, D). Analysis of GLI1 in the enthesis in Rab23 −/− samples revealed that GLI1 expression was weak and diffuse (Figures 4C, D). Concomitant to these findings, we found that GLI1 showed poor expressions at the patellar tendon-like structure and in the fibrocartilaginous structure of the cruciate ligaments in Rab23 −/− samples (Figures 4C, D).
## Aberrant expression of TGFβR2 and phospho-pErk44/42 in the Rab23
−/−
patellofemoral sample
TGFβ signaling has been shown to positively regulate Scx expression in early mouse limb explants and could direct mouse mesodermal stem cells toward the tendon lineage (Havis et al., 2014). TGFβ signaling is critical during patella bone initiation as the conditional knockout of Tgfβr2 in mouse early limb mesenchymal cells cause failure of Sox9 and Scx double-positive patella progenitor specification (Eyal et al., 2015). TGFβ signaling also has been implicated in committing mouse mesodermal stem cells toward the tendon lineage (Havis et al., 2014). However, in mice, the FGF/ERK MAPK pathway negatively regulates Scx expression in undifferentiated limb mesodermal cells (Havis et al., 2014). Our previous study showed that a deficiency of RAB23 caused aberrant FGF signaling (Hasan et al., 2020).
Here, we showed that Rab23 −/− mice failed to specify patella progenitors and largely affected the Scx expression. We then try to understand whether RAB23 deficiency could affect the expression of TGFβR2 and phospho-pErk$\frac{44}{42.}$ We analyzed TGFβR2 and phospho-pErk$\frac{1}{2}$ ($\frac{44}{42}$) expression in Wt and Rab23 −/− patellofemoral samples at E14.0 and E15.5, respectively. We found that TGFβR2 was expressed in the future patella site, in the patella tendon, quadriceps tendon, entheses, and in the cruciate ligaments in Wt samples at E14.0 and E15.5 (Figure 5A). In contrast, Rab23 −/− patellofemoral samples showed aberrant and rudimentary TGFβR2 expression in these embryonic stages (Figure 5A). Analysis of phospho-pErk$\frac{44}{42}$ in Wt samples showed its expression in the patella tendon and most prominently in the patellofemoral junction except cruciate ligaments, and the expression was also observed around the edge of the femur head except that the patella forming site at E14.0 (Figure 5A). At E15.5, the phospho-pErk$\frac{44}{42}$ expression persisted in the boundary cells of patellofemoral structure, and the expression was also observed beneath the quadriceps tendon (Figure 5A). Rab23 −/− patellofemoral samples showed aberrant and reduced expression of phospho-pErk$\frac{44}{42}$ in both embryonic stages (Figure 5A).
**FIGURE 5:** *Analysis of TGFβR2, phospho-pErk44/42, and BMP signaling during patella, tendonogenesis, and ligamentogenesis. (A) Immunohistochemical expression analysis of TGFβR2 and phospho-pErk44/42 in sagittal sections of the hind limbs obtained from E14.0 and E15.5 Wt and Rab23
−/−
embryos. In Wt samples, TGFβR2 is expressed in the patella site, patella tendon, quadriceps tendon, rectus femoris, and vastus medialis tendon and also in the cruciate ligaments in Wt samples. In Rab23
−/−
samples, TGFβR2 expression remained rudimentary and aberrant at both the embryonic stages analyzed. The Wt sample at E14.0 shows the expression of phospho-pErk44/42 in the patellofemoral junction except for the cruciate ligament. At E15.5, phospho-pErk44/42 shows expression in the boundary cells of the patellofemoral structure and also beneath the quadriceps tendon. This expression was altered in Rab23
−/−
samples (n = 3 samples for each age and genotype). p: patella, f: femur, and t: tibia. Scale bar: 200 μm. (B) Immunohistochemical expression analysis of BMP signaling components BMP4, BMPR1, and pSMAD 1/5/8 in sagittal sections of the hind limbs obtained from E14.0 and E15.5 Wt and Rab23
−/−
embryos. BMPR1, BMP4, and pSMAD 1/5/8 expression images show that all these markers are expressed in the patella site, patella tendon, quadriceps tendon, rectus femoris, and vastus medialis tendon (yellow arrow) in E14.0 and E15.5 samples. In Rab23
−/−
samples, BMPR1, BMP4, and pSMAD 1/5/8 expressions are barely detectable in these structures at E14.0 (white arrow) and show expression mostly in the femur head toward the rectus femoris and vastus medialis tendon (white arrow) at E15.5. To highlight the structural protein in the tendon and ligament cells, sagittal sections from E14.0 and E15.5 hind limbs were stained with Tenascin-C (lower panel). Dotted circle indicates the future patella site. (n = 3 samples for each age and genotype). p: patella, f: femur, and t: tibia. Scale bar: 200 μm.*
## Aberrant BMP4/BMPR1/pSmad1/5/8 expression during patella formation in Rab23
−/−
mice
As BMP signaling is known to regulate patella formation (Eyal et al., 2015; Eyal et al., 2019), we analyzed BMP4, BMPR1, and pSmad$\frac{1}{5}$/8 expression in Wt and Rab23 −/− samples at E14.0 and E15.5. In Rab23 −/− samples, we found a loss of expression of BMPR1, BMP4, and pSmad$\frac{1}{5}$/8 in the patella progenitor cells (Figure 5B). Also, these proteins were barely expressed in the patellar tendon-like structure and in the quadriceps tendon-like structures in Rab23 −/− samples (Figure 5B).
## RAB23 deficiency causes aberrant patella tendon and cruciate ligament formation
To understand whether RAB23 shows the regulation of tenocyte in the development of the patella tendon and cruciate ligament, we analyzed the expression of Scx, an early marker for tendon and ligament progenitors, and Tenascin-C, a structural matrix protein marker for tendon and ligament in Wt and Rab23 −/− samples at E14.0 and 15.5. The results showed that Rab23 −/− samples represented reduced and disorganized scleraxis expression in the cruciate ligament (Figure 6A). During the analysis of the structural protein Tenascin-C, a downstream target gene of Scx revealed that Tenascin-C was poorly expressed in the cruciate ligament and in the meniscus in Rab23 −/− samples (Figure 6A). The patella tendon, which connected the patella to the tibia, showed robust expression of Scx in Wt samples. In Rab23 −/− samples, as the patella was not formed, the patella tendon also failed to develop; instead, a ligament-like structure connected the femur to the tibia (Figure 6A). Scx and Tenascin-C expression in this structure was found aberrant and disorganized. We further aimed to follow the development of this structure including the cruciate ligament. Here, we analyzed the expression of COL1A1, another matrix protein of tenocytes, which provides structural integrity to the cruciate ligament and the patella tendon. The results show that Rab23 −/− mice at E15.5 and E17.5 failed to mark the cruciate ligament by COL1A1 and the patella tendon-like structure was significantly thin and malformed. The Wt samples showed robust expression of COL1A1 and marked very well the patella tendon and cruciate ligament (Figure 6B). These results collectively indicate that RAB23 regulates tendonogenesis and ligamentogenesis of the knee.
**FIGURE 6:** *RAB23 regulates tendonogenesis and ligamentogenesis and cell proliferation in the patella tendon and cruciate ligaments. (A) Immunohistochemical expression analysis of early tenocyte-expressing marker Scx and structural protein marker Tenascin-C in the Wt and Rab23
−/−
mice in the sagittal sections of hind limbs obtained from E14.0 and E15.5. Wt samples show Scx and Tenascin-C expression in the patella tendon at both the embryonic stages, where Tenascin-C confirms well-developed patella tendon. Rab23
−/−
samples at E14.0 shows less Scx expression in the patella tendon-like structure, and as Rab23
−/−
samples at E15.5 failed to develop patella and patella tendon, a musculostructure connected the femur to the tibia (arrow). Scx is expressed in this structure; however, subsequent Tenascin-C expression revealed that musculostructures are disrupted in Rab23
−/−
samples. Similar to patella tendon, analysis of Scx and Tenascin-C expressions in the cruciate ligaments show that in Wt samples, both markers are expressed well to mark the cruciate ligaments. However, both markers failed to mark cruciate ligaments in Rab23
−/−
samples. To highlight the cartilage cells of the patella, patella tendon sagittal sections from E14.0 and E15.5 hind limbs were stained with Alcian blue and fast red (upper panel) (n = 3 samples for each age and genotype). f: femur and t: tibia. Scale bar: 200 μm. (B) Immunohistochemical expression analysis of SOX9 and structural integrity protein of tendon and ligaments COL1A1 in the Wt and Rab23
−/−
mice sagittal-sectional tissues of hind limbs obtained from E15.5 and E17.5. Wt samples show COL1A1 expression that marks the cruciate ligament and patella tendon at both the embryonic stages (yellow arrow). However, Rab23
−/−
samples show COL1A1 expression that barely detected cruciate ligament at E15.5 (white arrow) and failed to demonstrate the cruciate ligaments at E17.5 (white arrow). Rab23
−/−
samples failed to develop patella and showed weaker COL1A1 expression in the ligament-like structure that connects the femur and the tibia. (n = 3 samples for each age and genotype). f: femur and t: tibia. Scale bar: 200 μm. (C) EdU pulsed cell proliferation analysis of patella tendon (A) and patella tendon-like structure (a´) in Wt and Rab23
−/−
samples at E15.5 and E17.5. Wt samples at both the embryonic stages show significantly more cell proliferation in the patella tendon (A) compared to patella tendon-like (a´) structure in Rab23
−/−
samples as the counting of all cells (blue + EdU + cells) and the ratio of EdU
+
cells (EdU
+
cells/all cells) are higher in Wt samples. Rab23
−/−
patella tendon-like structure is malformed at both E15.5 and E17.5 samples. Further observations by Alcian blue and fast red staining revealed that Rab23
−/−
samples fail to develop cruciate ligaments, therefore, EdU-positive cells in Rab23
−/−
samples show an irregular pattern of cell proliferation compared to Wt samples in the junction between tibia and femur (B) (white arrow). (n = 3 samples for each age and genotype). Nuclei were stained with Hoechst (blue), EdU
+
cells (red). Statistical significance was defined as p value ˂ 0.05 (*) and p value ˂ 0.02 (**). f: femur and t: tibia. Scale bar: 100 μm.*
We have previously reported that RAB23 regulates cell proliferation during early calvarial bone and suture development (Hasan et al., 2020). To understand whether RAB23 could influence cell proliferation during patella tendon and ligament development, we analyzed EdU pulsed Wt and Rab23 −/− patella tendon samples at E15.5 and E17.5 (Figure 6C). The patella tendon in Wt samples showed robust cell proliferation in E15.5 and E17.5. However, Rab23 −/− samples developed a tendon-like structure instead of a patella tendon which showed significantly less proliferative cells at both the embryonic stages (Figure 6C). Further observations of the region of cruciate ligaments in Rab23 −/− samples showed the pattern of proliferation in this region was aberrant than Wt samples (Figure 6C). While Wt samples showed proliferating cells in and around the cruciate ligaments, Rab23 −/− samples showed less proliferative cells in the region of cruciate ligaments; instead, Rab23 −/− samples showed proliferative cells closer to the articular cartilage site of the tibia (Figure 6C). Moreover, we showed that the cruciate ligaments in Rab23 −/− samples were poorly detectable at E15.5 samples (Figure 6C).
## Rab23
−/−
mice show multiple skeletal patterning defects
We further analyzed the skeleton of Rab23 −/− mice and compared them with Wt littermates to understand the global effect of RAB23 deficiency on skeletal development and patterning. Here, we found that Rab23 −/− mice show multiple axial and appendicular skeletal patterning defects (Figure 7). Our results demonstrated that Rab23 −/− mice show the craniofacial bones, including parietal, occipital, and palatine bones, are misshaped (Table 1) and showed shorter mandibular bones including malformed coronoid, condylar, and angular processes (Figure 7B). The Meckel’s cartilage in Rab23 −/− mice are largely missing (Figure 7B). Analysis of the vertebral column showed neck curvature defect, scoliosis (Figures 7A–C), fused vertebral column, defective curvature of the ribs, fused ribs, bifid ribs, and bifurcated sternum (Figures 7D–G). In addition to that, Rab23 −/− mice showed malformed deltoid tuberosity (Supplementary Figure S5) and defective joint morphogenesis (Supplementary Figure S6).
**FIGURE 7:** *Rab23
−/−
mice show diverse skeletal phenotypes. (A) Alizarin red (bone) and Alcian blue (cartilage) staining of whole WT and Rab23
−/−
mice at E18.5 showing the craniofacial, axial, and appendicular skeleton except for the fore and hind limbs ($$n = 5$$ for each age and genotype). (B) Alizarin red, Alcian blue staining, and µ-CT images of the mandible of Wt and Rab23
−/−
mice at E18.5. The mandibular processes; cor: coronoid process, con: condylar process, and ang: angular processes are poorly developed in Rab23
−/−
mice. Rab23
−/−
mandibles are smaller than Wt samples ($$n = 6$$ for each age and genotype). Statistical significance was defined as p value ˂ 0.001 (***). (C–E) μ-CT images of the vertebral column of Wt and Rab23
−/−
mice at E18.5. Rab23
−/−
mice show neck flexure defect (C. arrow), scoliosis (D), and fused vertebrae thoracic T6-T8 (E, arrow). Scale bar: 1 mm (C, D). (F, G) Alizarin red, Alcian blue staining, and µ-CT images of ribs of Wt and Rab23
−/−
mice at E18.5. Rab23
−/−
mice show split sternum (F, arrow) and bifid ribs (F and G, arrowhead) ($$n = 5$$ for each age and genotype). Scale bar: 1.5 mm (E), 1 mm (F), and 2 mm (G).* TABLE_PLACEHOLDER:TABLE 1
## Discussion
In this study, we demonstrate that the small GTPase protein RAB23 plays a critical role in knee and knee joint development. The knee bone, also known as the patella, functions during movement: providing structural support and stability to the knee joint (Lennox et al., 1994; Sarin et al., 1999). Patella is the largest sesamoid bone in mammals known to originate from Scx and SOX9 double-positive patella chondroprogenitor cells (Eyal et al., 2015; Eyal et al., 2019). We show that RAB23-deficient mice fail to develop patella and that missing patella in Rab23 −/− mice was not due to the programmed cell death (Supplementary Figure S1). We demonstrate that apart from SOX9 expression, RAB23 deficiency broadly misregulated Scx expression, which is essential for patella progenitor, patella tendon, quadriceps tendon, cruciate ligament formation, and their entheses formation and at the same time establishment of the patellofemoral joint. Our study revealed that SOX9 and Scx double-positive patella chondroprogenitor populations were lost in the patella site in Rab23 −/− mice (Figure 2). Since no patella anlagen formed in Rab23 −/− samples, the expansion of chondrogenesis has been observed toward the femur head (Figure 3 and Supplementary Figure S4). The missing patella also has been noticed in mice with conditional inactivation of Sox9 (Sugimoto et al., 2013).
After the initiation of patella anlagen, the mechanical load is required for patellofemoral joint formation. The autosomal recessive mutation in mdg (muscular dysgenesis) mice, which lack contractility of skeletal muscles, results in a failure of patellofemoral joint development with the patella remains in part of the femur (Pai, 1965a; Pai, 1965b; Eyal et al., 2015). We observed that while collecting the embryos at E18.5, Wt embryos could stretch their hind limbs, however, Rab23 −/− mice hind limbs remained static. Similarities between mdg mutant mice and Rab23 −/− mice may suggest that lack of skeletal muscle contraction could contribute to the patellofemoral phenotypes observed in Rab23 −/− mice. Defective joint formation also has been reported in TGFβ2 deficient mice (Spagnoli et al., 2007; Wang et al., 2014; MacFarlane et al., 2017). Patellofemoral joint establishment also requires higher cell proliferation in the boundary cells and in the interzone cells, where the cells gradually lose their chondrocyte characteristics and become flat and elongated (Eyal et al., 2015). Such events were observed missing in Rab23 −/− samples. Also, Rab23 −/− samples failed to organize GDF5-expressing boundary cells (Figure 3). Scx is known as the earliest marker for tendon and ligament development (Schweitzer et al., 2001; Kuo and Tuan, 2008). We found that Scx, along with matrix structural proteins COL1A1 and Tenascin-C, is largely affected in Rab23 −/− mice which rendered defective tendonogenesis, ligamentogenesis, and joint formation (Figure 6 and Supplementary Figure S3). FGF/ERK MAPK signaling, which is implicated as a positive regulator of SCX expression in chick undifferentiated limb cells, functions negatively on Scx expression in mouse undifferentiated limb mesodermal cells (Havis et al., 2014; Havis et al., 2016). In mouse patellofemoral samples, we find that RAB23 deficiency affected FGF/ERK expression (Figure 5). Our previous study also showed that RAB23 deficiency affects FGF/ERK signaling during early mouse calvarial bone and suture development (Hasan et al., 2020). A study showed that ectodermal signals cause the induction of tendon progenitors in the lateral plate mesoderm (LPM), and subsequently, LPM gives rise to the limb tendons which are Scx positive (Schweitzer et al., 2001). Upon induction, TGFβ signaling is required for the maintenance of tendon progenitors, and thus, Tgfβ2 −/−; Tgfβ2 −/− mice failed to develop limb tendons (Pryce et al., 2009). Here, we suggest that similar to TGFβ signaling, RAB23 is required for the maintenance of tendon progenitors.
The TGFβ2 signaling pathway plays a pivotal role during early fetal and embryonic development in mice (Sanford et al., 1997; Dunker and Krieglstein, 2002; Oka et al., 2007). Our study showed that TGFβR2 is expressed in the future patella site and in all the tendon and ligaments in Wt patellofemoral samples, and in Rab23 −/− samples, TGFβR2 expression was rudimentary and aberrant (Figure 5). This phenomenon suggests that RAB23 might regulate Scx expression through TGFβ2 signaling. BMP signaling is required for the growth and differentiation of patella progenitors to form patella anlagen, and subsequently, patella anlagen separates from the femur by mechanical stimuli from patella tendon and quadriceps tendon and incorporates into the patella tendon by establishing the patellofemoral joint. Patellofemoral joint formation requires the expression of genes Gdf5 and Tnmd that marks the interzone (Eyal et al., 2015; Eyal et al., 2019). Hedgehog signaling has been shown to be essential for limb patterning and growth plate chondrocyte proliferation and differentiation (Long et al., 2001). GLI1, which is the readout of Hh signaling, is expressed in the fibrocartilage junction, also known as enthesis (Liu et al., 2012) and is required for the differentiation, establishment, and maturation of entheses (Liu et al., 2013; Felsenthal et al., 2018; Zhang et al., 2020). A study showed that tendon-bone attachment cells are bi-fated and activates a mixture of tenocyte and chondrocyte transcriptomes (Kult et al., 2021). The establishment of such entheses requires Scx and SOX9 double-positive progenitors (Sugimoto et al., 2013), and the function of *Scx is* further extended as it is also required for the maturation of the entheses (Yoshimoto et al., 2017). The disruption of GLI1, Scx, and SOX9 expressions in Rab23 −/− mice could explain why the musculoskeletal system was defective in these mice.
Our results suggest that RAB23 is required for the specification of patella chondroprogenitor cells, which co-expressed SOX9 and scleraxis (Figure 2). Since RAB23 is necessary for TGFβR2 and scleraxis expression during early embryonic stages, it, therefore, contributes to tendonogenesis and ligamentogenesis (Figures 5, 6). RAB23 modulation of GLI1, SOX9, and scleraxis expression collectively showed a role of RAB23 in the formation of entheses, also known as the musculoskeletal bridge (Figure 4). Meanwhile, BMP signaling is required for the subsequent growth and development of the patella anlagen to patella, and here, we showed that RAB23 deficiency largely affected the expression of BMP signaling components (Figure 5). Interestingly, a study on mice showed that limb-specific ablation of BMP2 can initiate patella bone formation but resulted in patella bone hypoplasia (Eyal et al., 2019). However, limb-specific ablation of BMP4 mice resulted in patella aplasia (Eyal et al., 2015; Eyal et al., 2019). These findings collectively indicate that BMP2 is required for the growth of patella bone and BMP4 is required for patella progenitor differentiation (Eyal et al., 2019). Since BMP2 and BMP4 may play downstream roles in TGFβ2 signaling during patella formation, we suggest that RAB23 may regulate BMP signaling either directly or indirectly via TGFβ signaling. The rationale behind the latter hypothesis is that RAB23, as a small GTPase of membrane trafficking protein, might be involved in TGFβR2 regulation by cargo turnover, relaying signals, receptor recycling, or degradation. Such function of RAB23 in the regulation of TGFβ2/BMP signaling might not be limited to just patella development but also the broader spectrum of musculoskeletal development. Since RAB23 and TGFβ2 signaling both regulate Scx expression to give rise to the patella progenitor cells and subsequent common steps in the development of the patella and surrounding tendons and ligaments, we compared the wider skeletal patterning phenotypes of Tgfβ2 −/− and Rab23 −/− mice (Sanford et al., 1997). They showed great similarities. Tgfβ2 −/− mice exhibit multiple axial and appendicular skeletal patterning defects including kyphoscoliosis, fused vertebral column, abnormal curvature of the ribs, fused ribs, bifid ribs, and bifurcation of the sternum in mice (Sanford et al., 1997; Oka et al., 2007). Rab23 −/− mice replicate all these specific skeletal phenotypes (Figure 7; Table 1) including malformed deltoid tuberosity (Supplementary Figure S5) and defective joint morphogenesis (Supplementary Figure S6). Our findings and phenotypic similarities between Tgfβ2 −/− and Rab23 −/− mice highlight the possible cross-talk between RAB23 and TGFB2 signaling during musculoskeletal development and patterning.
## 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.
## Ethics statement
This animal study was reviewed and approved by the Helsinki University Hospital, and the Southern Finland Council Animal Welfare and Ethics committee.
## Author contributions
MH, AK, KA, MT, and DR designed the experiments. MH, AK, KA, MT, and DR generated and processed the mice. MH, AK, and DR wrote the manuscript. MH, AK, KA, MT, and DR performed the experiments. MH performed μ-CT analysis. DR conceived the study and supervised the experimental design and interpretation. All authors were involved in experimental design and in drafting and approving the manuscript.
## 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.2023.1049131/full#supplementary-material
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|
---
title: 'The correlation of muscle quantity and quality between all vertebra levels
and level L3, measured with CT: An exploratory study'
authors:
- Jona Van den Broeck
- Martine J. Sealy
- Carola Brussaard
- Jasmijn Kooijman
- Harriët Jager-Wittenaar
- Aldo Scafoglieri
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC9996002
doi: 10.3389/fnut.2023.1148809
license: CC BY 4.0
---
# The correlation of muscle quantity and quality between all vertebra levels and level L3, measured with CT: An exploratory study
## Abstract
### Introduction
In patients with cancer, low muscle mass has been associated with a higher risk of fatigue, poorer treatment outcomes, and mortality. To determine body composition with computed tomography (CT), measuring the muscle quantity at the level of lumbar 3 (L3) is suggested. However, in patients with cancer, CT imaging of the L3 level is not always available. Thus far, little is known about the extent to which other vertebra levels could be useful for measuring muscle status. In this study, we aimed to assess the correlation of the muscle quantity and quality between any vertebra level and L3 level in patients with various tumor localizations.
### Methods
Two hundred-twenty Positron Emission Tomography (PET)-CT images of patients with four different tumor localizations were included: 1. head and neck ($$n = 34$$), 2. esophagus ($$n = 45$$), 3. lung ($$n = 54$$), and 4. melanoma ($$n = 87$$). From the whole body scan, 24 slices were used, i.e., one for each vertebra level. Two examiners contoured the muscles independently. After contouring, muscle quantity was estimated by calculating skeletal muscle area (SMA) and skeletal muscle index (SMI). Muscle quality was assessed by calculating muscle radiation attenuation (MRA). Pearson correlation coefficient was used to determine whether the other vertebra levels correlate with L3 level.
### Results
For SMA, strong correlations were found between C1–C3 and L3, and C7–L5 and L3 ($r = 0.72$–0.95). For SMI, strong correlations were found between the levels C1–C2, C7–T5, T7–L5, and L3 ($r = 0.70$–0.93), respectively. For MRA, strong correlations were found between T1–L5 and L3 ($r = 0.71$–0.95).
### Discussion
For muscle quantity, the correlations between the cervical, thoracic, and lumbar levels are good, except for the cervical levels in patients with esophageal cancer. For muscle quality, the correlations between the other levels and L3 are good, except for the cervical levels in patients with melanoma. If visualization of L3 on the CT scan is absent, the other thoracic and lumbar vertebra levels could serve as a proxy to measure muscle quantity and quality in patients with head and neck, esophageal, lung cancer, and melanoma, whereas the cervical levels may be less reliable as a proxy in some patient groups.
## 1. Introduction
Malnutrition and sarcopenia are highly prevalent in patients with cancer [1, 2]. These nutrition (-related) disorders are linked to a combination of reduced food intake, loss of muscle quantity and quality, with or without the loss of fat mass, and poor physical performance (3–5). Previous studies show that low muscle quantity and quality are firmly associated with poorer clinical outcomes in patients with cancer [2, 6, 7]. Patients with cancer with low muscle quantity and quality also have a higher risk of cancer-induced fatigue, lower quality of life, and mortality [1, 8, 9]. When chemotherapy treatment is given to patients with cancer, it is often based on the body surface area (BSA). However, the BSA does not sufficiently take into account the interpersonal variations of body composition in patients with cancer, which could result in a higher risk of toxicity and incomplete treatment (7, 10–12). Therefore, in patients with cancer, it is important to measure muscle quantity [6]. In addition, measuring muscle quantity is also an important part of evaluation of the nutritional status of the patient [5, 13].
To define muscle quantity, skeletal muscle cross-sectional area (SMA) and skeletal muscle index (SMI) can be measured with computed tomography (CT), a gold standard for body composition measurement [1]. The SMI shows the relative muscle quantity, as it is corrected for height (SMI = SMA/height2) [2]. For this purpose, the third lumbar vertebra level (L3) is used, as the SMA correlates strongly with the muscle mass of the whole body [13, 14]. It has also been shown that the levels above and below (±10 cm) L3 correlate well with the muscle mass of the entire body [14]. However, a whole body CT scan is not always available in patients with cancer [7, 15]. When the lumbar levels are not included in the CT scan image, for example in patients with head and neck cancer [7], it is unclear which vertebra levels can be used to estimate whole body muscle mass. In earlier studies in patients with head and neck cancer, the cervical level 3 (C3) and thoracic level 4 (T4) were used to measure muscles, and these levels showed a good correlation with L3 [7].
According to the European Working Group on Sarcopenia in Older People, muscle quality can be measured by muscle radiation attenuation (MRA), using CT images [5]. Muscle quality is defined as muscle strength or power per unit of muscle mass and is closely intertwined with muscle strength [16]. Intermuscular adipose tissue is an important factor underpinning muscle quality and also predicts muscle function [17]. The intermuscular adipose tissue is located within the muscle, under the fascia, and encompasses intramuscular fat and low-density lean tissue [18]. Muscle radiation attenuation closely correlates with direct measurements of muscle lipid content and therefore determines infiltration of fat into the muscle (19–21).
In addition, limited evidence regarding the correlation of muscle quantity and quality between vertebra levels other than L3 and the L3 level is available [22]. As a first step in the search for which other vertebra levels, other than L3, could be used to determine whole body muscle mass, we aimed to examine the correlations between all vertebra levels with L3 for muscle quantity and quality in a sample of patients with various tumor localizations.
## 2.1. Participants
Positron Emission Tomography CT (PET-CT) images of the participants were retrospectively extracted from medical records of the Radiology department of the University Hospital Brussels from December 2019 until February 2021. Patients aged ≥18 years with any of the following four localizations of newly diagnosed tumors were included: 1. head and neck cancer, 2. esophageal cancer, 3. lung cancer, and 4. melanoma. We excluded participants receiving treatment for current cancer at the time of the PET-CT scan and who had a previous diagnosis of cancer at another tumor localization. PET-CT images were included if they were performed between 2014 and February 2021. Sex, age (years), body weight (kg), body height (m), body mass index (BMI; kg/m2), cancer stage, and Charlson Comorbidity Index (CCI) [23] were retrieved from the patients’ medical chart.
## 2.2. Scanning procedure
The PET-CT images were performed with three different CT devices: Philips GEMINI TF TOF 64, SIEMENS Biograph20, and SIEMENS Biograph128. The patients were scanned helically with a slice thickness of 2 mm and 120 kilovoltage peak (kVp). An intravenous iodinated contrast agent was used in all patients, except for $15\%$ of the patients with a contra-indication for this contrast: i.e., the contrast was recently applied for another CT procedure in the short term or the patients had problems with their kidneys.
## 2.3. Image analysis
MIM software (Version 7.0.1) was used to process the images. The whole-body scan was uploaded, after which 24 points were selected manually in the sagittal plane by a researcher (JV), as shown in Figure 1. The researcher selected images based on the center of each vertebral body. With the Launch Workflow procedure, 24 transverse slices were taken at the chosen points. This procedure allows a consistent and precise image selection. The slices were used to contour the muscles, as shown in Figures 2–4. Trunk muscles included in the contouring were the psoas, paraspinal, and abdominal wall muscles [2]. In total, 12 examiners participated in this study to contour the muscles. In each slice, the muscles were measured twice. The two measurements were each performed by a different examiner, i.e., students from the Medical Imaging and Radiotherapeutic Techniques training program of Hanze University of Applied Sciences, Groningen, Netherlands, who were trained in muscle anatomy by an expert. During the process, the contouring by the examiners was regularly checked by this expert. Examiners were blinded to each other’s measurements and the characteristics of the patient. To contour the muscles, the Hounsfield units (HU) were set at a range lock between −29 and 150 HU [24]. After contouring, SMA and MRA were calculated with the MIM software program. To calculate SMI, SMA was corrected for squared height in meters (cm2/m2). SMA was recorded in cm2, SMI in cm2/m2, and MRA in HU.
**FIGURE 1:** *Twenty-four manually selected points on the vertebral column.* **FIGURE 2:** *Contouring of the muscles at cervical level 3.* **FIGURE 3:** *Contouring of the muscles at thoracic level 4.* **FIGURE 4:** *Contouring of the muscles at lumbar level 3.*
## 2.4. Statistical analysis
IBM SPSS statistics 26 was used to perform the statistical analyses. A Shapiro–Wilk test was performed to examine the normality of the distribution of the data. Normally distributed data are presented as mean and standard deviation (SD). Not normally distributed data are presented as median and interquartile range. An intraclass correlation coefficient (ICC) was calculated to analyze interrater reliability. When the data were not normally distributed, bootstrapping was applied to indicate whether the ICC was likely to be affected by the distribution of the data. With a high bootstrapping value (≥0.90), the ICC was not likely to be effected by the distribution of the data and the ICC value was accepted. When ICC values ranged between 0.0 and 0.20, the reliability was considered as slight, between 0.21 and 0.50 as poor, between 0.51 and 0.75 as moderate, between 0.76 and 0.90 as good, and 0.91 or above as excellent [25].
Next, Pearson correlation coefficients were calculated to assess whether the other levels of the spine correlated with the L3 level. Therefore, we took the average value of both examiners for each vertebra level. Finally, Pearson correlation coefficients were determined to analyze the correlation between all other levels with the L3 level, to study whether the tumor localization influenced the reliability. A Pearson correlation coefficient ≥0.70 is considered a strong correlation [25]. Post hoc power analyses, using G*Power, were performed to analyze the power for each correlation. Power of 0.80 or higher was considered sufficient. For all analyses, the level of significance was set at $p \leq 0.05.$
## 3. Results
In total, 220 patients, including 34 patients with head and neck cancer, 45 with esophageal cancer, 54 with lung cancer, and 87 with melanoma, were included. Characteristics of the included patients are shown in Table 1. The descriptive data for SMA, SMI, and MRA at all vertebral levels are shown in Table 2.
The ICC values for the interrater reliability of the SMA and MRA for all vertebra levels ranged from 0.95 to 1.00. All interrater reliability values are shown in Table 3. The power was 1.00.
**TABLE 3**
| Unnamed: 0 | SMA | SMA.1 | MRA | MRA.1 |
| --- | --- | --- | --- | --- |
| | Pearson correlation | Bootstrap [95% interval] | Pearson correlation | Bootstrap [95% interval] |
| C1 | 0.96 | 0.94–0.97 | 0.98 | 0.98–0.99 |
| C2 | 0.98 | 0.98–0.99 | 0.99 | |
| C3 | 0.99 | 0.99–1.00 | 1.00 | |
| C4 | 1.00 | 0.99–1.00 | 1.00 | |
| C5 | 1.00 | 0.99–1.00 | 1.00 | |
| C6 | 0.99 | 0.99–1.00 | 0.99 | |
| C7 | 0.97 | | 0.98 | 0.97–0.99 |
| T1 | 0.97 | | 0.99 | |
| T2 | 0.95 | 0.93–0.97 | 0.98 | |
| T3 | 0.95 | | 0.99 | |
| T4 | 0.96 | 0.92–0.97 | 0.99 | 0.98–0.99 |
| T5 | 0.98 | 0.96–0.99 | 0.98 | 0.96–0.99 |
| T6 | 0.98 | 0.97–0.99 | 1.00 | |
| T7 | 0.99 | 0.99–1.00 | 1.00 | 1.00–1.00 |
| T8 | 0.99 | 0.99–1.00 | 1.00 | 0.99–1.00 |
| T9 | 0.99 | 0.99–0.99 | 1.00 | |
| T10 | 0.99 | 0.99–0.99 | 1.00 | |
| T11 | 0.98 | 0.98–0.99 | 0.99 | |
| T12 | 0.98 | 0.98–0.99 | 0.99 | |
| L1 | 0.98 | 0.98–0.99 | 1.00 | |
| L2 | 0.99 | 0.98–0.99 | 1.00 | |
| L3 | 1.00 | 1.00–1.00 | 1.00 | |
| L4 | 0.99 | 0.99–1.00 | 0.99 | |
| L5 | 0.99 | 0.99–1.00 | 1.00 | |
The Pearson correlation coefficients between the other vertebra levels and L3 are shown in Table 4. All correlations for SMA, SMI, and MRA were statistically significant. For SMA, correlations ranged from $r = 0.49$ to $r = 0.95.$ Strong correlations were found between C1–C3 and L3, and C7–L5 and L3 ($r = 0.72$–0.95). For SMI, Pearson correlation coefficients ranged from $r = 0.49$ to $r = 0.93.$ Strong correlations were found between the levels C1–C2, C7–T5, T7–L5, and L3 ($r = 0.70$–0.93), respectively. For MRA, the correlation ranged from $r = 0.48$ to $r = 0.95.$ Strong correlations were found between T1–L5 and L3 ($r = 0.71$–0.95). The power was 1.00.
**TABLE 4**
| L3 | L3.1 | L3.2 | L3.3 | L3.4 | L3.5 | L3.6 | L3.7 | L3.8 | L3.9 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | SMA | SMA | SMA | SMI | SMI | SMI | MRA | MRA | MRA |
| | Pearson correlation | p-Value | Bootstrap [95% interval] | Pearson correlation | p-Value | Bootstrap [95% interval] | Pearson correlation | p-Value | Bootstrap [95% interval] |
| C1 | 0.77 | <0.001 | 0.70–0.82 | 0.71 | <0.001 | 0.61–0.78 | 0.61 | <0.001 | 0.53–0.71 |
| C2 | 0.77 | <0.001 | 0.71–0.82 | 0.71 | <0.001 | 0.61–0.79 | 0.63 | <0.001 | |
| C3 | 0.72 | <0.001 | 0.62–0.80 | 0.67 | <0.001 | 0.53–0.78 | 0.59 | <0.001 | |
| C4 | 0.49 | <0.001 | 0.33–0.65 | 0.49 | <0.001 | 0.27–0.67 | 0.58 | <0.001 | |
| C5 | 0.53 | <0.001 | 0.42–0.63 | 0.51 | <0.001 | 0.63–0.64 | 0.56 | <0.001 | |
| C6 | 0.62 | <0.001 | 0.52–0.70 | 0.57 | <0.001 | 0.45–0.68 | 0.48 | <0.001 | |
| C7 | 0.73 | <0.001 | | 0.68 | <0.001 | | 0.59 | <0.001 | 0.50–0.70 |
| T1 | 0.76 | <0.001 | | 0.70 | <0.001 | 0.62–0.77 | 0.71 | <0.001 | |
| T2 | 0.77 | <0.001 | 0.70–0.82 | 0.70 | <0.001 | 0.61–0.77 | 0.75 | <0.001 | |
| T3 | 0.82 | <0.001 | | 0.77 | <0.001 | 0.68–0.83 | 0.79 | <0.001 | |
| T4 | 0.86 | <0.001 | 0.82–0.89 | 0.81 | <0.001 | 0.75–0.86 | 0.81 | <0.001 | 0.76–0.85 |
| T5 | 0.79 | <0.001 | 0.74–0.83 | 0.72 | <0.001 | 0.65–0.78 | 0.82 | <0.001 | 0.77–0.86 |
| T6 | 0.74 | <0.001 | 0.69–0.79 | 0.65 | <0.001 | 0.58–0.72 | 0.83 | <0.001 | |
| T7 | 0.79 | <0.001 | 0.72–0.84 | 0.71 | <0.001 | 0.62–0.78 | 0.85 | <0.001 | 0.81–0.89 |
| T8 | 0.80 | <0.001 | 0.75–0.84 | 0.73 | <0.001 | 0.70–0.79 | 0.86 | <0.001 | 0.83–0.90 |
| T9 | 0.80 | <0.001 | 0.75–0.85 | 0.74 | <0.001 | 0.67–0.81 | 0.87 | <0.001 | |
| T10 | 0.85 | <0.001 | 0.80–0.89 | 0.81 | <0.001 | 0.74–0.87 | 0.87 | <0.001 | |
| T11 | 0.91 | <0.001 | 0.88–0.93 | 0.89 | <0.001 | 0.84–0.92 | 0.90 | <0.001 | |
| T12 | 0.92 | <0.001 | 0.89–0.94 | 0.90 | <0.001 | 0.85–0.93 | 0.92 | <0.001 | |
| L1 | 0.92 | <0.001 | 0.90–0.94 | 0.91 | <0.001 | 0.87–0.93 | 0.93 | <0.001 | |
| L2 | 0.95 | <0.001 | 0.93–0.96 | 0.93 | <0.001 | 0.90–0.95 | 0.95 | <0.001 | |
| L4 | 0.92 | <0.001 | 0.89–0.94 | 0.89 | <0.001 | 0.84–0.93 | 0.94 | <0.001 | |
| L5 | 0.92 | <0.001 | 0.89–0.93 | 0.89 | <0.001 | 0.85–091 | 0.88 | <0.001 | |
The correlations between the other levels and L3 per tumor localization are shown in Table 5. All p-values were significant (p ≤ 0.001) and the bootstraps confirmed the correlation values, except for the SMA and SMI at the level of C4–C6 in the patients with esophageal cancer. Level T4 is the uppermost level in the vertebral column that reached a strong correlation with L3 for SMA, SMI, and MRA in all tumor localizations. The power analysis shows that for the smallest group (patients with head and neck cancer, $$n = 34$$) the power was 0.80 for correlations of $r = 0.60$ and higher. For the largest group (patients with melanoma, $$n = 87$$) the power was 0.80 for all correlations.
**TABLE 5**
| Unnamed: 0 | Head and neck cancer L3 (n = 34) | Head and neck cancer L3 (n = 34).1 | Head and neck cancer L3 (n = 34).2 | Esophageal cancer L3 (n = 45) | Esophageal cancer L3 (n = 45).1 | Esophageal cancer L3 (n = 45).2 | Lung cancer L3 (n = 54) | Lung cancer L3 (n = 54).1 | Lung cancer L3 (n = 54).2 | Melanoma L3 (n = 87) | Melanoma L3 (n = 87).1 | Melanoma L3 (n = 87).2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | SMA | SMI | MRA | SMA | SMI | MRA | SMA | SMI | MRA | SMA | SMI | MRA |
| C1 | 0.60 | 0.47 | 0.87 | 0.69 | 0.62 | 0.64 | 0.76 | 0.73 | 0.76 | 0.78 | 0.74 | 0.35 |
| C2 | 0.71 | 0.62 | 0.86 | 0.57 | 0.48 | 0.80 | 0.76 | 0.70 | 0.74 | 0.78 | 0.74 | 0.39 |
| C3 | 0.78 | 0.72 | 0.89 | 0.63 | 0.51 | 0.70 | 0.81 | 0.74 | 0.77 | 0.65 | 0.64 | 0.30 |
| C4 | 0.64 | 0.61 | 0.74 | X | X | 0.79 | 0.70 | 0.64 | 0.66 | 0.44 | 0.50 | 0.43 |
| C5 | 0.47 | 0.46 | 0.76 | X | X | 0.62 | 0.52 | 0.45 | 0.64 | 0.54 | 0.57 | 0.45 |
| C6 | 0.70 | 0.63 | 0.70 | X | X | 0.49 | 0.59 | 0.67 | 0.46 | 0.62 | 0.59 | 0.42 |
| C7 | 0.72 | 0.57 | 0.71 | 0.49 | 0.52 | 0.61 | 0.64 | 0.59 | 0.53 | 0.77 | 0.75 | 0.56 |
| T1 | 0.74 | 0.60 | 0.82 | 0.58 | 0.58 | 0.74 | 0.69 | 0.61 | 0.81 | 0.79 | 0.74 | 0.60 |
| T2 | 0.61 | 0.43 | 0.85 | 0.63 | 0.62 | 0.74 | 0.83 | 0.76 | 0.84 | 0.86 | 0.82 | 0.66 |
| T3 | 0.68 | 0.54 | 0.83 | 0.76 | 0.73 | 0.80 | 0.88 | 0.81 | 0.87 | 0.86 | 0.84 | 0.74 |
| T4 | 0.82 | 0.73 | 0.85 | 0.79 | 0.74 | 0.80 | 0.87 | 0.80 | 0.87 | 0.85 | 0.83 | 0.77 |
| T5 | 0.76 | 0.66 | 0.88 | 0.71 | 0.64 | 0.84 | 0.82 | 0.77 | 0.86 | 0.76 | 0.70 | 0.76 |
| T6 | 0.71 | 0.58 | 0.88 | 0.73 | 0.67 | 0.88 | 0.81 | 0.76 | 0.87 | 0.67 | 0.56 | 0.78 |
| T7 | 0.77 | 0.67 | 0.91 | 0.76 | 0.71 | 0.89 | 0.80 | 0.74 | 0.88 | 0.74 | 0.64 | 0.79 |
| T8 | 0.75 | 0.64 | 0.91 | 0.72 | 0.66 | 0.85 | 0.83 | 0.80 | 0.88 | 0.78 | 0.71 | 0.84 |
| T9 | 0.74 | 0.62 | 0.91 | 0.75 | 0.70 | 0.87 | 0.86 | 0.83 | 0.90 | 0.76 | 0.71 | 0.82 |
| T10 | 0.81 | 0.71 | 0.90 | 0.78 | 0.76 | 0.92 | 0.91 | 0.88 | 0.90 | 0.82 | 0.78 | 0.83 |
| T11 | 0.88 | 0.81 | 0.93 | 0.84 | 0.83 | 0.91 | 0.93 | 0.91 | 0.92 | 0.91 | 0.90 | 0.86 |
| T12 | 0.86 | 0.79 | 0.94 | 0.88 | 0.87 | 0.93 | 0.93 | 0.91 | 0.95 | 0.92 | 0.91 | 0.89 |
| L1 | 0.90 | 0.86 | 0.95 | 0.91 | 0.90 | 0.92 | 0.90 | 0.89 | 0.96 | 0.93 | 0.92 | 0.91 |
| L2 | 0.92 | 0.87 | 0.97 | 0.93 | 0.92 | 0.93 | 0.90 | 0.85 | 0.97 | 0.97 | 0.96 | 0.92 |
| L4 | 0.90 | 0.88 | 0.98 | 0.87 | 0.84 | 0.93 | 0.88 | 0.79 | 0.95 | 0.93 | 0.92 | 0.92 |
| L5 | 0.87 | 0.84 | 0.94 | 0.86 | 0.83 | 0.88 | 0.90 | 0.85 | 0.85 | 0.92 | 0.91 | 0.87 |
## 4. Discussion
This is the first study to assess the correlation of muscle quantity and quality between all other vertebra levels and L3. For muscle quantity, i.e., SMA and SMI, most cervical, thoracic, and lumbar levels show a strong correlation with L3. Notably, in the group of patients with esophageal cancer, none of the cervical levels correlate strongly with L3 for SMA and SMI. For muscle quality, i.e., MRA, all thoracic and lumbar levels show a strong correlation with the muscle quality of L3, whereas the cervical levels do not. However, in patients with head and neck cancer, all levels, including the cervical, show a strong correlation with muscle quality at the L3 level. Also, in the patients with esophageal and lung cancer, some cervical levels show a strong correlation.
Our findings are in line with previous studies that determined the correlation between other vertebra levels and L3. For example, in patients with head and neck cancer, a strong correlation between the other lumbar levels and L3 was previously found [14, 26, 27]. In patients with various types of advanced cancer, only thoracic levels, T5, T8, T10, and T12, have been studied, and moderate correlations for SMI and MRA between T5, T8, T10, and L3 were found [14, 26, 27]. In patients with oral squamous cell carcinoma, the correlation between T12 and L3, was strong which is in agreement with the results of our study [28]. However, for level C3, results are ambiguous in head and neck cancer patients and C3 was reported to not correlate well with L3 in patients with low muscle mass [29]. In contrast, a strong correlation between the muscles at C3 and L3 in patients with head and neck cancer was found in our study. In patients with head and neck cancer, it is more difficult to measure the cervical muscles, because the tumor is located in the cervical region [26]. For example, when contouring the sternocleidomastoid muscle, the SMA may be overestimated because the lymph node stations are located around this muscle [30]. Doubling the SMA of the healthy sternocleidomastoid muscle to compensate for the lack of the SMA of the affected muscle can be considered, to avoid the muscle quantity being influenced by the tumor at the level of the affected sternocleidomastoid muscle [26]. Moreover, a study in patients with head and neck cancer showed no significant difference in the correlation between C3 and L3 when comparing a group of patients with head and neck cancer with healthy participants [26]. Unfortunately, we cannot explain why the cervical levels of the patients with esophageal cancer lacked correlation with L3 for muscle quantity. Further research is needed to identify determinants for the this correlation. Cervical MRA values in this study were more homogeneous for patients with melanoma compared to values for patients with other cancer types. This could explain the correlation between cervical levels and L3 being lower in the patients with melanoma compared to the other patients.
Our results confirm excellent interrater reliability of measuring SMA and MRA by CT scan analysis as found in previous research (31–33). Previous research demonstrated that longer time between measurements limits reliability. For example, when participants walk around for a while between the two measurements, the reliability for contouring the SMA was only acceptable [31]. In our study, muscle contouring was performed twice on the same CT image. Moreover, the HU values were set and the segmentation was performed semi-automatically. A factor that may influence reliability of MRA is the accuracy of the contouring of the muscles. If intramuscular fat is incorporated in the SMA due to incorrect contouring, this could negatively affect reliability of MRA. In our study, the HU values corrected the contouring of the muscles, to ensure that only muscle tissue was contoured.
The current study has some limitations. Firstly, in $85\%$ of our participants, intravenous contrast was used while taking the PET-CT. Previous research has demonstrated that the use of contrast fluid influences the SMI and MRA [32]. More research is needed to determine whether contrast fluid influences the correlation between different vertebral levels [32]. Secondly, while we have included a diverse group of patients with cancer with high incidence rates in the Belgian population [33], the sample size for each tumor localization group was small. Moreover, the proportion of women in our study was not large, due to using a convenience sample that reflects the distribution of sex in the patient populations. Therefore, more research with larger sample sizes and equal sex distribution is needed to confirm our conclusions. Thirdly, we were not able to correlate the vertebra levels to whole body muscle mass. Evaluation of whole body muscle mass requires complete inclusion of the arms in the scan, and unfortunately the diameter of the CT scan was set too small, based on the trunk, and therefore did not include the arms.
In the current study, we found that other levels are strongly correlated with L3. However, if a CT scan at the L3 level is not available the other thoracic and lumbar vertebra levels could serve as a proxy to measure muscle quantity in patients with head and neck-, lung-, esophageal cancer, and melanoma, whereas the cervical levels may be less reliable as a proxy in some patient groups. Future research is needed to develop prediction equations to estimate whole body muscle mass from the vertebra levels correlating well with the L3 level.
## 5. Conclusion
In patients with head and neck cancer, lung cancer, and melanoma, muscle quantity is strongly correlated between some cervical, and all thoracic and lumbar levels and L3. In esophageal patients, only the thoracic and lumbar levels are strongly correlated. For muscle quality, the cervical, thoracic, and lumbar levels and L3 are well correlated in the head and neck, esophageal, and lung patients, but in patients with melanoma the cervical levels do not correlate well with L3. If visualization of L3 on the CT scan is absent, we suggest that the other thoracic and lumbar vertebra levels could serve as a proxy to measure muscle quantity in patients with head and neck-, lung-, esophageal cancer, and melanoma, whereas the cervical levels may be less reliable as a proxy in some patient groups. Further research should determine whether our conclusions can be confirmed and that these levels can also be used to estimate whole body muscle mass by examining the correlation of these levels with whole body muscle mass.
## 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 the Commissie Medische Ethiek Brussel. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
JV: acquisition, analysis, and interpretation of data, drafting the work, provide approval for publication, and agrees to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. MS, HJ-W, and AS: design of the work, interpretation of data, revising the work, and agrees to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. CB: acquisition of data and revising the work. JK: analysis of data and revising the work. 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.
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|
---
title: 'Assessing causality between different risk factors and pulmonary embolism:
A Mendelian randomization study'
authors:
- Jian-ming Wei
- Yan-li Song
- Huan Zeng
- Wen-wen Yan
- Xue-bo Liu
journal: Frontiers in Cardiovascular Medicine
year: 2023
pmcid: PMC9996005
doi: 10.3389/fcvm.2023.1057019
license: CC BY 4.0
---
# Assessing causality between different risk factors and pulmonary embolism: A Mendelian randomization study
## Abstract
### Objectives
Mendelian randomization (MR) was used to estimate the causal relationship between body mass index (BMI), ever smoked, heart failure, alcohol intake frequency, inflammatory bowel disease (IBD), and pulmonary embolism (PE). This study aimed to investigate whether there is a causal relationship between BMI, the presence of smoking, heart failure, frequency of alcohol intake, IBD, and PE.
### Methods
Pooled data on PE from a published GWAS meta-analysis involving approximately 461,164 participants of European ancestry were selected. A publicly available pooled dataset of BMI [461,460], ever smokers [461,066], heart failure [977,323], IBD [75,000], and frequency of alcohol intake [462,346] was used from another independent GWAS. MR was performed using established analysis methods, including Wald ratios, inverse variance weighted (IVW), weighted median (WM), and MR-Egger. Also, the final expansion was validated with multivariate MR.
### Results
In the IVW model, genetically elevated BMI was causally associated with PE [OR = 1.002, $95\%$ CI (1.001, 1004), $$P \leq 0.039$$]. Cochran’s Q test was used to detect heterogeneity in the MR-*Egger analysis* ($$P \leq 0.576$$). Therefore, the effect of gene-level heterogeneity was not considered. In the MR analysis of other risk factors, we observed genes for ever smoking [IVW OR = 1.004, $95\%$ CI (0.997, 1.012)], heart failure [IVW OR = 0.999, $95\%$ CI (0.996, 1.001)], IBD [IVW OR = 1.000, $95\%$ CI (0.999, 1.001)], and frequency of alcohol intake [IVW OR = 1.002, $95\%$ CI (1.000, 1.004)] were not causally associated with PE. Analysis using multivariate MR expansion showed no causal effect of BMI on PE considering the effect of height as well as weight ($$P \leq 0.926$$).
### Conclusion
In European populations, a causal relationship exists between BMI and PE: increased BMI leads to PE. In contrast, ever smoking, heart failure, frequency of alcohol intake, and IBD are not directly associated with PE. There was no causal effect of BMI with PE in multivariate Mendelian randomized analysis.
## Introduction
Venous thromboembolism (VTE), including deep vein thrombosis (DVT) and pulmonary embolism (PE), affects nearly 10 million people of all ethnicity worldwide each year [1]. In Europe, 8–13 per 1,000 women aged 15–55 years and 2–7 per 1,000 men die from PE [2]. Therefore, early identification and active intervention of risk factors in patients with PE is essential in clinical practice. There is a need to explore the etiology and prevent the occurrence of PE.
Venous thromboembolism is a common venous thrombotic event, and its common risk factors include ever smoking and obesity (3–5). In addition, heart failure has been reported to increase the risk of PE [6]. Several studies have shown that the risk of VTE is 2–3 times higher in patients with inflammatory bowel disease (IBD) than in the general population [7]. Moderate frequency of alcohol intake has been differentially associated with hemostasis and fibrinolytic factor levels [8]. However, the relationship between the frequency of alcohol intake and the risk of developing PE remains uncertain.
Previous literature has suggested that risk factors for PE include body mass index (BMI), ever smoking, heart failure, IBD, and frequency of alcohol intake. Although there is considerable evidence to support the association of these risk factors with PE, the direction of causality between these risk factors and PE remains unclear [9]. Due to the influence of confounding factors, the available clinical findings do not directly imply a causal relationship. MR methods were subsequently introduced to reduce the impact of acquired confounding factors [10].
Mendelian randomization (MR) is a powerful tool in epidemiology to estimate the causal effect of exposure on outcome in the presence of unobserved confounders by exploiting genetic variation as instrumental variables (IVs) of exposure [11]. Nowadays, causality based on the MR of inferred variables is widely used. In this MR analysis, we aimed to demonstrate whether there is a causal relationship between BMI, ever smoking, heart failure, frequency of alcohol intake, IBD, and PE.
## Overview of the study design
An overview of the multifactorial MR analysis study is shown in Figure 1. In summary, we first assessed the causal effects of BMI, ever smoking, heart failure, frequency of alcohol intake, and IBD on PE. Also, genetic variation was considered IV only if three strict assumptions were met [12]. First, genetic variation was highly correlated with exposure. Second, genetic variants are independent of confounding factors (e.g., BMI, sex, and age). Finally, genetic variation directly affects the outcome through the exposure pathway. We analyzed this using a recently pooled dataset of genome-wide association studies (GWAS) on BMI, ever smoked, heart failure, frequency of alcohol intake, and IBD on PE.
**FIGURE 1:** *Design of the Mendelian randomization (MR) study. “X” indicates that the genetic variant is not associated with confounding factors or cannot be directly involved in the outcome through the exposure pathway. “√” means that the genetic variant is highly correlated with exposure. SNP, single nucleotide polymorphism.*
## Data sources and SNP selection for BMI, ever smoked, heart failure, frequency of alcohol intake, and IBD
We selected pooled data for PE from published GWAS meta-analyses involving approximately 461,164 participants of European ancestry. We also used a publicly available pooled dataset of BMI (including 461,460), ever smoking (including 46,106), heart failure (including 977,323), IBD (including 75,000), and alcohol intake frequency (including 462,346) from another independent GWAS. The GWAS reported data related to BMI, ever smoking, heart failure, IBD, and alcohol intake frequency with genome-wide significance at the PE level ($P \leq 5$ × 10–8). Also, these single nucleotide polymorphisms (SNPs) were tested for linkage disequilibrium (LD) to cluster the independence of SNPs. Genome-wide significant independent (r2 < 0.001) variants associated with BMI, ever smoking, heart failure, IBD, and alcohol intake frequency were extracted from the GWAS. In addition, R2 and F statistics were calculated to assess the strength of IVs based on the sample size of the exposure dataset, the number of IVs, and genetic variance. Finally, we searched the Phenoscanner database1 for all exposure-associated SNPs and their proxies to determine if there were SNPs associated with confounders ($P \leq 5$ × 10–8). We manually removed these SNPs to avoid polymorphic effects.
## MR analysis
We used the two-sample MR package in the R language for the analysis. We mainly used inverse variance weighted (IVW) for MR analysis, and predictions were made by weighted regression of SNP-specific Wald ratios (i.e., β outcome/β exposure). Several sensitivity analyses were performed, including weighted median (WM) and MR-Egger regression methods. IVW estimates are inverse variance-weighted means of ratio estimates from two or more instruments [13]. The WM estimate is the median of the weighted empirical distribution function of the individual SNP ratio estimates. MR-Egger regression consists of a weighted linear regression of SNP schizophrenia on SNP biomarker effect estimates [14]. The MR-Egger regression method is robust to horizontal pleiotropy. We quantified the level of heterogeneity by using Cochran’s Q statistics and I2 statistics [15]. The larger the value of I2 was, the more significant the heterogeneity. In addition, the “leave-one-out analysis,” which removed each SNP in turn, ensured the reliability of the results.
## Causal effects of BMI, ever smoking, heart failure, frequency of alcohol intake, and IBD on PE
Single nucleotide polymorphisms associated with BMI, ever smoking, heart failure, frequency of alcohol intake, and IBD were collected and downloaded from the phenotype database. Confounders were controlled using (see text footnote 1), and SNPs directly associated with weight, height, BMI, smoking, heart failure, alcohol intake, and IBD were excluded. SNPs associated with outcome PE were also excluded. In addition, 172, 57, 10, 71, and 21 genetic variants not associated with LD were identified as IVs, respectively. To verify the influence of each SNP on the overall causal estimate, leave-one-out analysis was performed (as shown in Figure 2). Forest plot shows the odds ratio (OR) with a horizontal line representing $95\%$ CI for the BMI, ever smoking, heart failure, frequency of alcohol intake, and IBD -associated SNP allele for PE risk (as shown in Figure 3). Scatter plot to visualize causal effect of BMI, ever smoking, heart failure, alcohol intake frequency, and IBD on total PE risk (as shown in Figure 4). Funnel plots to visualize overall heterogeneity of MR estimates for the effect of BMI, ever smoking, heart failure, alcohol intake frequency, and IBD on PE randomization (as shown in Figure 5). The F-statistics of IVs for BMI, ever smoking, heart failure, alcohol intake frequency, and IBD were above the threshold of 10, indicating that IVs were vital instruments, thus reducing bias in IV estimation.
**FIGURE 2:** *“Leave-one-out” analysis shows the effects of body mass index (BMI), ever smoking, heart failure, frequency of alcohol intake, and inflammatory bowel disease (IBD)-related single nucleotide polymorphism (SNPs) on pulmonary embolism (PE). Mendelian randomization (MR) estimated effect sizes for BMI (A), ever smoking (B), heart failure (C), frequency of alcohol intake (D), and IBD (E) are shown. Data are expressed as β values and 95% CI.* **FIGURE 3:** *Forest plots of the causal effects of body mass index (BMI), ever-smoking, heart failure, alcohol intake frequency, and inflammatory bowel disease (IBD)-related single nucleotide polymorphism (SNPs) on pulmonary embolism (PE). The Mendelian randomization (MR) estimated effect sizes for BMI (A), ever smoking (B), heart failure (C), frequency of alcohol intake (D), and IBD (E) are shown. Data are expressed as beta values and 95% CI.* **FIGURE 4:** *Scatter plots of the causal effects of body mass index (BMI), ever smoking, heart failure, alcohol intake frequency, and inflammatory bowel disease (IBD)-related single nucleotide polymorphism (SNPs) on pulmonary embolism (PE). The effect sizes of Mendelian randomization (MR) estimates for BMI (A), ever smoking (B), heart failure (C), frequency of alcohol intake (D), and IBD (E) are shown.* **FIGURE 5:** *Funnel plot of the causal effects of body mass index (BMI), ever smoking, heart failure, alcohol intake frequency, and inflammatory bowel disease (IBD)-related single nucleotide polymorphism (SNPs) on pulmonary embolism (PE). The Mendelian randomization (MR) estimated effect sizes for BMI (A), ever smoking (B), heart failure (C), alcohol intake frequency (D), and IBD (E) are shown.*
As shown in Figure 6, pleiotropy was detected by P for Intercept ($P \leq 0.05$). Therefore, we used IVW in the random effects model. Hereditarily elevated BMI was causally associated with PE in the IVW model (OR = 1.002, $95\%$ CI: 1.001–1004, $$P \leq 0.039$$). Heterogeneity was detected by MR-*Egger analysis* with Cochran’s Q test ($$P \leq 0.576$$). Therefore, the effect of gene-level heterogeneity was not considered.
**FIGURE 6:** *Mendelian randomization (MR) analysis for the causality of body mass index (BMI), ever smoking, heart failure, alcohol intake frequency, and inflammatory bowel disease (IBD) with the risk of pulmonary embolism (PE). MR, Mendelian randomization; IVW, inverse variance weighted; OR, odds ratio; CI, confidence interval; WM, weighted median.*
In the MR analysis of other risk factors, we observed genes for ever smoking (IVW OR = 1.004, $95\%$ CI: 0.997–1.012), heart failure (IVWOR = 0.999, $95\%$ CI: 0.996–1.001), IBD (IVWOR = 1.000, $95\%$ CI: 0.999–1.001), and alcohol intake frequency (IVW OR = 1.002, $95\%$ CI: 1.000–1.004) levels were not causally related to PE. Cochran’s Q test showed heterogeneity only in the case of ever smoking but not in other factors. In addition, MR-*Egger analysis* showed non-directional pleiotropy in all directions for IVs (P for Intercept = 0.081 for ever smoking, P for Intercept = 0.181 for heart failure, P for Intercept = 0.126 for IBD, and P for Intercept = 0.285 for frequency of alcohol intake). Analysis using multivariate MR expansion showed no causal effect of BMI on PE considering the effect of height as well as weight ($$P \leq 0.926$$).
## Discussion
This study used MRs for BMI, ever smoking, heart failure, alcohol intake frequency, and IBD from the available GWAS database to infer a causal relationship between BMI and PE. However, there was no causal relationship between ever smoking, heart failure, alcohol intake frequency, and IBD and PE. It was observed that ever smoking, heart failure, IBD, and frequency of alcohol intake could not directly cause PE at the genetic level.
A systematic review and dose-response meta-analysis of 3910747 participants showed a significant association between lower BMI (underweight vs. normal BMI) and a reduced risk of PE (HR: 0.80, $95\%$ CI: 0.70–0.92, I2 = $9\%$) and a higher risk of PE in obese than those participants presenting with a healthy BMI (HR: 2.24, $95\%$ CI: 1.93–2.60, I2 = $0\%$) [16]. Another MR study used BMI-related SNPs from the UK Biobank as an instrumental variable to assess the association between 367,703 participants with cardiovascular disease. It was concluded that BMI was positively associated with PE. The OR for PE was 1.06 ($95\%$ CI: 1.02–1.11; $$P \leq 2.6$$ × 10–3) for every 1 kg/m2 increase in BMI [9]. However, the study did not observe a different causal relationship for PE.
At the molecular level, high-throughput proteomics and cross-validated regularized regression modeling approaches showed 11 proteins (CLEC4C, FABP4, FLT3LG, IL-17G, LEP, LYVE1MASP1, ST2, THBS2, THBS4, and TSLP) were consistently associated with BMI in plasma in the context of acute VTE (17–27). Furthermore, the absence of serum leptin is inversely associated with recurrence and death from VTE at high body weight or high circulating concentrations of MMP-2 [28]. Whether the body influences VTE through the regulation of leptin remains to be further refined.
This study has several limitations. First, the results of this study were only set in a European population, and extrapolation to other populations still requires further validation. Second, the findings were not stratified by BMI, and the effect of different BMI strata on PE could not be further clarified. Third, some risk factors involved several SNPs. Therefore, a larger GWAS and more SNPs are needed as tools to replicate the MR study to improve the ability to test associations. More importantly, the MR design still needs to be validated by RCT.
Nevertheless, the present study undoubtedly has some advantages. First, it involved a large cohort, enabling multifactorial causal inference of an outcome event in a real-world study. Because the effects of confounding factors were controlled for and tested by various methods, the conclusion obtained had a solid evidence base and could better guide clinical practice. Second, the study population was PE, a common cardiovascular disease with high annual morbidity and mortality. This study explored the effects of five common factors, namely, BMI, ever smoking, heart failure, frequency of alcohol intake, and IBD, on PE with good generalizability. These five factors are common in current clinical practice.
Although existing clinical studies have shown that previous smoking, heart failure, frequency of alcohol consumption, and IBD are associated with PE, there was no causal effect between the above risk factors and PE using single-factor MR analysis, and no causal effect between BMI and PE in the multifactor MR extension analysis, corrected for height and weight. The possible reasons for this are as follows: [1] whether there are other different mediators mediating the association between the above risk factors and PE, but considering the limited data obtained in this study, further expansion of the analysis of the mediators of the above risk factors cannot be supported for the time being; [2] the data from the existing study are limited to the European population, and the expansion of this conclusion needs to consider the heterogeneity of different populations, and more data are needed to verify the causal effect of the above risk factors and PE in the future. In the future, more data are needed to verify the causal effects of the above risk factors and PE; meanwhile, the above findings need to be further validated by RCT. There are no clinical studies available that suggest that the association between PE and ever smoking, heart failure, frequency of alcohol consumption, or IBD is mediated by other intermediate risk factors, which is something that needs to be further explored in the future in this study. [ 3] Restricted by the number of SNPs as well as the F-value, the higher the number of SNPs and the larger the F-value, the more statistically significant the effect.
In conclusion, there was a causal relationship between BMI and PE in the European population. Increased BMI could lead to PE, while the presence or absence of smoking, heart failure, frequency of alcohol intake, and IBD are not directly causally related to PE. There was no causal effect of BMI with PE in multivariate Mendelian randomized analysis. Either univariate or multivariate MR needs to be further validated by RCT in the future.
## Data availability statement
The original contributions presented in this study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.
## Author contributions
J-MW, Y-LS, and HZ performed the material preparation, data collection, and analysis. J-MW, W-WY, and X-BL wrote the first draft of the manuscript. W-WY and X-BL commented on previous versions of the manuscript. All authors contributed to the study conception and design and read, revised, and approved the final manuscript.
## 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.2023.1057019/full#supplementary-material
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|
---
title: Oligosaccharides isolated from Rehmannia glutinosa protect LPS-induced intestinal
inflammation and barrier injury in mice
authors:
- Xiao Li
- Rong Gui
- Xuefang Wang
- Erjuan Ning
- Lixian Zhang
- Yi Fan
- Ling Chen
- Liqin Yu
- Jie Zhu
- Zhining Li
- Lei Wei
- Wei Wang
- Zihong Li
- Yue Wei
- Xuebing Wang
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC9996025
doi: 10.3389/fnut.2023.1139006
license: CC BY 4.0
---
# Oligosaccharides isolated from Rehmannia glutinosa protect LPS-induced intestinal inflammation and barrier injury in mice
## Abstract
### Objectives
We investigated the protective effect of *Rehmannia glutinosa* oligosaccharides (RGO) on lipopolysaccharide (LPS)-induced intestinal inflammation and barrier injury among mice.
### Methods
RGO is prepared from fresh rehmannia glutinosa by water extraction, active carbon decolorization, ion exchange resin impurity removal, macroporous adsorption resin purification, and decompression drying. LPS could establish the model for intestinal inflammation and barrier injury in mice. Three different doses of RGO were administered for three consecutive weeks. Then the weight, feces, and health status of the mice were recorded. After sacrificing the mice, their colon length and immune organ index were determined. The morphological changes of the ileum and colon were observed using Hematoxylin-eosin (H&E) staining, followed by measuring the villus length and recess depth. RT-qPCR was utilized to detect the relative mRNA expression of intestinal zonula occludens-1 (ZO-1) and occludin. The expression of inflammatory factors and oxidation markers within ileum and colon tissues and the digestive enzyme activities in the ileum contents were detected using ELISA. The content of short-chain fatty acids (SCFAs) in the colon was determined with GC. The gut microbial composition and diversity changes were determined with 16S-rRNA high-throughput sequencing. The association between intestinal microorganisms and SCFAs, occludins, digestive enzymes, inflammatory factor contents, and antioxidant indexes was also analyzed.
### Results
RGO significantly increased the weight, pancreatic index, thymus index, and colon length of mice compared with the model group. Moreover, it also improved the intestinal tissue structure and increased the expression of intestinal barrier-related junction proteins ZO-1 and Occludin. The contents of IL-6, IL-17, IL-1β, and TNF-α in the intestinal tissues of mice were significantly reduced. Additionally, the activities of superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), and catalase (CAT) were elevated. In contrast, the malondialdehyde (MDA) content decreased. Trypsin and pancreatic lipase activities in the ileum enhanced, and the SCFA contents such as acetic acid, propionic acid, and butyric acid in the colon increased. The study on intestinal flora revealed that RGO could enhance the abundance of intestinal flora and improve the flora structure. After RGO intervention, the relative abundance of Firmicutes, Lactobacillus, and *Akkermania bacteria* in the intestinal tract of mice increased compared with the model group, while that of Actinomycetes decreased. The intestinal microbiota structure changed to the case, with probiotics playing a dominant role. The correlation analysis indicated that Lactobacillus and *Ackermann bacteria* in the intestinal tract of mice were positively associated with SCFAs, Occludin, ZO-1, pancreatic amylase, SOD, and CAT activities. Moreover, they were negatively correlated with inflammatory factors IL-6, IL-17, IL-1β, and TNF-α.
### Conclusions
RGO can decrease LPS-induced intestinal inflammation and intestinal barrier injury in mice and protect their intestinal function. RGO can ameliorate intestinal inflammation and maintain the intestinal barrier by regulating intestinal flora.
## Introduction
The incidence rate of intestinal inflammatory diseases has gradually increased in recent years, becoming a global health management problem [1]. The intestinal tract is the leading site of digestion and absorption, and the intestinal mucosa has rich blood vessels. Inflammation will lead to intestinal barrier injury. Simultaneously, intestinal barrier injury is also involved in various intestinal diseases, closely associated with inflammatory bowel disease, bacterial enteritis, and Crohn's disease [2]. Intestinal injury can cause emaciation, malnutrition, stunted growth, and even death of patients, among severe cases [3]. The timely and appropriate application of enteral nutrition can effectively enhance the nutritional status of patients and alleviate the release of intestinal inflammatory factors. Moreover, it effectively improves intestinal mucosal injury, significantly maintaining the health of the intestinal system [4]. Hormones and antibiotics for treating intestinal inflammation have noticeable therapeutic effects, but they can also cause potential damage to the body. Therefore, using natural nutritional agents without side effects for treatment and prevention is a new method for treating intestinal inflammatory diseases.
Rehmannia glutinosa is the root tuber of the Scrophulariaceae plant Rehmannia. It is a traditional Chinese medicine with the functions of nourishing Yin, clearing heat, tonifying blood, and stopping bleeding. Rehmannia has a long history of consumption in China. Around 1,000 years ago, in Huaiqing Prefecture and other Rehmannia-producing areas in Henan Province, people “pickled the Rehmannia into pickles, and soaked in wine and tea for consumption.” *Rehmannia is* still shredded and served cold or boiled into porridge. Studies have depicted that the main chemical components of *Rehmannia glutinosa* are iridoid glycosides, oligosaccharides, polysaccharides, and amino acids, which are the material basis for it to play its role [5]. Oligosaccharides are low molecular weight sugar polymers formed by the condensation of 3–9 monosaccharides by glycosidic bonds [6]. These oligosaccharides cannot be easily digested and hydrolyzed in the small intestine. Instead, they are utilized by probiotics after entering the hindgut to enhance the proliferation of beneficial bacteria and contribute to the stability of intestinal microecology [7]. Several studies have demonstrated that intestinal microorganisms are closely related to intestinal health and function. Once the balance of intestinal flora is destroyed, it can lead to excessive consumption of the mucosal layer and accelerated apoptosis of intestinal mucosal epithelial cells, thereby damaging the intestinal mucosal barrier. Many pathogenic bacteria could invade to trigger a strong intestinal immune response, increasing the secretion of various intestinal inflammatory factors and ultimately inducing digestive and absorption dysfunction (8–10). However, it has not been reported whether RGO can ameliorate intestinal inflammation and maintain the intestinal barrier by regulating intestinal microbiota.
LPS comprises lipids and polysaccharides at the outermost layer of the cell wall of gram-negative bacteria and is an inflammatory stimulator [9]. Studies have indicated that LPS can bind to TLR4 receptors on the cell surface. They activate nuclear factor-κB (NF-κB) through the MyD88 pathway and then enter the nucleus to induce the synthesis and release of cytokines. These include tumor necrosis factor-α (TNF-α), interferon-γ (IFN-γ), interleukin-6 (IL-6), etc., which causes intestinal inflammation [11]. Long-term exposure to heterogeneous LPS can destroy the intestinal mucosal barrier, causing intestinal flora homeostasis [12]. The amount of SCFAs produced can indirectly reflect the balance of intestinal flora, and the content of intestinal tight junction protein can also reflect the health of intestinal barrier. Therefore, in this study, LPS was used to construct an animal model for intestinal inflammation and barrier injury using mice to explore the protective effect of RGO on LPS-induced intestinal inflammation and barrier injury in mice. Moreover, the relationship between RGO and intestinal microorganisms was also assessed, thus providing the theoretical basis for applying RGO in intestinal inflammatory diseases.
## Materials and reagents
Fresh Rhizomes of *Rehmannia glutinosa* were obtained from Wuzhi County, Henan Province, China (35° 1′23″ north latitude, 113° 18′76″ east longitude) in December 2021. Stachyose (NO. 112031-201701), sucrose (NO.1 11507-202105), raffinose (NO. 190225-201901), and verbascose (NO. 111530-201914) were purchased from China Institute for Food and Drug Control.
Dexamethasone tablets were purchased from Tangshan Longkang Pharmaceutical Co., Ltd.
The lipopolysaccharide (LPS) was purchased from Sigma Company in the United States. The detection kits for TNF-α, IL-6, IL-17, and IL-1β were purchased from Shanghai Enzyme-linked Biotechnology Co., Ltd. Moreover, the detection kits for SOD, MDA, GSH-Px, and CAT were purchased from the Nanjing Jiancheng Bioengineering Research Institute. Additionally, the digestion enzyme test kits were purchased from Beijing SOLEBAR Technology Co., Ltd. All other chemicals, solvents, and reagents were of pure analytical grade.
## Preparation of RGO
Fresh Rhizomes of *Rehmannia glutinosa* were cut into small pieces of 5–10 mm after being washed, added four times the amount of water, and extracted twice at 90°C, at 1 h duration. The two extracts were combined, adding activated carbon (2 g/100 ml) and activated clay (2 g/100 ml) to the extract. It was stirred and decolored at 80°C for 30 min, then centrifuged. The supernatant was passed through 001 × 7 cation exchange resin column (diameter: high = 6:1), D201 type anion exchange resin column (diameter: high = 6:1), D101 macroporous adsorption resin column (diameter: high = 10:1) one by one, sample volume (mL): resin column volume = 1:1.5, flow rate was 500 mL/h. Finally, the macroporous adsorption resin effluent was collected and concentrated and dried at 60°C to get white powder, that is RGO.
The type and content of oligosaccharides in RGO were detected using high-performance liquid chromatography (HPLC) (Agilent1260), configured using a Refractive Index Detector (RID) [13]. The standard reference substances of sucrose, stachyose, raffinose, and mulberry sugar were weighed precisely and prepared with $70\%$ acetonitrile aqueous solution into the standard reference solution with a concentration of 0.5 mg/mL, respectively. The RGO powder was also weighed precisely, and prepared with $70\%$ acetonitrile aqueous solution into the sample solution with a concentration of 1 mg/mL. The chromatographic column was Agilent ZORBOX NH2 (4.6 mm × 250 mm, 5 μm); the mobile phase was acetonitrile: water (7:3); the injection volume was 10 μL, the flow rate was 1.0 mL/min, and the temperature of column incubator was 40°C. The temperature of the detection was 50°C with RID. The types of oligosaccharides in RGO were determined by comparing the HPLC peaks of reference substance with those in RGO, and the content of oligosaccharides was calculated by external standard method.
## Animal and experimental design
We purchased 36 KM mice from Henan Scribes Biotechnology Co., Ltd. The mice were fed for 7 days before the experiment to acclimate to the environment. The mice were randomly divided into six groups, namely normal (N), model (M), treatment (T), RGO low dose (RL, 0.25 g/kg), RGO medium dose (RM, 0.5 g/kg), and RGO high dose (RH, 1 g/kg) groups. Six mice were in each group, and the test period was 21 days. Except for the normal group, on the 9th, 13th, 17th, and 21st days, 0.2 mL of normal saline was injected intraperitoneally. 0.2 mL of 1 mg/kg LPS was injected intraperitoneally in all other groups to develop mice models of intestinal inflammation and barrier injury [14, 15]. From the first day of the test, mice in RL, RM, and RH groups were provided 0.2 mL of RGO solution by gavage once a day for 21 days. The mice in the N, M, and T groups were gavaged with an equal volume of normal saline daily. After each intraperitoneal LPS injection, the mice in the T group were gavaged with 0.2 mL dexamethasone solution at 0.5 mg/kg dose 30 min. All the groups were fed adequate food and free to drink water. The weight of mice was determined on the 1st, 7th, 14th, and 21st days of the test. Additionally, the food intake, fecal properties, and health status of mice were observed and recorded daily.
## Sample collection
On the 21st day of the test, the mice were sacrificed under ether anesthesia after 6 h of LPS treatment and dissected. The thymus, spleen, and liver tissues were collected, rinsed with PBS solution, blotted dry using filter paper, and weighed. Then the immune organ index of the mice was calculated. The whole colon was taken, and the length of the colon was measured. The ileum and colon tissues and their contents were collected to detect related indicators. A part of the intestinal tissue was placed in $4\%$ paraformaldehyde for histopathological analysis. Then, the rest of the intestinal tissue and contents were stored at −80°C.
Immune organ index = weight of organ (mg)/body weight (g).
## Histopathology
The ileum and colon tissues of mice were extracted from a $4\%$ paraformaldehyde solution. About 1 cm of the middle segment was dehydrated using ethanol, hyalinized in xylene, embedded in paraffin, and sectioned (4–6 μm). The pathologic slices were made with Hematoxylin-eosin (H&E) staining, and the morphology of intestinal tissues was observed using a light microscope (Olympus, DP-72, Tokyo, Japan). The jejunum villus length, the colonic fold height, and the depth of the crypt of the ileum and colon were evaluated.
## RT-qPCR detection
The total RNA of the ileum and colon mucosa was extracted using the Trizol method. Then, 1,000 ng of total RNA was reverse transcribed into cDNA with RT Super Mix and stored at −80°C. β-actin was utilized as the reference gene. The primer sequences of the target gene and the reference gene are shown in Table 1. The cDNA was used as the template for real-time PCR [16]. Reaction procedures: High-temperature denaturation was performed at 95°C for 10 min, renaturation at 95°C for 15 s, and primer extension at 60°C for the 60 s. This was repeated for 40 cycles. Cycle threshold (Ct) values were utilized for the relative quantification of RT-qPCR amplification. The Ct value method compared the expression of target genes associated with β-actin.
**Table 1**
| Gene name | Upstream primer | Downstream primer |
| --- | --- | --- |
| ZO-1 | 5′-GGGTCATCATCTCTGCACCT-3′ | 5′-GGTCATAAGTCCCTCCACGA-3′ |
| Occludin | 5′-AACAACCCCTTCCAAGTTCC-3′ | 5′-CTCCCAGAGTTCCGATTCAC-3′ |
| β-actin | 5′-ACCTCCAGGACGACGACTTTGAT-3′ | 5′-GTGTCTTCTGCACGTACTCCA-3′ |
## Enzyme-linked immunosorbent assay
Precooled normal saline was added to the ileum and colon tissues of mice at the ratio of weight (g): volume (ml) = 1:10, respectively. The homogenate was mechanically homogenized at 3,000 r/min and centrifuged for 15 min under ice-water bath conditions. The supernatant was taken, and according to the kit's instructions, the contents of IL-6, IL-17, TNF-α, IL-1β, MDA, SOD, GSH-Px, and CAT were determined.
## Digestive enzyme detection
Precooled normal saline was added to the middle ileum of mice at the ratio of weight (g): volume (ml) = 1:10. A high-speed grinder centrifuged them at 3,000 r/min for 15 min, and the supernatant was taken. The activities of trypsin, lipase and amylase were determined based on the kit's instructions.
## SCFA content in feces
The colon contents of mice were weighed accurately. Methanol was added at a ratio of 1:5 (mg: μL). The mixture was stirred for 30 s to form a uniform suspension. A small amount of concentrated sulfuric acid solution was added to adjust the pH to 2–3. The samples were left at room temperature for 10 min through continuous shaking. Then, the samples were centrifuged at 12,000 rpm for 10 min. Ten microliter of supernatant was taken, and the content of SCFA was determined through the Shimadzu GC-2014C gas chromatograph (Shimadzu, Japan), the flame ionization detector, and the DB-FFAP capillary column (30 m × 0.25 m × 0.25 mm) [17].
## 16S rRNA high-throughput sequencing analysis of intestinal flora
The colonic contents of mice were obtained through 16S rRNA high-throughput sequencing, commissioned by Shanghai Parsono Biotechnology Co., Ltd.
## Statistical analysis
Microsoft Excel software were used for Preliminary statistical. GraphPad Prism 8 software was used for image processing. All data were collected in triplicate and the average value was used for analysis and the data were statistically compared for significant differences by one-way analysis of variance (One-Way ANOVA) and Duncan's multiple comparisons using SPSS 22.0 software.
## Identification and content determination of RGO
Determination of main components of RGO by HPLC, The peak positions of sucrose, raffinose, stachyose and verbascose were 7.164, 10.052, 15.001, 22.778 min, respectively, The RGO isolated in this experiment have chromatographic peaks in the corresponding position. The contents of sucrose, raffinose, stachyose, and verbascose in the prepared RGO were 7.52, 5.19, 81.02, and $4.85\%$, respectively, Moreover, the total amount of oligosaccharides was $91.06\%$. The HPLC chromatogram is shown in Figure 1.
**Figure 1:** *RGO HPLC chromatogram (1 sucrose, 2 raffinose, 3 stachyose, 4 verbascose).*
## Effects of RGO on body weight, organ index, and colon length in LPS mice
As shown in Figure 1, no significant difference was observed in the body mass of mice from each group before the intraperitoneal LPS injection. After the LPS injection, the mice in the M group showed symptoms of movement retardation, in appetence, soft stools, tears, listlessness, and messy fur. During the experiment, the body weight gain of mice in the M group was significantly decreased ($P \leq 0.01$) compared with the N group. Moreover, the body weight gain of mice in RL, RM, RH, and T groups was significantly increased compared with the M group ($P \leq 0.01$). Among them, the weight gain in the RL and T groups was significantly lower than in the N group ($P \leq 0.05$). The body weight gain in the RH group revealed no significant difference from that in the N group ($P \leq 0.05$). The body weight gain in the RM group was significantly higher than the N group ($P \leq 0.05$) (Figures 2A, B). Thus, RGO could inhibit LPS-induced weight loss within mice.
**Figure 2:** *Changes of body weight, immune organ index and colon length in mice during the test. (A) Weekly body weight of mice. (B) Body weight gain of mice. (C) Liver index. (D) Pancreatic index. (E) Thymus index. (F) Colon of mice. (G) Length of colon. Compared with group N: *P < 0.05, **P < 0.01; compared with group M. #P < 0.05, ##P < 0.01.*
The liver index of mice in each experimental group had no significant difference compared with group N. In contrast, the pancreas and thymus indexes of mice in the M and T groups were significantly reduced ($P \leq 0.01$). Compared with the M group, the pancreas index of mice in the RM group was significantly elevated ($P \leq 0.01$). Moreover, the thymus index of mice in RL, RM, RH, and T groups was significantly increased ($P \leq 0.01$) (Figures 2C–E). We also determined the colon length of mice within each experimental group. Compared with the N group, the colon length of mice in the M and T groups was significantly reduced ($P \leq 0.01$). However, the colon length of mice in RL, RM, and RH groups increased significantly compared with the M group ($P \leq 0.01$) (Figures 2F, G). These results demonstrated that LPS induced atrophy of the pancreas, thymus, and colon in mice. RGO intervention could decrease the atrophy of the pancreas, thymus, and colon in LPS mice and protect them.
## Effects of RGO on intestinal epithelial barrier in LPS mice
We used H&E-stained to detect the pathological changes in the ileum and colon and investigate the effect of RGO on the intestinal epithelial barrier of LPS mice (Figures 3A, B). From the pathological section of the ileum and colon, it could be observed that the intestinal mucosa of mice in the N group was intact. Moreover, the villi of the small intestine were arranged closely and regularly, and the morphology of epithelial cells was normal. In the M group, intestinal gland necrosis, villus shortening, necrosis, and abscission of part of villous epithelium in the ileum, intestinal gland necrosis, goblet cell abscission, and other pathological conditions in the colon were observed. In each administration group, the exfoliation of intestinal epithelial cells was milder, the villus was arranged orderly, inflammatory cell infiltration was reduced, and infection symptoms were alleviated. The villus length and crypt depth of the ileum and the height and crypt depth of colonic folds were evaluated (Figures 3C, D). Compared with group N, the ileal villi length of mice in group M was significantly shortened ($P \leq 0.01$). Additionally, the height of colonic folds was significantly reduced ($P \leq 0.01$), and the depth of the ileal colonic recess was significantly enhanced ($P \leq 0.01$). This indicated that LPS caused intestinal villi damage, recess deepening, and intestinal epithelial barrier injury among mice. The villus length of the ileum of the mice in the RM and RH groups increased significantly ($P \leq 0.01$) compared with the M group. Moreover, the height of colonic folds in the RH group increased significantly ($P \leq 0.01$), and the depth of the ileal crypt in the RL, RM, and RH groups significantly decreased ($P \leq 0.01$).
**Figure 3:** *Effect of RGO on intestinal epithelial barrier in LPS mice. (A) Pathological assessment of H&E-stained mice ileum sections; magnification 10× and 40×. (B) Pathological assessment of H&E-stained mice colon sections; magnification 10× and 40×. (C) Villus length and folds height. (D) Crypt depth. (E)
ZO-1 mRNA relative expression level. (F)
Occludin mRNA relative expression level (a. Intestinal gland necrosis; b. Epithelial cell exfoliation; c. The villus becomes shorter; d. Necrosis of intestinal gland basal layer). Compared with group N: *P < 0.05, **P < 0.01; compared with group M. #P < 0.05, ##P < 0.01.*
RT-qPCR detected the relative mRNA expression of the intestinal barrier-related junction proteins Occludin and ZO-1 (Figures 3E, F). The results indicated that the relative mRNA expression of Occludin and ZO-1 in mice ileum and colon mucosa in group M was lower than in group N. The relative mRNA expression of Occludin and ZO-1 in mice ileum and colon mucosa in RL, RM, and RH groups was significantly increased compared with the M group ($P \leq 0.01$). These results indicated that RGO could enhance the intestinal morphology of LPS mice, increase the length of the intestinal villus, the height of the colonic fold, and the depth of the ileocolic crypt. Furthermore, it can increase the relative mRNA expression of intestinal tight junction protein and alleviate intestinal morphology and epithelial barrier damage in LPS mice.
## Effects of RGO on intestinal inflammation and oxidative indexes in LPS mice
ELISA could detect the expression of related inflammatory factors in the ileum and colon and identify the effect of RGO on intestinal inflammation in LPS mice (Figures 4A, B). The results indicated that compared with the N group, the levels of IL-6, IL-17, IL-1β, and TNF-α in the ileum and colon tissues of the M group elevated significantly ($P \leq 0.01$). Compared with the M group, the levels of IL-6, IL-17, IL-1β, and TNF-α in the ileum of mice in RL, RM, and RH groups were significantly decreased ($P \leq 0.01$). However, there was no significant difference in the level of IL-6 in the colon between the RL and M groups. IL-6, IL-17, IL-1β, and TNF-α levels in the colon of RL, RM, and RH groups were significantly reduced ($P \leq 0.01$). These results depicted that RGO intervention could decrease the levels of intestinal inflammatory factors in LPS mice.
**Figure 4:** *The effect of RGO on intestinal inflammatory factors and oxidative indexes in LPS mice. (A) IL-6, IL-17, IL-1β, and TNF-α levels in the ilenm. (B) IL-6, IL-17, IL-1β, and TNF-α levels in the colon. (C) MDA, SOD, GSH-Px, and CAT levels in the ilenm. (D) MDA, SOD, GSH-Px, and CAT levels in the colon. Compared with group N: *P < 0.05, **P < 0.01; compared with group M. #P < 0.05, ##P < 0.01.*
Inflammatory injury is often accompanied by oxidative damage to LPS-induced intestinal mucosal injury. Therefore, intestinal oxidative indicators were also measured in LPS mice. The results indicated that in the ileum tissue of mice, compared with group N, the MDA level in group M increased significantly ($P \leq 0.01$). In contrast, the SOD, GSH-Px, and CAT levels decreased significantly ($P \leq 0.01$). Compared with the M group, the MDA levels in RL, RM, RH, and T groups decreased significantly ($P \leq 0.01$). However, the SOD and GSH-Px levels increased significantly ($P \leq 0.01$). CAT levels in RL, RM and T groups also increased significantly ($P \leq 0.01$) (Figure 4C). In the colon tissue of mice, the level of MDA in group M was significantly elevated compared with group N ($P \leq 0.01$). In contrast, the SOD, GSH-Px, and CAT levels were significantly decreased ($P \leq 0.01$). After RGO intervention, the SOD, GSH-Px, and CAT levels in T, RM, and RH groups were significantly increased compared with group M ($P \leq 0.01$). The SOD and CAT levels in group RL were also significantly enhanced ($P \leq 0.01$), but the GSH-Px levels were insignificant (Figure 4D). These results indicated that RGO intervention could improve the antioxidant capacity of the intestinal tract of mice.
## Effects of RGO on intestinal digestive enzymes in LPS mice
Compared with the N group, the pancreatic amylase, lipase, and trypsin activities in the ileum of mice in the M group were significantly decreased ($P \leq 0.01$). Compared with the M group, the activities of pancreatic lipase in RL, RM, and RH groups were significantly enhanced ($P \leq 0.01$). Trypsin activity in group RM was significantly increased ($P \leq 0.01$). However, there was no significant difference in pancreatic amylase activity (Figure 5). These results indicated that RGO intervention could restore the activities of trypsin and lipase within the intestinal tract of LPS mice.
**Figure 5:** *(A–C) Effect of RGO on digestive enzyme activity in mice. Compared with group N: *P < 0.05, **P < 0.01; compared with group M. #P < 0.05, ##P < 0.01.*
## Effects of RGO on the content of intestinal SCFAs in LPS mice
SCFAs are metabolites of the intestinal flora. Compared with the N group, acetic acid, propionic acid, and butyric acid in the colon contents of the mice in the M group were significantly decreased ($P \leq 0.01$). It depicted that LPS had an inhibitory effect on the production of SCFAs. Compared with the M group, acetic acid, propionic acid, and butyric acid in RL, RM, and RH groups were significantly elevated ($P \leq 0.01$). Among them, the contents of acetic acid and propionic acid in the RM group were the highest, while the butyric acid in the RL group was the highest (Figure 6). These results indicated that RGO intervention could enhance the content of SCFAs in the intestinal tract of LPS mice. Moreover, we also suggested that RGO could elevate the content of SCFAs by controlling the composition of the intestinal microorganisms.
**Figure 6:** *(A–C) Effect of RGO on SCFAs in mice. Compared with group N: *P < 0.05, **P < 0.01; compared with group M. #P < 0.05, ##P < 0.01.*
## Effects of RGO on intestinal flora of LPS mice
16S rRNA could determine the diversity and species richness of intestinal microorganisms in mice from different treatment groups. Alpha diversity analysis revealed that the species richness index Chao1 ofN, RL, RM, and RH groups was much higher than that of M and T groups, showing significant differences ($P \leq 0.05$). At the same time, the Shannon diversity index and Simpson diversity index were also higher than the M and T groups, without any significant difference (Figure 7A). The Rarefaction curves revealed that all curves tended to be parallel as the number of sequences increased, depicting that the sequencing data met the analysis needs (Figure 7B). Exclusivity analysis of OTUs showed that 14,738 OTUs were obtained using alignment. The proportions of annotated OTUs at the phyla, genus, and species levels were 98.14, 40.73, and $5.68\%$, respectively. The number of bacterial species in the RL, RM, RH, and N groups was significantly higher than in M and T groups ($P \leq 0.05$). There was no significant difference between the M and T groups. Compared with the M group, the number of species in RL, RM, and RH groups elevated by $14.55\%$ ($P \leq 0.05$), $19.09\%$ ($P \leq 0.05$), and $22.74\%$ ($P \leq 0.05$), respectively (Figure 7C).
**Figure 7:** *Effect of RGO on intestinal flora diversity in mice. (A) Alpha diversity index. (B) Dilution curve. (C) OTUs exclusivity analysis.*
At the phyla level, the microflora with a larger abundance of mouse intestinal microbes was mainly Firmicutes, Bacteroides, Proteobacteria, and Actinobacteria. Firmicutes and Bacteroides had the highest relative abundance, having more than $90\%$ of the total microbial biomass (Figure 8A). Compared with group N, the relative abundance of Firmicutes among intestinal microorganisms of mice in group M was significantly decreased. The relative abundance of Proteobacteria was significantly enhanced ($P \leq 0.01$). The abnormal expansion of Proteobacteria reduced the ability to regulate the balance of the intestinal microbial community. The increase in Proteobacteria was considered a potential feature of ecological imbalance and disease risk [18]. After RGO intervention, compared with the M group, the relative abundance of Proteobacteria in RL, RM, and RH groups decreased significantly ($P \leq 0.01$), while the relative abundance of Firmicutes increased. The relative abundance of Firmicutes in the RH group increased significantly ($P \leq 0.01$).
**Figure 8:** *Effect of RGO on intestinal microflora structure in mice. (A) Phylum level composition analysis. (B) Genus level composition analysis. (C) Species level cluster analysis. Compared with group N: *P < 0.05, **P < 0.01; compared with group M. #P < 0.05, ##P < 0.01.*
At the genus level, Lactobacillus and Bacteroides were the primary genera of intestinal microorganisms in mice (Figure 8B). The relative abundance of the two genera in group N was 41.40 and $7.10\%$, respectively. Compared with group N, the relative abundance of Lactobacillus in the M and T groups was reduced to 29.87 and $17.68\%$, respectively. Lactic acid bacteria have been a common probiotic to regulate intestinal ecological balance. After RGO intervention, the relative abundance of Lactobacillus in the RH group ($57.12\%$) was significantly higher than that in the M group ($P \leq 0.01$). In contrast, the relative abundance of Bacteroides, Prevotella, and Oscillospira had no significant change.
At the species level, intestinal microorganisms among mice with relatively high abundance included Bacteroides barnesiae, Lactobacillus vaginalis, Lactobacillus hamsteri, Akkermania musciniphila, and Lactobacillus salivarius. Compared with the M group, the relative abundance of *Akkermania musciniphila* in the RM and RH groups was significantly enhanced ($P \leq 0.01$). Moreover, the relative abundance of Lactobacillus vaginalis, Lactobacillus hamster, and *Lactobacillus salivarius* in the RH group significantly increased ($P \leq 0.01$). Cluster analysis revealed that the intestinal flora structure of the *Rehmannia glutinosa* oligosaccharides group was clustered into one group. Thus, the effects of different doses of RGO on intestinal microorganisms were consistent (Figure 8C). Therefore, RGO regulates intestinal flora imbalance in LPS mice.
## Correlation between intestinal microorganisms and SCFAs, tight junction proteins, digestive enzymes, inflammatory factors, and antioxidant indexes
The analysis revealed that Lactobacillus, Akkermansia, and *Alistipes massiliensis* in the intestinal tract of mice were positively associated with SCFAs, Occludin, ZO-1, pancreatic amylase, and SOD activities (Figure 9). Moreover, they were negatively correlated with inflammatory factors. Such as IL-6, IL-17, IL-1β, and TNF-α. However, Mucispirillum schaedleri and Desulfovibrio have been negatively associated with SCFAs, tight junction associated proteins, digestive enzymes, and SOD activities and positively correlated with inflammatory factors. RGO intervention significantly elevated the relative abundance of Lactobacillus, Lactobacillus, and Akkermansia, combined with the structural changes of intestinal flora. Therefore, we speculate that RGO may be by increasing the proportion of beneficial bacteria in the intestine to enhance SCFA production, enhance the expression of intestinal tight junction proteins, inhibit the release of inflammatory factors, and elevate antioxidant activity. Moreover, RGO alleviated intestinal inflammation and barrier injury due to LPS. The mechanism of RGO relieving intestinal inflammation in mice needs to be further analyzed.
**Figure 9:** *Correlation analysis of intestinal flora. *p < 0.05, **p < 0.01.*
## Discussion
The difficulty in preventing intestinal inflammation and the strong recurrence are the problems in treating this disease [19]. Therefore, human health must appropriately apply natural and non-irritating bioactive substances to enhance intestinal inflammation and maintain intestinal barrier function [1]. Rehmannia glutinosa oligosaccharide, a natural bioactive substance, can proliferate probiotics and improve immunity [6], with high application value. Therefore, this study explored the effects of RGO on LPS-induced intestinal inflammation and barrier injury among mice.
LPS exists in the outer membrane of Gram-negative bacteria, stimulating macrophages to secrete proinflammatory cytokines, including IL-6, IL-1β, and TFN-α, while promoting the synthesis and release of inflammatory cytokines [20]. The excessive secretion of IL-6, IL-17, IL-1β, and TNF-α has a crucial role in the pathogenesis of intestinal inflammation [21]. TNF-α can enhance intestinal permeability by regulating the integrity of intestinal epithelial cells [22, 23]. Therefore, blocking the secretion of these cytokines can be an effective strategy for treating intestinal inflammation. This study revealed that RGO could reduce the excessive secretion of inflammatory factors IL-6, IL-17 and IL-1β, and TNF-α within the intestine due to LPS.
Studies have indicated that the body has a strong oxidative stress response, and the antioxidant capacity of cells will be reduced when intestinal inflammation occurs. Excessive free radicals will act on lipid peroxidation, producing a large amount of MDA, thereby damaging the structure and function of proteins [12]. SOD, GSH-Px, and CAT are the most important antioxidant enzymes in the body. Their main functions are to eliminate free radicals and reactive oxygen species, thus preventing peroxide production [24]. RGO can alleviate the oxidative stress due to LPS, specifically manifested by enhancing the activities of SOD, GSH-Px, and CAT and inhibiting the enhancement of MDA levels.
The villus height and crypt depth could reflect the digestion and absorption ability of the small intestine. The higher the villus, the larger the number of peripheral intestinal epithelial cells. Moreover, the larger the contact area between the intestine and nutrients, the stronger the digestion and absorption of nutrients. The depth of the crypt becomes shallow, depicting that the maturation rate of intestinal epithelial tissue increases [25]. Additionally, the ability to secrete digestive fluid is more substantial, and the intestinal absorption capacity is stronger. The plica is rich in glands and lymphoid tissue; the higher the plicae, the more excellent intestinal transport, and absorption capacity. Therefore, the higher the villi and plicae, the shallower the crypts and has better the capacity of the intestinal tract to digest and absorb nutrients. This experiment showed that the intervention of RGO could enhance the length of the ileal villi and the height of the colonic plicae. Moreover, it can reduce the depth of crypts in LPS mice, suggesting that RGO could restore the structural damage from the LPS-induced intestinal inflammation among mice.
Occludin and ZO-1 are essential tight junction proteins of the intestinal barrier structure [26]. They have an important role in maintaining epithelial cell structure, regulating the transport of related ions, and controlling and regulating intestinal permeability [27]. The results indicated that LPS could significantly reduce the expression of Occludin and ZO-1 in the intestinal tight junction and increase intestinal permeability. Therefore, there is increased fecal water content and soft and loose stool in LPS mice [26]. After the RGO intervention, the expression of Occludin and ZO-1 was significantly enhanced. In combination with the results of intestinal tissue sections, RGO could improve intestinal permeability and water reabsorption capacity. Moreover, it can adjust the balance of water and salt and alleviate diarrhea symptoms by up-regulating the content of intestinal tight junction proteins. Thus, it maintains the integrity of intestinal tissue.
Digestive enzyme activity is one of the essential indicators to evaluate intestinal function, and its primary role is to aid digestion [28, 29]. LPS can significantly decrease the activity of pancreatic digestive enzymes in mice. At the same time, the intervention of RGO can substantially improve the activity of pancreatic digestive enzymes, demonstrating that RGO plays a significant role in restoring intestinal dysfunction.
Ninety-five percent of SCFAs in the intestine are acetic acid, propionic acid, and butyric acid, and after being absorbed by the intestine, they store energy and reduce osmotic pressure. SCFAs play an essential role in regulating the normal function of the large intestine and the morphology and function of colonic epithelial cells [30, 31]. SCFAs can also enhance the absorption of sodium, especially butyric acid, which can increase the production of Lactobacillus. It also reduces the number of *Escherichia coli* and serves as a significant energy source for intestinal mucosal cells [32]. This study found that RGO could significantly up-regulate the production of acetic acid, propionic acid, and butyric acid in the intestine. SCFAs can promote the repair of intestinal mucosa damage, restore its function, regulate oxidative stress response, and inhibit the production of inflammatory cytokines as the energy source of intestinal epithelial cells, thus exerting anti-inflammatory effects.
When intestinal inflammation occurs, it can cause an imbalance of intestinal flora. Therefore, it is also crucial to correct intestinal flora and treat intestinal diseases and eliminate inflammation [33]. The study observed that endogenous specific anaerobes (Lactobacillus, Bifidobacterium, etc.) could compete against potential aerobic pathogenic bacteria (Enterococcus faecalis, Escherichia coli, etc.), and their content could reflect the balance of intestinal flora [34]. The results showed that RGO could regulate the intestinal floral structure and restore the balance of intestinal flora in LPS-induced inflammatory mice. This may be facilitated by increasing the relative expression of 16S rRNA genes of intestinal probiotics, including Firmicutes, Lactobacillus, and Akkermansia. Most Firmicutes are beneficial bacteria, such as Lactobacillus, Fecal bacilli, and Lactobacillus. They produce acetic acid and butyric acid in the intestine to enhance the development of intestinal epithelial cells while preventing pathogens from interfering with intestinal health [35, 36]. Bacteroides can promote the digestion and absorption of lipids, proteins, and carbohydrates. Moreover, they resist the adhesion of invasive intestinal pathogens by colonizing the intestinal mucosa surface [37, 38]. Studies have revealed that Akkermansia can delay aging, inhibit neurodegenerative diseases, lower lipids, and weight loss, and assist cancer immunotherapy [39]. These results indicated that RGO could induce changes in the composition or metabolism of intestinal microbiota, enhance the proliferation of probiotics in intestinal microbiota, and inhibit the growth of harmful bacteria such as Proteobacteria. Thus, it affects the structure of intestinal microbiota, regulates the intestinal microecological balance, and maintains the richness and diversity of intestinal microbiota while promoting the health of the host organism. Simultaneously, correlation analysis revealed that beneficial bacteria in the intestine (including Lactobacillus, Akkermansia, etc.) were positively associated with intestinal SCFA, tight junction protein, digestive enzyme, and SOD activity. Moreover, they were negatively correlated with the content of inflammatory factors. Thus, RGO may improve intestinal function, reduce inflammation and restore intestinal health by controlling the homeostasis of intestinal flora.
Therefore, RGO can regulate the intestinal floral structure in LPS mice and increase the abundance of intestinal flora. At the same time, it can increase the production of SCFAs in the intestine, decreasing intestinal inflammation, repairing intestinal barrier injury, and maintaining intestinal health. RGO has potential application value in treating and preventing intestinal inflammatory diseases.
## 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.
## Ethics statement
The animal study was reviewed and approved by Animal Ethics of the Henan Agricultural University.
## Author contributions
XL and XuebW provided experimental plans and ideas. XL, RG, XuefW, EN, LZ, LC, JZ, ZhL, and YW conducted experiments, sorted out data, and drew drawings. XL, RG, YF, LY, LW, WW, and ZiL helped analyze data and participated in paper writing. All authors have read and agreed to submit the manuscript.
## Conflict of interest
XL, XuefW, EN, LZ, LC, LY, JZ, ZhL, LW, WW, ZiL, and YW were employed by Henan Natural Products Biotechnology Co., Ltd. YF was employed by Henan High Tech Industry Co., Ltd. The remaining 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
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|
---
title: 'Association between metabolic obesity phenotypes and multiple myeloma hospitalization
burden: A national retrospective study'
authors:
- Yue Zhang
- Xiude Fan
- Chunhui Zhao
- Zinuo Yuan
- Yiping Cheng
- Yafei Wu
- Junming Han
- Zhongshang Yuan
- Yuanfei Zhao
- Keke Lu
journal: Frontiers in Oncology
year: 2023
pmcid: PMC9996033
doi: 10.3389/fonc.2023.1116307
license: CC BY 4.0
---
# Association between metabolic obesity phenotypes and multiple myeloma hospitalization burden: A national retrospective study
## Abstract
### Background & purpose
Obesity and metabolic disorders were associated with increased risk of MM, a disease characterized by high risk of relapsing and require frequent hospitalizations. In this study, we conducted a retrospective cohort study to explore the association of metabolic obesity phenotypes with the readmission risk of MM.
### Patients & methods
We analyzed 34,852 patients diagnosed with MM from the Nationwide Readmissions Database (NRD), a nationally representative database from US. Hospitalization diagnosis of patients were obtained using ICD-10 diagnosis codes. According to obesity and metabolic status, the population was divided into four phenotypes: metabolically healthy non-obese (MHNO), metabolically unhealthy non-obese (MUNO), metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO). The patients with different phenotypes were observed for hospital readmission at days 30-day, 60-day, 90-day and 180-day. Multivariate cox regression model was used to estimate the relationship between obesity metabolic phenotypes and readmissions risk.
### Results
There were 5,400 ($15.5\%$), 7,255 ($22.4\%$), 8,025 ($27.0\%$) and 7,839 ($35.6\%$) unplanned readmissions within 30-day, 60-day, 90-day and 180-day follow-up, respectively. For 90-day and 180-day follow-up, compared with patients with the MHNO phenotype, those with metabolic unhealthy phenotypes MUNO (90-day: $$P \leq 0.004$$; 180-day: P = < 0.001) and MUO (90-day: $$P \leq 0.049$$; 180-day: $$P \leq 0.004$$) showed higher risk of readmission, while patients with only obesity phenotypes MHO (90-day: $$P \leq 0.170$$; 180-day: $$P \leq 0.090$$) experienced no higher risk. However, similar associations were not observed for 30-day and 60-day. Further analysis in 90-day follow-up revealed that, readmission risk elevated with the increase of the combined factor numbers, with aHR of 1.068 (CI: 1.002-1.137, $$P \leq 0.043$$, with one metabolic risk factor), 1.109 (CI: 1.038-1.184, $$P \leq 0.002$$, with two metabolic risk factors) and 1.125 ($95\%$ CI: 1.04-1.216, $$P \leq 0.003$$, with three metabolic risk factors), respectively.
### Conclusion
Metabolic disorders, rather than obesity, were independently associated with higher readmission risk in patients with MM, whereas the risk elevated with the increase of the number of combined metabolic factors. However, the effect of metabolic disorders on MM readmission seems to be time-dependent. For MM patient combined with metabolic disorders, more attention should be paid to advance directives to reduce readmission rate and hospitalization burden.
## Introduction
Multiple myeloma (MM), the second most common hematological malignancy, accounts for $1\%$ of all tumor diseases [1]. Although the treatment of MM makes significant progress, MM remains a severe and incurable disease, and most patients relapse and require frequent hospitalizations [2, 3]. Hospital readmission is a indicator of medical resource utilization and nursing quality evaluation, and also an important predictor of disease outcomes, such as increased morbidity, raised mortality, and the loss of functional independence [4, 5]. The annual cost of readmissions within 30 days after discharge accounts for more than $17 billion of avoidable Medicare expenses in the United States [6]. It is foreseeable that repeat hospitalization of MM patients will be a significant burden on the healthcare system and patients. Therefore, there is an urgent need to identify risk factors of readmission to reduce this significant and continuous burden.
Obesity, an urgent and growing global public health threat, is a significant risk factor for chronic diseases such as cardiovascular disease (CVD), diabetes, and certain types of cancer [7, 8]. Although evidence suggests that obesity is associated with higher incidence and mortality in MM [9], the effect of obesity on MM is still controversial [10, 11]. Studies suggested that metabolically healthy obese (MHO) individuals are at lower risk of CVD, cancers and mortality than those metabolically unhealthy obese (MUO) [12, 13]. Similarly, normal-weight individuals with unhealthy metabolic characteristics (metabolically unhealthy non-obese [MUNO]) have increased risk of CVD than those with metabolically healthy status (metabolically healthy non-obese [MHNO]) [13]. These differences implied that in addition to obesity, taking into account the coexistence of metabolic abnormalities can more effective in identifying risk factors for obesity-related diseases (9–11). Given the conflicting evidence on the relationship between obesity and MM, it seems to be more appropriate to consider the obesity and metabolic jointly to study the risk factors for MM.
Therefore, to accurately and systematically explore the potential modifiable risk factors for MM, we conducted a retrospective cohort study using the Nationwide Readmissions Database (NRD), a large contemporary nationwide database from the United States. We evaluated the differences in readmission risk among patients with different combinations of obesity and metabolic status to identify risk factor of readmission, and provide a clinical reference for optimizing patient care and minimizing the medical burden.
## Data sources
We conducted a retrospective cohort study using 2018 data from the NRD database (https://hcup-us.ahrq.gov/db/nation/nrd/nrddbdocumentation.jsp). The NRD is a longitudinal database developed and maintained by the Agency for Healthcare Research and Quality from the US. The NRD 2018 contains data from 28 states including clinical and non-clinical information at the hospital and patient levels, representing 60 percent of total population and 58.7 percent of hospitalizations in the United States [14]. Rehabilitation and long-term care hospitals are not included. Based on patient linkage numbers, the NRD can track individuals across hospitals within a state. Because of the de-identified nature of the data, this study was determined to be exempt from ethics board review by the local ethics committee, Biomedical Research Ethic Committee of Shandong Provincial Hospital.
## Population
We used the International Classification of Diseases-Tenth Revision-Clinical Modification (ICD-10-CM) codes (c90.x based) to identify patients with the first 30 diagnoses including MM on the index discharge. Due to data limitations, we did not consider the clinical stage, karyotype, oncogenic mutations and treatment strategies of MM patients in this study. The codes used to diagnose disease were listed in Supplementary Table 1. Patients with missing important hospitalization information, aged under 18 years old, pregnant, died during the index hospitalization, and low body weight [body mass index (BMI) ≤ 19.9 kg/m2] were excluded. Because the NRD cannot track admissions for the following year, we excluded those discharged in December for the 30-day follow-up study; for 60-day, we excluded those who were discharged in November and December; for 90-day, we excluded those who were discharged from October to December, and for the 180-day, we excluded those who were discharged from July to December. Detailed inclusion and exclusion criteria were shown in Figure 1.
**Figure 1:** *Flow chart of data screening.*
## Patient characteristics
For each patient, demographic variables and hospitalization characteristics were collected, which included age, gender, primary payer status (Medicare, Medicaid, private insurance, self-pay, free, and other types), median household income by ZIP code, discharge disposition, location, length of stay (LOS), total charges and comorbidities. Detailed information was provided in Table 1.
**Table 1**
| Variables | Total | MHNO | MUNO | MHO | MUO | P value* |
| --- | --- | --- | --- | --- | --- | --- |
| No. of cases | 34852 | 17305 (49.7%) | 12829 (36.8%) | 2130 (6.1%) | 2588 (7.4%) | |
| Age (years) (mean (SD)) | 69.8 (11.4) | 68.2 (12.1)a | 73.3 (9.8)b | 64.4 (11.6)c | 68.5 (9.9)a | <0.001 |
| Age ≥ 65 | 24178 (69.4%) | 10908 (63.0%)a | 10475 (81.7%)b | 1069 (50.2%)c | 1726 (66.7%)d | <0.001 |
| Male | 19262 (55.3%) | 9388 (54.3%)a | 7519 (58.6%)b | 1064 (50.0%)c | 1291 (49.9%)c | <0.001 |
| Primary payer | | | | | | <0.001 |
| Medicare | 24389 (70.0%) | 11130 (64.3%)a | 10228 (79.7%)b | 1202 (56.4%)c | 1829 (70.7%)d | |
| Medicaid | 2078 (6.0%) | 1177 (6.8%)a | 535 (4.2%)b | 205 (9.6%)c | 161 (6.2%)a | |
| Private insurance | 7281 (20.9%) | 4376 (25.3%)a | 1725 (13.4%)b | 646 (30.3%)c | 534 (20.6%)d | |
| Self-pay | 332 (1.0%) | 198 (1.1%)a | 102 (0.8%)b | 17 (0.8%)a, b | 15 (0.6%)a, b | |
| Free | 66 (0.2%) | 45 (0.3%)a | 11 (0.1%)a | 5 (0.2%)a, b | 5 (0.2%)a, b | |
| Other | 706 (2.0%) | 379 (2.2%)a | 228 (1.8%)a | 55 (2.6%)a | 44 (1.7%)a | |
| Median household income by ZIP code | | | | | | <0.001 |
| 0-25th | 8406 (24.1%) | 3929 (22.7%)a | 3262 (25.4%)b, c | 505 (23.7%)a, c | 710 (27.4%)b | |
| 26-50th | 8985 (25.8%) | 4390 (25.4%)a | 3294 (25.7%)a, b | 581 (27.3%)a, b | 720 (27.8%)b | |
| 51-75th | 8870 (25.5%) | 4456 (25.7%)a | 3218 (25.1%)a | 572 (26.9%)a | 624 (24.1%)a | |
| 76-100th | 8591 (24.6%) | 4530 (26.2%)a | 3055 (23.8%)b | 472 (22.2%)b, c | 534 (20.6%)c | |
| Disposition | | | | | | <0.001 |
| Routine | 20250 (58.1%) | 10840 (62.6%)a | 6843 (53.3%)b | 1228 (57.7%)c | 1339 (51.7%)d | |
| Short-term Hospital | 398 (1.1%) | 190 (1.1%)a | 161 (1.3%)a | 27 (1.3%)a | 20 (0.8%)a | |
| Skilled Nursing/ Other Facility | 6179 (17.7%) | 2717 (15.7%)a | 2559 (19.9%)b | 387 (18.2%)b | 516 (19.9%)b | |
| Home Health Care | 7787 (22.3%) | 3427 (19.8%)a | 3189 (24.9%)b, c | 473 (22.2%)a, c | 698 (27.0%)b | |
| Against Medical Advice | 225 (0.6%) | 125 (0.7%)a | 71 (0.6%)a | 15 (0.7%)a | 14 (0.5%)a | |
| Discharge alive, destination unknown | 13 (0.0%) | 6 (0.0%)a | 6 (0.0%)a | 0 (0.0%)a | 1 (0.0%)a | |
| Location | | | | | | 0.007 |
| Large central counties | 10033 (28.8%) | 4871 (28.1%)a | 3847 (30.0%)b | 571 (26.8%)a | 744 (28.7%)a, b | |
| Large fringe counties | 9795 (28.1%) | 4813 (27.8%)a | 3652 (28.5%)a | 594 (27.9%)a | 736 (28.4%)a | |
| Medium metro counties | 7309 (21.0%) | 3697 (21.4%)a | 2606 (20.3%)a | 478 (22.4%)a | 528 (20.4%)a | |
| Small metro counties | 3269 (9.4%) | 1652 (9.5%)a | 1144 (8.9%)a | 218 (10.2%)a | 255 (9.9%)a | |
| Micropolitan counties | 2489 (7.1%) | 1250 (7.2%)a | 895 (7.0%)a | 159 (7.5%)a | 185 (7.1%)a | |
| Not metro/ micropolitan counties | 1957 (5.6%) | 1022 (5.9%)a | 685 (5.3%)a | 110 (5.2%)a | 140 (5.4%)a | |
| LOS (days) (mean (SD)) | 7.4 (8.9) | 7.4 (8.9)a | 6.9 (8.4)b | 9.5 (11.6)c | 8.1 (8.4)d | <0.001 |
| LOS > 7 days | 10861 (31.2%) | 5376 (31.1%)a | 3696 (28.8%)b | 860 (40.4%)c | 929 (35.9%)d | <0.001 |
| Total Charges ($) (mean (SD)) | 87260.1 (121839.4) | 88067.5 (126436.0)a | 80442.4 (109012.6)b | 112913.9 (146002.6)c | 94544.4 (125521.9)b | <0.001 |
| Comorbidities | Comorbidities | Comorbidities | Comorbidities | Comorbidities | Comorbidities | Comorbidities |
| Metabolically unhealthy | 15417 (44.2%) | 0 (0.0%)a | 12829 (100.0%)b | 0 (0.0%)a | 2588 (100.0%)b | <0.001 |
| Obesity | 4718 (13.5%) | 0 (0.0%)a | 0 (0.0%)a | 2130 (100.0%)b | 2588 (100.0%)b | <0.001 |
| Hyperglycemia | 10108 (29.0%) | 846 (4.9%)a | 7272 (56.7%)b | 168 (7.9%)c | 1822 (70.4%)d | <0.001 |
| Dyslipidemia | 13126 (37.7%) | 1343 (7.8%)a | 9801 (76.4%)b | 150 (7.0%)a | 1832 (70.8%)c | <0.001 |
| Hypertension | 24418 (70.1%) | 8306 (48.0%)a | 12417 (96.8%)b | 1198 (56.2%)c | 2497 (96.5%)d | <0.001 |
| Heart failure | 7826 (22.5%) | 2661 (15.4%)a | 3880 (30.2%)b | 371 (17.4%)a | 914 (35.3%)c | <0.001 |
| Renal failure | 17244 (49.5%) | 7233 (41.8%)a | 7507 (58.5%)b | 956 (44.9%)c | 1548 (59.8%)d | <0.001 |
| Coronary heart disease | 6752 (19.4%) | 1845 (10.7%)a | 4009 (31.2%)b | 206 (9.7%)a | 692 (26.7%)c | <0.001 |
| Neoplastic anemia | 2938 (8.4%) | 1470 (8.5%)a | 1072 (8.4%)a | 185 (8.7%)a | 211 (8.2%)a | 0.891 |
| Neutropenia | 2034 (5.8%) | 1295 (7.5%)a | 490 (3.8%)b | 159 (7.5%)a | 90 (3.5%)b | <0.001 |
| Depression | 4058 (11.6%) | 1823 (10.5%)a | 1574 (12.3%)b | 281 (13.2%)b, c | 380 (14.7%)c | <0.001 |
## Definition
The readmission was defined as unplanned readmission to the hospital due to any diagnosis within 30 days, 60 days, 90 days, and 180 days from index discharge. If there were more than one readmission, only the first readmission was counted. Over-weight and obesity was defined as BMI ≥ 25 kg/m2 [15, 16]. There was still lack of consensus on the definition of metabolic health status. However, many studies defined metabolic unhealthy status as presence of ≥2 metabolic risk factors [17, 18]. According to the Adult Treatment Panel III (ATP-III) criteria and the International Diabetes Federation (IDF) consensus [19, 20], we defined metabolic risk factors including [1] hypertension: primary hypertension or secondary hypertension or undiagnosed elevated blood pressure; [2] dyslipidemia: high serum triglyceride (TGs) levels or high high-density lipoprotein (HDL)-cholesterol levels, etc.; [ 3] hyperglycemia: pre-diabetes or diabetes mellitus or other specific diabetes. Abdominal obesity was not included in the models because of the collinearity of waist circumference and BMI. Metabolically unhealthy status was defined as with two or more of the above metabolic risk factors. The codes used were listed in Supplementary Table 1. Based on the obesity and metabolic status, individuals were classified into four different phenotypes; [1] metabolically healthy non-obesity (MHNO); [2] metabolically unhealthy non-obesity (MUNO); [3] metabolically healthy obesity (MHO); and [4] metabolically unhealthy obesity (MUO). For 90-day analysis, based on combined metabolic risk factor type only, individuals were further divided into: [1] no metabolic risk factor; [2] only with hyperglycemia; [3] only with dyslipidemia; [4] only with hypertension. In addition, based on the number of combined metabolic risk factors, individuals were further divided into: [1] no metabolic risk factor; [2] one metabolic risk factor; [3] two metabolic risk factors; [4] three metabolic risk factors.
## Outcomes
The primary outcome of the study was all-cause unplanned readmissions of different metabolic obesity phenotypes for four follow-up days among patients with MM. The secondary outcomes were the LOS, mean total hospitalization charges, and readmission mortality during the readmission.
## Statistical analysis
We used descriptive statistics to compare the demographic and admission characteristics of patients in different metabolic obesity phenotypes. The Chi-square test was used to analyze categorical variables, and the ANOVA was used to analyze continuous variables. Categorical variables were presented as counts with percentages. Continuous variables were presented as means and standard deviation (SD). We estimated the unadjusted hazard ratio (HR) with the univariate Cox proportional hazards model. In addition, the multivariate Cox regression model was used to analyze adjusted HR (aHR) value including the potential confounding factors. Based on the associations found from the literature and univariate analysis, we adjusted factors such as age, gender, elective versus non-elective admission, primary payer, disposition of patient, resident, length of stay, total charges, emergency record, same day events, patient location, antineoplastic chemotherapy, and stem cells transplant status. All calculated P-values were two-sided, and the threshold for significance was set at $P \leq 0.05.$ All statistical analyses were performed by SPSS software (version 26.0; SPSS, Chicago, IL).
## Patient baseline characteristics
From 17,686,511 discharge records in NRD 2018, we included 34,852 patients diagnosed with MM at index discharge in the cohort study (Figure 1). For 30-day, 60-day, 90-day and 180-day follow-ups, we analyzed 34,852, 32,334, 29,747 and 22,032 participants, respectively (Figure 1). Table 1 provided the demographic characteristics and common comorbidities of patients with different metabolic obesity phenotypes.
The mean age of the population was 69.8 years old, and the elderly aged 65 or above accounted for $69.4\%$ (Table 1). The majority of patients were male ($55.3\%$) and used privately insured ($70.0\%$). There were 4718 ($13.5\%$) patients with obesity and 15417 ($44.2\%$) patients with metabolically unhealthy status.
The MHNO group was the largest of the four phenotypes, and the MHO had the longest mean LOS (9.5 days) and highest mean total charges ($112913.9). Compared with MHNO and MHO groups, MUNO and MUO groups had more elder patients (>65 years) and higher prevalence of heart failure, renal failure, and coronary heart disease. MUNO, MHO, and MUO groups had higher prevalence of depression than the MHNO group (Table 1).
## Readmission risk
For 30-day, 60-day, 90-day and 180-day follow-up, we observed that 5,400 ($15.5\%$), 7,255 ($22.4\%$), 8,025 ($27.0\%$) and 7,839 ($35.6\%$) patients experienced unplanned readmissions (Supplementary Table 2), respectively. Figure 2 showed the readmission rate among different metabolic obesity phenotypes in the four cohorts. The MUNO and MUO groups had higher readmission rate than MHNO and MHO groups in 60-day, 90-day, and 180-day studies, respectively (Figure 2).
**Figure 2:** *Readmission rate among different metabolic obesity phenotypes in four follow-up days. MHNO, metabolically healthy non-obese; MUNO, metabolically unhealthy non-obese; MHO, metabolically healthy obese; MUO, metabolically unhealthy obese.*
In the 30-day and 60-day research, we observed that there were no differences in readmission risk among different metabolic obesity phenotypes (Figures 3A, B, Supplementary Table 2). In the 90-day and 180-day research, patients in the metabolically unhealthy groups (MUNO and MUO) had a higher readmission rate than those in the metabolically healthy group (MHNO and MHO), respectively (Supplementary Table 2). In 90-day analysis, patients in the MUNO group [aHR = 1.07 (1.023-1.128), $$P \leq 0.004$$] and the MUO group [aHR = 1.089 (1.000-1.186), $$P \leq 0.049$$] had higher readmission risk than those in the MHNO group (Figure 3C, Supplementary Table 2). In 180-day analysis, we also observed higher risk of readmission in the MUNO group [aHR = 1.092 (1.039-1.147), P = < 0.001] and the MUO group [aHR = 1.133 (1.040-1.234), $$P \leq 0.004$$] than MHNO (Figure 3D, Supplementary Table 2), respectively.
**Figure 3:** *Readmission risk among different metabolic obesity phenotypes in four follow-up days. (A) Readmission risk in 30-day. (B) Readmission risk in 60-day. (C) Readmission risk in 90-day. (D) Readmission risk in 180-day. The model was adjusted for age, sex, elective admission, primary payer, disposition of patient, resident, length of stay, total charges, emergency record, same day events, patient location, antineoplastic chemotherapy, and stem cells transplant status. aHR, adjusted hazard ratio; CI, confidence interval; MHNO, metabolically healthy non-obese; MUNO, metabolically unhealthy non-obese; MHO, metabolically healthy obese; MUO, metabolically unhealthy obese.*
The 90-day data was then processed for further analysis. When stratified by age, higher risk in the MUNO group [aHR = 1.063 (1.005-1.125), $$P \leq 0.032$$] and the MUO group [aHR = 1.12 (1.009-1.242), $$P \leq 0.033$$] than in the MHNO was only observed in the elderly population aged 65 or older (Supplementary Table 3). As shown in subgroups based on metabolic risk factor type (Table 2), patients with hyperglycemia only [HR = 1.193 (1.044-1.364), $$P \leq 0.01$$] and hypertension only [HR = 1.091 (1.022-1.164), $$P \leq 0.009$$] had increased risk of readmission compared with those without metabolic risk factors. However, similar result was not observed after correction for confounding factors. In subgroups based on the number of metabolic risk factors, we found that patients with metabolic risk factors had higher readmission risk than those without metabolic risk factors. Furthermore, the risk elevated with the increase of the number of combined factors (Table 2), with aHR of 1.068 ($$P \leq 0.043$$, combined with one metabolic risk factor), 1.109 ($$P \leq 0.002$$, combined with two metabolic risk factors), and 1.125 ($$P \leq 0.003$$, combined with three metabolic risk factors).
**Table 2**
| Unnamed: 0 | Total Number | Number of Readmissions | HR (95% CI) | P value | aHR* (95% CI) | P value.1 |
| --- | --- | --- | --- | --- | --- | --- |
| Metabolic obesity phenotypes | Metabolic obesity phenotypes | Metabolic obesity phenotypes | Metabolic obesity phenotypes | Metabolic obesity phenotypes | Metabolic obesity phenotypes | Metabolic obesity phenotypes |
| MHNO | 14810 | 3804 (25.7%) | Reference | Reference | Reference | Reference |
| MUNO | 10930 | 3098 (28.3%) | 1.113 (1.061-1.167) | < 0.001 | 1.074 (1.023-1.128) | 0.004 |
| MHO | 1829 | 497 (27.2%) | 1.063 (0.968-1.168) | 0.198 | 1.068 (0.972-1.174) | 0.170 |
| MUO | 2178 | 626 (28.7%) | 1.131 (1.039-1.231) | 0.004 | 1.089 (1.000-1.186) | 0.049 |
| Metabolic risk factor type | Metabolic risk factor type | Metabolic risk factor type | Metabolic risk factor type | Metabolic risk factor type | Metabolic risk factor type | Metabolic risk factor type |
| No Metabolic Risk Factor | 6346 | 1560 (24.6%) | Reference | Reference | Reference | Reference |
| Only With Hyperglycemia | 866 | 249 (28.8%) | 1.193(1.044-1.364) | 0.010 | 1.141 (0.997-1.305) | 0.056 |
| Only With Dyslipidemia | 1275 | 328 (25.7%) | 1.054(0.936-1.188) | 0.383 | 1.061 (0.940-1.196) | 0.339 |
| Only With Hypertension | 8152 | 2164 (26.5%) | 1.091(1.022-1.164) | 0.009 | 1.056 (0.988-1.129) | 0.109 |
| Metabolic risk factor numbers | Metabolic risk factor numbers | Metabolic risk factor numbers | Metabolic risk factor numbers | Metabolic risk factor numbers | Metabolic risk factor numbers | Metabolic risk factor numbers |
| No Metabolic Risk Factor | 6346 | 1560 (24.6%) | Reference | Reference | Reference | Reference |
| One Metabolic Risk Factor | 10293 | 2741 (26.6%) | 1.095 (1.029-1.166) | 0.004 | 1.068 (1.002-1.137) | 0.043 |
| Two Metabolic Risk Factors | 9012 | 2540 (28.2%) | 1.162 (1.091-1.238) | < 0.001 | 1.109 (1.038-1.184) | 0.002 |
| Three Metabolic Risk Factors | 4096 | 1184 (28.9%) | 1.198 (1.110-1.292) | < 0.001 | 1.125 (1.040-1.216) | 0.003 |
## Death and healthcare during readmission
Of the patients discharged from their index hospitalization and followed for 30-days, 60-days, 90-days, and 180-days, 5400 ($15.5\%$), 7255 ($22.4\%$), 8025 ($27.0\%$), 7839 ($35.6\%$) were readmitted, respectively. During the readmission, the MUO group were more likely to have LOS over 7 days than those in the MHNO group at 30-days ($36.3\%$ vs. $28.5\%$, $p \leq 0.05$), 60-days ($35.1\%$ vs. $28.1\%$), $p \leq 0.05$), and 90-days ($32.9\%$ vs. $27.3\%$, $p \leq 0.05$) (Table 3). At 90-day readmission, patients in the MHO group had higher total charges than the MUNO group ($ 88752.3 vs. $ 73876.1, $p \leq 0.05$; Table 3). However, no significant difference in mortality was found among the four metabolic obesity phenotypes.
**Table 3**
| Variables | Total | MHNO | MUNO | MHO | MUO | P value* |
| --- | --- | --- | --- | --- | --- | --- |
| 30-day | 30-day | 30-day | 30-day | 30-day | 30-day | 30-day |
| total charge ($) (mean (SD)) | 81234.7 (125096.4) | 81443.5 (119524.0) | 78280.3 (127669.8) | 97277.6 (170249.6) | 81190.3 (100843.0) | 0.084 |
| LOS>7 days | 1604 (29.7%) | 756 (28.5%)a | 592 (29.5%)a | 109 (32.4%)a, b | 147 (36.3%)b | 0.009 |
| Died | 415 (7.7%) | 187 (7.0%) | 163 (8.1%) | 33 (9.8%) | 32 (7.9%) | 0.236 |
| 60-day | 60-day | 60-day | 60-day | 60-day | 60-day | 60-day |
| total charge ($) (mean (SD)) | 79189.5 (119116.5) | 79462.1 (115793.5)a | 76875.6 (120066.0)a | 90473.4 (155932.8)a | 79680.9 (98411.9)a | 0.002 |
| LOS>7 days | 2100 (28.9%) | 976 (28.1%)a | 784 (28.5%)a | 141 (30.9%)a, b | 199 (35.1%)b | 0.005 |
| Died | 494 (6.8%) | 233 (6.7%) | 184 (6.7%) | 38 (8.3%) | 39 (6.9%) | 0.613 |
| 90-day | 90-day | 90-day | 90-day | 90-day | 90-day | 90-day |
| total charge ($) (mean (SD)) | 76972.9 (113977.2) | 78094.8 (112106.4)a,b | 73876.1 (113771.1)a | 88752.3 (148659.1)b | 76129.4 (92002.0)a,b | 0.046 |
| LOS>7 days | 2232 (27.8%) | 1 040 (27.3%)a | 839 (27.1%)a | 147 (29.6%)a, b | 206 (32.9%)b | 0.018 |
| Died | 529 (6.6%) | 253 (6.7%) | 197 (6.4%) | 41 (8.2%) | 38 (6.1%) | 0.425 |
| 180-day | 180-day | 180-day | 180-day | 180-day | 180-day | 180-day |
| total charge ($) (mean (SD)) | 75536.0 (109226.8) | 76448.0 (108947.0) | 72910.9 (107397.7) | 86417.3 (136745.0) | 74623.8 (94644.7) | 0.079 |
| LOS>7 days | 2116 (27.0%) | 990 (26.7%)a | 786 (25.8%)a | 150 (31.5%)a | 190 (30.8%)a | 0.008 |
| Died | 458 (5.8%) | 212 (5.7%) | 181 (5.9%) | 32 (6.7%) | 33 (5.4%) | 0.781 |
## Discussion
With the increasing prevalence of MM in elderly patients, it is important to focus on its risk factors to identify high-risk patients and reduce the burden of disease. In this retrospective cohort study based on a representative NRD database, we analyzed 34,852 patients diagnosed with MM from the National Readmission Database (NRD) in the United States. According to obesity and metabolic status, the population was divided into four phenotypes: metabolically healthy non-obese (MHNO), metabolically unhealthy non-obese (MUNO), metabolically healthy obese (MHO), and metabolically unhealthy obese (MUO). The patients with different phenotypes were observed for hospital readmission at days 30-day, 60-day, 90-day and 180-day. We found that the metabolically unhealthy (MUNO and MUO) individuals had higher risk of 90-day and 180-day readmission than MHNO individuals. In the age subgroup analysis of 90-day, we observed similar results only in the older group (≥ 65 years of age). Moreover, further analysis found that the risk of readmission increased significantly as the number of metabolic risk factors increased. Analysis of hospitalization characteristics of readmission patients showed that patients with metabolically unhealthy obesity (MUO) were at a higher risk of long-term hospitalization than patients in other groups.
MM is a hematological malignancy with genetic abnormalities. Patients with MM had higher incidence of some modifiable risk factors, such as obesity, hyperglycemia, dyslipidemia, and hypertension (21–23). This is more likely due to the metabolic dysregulation of MM. To ensure energy demands for rapid cell proliferation and tumor growth, myeloma cells reprogram the metabolic pathways, involving metabolic reorganization of glycolysis and oxidative phosphorylation, abnormal fatty acid metabolism, and chronic inflammation [24, 25]. Overweight and obesity are associated with increased morbidity and mortality risk of MM through inflammatory cytokines, leptin, insulin, and insulin-like growth factor levels [26]. Previous research found that obesity could increase the number and size of bone marrow adipocytes to obtain energy and induce the overexpression of protumor cytokines [27]. For MM patients with diabetes, tumor cells can evade apoptosis by insulin resistance, hyperinsulinemia, and overproduction of insulin-like growth factor 1 [25]. Dyslipidemia in MM has also been widely reported. The prevailing view is that the binding of paraproteins to serum lipoproteins and related tissue may result in reduced lipoproteins clearance [21, 28]. However, some studies have also pointed out that myeloma cells are dependent upon exogenous cholesterol for survival, such as low density lipoprotein that is an important antiapoptotic drug and may prevent myeloma cell apoptosis and promote myeloma cell survival [29]. In addition, the higher incidence of hypertension is related to the increased cardiovascular complications in MM patients [23].
However, previous studies did not compare the effects of obesity and metabolic disorders on MM. Our analyses indicated that metabolic status rather than obesity was the determinant risk of MM readmissions. This seems to confirm that metabolic disorders have a stronger effect on MM than obesity in some respects. We found that the prevalence of cardiovascular complications was significantly higher in the metabolically unhealthy (MUNO and MUO) groups than in the metabolically healthy (MHNO and MHO) groups, which suggests that the readmission risk of metabolically unhealthy patients may be related to a higher burden of comorbidities. This was consistent with the conclusions of some previous studies (13, 30–32). In a prospective cohort study with 30 years follow-up, metabolically unhealthy individuals are at higher cardiovascular disease risk across all BMI categories, and the transition from metabolically healthy status to unhealthy phenotypes is also associated with increased cardiovascular disease risk [33]. MUO is more likely to show insulin resistance, adverse fat distribution, higher inflammation markers, and adipose tissue dysfunction than MHO [13, 34]. In fact, these factors are associated with higher risk of atherosclerosis and cardiovascular disease [35, 36]. Furthermore, as an elderly disease, MM is more likely to be affected by severe comorbidities. Additionally, metabolic disorders may affect the clinical manifestations of MM in terms of the drug efficacy and tumor cell activity [25]. Metabolically unhealthy patients may face a lower intensity of treatment [21, 34] and poorly executed care [25], which will affect the treatment efficacy of the disease.
Although there has been substantial evidence to show that obesity is the only modifiable risk factor for MM [26, 37], other studies have observed different phenomena. Increased BMI was not significantly associated with adverse outcomes in MM patients [10, 38] and was even associated with lower morbidity and longer overall survival [11]. As suggested in recent literature, the influence of BMI on MM may depend on different disease stages [26]. For normal individuals or patients in the early stages of the disease, elevated BMI was associated with higher mortality and disease progression, while for MM patients in a transplant or relapsing state, a higher BMI meant patients could tolerate better treatment and experience less disease-related weight loss [26]. In summary, the role of obesity in MM is complex. However, previous studies did not consider the correlation between obesity and metabolic disorders. Our study found that the MM-related readmission risk, as a result of disease progression or unexpected complications, was affected by metabolic disorders, which may imply that modifiable metabolic risk factors play a more significant role than obesity in the short-term disease progression of MM patients.
As mentioned above, all metabolic risk factors can induce adverse outcomes in MM patients through multiple pathways and mechanisms. Our analysis demonstrated the cumulative effect of metabolic risk factors numbers on the risk of MM readmission, which was supported by previous studies [39]. This result is worrying for MM patients with multiple metabolic risk factors, especially for the elderly patients. Interestingly, we also observed that the association between metabolically unhealthy status and readmission risk appeared in 90-day and 180-day studies, rather than in 30-day and 60-day studies. Disease progression within 60 days after treatment is commonly referred to as refractory MM [40], which is often related to cytogenetic risk [40, 41]. This might indicate that, unlike the short-term influence of refractory mechanisms, metabolic disorders influence the longer-term development of MM.
Although few studies directly explore the association between metabolic disorders and obesity in MM, based on our findings, we speculate that the higher risk of readmission in metabolically unhealthy patients with MM is related, at least in part, to the above mechanisms. From our study, we have reason to believe that metabolic disorders have a greater impact on MM readmissions than obesity. We found that patients with metabolically unhealthy status had a higher readmission rate and readmission risk than those with metabolically healthy status. Meanwhile, patients with metabolic abnormalities and obesity may have a longer time in re-hospitalization stay. Through targeted interventions, such as diet, exercise and drug treatment, metabolic risk factors and quantity can be controlled to reduce the occurrence of adverse disease outcomes reduce the disease burden of MM. Readmission is considered to be a result of disease progression or unexpected complications on initial admission. Although our study may help to identify people who have a high risk of readmission, it is not clear whether the readmission of metabolically unhealthy patients is due to progression of the disease itself or related complications. Therefore, further investigations are necessary to explore the mechanisms of the effect of metabolic disorders on the development of MM.
Although a large sample size of patients was used for follow-up, our study had many other limitations. First, all diseases were diagnosed based on the ICD-10 codes, and we could not verify the accuracy of disease diagnoses in our study. Secondly, obesity defined by BMI did not consider the effect of fat distribution on the disease, which had implications for MM outcomes [15]. Moreover, due to data limitations, the stage, karyotype, oncogenic mutations and treatment regimen information of MM patients were not available, which could have influenced the results. To reduce these effects, we corrected for patient antitumor chemotherapy and stem cell transplant status in the analysis, which were the primary treatment for MM. Although we did not perform a subgroup analysis based on MM stage and treatment type due to the limitations of the database, the results of this study have good representativeness and reliability among MM patients based on the large patient population in the NRD database. Further studies in more detailed cohorts are required to validate the relationship of obesity and metabolism to MM specific disease characteristics. In addition, the analyzed data were from NRD 2018, before the coronavirus disease 2019 (COVID-19) epidemic, and we were unable to assess the NRD data during the pandemic. Therefore, our analysis could not take into account the effect of COVID-19 on the results. Recent studies have shown that COVID-19 increased rehospitalizations and mortality rates in patients and had adverse effects on cancer patients, including MM (42–44). However, obesity and metabolic risk factors contribute to COVID-19 infection and adverse outcomes and increase the risk of hospitalization [45, 46]. Thus, we speculate that during the pandemic, COVID-19, obesity and metabolic risk factors could increase the risk of readmission and adverse outcomes in MM patients jointly. Future clinical practice should focus on patients with obesity and metabolic abnormalities, control the occurrence of metabolic abnormalities and the number of metabolic risk factors, and prevent the adverse effects of obesity and metabolism on patients with cancer and COVID-19.
## Conclusion
Taken together, we observed that metabolic disorders, not obesity, were independently associated with higher readmission risk in patients with MM. Moreover, as the number of metabolic risk factors increased, the risk of MM readmission elevated. In the management of MM, attention should be paid to advance directives and optimized nursing for patients with metabolic disorders to reduce the readmission rate and hospitalization burden.
## 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 Biomedical Research Ethic Committee of Shandong Provincial Hospital. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
We kindly acknowledge the participants and researchers who participated in this study. YZ, XF, KL and YFZ contributed to the design of the study protocol. YZ performed the data analyses and wrote the first draft of the manuscript. JH and ZSY contributed to the analysis plan. Chunhui Zhao contributed to the data analysis. ZNY, YC and YW revised the manuscript to create the final version. All authors proofread the manuscript and agreed with the submission and 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/fonc.2023.1116307/full#supplementary-material
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|
---
title: 'The association of insufficient gestational weight gain in women with gestational
diabetes mellitus with adverse infant outcomes: A case-control study'
authors:
- Dabin Huang
- Mulin Liang
- Bin Xu
- Shan Chen
- Yan Xiao
- Hui Liu
- Dan Yin
- Jun Yang
- Ling Wang
- PianPian Pan
- Yihui Yang
- Wei Zhou
- Juncao Chen
journal: Frontiers in Public Health
year: 2023
pmcid: PMC9996046
doi: 10.3389/fpubh.2023.1054626
license: CC BY 4.0
---
# The association of insufficient gestational weight gain in women with gestational diabetes mellitus with adverse infant outcomes: A case-control study
## Abstract
### Background
To investigate the association between insufficient maternal gestational weight gain (GWG) during dietary treatment, and neonatal complications of small-for-gestational-age (SGA) infants born to mothers with Gestational diabetes mellitus (GDM).
### Methods
A retrospective case-control study was conducted, involving 1,651 infants born to mothers with GDM. The prevalence of a perinatal outcome and maternal GWG were compared among SGA, adequate- (AGA), and large-for-gestational-age (LGA); association with birth weight and GWG was identified using Pearson's correlation analysis; binary logistic regression was performed to determine the odds ratio (OR) associated with SGA.
### Results
In total, 343 SGA, 1025 AGA, and 283 LGA infants met inclusion criteria. The frequency of SGA infants who were siblings (41.7 vs. 4.3 vs. $1.9\%$) and composite of complications (19.2 vs. 12.0 vs. $11.7\%$) were higher in SGA infants than in those in AGA or LGA infants group (both $P \leq 0.01$). GWG and pre-partum BMI were lower among the SGA mothers with GDM group (11.7 ± 4.5 kg, 25.2 ± 3.1 kg/m2) than AGA (12.3 ± 4.6 kg, 26.3 ± 3.4 kg/m2) or LGA (14.0 ± 5.1 kg, 28.7 ± 3.9 kg/m2) mothers with GDM group. Binary logistic regression showed that siblings who were SGA (AOR 18.06, $95\%$ CI [10.83–30.13]) and preeclampsia (AOR 3.12, $95\%$ CI [1.34–7.30]) were associated with SGA, but not GWG below guidelines ($P \leq 0.05$). The risk of SGA (25.7 vs. 19.1 vs. $14.2\%$) and FGR (15.3 vs. 10.9 vs. $7.8\%$) was higher in GWG below guidelines group than those in GWG above and within guidelines group, the risk of low Apgar score (6.4 vs. 3.0 vs. $2.8\%$) was higher in GWG above guidelines group than that in GWG below and within guidelines group ($P \leq 0.05$).
### Conclusion
Our findings demonstrated that GWG above and below guidelines, compared with GWG within guidelines, had a higher risk of adverse infant outcomes. Our findings also suggested that GWG below guidelines did not increase the risk for SGA, though SGA infants had more adverse outcomes among neonates born to mothers with GDM.
## Introduction
Gestational diabetes mellitus (GDM), a common pregnancy complication, affects 1–$28\%$ of all pregnancies. Maternal GDM contributes to short- and long-term health risks both for mothers (including polycystic ovarian syndrome, obesity, and type 2 diabetes) and children (including respiratory distress syndrome, hypoglycemia, hyperbilirubinemia, obesity, and metabolic syndrome) [1]. The majority of newborns born to women with GDM are large-for-gestational-age (LGA) infants.
At present, the treatment of GDM includes dietary interventions and drugs. The current first-line therapy for GDM is dietary interventions, including lifestyle changes, weight management, physical activity, and medical nutrition therapy; 70–$80\%$ of GDM women were given dietary interventions to control blood glucose (2–4). Dietary interventions can reduce gestational weight gain (GWG). The targets of GWG during dietary interventions in many countries refer to the recommendations of the US Institute of Medicine (IOM), which were updated in 2009 based on pre-pregnancy body mass index (BMI) [5]. GWG is an important antenatal factor, a few studies demonstrated that insufficient GWG (GWG below the IOM guidelines) during pregnancy was associated with a higher incidence of small-for-gestational-age (SGA) [6, 7]. Studies demonstrated that SGA infants born to mothers with GDM had a higher risk of hypoglycemia and hyperbilirubinemia in infants and long-term cardiovascular hospitalizations in adulthood than LGA or appropriate-for-gestational-age (AGA) (8–10). However, it is not known if SGA associated with GDM is a risk factor for other perinatal complications. Moreover, the frequency of SGA born to mothers with GDM who were given dietary treatment has been reported to be as high as $11\%$, and this rate is increasing worldwide; the prevalence of insufficient GWG is also increasing [11, 12]. Additionally, there is limited research on the association of insufficient GWG with SGA infants born to mothers with GDM. To better understand the relationship between insufficient GWG during the dietary intervention and perinatal outcomes, the aim of this study was to examine whether abnormal GWG will increase the frequency of SGA and the risk of adverse outcomes.
## Study design and participants
This study was a population-based, retrospective case-control study and approved by the research ethical committee in Guangzhou Women and Children's Medical Centre of Guangzhou Medical University, Number 2014121402, date of approval 12 December 2014. The selection of participants is shown in Figure 1. All parents provided written informed consent, and the ethics committee approved this consent procedure. This study was conducted from December 2014 to March 2022.
**Figure 1:** *Flow diagram of study population. AGA, appropriate for gestational-age; GDM, gestational diabetes mellitus; LGA, large-for-gestational-age; SGA, small for gestational age.*
The inclusion criteria were the followings: [1] mothers were diagnosed with GDM with singleton pregnancy and live birth; [2] mothers were older than 18 years; [3] mothers/infant pairs with complete maternal (delivery data, complication during pregnancy) and neonatal data (birth data, in-hospital outcomes); 4) all newborns were given routine examinations. The exclusion criteria: [1] infants presented with chromosomal aberrations, and genetic syndromes; [2] mothers with concomitant pathology that could affect glucose metabolism (such as diabetes, maturity-onset diabetes in young, polycystic ovarian syndrome, and uncontrolled thyroid during pregnancy); [3] mothers weren't given regular obstetric examinations. Only one birth infant for mother was included in our study. GDM was diagnosed according to guidelines for the diagnosis and treatment of GDM in China [13]. The guidelines are similar to the American Diabetes Association and International Association of Diabetes and Pregnancy Study Group (IADPSG) guidelines [14]. After the diagnosis of GDM, all GDM women who underwent regular obstetric examinations in hospitals received individualized dietary consultation with a dietitian. Individual recommendations for diet and exercise were based on the guidelines for the treatment of GDM in the China [13], and estimations of daily energy intake and nutrients were computed using a food database. Dietary recommendations were based on the following principles: restricting dietary intake of saturated fat and exchanging carbohydrate-rich foods with a medium-to-high glycaemic index for foods with a lower glycaemic index to reduce the glycaemic load. All women were recommended to participate in the aerobic and strength-conditioning exercises. The women were informed about weekly maternal GWG in late pregnancy, based on their pre-pregnancy BMI, independent of their GWG before GDM diagnosis as follows: gain of 12.5–18 kg for under-weight women (BMI < 18.5 kg/m2), 11.5–16 kg for normal women (BMI 18.5–24.9 kg/m2) and 7–11 kg for overweight women (BMI 25–29.9 kg/m2), 5–9 kg for obese women (BMI ≥30 kg/m2), and physical activity for at least 30 min/day were recommended.
All infants were divided into the following three groups: SGA infants born to mothers with GDM, AGA infants born to mothers with GDM, and LGA infants born to mothers with GDM. The diagnostic criteria of SGA infants: infants whose birth weight is below the 10th percentile at gestational age. The diagnostic criteria of AGA infants: infants whose birth weight is between the 10th and 90th percentile at gestational age. LGA was defined by a birth weight >90th percentile at gestational age. Classification of all newborns was defined according to the Fenton growth chart. GWG was defined as the difference between the final weight before delivery (within the last week before giving birth) and the pre-pregnancy weight (within 3 months before pregnancy). Based on the guidelines for maternal GWG of IOM, GWG was divided into the following three groups for analysis: GWG below guidelines (insufficient GWG), GWG within guidelines (sufficient GWG), and GWG above guidelines (excessive GWG). We performed matching according to neonatal gestational age (difference was ≤ 3 days), sex, and maternal age (difference was ≤ 3 years).
## Data collection
Each mother/infant pair's demographic data, intervention condition, and medical information were collected individually using medical records. All mother/infant pairs underwent structured medical examinations and physical examinations. The following data on siblings of included infants also were recorded: maternal blood glucose after a 75-g OGTT, glycated-hemoglobin (Hb) level, gestational age at delivery, sex, birth weight and height of newborn, and mode of delivery. The weight and length/height of mother/infant pairs were measured by trained nurses using standard anthropometric methods, and pre-pregnancy weight was obtained according to maternal self-report. BMI was calculated by dividing weight in kilograms by the square of height in meters. Early thrombocytopenia was defined as a platelet count of < 150 × 10∧9/L in the first 72 h of life. Fetal growth restriction (FGR) was determined by ultrasound, early FGR was defined as the gestational age was < 32 weeks and the following criteria were present: estimated fetal weight (EFW) or abdominal circumference (AC) below the 3rd percentile for the gestational age or absent end-diastolic flow in umbilical artery (UA); late FGR was defined as the gestational age was>32 weeks and the following criteria were present: EFW or AC below the 3rd percentile for the gestational age. Hypoglycemia was defined as blood glucose < 35 mg/dl or plasma glucose < 40 mg/dl within the first 48 h of life. Low Apgar score was defined as a 1-min Apgar score ≤ 7. Necrotizing enterocolitis (NEC) was diagnosed according to Bell criteria. Neonatal respiratory distress syndrome (NRDS) was diagnosed by [1] evidence of respiratory failure, [2] administration of exogenous pulmonary surfactant [3] radiographic evidence. Symptomatic polycythemia was defined as venous hematocrit >65 % or hemoglobin >220 g/L. Prematurity was defined as gestational age at birth of < 37 weeks. The presence of at least one neonatal complication (early thrombocytopenia, hypoglycemia, low Apgar score, NRDS, NEC, or polycythemia) was assessed.
Data entry was performed by a trained clerk and a supervisor. Data were cross-checked by the co-author (VF) for any errors and discrepancies. Data entered incorrectly will be examined and corrected by the supervisor after confirmation with the participants or their obstetric records. Any revision of the original data will be tracked in detail.
## Statistical analysis
Sample size calculation was performed using PASS 15. With $90\%$ power and the assumption of relative risk = 2.0, we calculated that 256 SGA infants born to mothers with GDM were needed. Data were analyzed by using SPSS version 22 (SPSS, Chicago, IL, USA). Means (standard deviations) or median (range) was used to describe continuous variables; t-tests, ANOVA or kruskal-wallis test were used to analyze the differences in continuous variables. A two-sided chi-squared or Fisher's exact test was used for categorical variables presented as numbers and percentages. Binary logistic regression was used to determine risk factors for SGA infants born to mothers with GDM after adjusting for potential confounding variables. The association between birth weight and maternal GWG, pre-partum period BMI, and pre-pregnancy BMI was identified using Pearson's correlation analysis. Differences were considered statistically significant at a two-sided P value of < 0.05.
## Results
Overall, the final sample consisted of 1,651 mother/infant pairs. Of these, 343 SGA infants born to mothers with GDM, 1,025 AGA infants born to mothers with GDM, and 283 LGA infants born to mothers with GDM. There were 92 ($5.6\%$) premature births, with 8 ($2.3\%$) in the SGA infants born to mothers with GDM.
Relevant characteristics of mothers are presented in Table 1. We observed that GWG and Pre-partum BMI were lower in the SGA mothers with GDM group (11.7 ± 4.5 kg, 25.2 ± 3.1 kg/m2) than that in AGA (12.3 ± 4.6 kg, 26.3 ± 3.4 kg/m2) or LGA (14.0 ± 5.1 kg, 28.7 ± 3.9 kg/m2) mothers with GDM group (both $P \leq 0.001$). Preeclampsia was higher in SGA mothers with GDM than that in AGA mothers or LGA mothers (8.5 vs. 2.3 vs. $2.5\%$; $P \leq 0.001$). The incidence of primiparity ($61.5\%$) and GWG below guidelines ($50.4\%$) were highest in the SGA mothers group, and LGA mothers had the highest proportion of cesarean section ($41.4\%$) and GWG above guidelines ($39.2\%$).
**Table 1**
| Unnamed: 0 | SGA mothers with GDM (n = 343) | AGA mothers with GDM (n = 1,025) | LGA mothers with GDM (n = 283) | P-value |
| --- | --- | --- | --- | --- |
| Age, mean (SD), years | 31.6 (4.7) | 31.9 (4.3) | 32.7 (4.7)h | 0.013 |
| Rural residence No, (%) | 292 (85.1) | 870 (84.9) | 253 (89.4) | 0.148 |
| Primiparity (primiparous) No. (%) | 211 (61.5) | 558 (54.4)e | 100 (35.3)h | < 0.001 |
| Pre-pregnancy BMI, median (range), kg/m2 | 20.2 (11.8-30.3) | 21.1 (13.0-40.6)f | 23.9 (11.9-36.0)h | < 0.001 |
| Pre-pregnancy BMI < 18.5, No. (%) | 91 (26.5) | 168 (16.4)f | 21 (7.4)h | < 0.001 |
| Pre-pregnancy BMI 18.5–24.9, No. (%) | 210 (61.2) | 719 (70.1)f | 169 (59.7)h | < 0.001 |
| Pre-pregnancy BMI 25-29.9, No. (%) | 39 (11.4) | 117 (11.4) | 75 (26.5)h | < 0.001 |
| Pre-pregnancy BMI ≥ 30, No. (%) | 3 (1.1) | 21 (2.0) | 18 (6.4)h | < 0.001 |
| Pre-partum weight, median (range), kg | 62.3 (44.7-92.6) | 66.8 (44.5-124.1)f | 74.1 (40.7-118.3)h | < 0.001 |
| Pre-partum BMI, median (range), kg/m2 | 24.9 (19.1-34.0) | 26.0 (18.8-44.9)f | 28.3 (16.4-43.4)h | < 0.001 |
| GWG, mean (SD), kg | 11.7 (4.5) | 12.3 (4.6)f | 14.0 (5.1)h | < 0.001 |
| GWG below guidelines, aNo. (%) | 173 (50.4) | 441 (43.0)f | 59 (20.8)h | < 0.001 |
| GWG within guidelines, aNo. (%) | 121 (35.3) | 398 (38.8) | 113 (39.9) | 0.414 |
| GWG above guidelines, aNo. (%) | 49 (14.3) | 186 (18.1) | 111 (39.2)h | < 0.001 |
| Cesarean section, No (%) | 142 (41.4) | 339 (33.1)f | 131 (46.3)g | < 0.001 |
| Anemia, No (%) | 28 (8.2) | 61 (6.0) | 24 (8.5) | 0.182 |
| Drug or illicit use, No (%) | 1 (0.3) | 2 (0.2) | 1 (0.4) | 0.872 |
| Tobacco use, No (%) | 2 (0.6) | 11 (1.1) | 1 (0.3) | 0.277 |
| Hypothyroidism, No (%) | 7 (2.0) | 14 (1.4) | 6 (2.1) | 0.542 |
| ICP, No (%) | 6 (1.7) | 14 (1.4) | 4 (1.4) | 0.875 |
| Oligohydramnios, No (%) | 8 (2.3) | 13 (1.3) | 4 (1.4) | 0.362 |
| Pregnancy-induced hypertension, No, (%) | 19 (5.5) | 65 (6.3) | 20 (7.1) | 0.733 |
| Placenta previa, No (%) | 7 (2.0) | 15 (1.5) | 1 (0.4) | 0.191 |
| Placental abruption, No (%) | 5 (1.5) | 16 (1.6) | 0 (0)g | 0.110 |
| Placental malformation, bNo (%) | 10 (2.9) | 14 (1.4) | 3 (1.1) | 0.103 |
| Preeclampsia, cNo (%) | 29 (8.5) | 24 (2.3)f | 7 (2.5)h | < 0.001 |
| Single umbilical artery, No (%) | 2 (0.6) | 2 (0.2) | 0 (0) | 0.297 |
| Uterine malformation, dNo (%) | 3 (0.9) | 3 (0.3) | 0 (0) | 0.161 |
Table 2 shows the clinical characteristics of the infants. The frequency of SGA infants who were siblings (41.7 vs. 4.3 vs. $1.9\%$) and composite of complications (19.2 vs. 12.0 vs. $11.7\%$) were higher in SGA infants than in those in AGA or LGA infants group (both $P \leq 0.01$). The risk of symptomatic polycythemia was more frequent in LGA infants than that in the AGA or SGA infants group (3.2 vs. 0.3 vs. $0.6\%$, $P \leq 0.001$). The risk of early thrombocytopenia was increased in SGA infants compared to that in AGA infants (1.7 vs. $0.2\%$). However, there were no significant differences in the risk of Apgar score, hypoglycemia, hypoglycemia encephalopathy, NEC, NRDS, or perinatal death among the three groups (all $P \leq 0.05$).
**Table 2**
| Unnamed: 0 | SGA (343) | AGA (1025) | LGA (283) | P-value |
| --- | --- | --- | --- | --- |
| Gestational age, median (range), weeks | 38.3 (33.3–40.9) | 38.7 (28.9–41.1)a | 39.0 (28.1–41.1)c | < 0.001 |
| Sex (boys/girls) | 182:161 | 532:493 | 165:118 | 0.161 |
| Birth weight (BW), mean (SD), g | 2393.2 (236.6) | 3097.6 (325.9)a | 4050.1 (360.9)c | < 0.001 |
| Siblings who were SGA, No, (%) | 60 (41.7) | 23 (4.3)a | 4 (1.9)c | < 0.001 |
| Apgar score | Apgar score | Apgar score | Apgar score | Apgar score |
| 1 min low ( ≤ 7), No (%) | 10 (2.9) | 37 (3.6) | 7 (2.5) | 0.583 |
| 1 min low ( ≤ 3), No (%) | 3 (0.9) | 2 (0.2) | 1 (0.4) | 0.194 |
| Early thrombocytopenia, No (%) | 6 (1.7) | 2 (0.2)a | 3 (1.1) | 0.006 |
| Birth glucose, mean (SD), mmol/l | 3.6 (0.8) | 3.8 (0.9)a | 3.5 (0.6) | 0.001 |
| Hypoglycemia, No (%) | 37 (10.8) | 80 (7.8) | 18 (6.4) | 0.103 |
| Hypoglycemia encephalopathy, No (%) | 1 (0.3) | 1 (0.1) | 0 (0) | 0.545 |
| NEC, No (%) | 3 (0.9) | 3 (0.3) | 0 (0) | 0.161 |
| NRDS, No (%) | 2 (0.6) | 6 (0.6) | 0 (0) | 0.435 |
| Perinatal death, No (%) | 1 (0.3) | 2 (0.2) | 0 (0) | 0.686 |
| Polycythemia, No (%) | 2 (0.6) | 3 (0.3) | 9 (3.2)b | < 0.001 |
| Composite of complications, No (%) | 66 (19.2) | 123 (12.0)a | 33 (11.7)c | 0.002 |
Pearson's correlation analysis was used to determine the correlation between maternal factors and birth weight, and the results are shown in Table 3. The results illustrated that GWG, prepregnancy BMI, and prepartum period BMI were significantly weakly correlated with infants' birth weight (Pearson correlation coefficient 0.129, 0.113, and 0.170; all $P \leq 0.01$).
**Table 3**
| Unnamed: 0 | Pearson correlation coefficient(r) | P-value |
| --- | --- | --- |
| Prepartum period BMI | 0.17 | < 0.01 |
| Prepregnancy BMI | 0.113 | < 0.01 |
| Gestational weight gain | 0.129 | < 0.01 |
Neonates were further grouped by the guidelines for maternal GWG of IOM as follows: GWG below guidelines, GWG within guidelines, and GWG above guidelines. Table 4 shows the influence of GWG on perinatal outcomes. 673 ($40.8\%$) women had GWG below guidelines, 346 ($21.0\%$) had GWG above guidelines, and 632 ($38.2\%$) had GWG within guidelines. We observed that the risk of SGA (25.7 vs. 19.1 vs. $14.2\%$) and FGR (15.3 vs. 10.9 vs. $7.8\%$) was higher in GWG below guidelines group than those in GWG above and within guidelines group. However, the risk of low Apgar score (6.4 vs. 3.0 vs. $2.8\%$) was higher in GWG above guidelines group than that in GWG below or within guidelines group ($P \leq 0.05$). There were no significant differences in adverse women outcomes, early thrombocytopenia, NEC, hypoglycemia, or NRDS among these three groups.
**Table 4**
| Unnamed: 0 | GWG below guidelinesa (n = 673) | GWG within guidelinesa (n = 632) | GWG above guidelinesa (n = 346) | P-value |
| --- | --- | --- | --- | --- |
| Cesarean delivery, No (%) | 223 (33.1)b | 249 (39.4) | 124 (35.8) | 0.062 |
| FGR, No (%) | 103 (15.3)b | 69 (10.9) | 27 (7.8) | 0.001 |
| Pregnancy-induced, hypertension No (%) | 51 (7.6) | 35 (5.5) | 18 (5.2) | 0.203 |
| Preeclampsia, No (%) | 28 (4.2) | 25 (4.0) | 7 (2.0) | 0.194 |
| Early thrombocytopenia, No (%) | 7 (1.0) | 3 (0.5) | 1 (0.3) | 0.284 |
| Hypoglycemia, No (%) | 58 (8.6) | 54 (8.5) | 23 (6.6) | 0.505 |
| Low APGAR, No (%) | 20 (3.0) | 18 (2.8) | 22 (6.4)e | 0.010 |
| NEC, No (%) | 3 (0.4) | 2 (0.3) | 1 (0.3) | 0.897 |
| NRDS, No (%) | 5 (0.7) | 2 (0.3) | 1 (0.3) | 0.455 |
| Symptomatic polycythemia, No (%) | 2 (0.3) | 5 (0.8) | 7 (2.0) | 0.017 |
| SGA, No (%) | 173 (25.7)c | 121 (19.1) | 49 (14.2)d | < 0.001 |
Next, multivariate binary logistic regression analysis was performed to identify factors associated with SGA, and the results are shown in Table 5. Adjusting for other confounding variables, the following factors were associated with SGA: preeclampsia (AOR 3.12, $95\%$ CI [1.34–7.30]) and siblings who were SGA (AOR 18.06, $95\%$ CI [10.83–30.13]).
**Table 5**
| Risk factors | Adjusted odds ratioa | 95% confidence interval | P-value |
| --- | --- | --- | --- |
| GWG below guidelines | 1.49 | 0.92–2.21 | 0.12 |
| Preeclampsia | 3.12 | 1.34–7.30 | < 0.01 |
| Siblings who were SGA | 18.06 | 10.83–30.13 | < 0.01 |
To further evaluate the effect of risk factors for SGA on GWG among women with GDM, mothers were further grouped by risk factors for SGA as follows: GDM mothers with and without preeclampsia, GDM mothers with and without siblings who were SGA. However, groups did not differ significantly in pre-pregnancy BMI, GWG, the incidence of GWG below guidelines, GWG within guidelines, and GWG below guidelines (all $P \leq 0.05$) (Table 6).
**Table 6**
| Unnamed: 0 | Unnamed: 1 | GDM mothers with risk factora | GDM mothers without risk factor | P-value |
| --- | --- | --- | --- | --- |
| GDM mothers with and without preeclampsia | | N = 60 | N = 1591 | |
| | Age, mean (SD), years | 33.2 (5.3) | 32.0 (4.5) | 0.052 |
| | SGA, No, (%) | 29 (48.3) | 314 (19.7) | < 0.001 |
| | Pre-pregnancy BMI, median (range), kg/m2 | 22.8 (3.2) | 21.8 (3.8) | 0.149 |
| | GWG, mean (SD), kg | 11.7 (4.8) | 12.9 (8.0) | 0.377 |
| | GWG below guidelines, No. (%) | 21 (35.0) | 652 (40.9) | 0.355 |
| | GWG within guidelines, No. (%) | 28 (46.7) | 604 (38.0) | 0.173 |
| | GWG above guidelines, No. (%) | 11 (18.3) | 335 (21.1) | 0.611 |
| GDM mothers with and without siblings who were SGA | | N = 87 | N = 1564 | |
| | Age, mean (SD), years | 32.6 (4.8) | 31.9 (4.5) | 0.157 |
| | SGA, No (%) | 60 (69.0) | 283 (18.1) | < 0.001 |
| | Pre-pregnancy BMI, median (range), kg/m2 | 21.8 (3.2) | 21.7 (3.8) | 0.608 |
| | GWG, mean (SD), kg | 12.3 (4.8) | 12.5 (4.8) | 0.750 |
| | GWG below guidelines, No. (%) | 29 (33.3) | 644 (41.2) | 0.147 |
| | GWG within guidelines, No. (%) | 37 (42.5) | 595 (38.0) | 0.402 |
| | GWG above guidelines, No. (%) | 21 (24.2) | 325 (20.8) | 0.454 |
## Discussion
We conducted a population-based, case-control study to explore the association between dietary intervention and the frequency of SGA. The main results of our study suggested the following: [1] siblings who were SGA and pre-eclampsia could be the risk factors for SGA infants born to mothers with GDM, infants' birth weight is also correlated with GWG, prepregnancy BMI, and prepartum period BMI; [2] compared to AGA or LGA, SGA infants born to mothers with GDM have more adverse perinatal outcomes; [3] GWG above and below guidelines increased the risk of adverse infant outcomes.
Dietary advice and exercise interventions are the first-line therapy for pregnant women with GDM. Dietary advice and exercise interventions alone for the prevention of GDM have been widely assessed; dietary and exercise interventions have been proven to reduce GWG in these studies [15, 16]. So international guidelines recommend that pregnant women with GDM participated in aerobic and strength-conditioning exercises for 30 min at least 5 days per week along with medical nutritional therapy [17, 18]. The main purpose of dietary interventions for pregnant women with GDM is to prevent macrosomia due to its associated risks [19, 20]. However, some findings suggested that preventing SGA and related complications should be just as important in the care of pregnant women with GDM (21–23). The birth rate of SGA infants was similar to that of LGA infants in these studies; nevertheless, perinatal complications ($20.1\%$) and mortality ($1.6\%$) were worse in the SGA group than in the AGA or LGA groups [11, 24, 25]. Our findings were in agreement with these previous reports. In normal pregnancies, the risk of perinatal asphyxia, hypoglycemia, polycythemia, thrombocytopenia, and other neonatal complications is also higher in SGA neonates than in AGA or LGA neonates (26–28).
Dietary advice and exercise interventions can reduce GWG, Pearson's correlation analysis also showed that GWG was correlated with infants' birth weight in our study; but whether they can increase the incidence of SGA remains controversial [6, 15]. IOM recommended different targets for an adequate GWG depending on the pre-pregnancy BMI in 2009. In China, insufficient GWG (GWG below guidelines) occurs in $33.9\%$ of pregnant women with GDM [29]. In our study, $40.8\%$ of GDM women presented with a total GWG below the IOM guidelines; this variation may be due to rigorous lifestyle improvements. Recent studies have shown that insufficient GWG results in increased odds of preterm birth and SGA in normal pregnancies and women with GDM (30–33). However, Gou et al. [ 34] showed that insufficient GWG was not the risk factor for SGA. Adjusting for other confounding variables, the results of logistic regression analysis also showed that there was no positive correlation between SGA and insufficient GWG in our study. The inconsistency may be due to the different study populations, the calculation method of GWG and the adjusted confounding variables. Therefore, more and larger randomized controlled trials are needed to assess the relationship between insufficient GWG and SGA in Chinese.
Moreover, some previous studies reported that excessive GWG (GWG above guidelines) had increased the odds of hypertensive disorders of pregnancy, cesarean delivery, macrosomia, and LGA [35, 36]; but the effects of excessive GWG on adverse infant outcomes have not been analyzed in detail in these studies. GDM women with excessive GWG increased the risk for low Apgar score ($6.4\%$) in our study compared to that in GWG below or within guidelines group.
The recurrence risk among siblings is widely used to measure shared genetic contributions. The frequency of SGA siblings in SGA infants born to mothers with GDM group was 10-fold higher than that in the AGA group in our study. Therefore, our findings demonstrated that genetic factors and preeclampsia were risk factors for SGA infants born to mothers with GDM. Recently, Spanish scholars indicated that smoking and neonate prematurity were also risk factors for these infants [11]; however, we did not find this result in our study.
Our study presents novel information, not previously reported, as we considered the frequency of siblings who were SGA, neonatal early thrombocytopenia, and abnormal GWG among GDM women. Our findings are also helpful for clinicians to design more accurate care programs for pregnant women with GDM. Our study also has several limitations. First, the number of SGA infants born to mothers with GDM may be small. Second, the other limitation is reliance on self-reported pre-pregnancy weight. Third, the time for the blood routine examination and blood glucose test was varied for each infant, which might affect the results. Fourth, it was a retrospective observational study; therefore, selection and information bias cannot be excluded.
## Conclusion
In conclusion, our findings suggested that GWG below guidelines did not increase the risk for SGA, though SGA infants had more adverse outcomes among neonates born to mothers with GDM. However, SGA infants born to mothers with GDM remain a major concern. Moreover, we identified that genetic factors and pre-eclampsia could be the risk factors for SGA infants born to mothers with GDM. Our research also demonstrated that GWG above and below guidelines, compared with GWG within guidelines, had a higher risk of adverse infant outcomes. These findings suggest that it is necessary to maintain a reasonable GWG among pregnant women with GDM to reduce adverse perinatal complications.
## 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.
## Ethics statement
The studies involving human participants were reviewed and approved by Ethical Committee in Guangzhou Women and Children's Medical Centre of Guangzhou Medical University, Number 2014121402, date of approval 12 December 2014. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s), and minor(s)' legal guardian/next of kin, for the publication of any potentially identifiable images or data included in this article.
## Author contributions
DH, ML, BX, WZ, and JC designed the study, designed the data collection instruments, collected data, carried out the initial analyses, drafted the initial manuscript, and created the tables and figures. SC, YX, and JC conceptualized and designed the study, coordinated and supervised data collection, and helped draft the initial manuscript. HL, LW, PP, DY, YY, and JY designed the data collection instruments, collected data, and conducted the initial analyses. All authors contributed to the manuscript's critical revision, read, 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
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|
---
title: Characterization of hexokinase gene family members in Glycine max and functional
analysis of GmHXK2 under salt stress
authors:
- Shuai Chen
- Zengyuan Tian
- Yuqi Guo
journal: Frontiers in Genetics
year: 2023
pmcid: PMC9996050
doi: 10.3389/fgene.2023.1135290
license: CC BY 4.0
---
# Characterization of hexokinase gene family members in Glycine max and functional analysis of GmHXK2 under salt stress
## Abstract
Hexokinase (HXK) is a bifunctional enzyme involved in carbohydrate metabolism and sugar signal sensing. HXK gene family has been extensively discussed in many species, while the detailed investigations of the family in Glycine max have yet to be reported. In this study, 17 GmHXK genes (GmHXKs) were identified in the G. max genome and the features of their encoded proteins, conserved domains, gene structures, and cis-acting elements were systematically characterized. The GmHXK2 gene isolated from G. max was firstly constructed into plant expression vector pMDC83 and then transformed with Agrobacterium tumefaciens into Arabidopsis thaliana. The expression of integrated protein was analyzed by Western Blotting. Subcellular localization analysis showed that the GmHXK2 was located on both vacuolar and cell membrane. Under salt stress, seedlings growth was significantly improved in Arabidopsis overexpressing GmHXK2 gene. Furthermore, physiological indicators and expression of salt stress responsive genes involved in K+ and Na+ homeostasis were significantly lower in GmHXK2-silenced soybean seedlings obtained by virus-induced gene silencing (VIGS) technique under salt stress compared with the control plants. Our study showed that GmHXK2 gene played an important role in resisting salt stress, which suggested potential value for the genetic improvement of abiotic resistant crops.
## 1 Introduction
Sugars play an important role during plant life, serving not only as the sources of energy and carbon structural components for growth and development but also as signaling molecules involved in many physiological processes such as germination (Kim et al., 2016), flowering (Zhang et al., 2020), senescence (Xiao et al., 2000), stomatal closure (Lugassi et al., 2015), and response to abiotic and biotic stresses (Sarowar et al., 2008; Granot et al., 2014; Lugassi et al., 2015). In higher plants, sucrose comprise the majority of carbohydrates produced by photosynthesis. Following synthesis, sucrose can then be directly stored and converted into hexose (glucose and fructose) by either invertase or sucrose synthase (Siemens et al., 2011; Granot et al., 2014). Afterwards, hexose is phosphorylated by hexokinase to produce hexose-6-P, which enters the glycolysis pathway and generates energy and intermediate metabolites involved in physiological activities of plants (David-Schwartz et al., 2013). HXK acts as a hexose sensor and signal in signaling networks to sense external nutrients, light, and hormones in addition to its role as a regulator of plant growth (Jang et al., 1997; Kim et al., 2013). Therefore, it has been recognized as a bifunctional enzyme and the key connecting element between sugar signaling and plant hormone signaling (Jang et al., 1997; Perata et al., 1997; Moore, 2004).
HXKs are encoded by a gene family in many plant species (Granot et al., 2014). AtHXKs genes were first isolated and identified from A. thaliana (Minet et al., 1992), revealing six members among which three proteins can phosphorylate hexoses, while the other three proteins lack catalytic activity (Karve et al., 2008). HXK gene family members have been systematically identified in various plant species. 10, 9, 4, 7, 11, 17 HXK genes members were identified respectively in *Oryza sativa* (Cho et al., 2006), Zea mays (Zhang et al., 2014), *Lycopersicon esculentum* (Kandel-Kfir et al., 2006), *Manihot esculenta* (Geng et al., 2017), Physcomitrella patens (Olsson et al., 2003) and *Gossypium hirsutum* L (Dou et al., 2022) in their genome.
Different HXKs are located in the membranes of various subcellular organelles such as the mitochondrion, chloroplast, cytoplasm, nucleus, Golgi, and vacuole (da-Silva et al., 2001; Olsson et al., 2003; Kandel-Kfir et al., 2006; Wang et al., 2014). Based on N-terminal amino acid sequences, plant HXK can be classified as Type A, Type B, or Type C (Giese et al., 2005; Karve et al., 2010). Type A HXKs has a plastid signal peptide structure containing a chloroplast transit peptide of about 30 amino acids, and it is mainly located in chloroplasts (Kandel-Kfir et al., 2006). Type B HXKs, which is mainly located in plastid (Olsson et al., 2003), contain an N-terminal hydrophobic membrane anchor domain of about 24 amino acids. Type C HXKs do not have signal peptides or a membrane anchor; they are cytosolic and seem to be only present in monocotyledonous plants and the moss P. patens (Karve et al., 2010; Cheng et al., 2011; Nilsson et al., 2011). As such, the diversity of HXK structure and subcellular localization lead to significant differences in their functions (Matschinsky et al., 2006; Riera et al., 2008).
Under salt stress, signaling substances such as ROS, ABA, Ca2+ in plants activate transcription factors through signal transduction, such as MYB, NAC, ERF, bZIP, etc., ( Li et al., 2015; Wang et al., 2016; Baillo et al., 2019). The interaction between transcription factors and cis-regulatory elements promotes the expression of salt stress-responsive genes, inhibits the excessive accumulation of Na+ in plants and maintains the homeostasis of osmotic pressure. These include salt overly sensitive 1 (SOS1), high-affinity K+ transporter (HKT) and glutamate receptor-like channels (GLRs) located on the plasma membrane, sodium-hydrogen exchanger (NHX), cation/H+ exchanger (CHX and SALT3) and cyclic nucleotide-gated channels (CNGCs) located on the tonoplast and endomembranes, etc., ( Plett et al., 2010; Guan et al., 2014; Ma et al., 2014). *These* genes are involved in the process of salt stress and regulate cation concentration to protect plants from damage caused by ion toxicity.
Soybean (Glycine. max L.) is the third most valuable plant crop worldwide as an oilseed crop (Bilal et al., 2020). However, the growth, production and quality of soybean are threatened by environment stress. With the increase in problems of soil salinity, salt stress has become one of the major stresses affecting growth and development of soybean (Leisner et al., 2017; Bilal et al., 2018). To date, little is known about any G. max HXK genes and their function in growth and development under abiotic salt stresses. In this study, we identified HXK gene family members of G. max based on the genome database. Next, we investigated the homology and phylogenetic relationship, gene structure, conserved motifs and cis-elements. The promoter sequences of most GmHXKs included many cis-elements including ABRE, ERE and LTR. It was supposed that GmHXKs were related with ABA signal pathway. Then, we assayed the expression pattern of GmHXKs under salt and drought stress, and found expression of GmHXK2 in roots was increased gradually after treatment with NaCl or drought stress for 72 h. GmHXK2 silenced plants obtained by VIGS technique were severely damaged after treatment with NaCl. We measured physiological indicators and the expression of some genes such as GmSOS1, GmSALT3, GmHKT1, GmbZIP44,etc., which were reported had a role in salt tolerance, in GmHXK2 soybean silenced plants under salt stress conditions. Moreover, the DNA sequence of GmHXK2 was cloned and constructed into the plant expression vector pMDC83, and it then transformed with Agrobacterium tumefaciens to A. thaliana. We analyzed the seed germination, plant growth and salt tolerance of homozygous T3 transgenic plants WT35 S:GmHXK2. Our study provided essential information concerning the physiological functions of GmHXK2 and an important theoretical basis for genetic engineering soybeans to better tolerate to salt stress.
## 2.1 Identification of the HXK gene family members in G. max
HMMER 3.0 software was used to search the soybean genome database for genes containing the structural domain of hexokinase (PF03727 and PF00349) and 17 soybean hexokinase genes were retrieved finally. The HXK gene sequences of G. max was downloaded from Ensembl Plants (http://plants.ensembl.org/index.html). The physical and chemical parameters of the proteins, including molecular weight (MV) and theoretical isoelectric point (PI), were computed by ExPASy (http://web.expasy.org/protparam/). The presence of a chloroplast transit peptide (cTP) in the protein sequence was predicted using the ChloroP 1.1 Server (http://www.cbs.dtu.dk/services/ChloroP/). The existence of transmembrane helices (TMHs) in the protein was predicted using TMHMM Server v. 2.0 (http://www.cbs.dtu.dk/services/TMHMM-2.0/). Chromosomal localizations of GmHXKs were mapped in Map Gene2Chromosome v2 (http://mg2c.iask.in/mg2c_v2.0/).
## 2.2 Multiple alignment, phylogenetic and expression pattern analysis
The sequences of the GmHXKs proteins obtained from the G. max genome were aligned using DNAMAN 7.0 software to search for conserved domains by inspection using sites present in AtHXK1 as a reference. To compare evolutionary relationships, the putative HXKs from G. max, A. thaliana, Solanum lycopersicum, O. sativa and *Nicotiana tabacum* were used to construct the phylogenetic tree using MEGA-X with the neighbor-joining (NJ) method and 1,000 bootstrap replicates (Kumar et al., 2018). Expression data on GmHXK gene family members at different developmental stages and in different tissues under normal conditions were downloaded from the Soybase (https://www.soybase.org/). Data on differential expression for only 14 members were eventually obtained and used for subsequent analysis.
To analyze expression pattern of soybean seedlings under salt stress, soybean seedlings were grown in a growth chamber under greenhouse conditions of 28°C under a16-h light/8-h dark cycle. Three-week-old seedlings were treated with $0.5\%$ NaCl (salt stress) or drought treatment ($10\%$PEG 6000). The root samples of the seedlings were collected after treatment for 2-h, 8-h, 24-h, and 72-h. Then, different samples were frozen quickly in liquid nitrogen, and stored at −80°C for RNA extraction and analysis. Total RNA was isolated using the Plant RNA Kit (CWBIO, Beijing, China), and its concentration and purity were determined by Nanodrop2000 nucleic acid analyzer (Thermo, America). First-strand cDNA was synthesized from 0.5 µg of total RNA using the HiFi-MMLV cDNA Kit (CWBIO, Beijing, China), and then used as a template for qRT-PCR analysis using gene-specific primers (Supplementary Table S1). Data analysis of RT-qPCR was performed using 2−ΔΔCT method (Livak and Schmittgen, 2001).
## 2.3 Gene structure, conserved motifs and cis-elements analysis
Gene structures were analyzed using Gene Structure Display Serve 2.0 (https://gsds.cbi.pku.edu. cn/) to investigate the exon-intron organizations of GmHXK genes based on their information given in the Ensembl Plants. The novel motifs of GmHXKs were searched using MEME (http://meme-suite. org/tools/meme) (Bailey et al., 2009). The parameters were set as follows: the site distribution was set to any number of repetitions (anr), the number of motifs was set to 10, and all other optional parameters remained default (He et al., 2019). The combination of gene structures, motifs, and phylogenetic tree was then generated using TBtools. In addition, the cis-acting regulatory elements in the 2000-bp genomic sequence upstream of the coding GmHXK gene sequences were investigated using the online PlantCARE databases. ( http://bioinformatics.psb.ugent.be/webtools/plantcare/html/).
## 2.4 Construction of plant expression vector and transformation of arabidopsis
GmHXK2 was obtained by PCR using the soybean genome DNA as a template with the primes, which are specific for GmHXK2 gene. The PCR was performed using standard conditions: initial denaturation at 94°C for 2 min followed by 35 cycles of 94°C for 30 s, 56°C for 30 s and 72°C for 1 min, followed by a final extension of 72°C for 5 min. The PCR products of GmHXK2 were gel purified using gel purification kit (TaKaRal MiniBEST Agarose Gel DNA Extraction Kit Ver.4.0) and used to construct the entry vector (Invitrogen, United States of America) by TOPO cloning reaction according to the manufacturer’s instructions. The entry vector containing the gene of the correct orientation and sequence was used to construct the target vector pMDC83 via the LR Clonase II enzyme mediated gateway cloning reaction according to manufacturer’s protocol. The recombinant plasmid pMDC83 with the hygomycin phosphotransferase gene under the regulation of double CaMV 35S promoter was generated, with GFP fused at the C-terminus of GmHXK2. Then, sequencing was performed to confirm the insertion into the correct gene in pMDC83. Next, the integrated vector was introduced into A. tumefaciens strains GV3101 by the liquid nitrogen freeze-thaw method. The A. tumefaciens transformation was transformed into WT Arabidopsis using the floral dipping technique. Transformed Arabidopsis plants were grown in a greenhouse under a $\frac{16}{8}$ h light/dark cycle at $\frac{24}{22}$°C with $70\%$ relative humidity. Seeds harvested from the transformed plants (T0) were grown on $\frac{1}{2}$ MS medium containing 20 mg L-1 hygromycin under the same growth conditions. Homozygous T3 progeny WT35S::GmHXK2 derived from T2 population were selected and confirmed by PCR for further salt tolerance analysis.
## 2.5 Molecular analysis of transgenic arabidopsis plant and analysis on subcellular localization of GmHXK2
Genomic DNA of from the putative T3 transgenic Arabidopsis was isolated and analyzed by PCR amplification using specific primers. The amplification fragments were monitored in transgenic Arabidopsis with 35S:GmHXK2-GFP in pMDC83. No transformed seedlings were used as control.
The roots from 4-week-old seedlings expressing GFP in the transgenic Arabidopsis transformed with 2 × 35S::GmHXK2-GFP in pMDC83 were monitored using confocal laser-scanning microscopes. Images were captured at an excitation of 480 nm and emission between 515 and 565 nm for GFP. The subcellular localization of GmHXK2-GFP fusion proteins was confirmed.
## 2.6 Western blot analysis of transgenic arabidopsis plants
WT and WT35S::GmHXK2 transgenic Arabidopsis plants were grown on $\frac{1}{2}$ MS medium containing 20 mg L-1 hygromycin. After 15 days of cultivation, total proteins of the seedlings were extracted from WT and transgenic Arabidopsis seedlings with a buffer consisting of 50 mM Tris/HCl (pH 8.0), 150 mM NaCl, 1 mM EDTA, and $0.2\%$ (w/v) Triton X-100, $4\%$ β-mercaptoethanol, 1 mM dithiothreitol (DTT), and $1\%$ (v/v) protease inhibitor cocktail of which were then used for protein quantification using the BCA Protein Quantitative Kit (Boster). The protein samples (200 μg amounts) were electrophoresed in $8\%$ SDS-PAGE and the gels were transferred to nitrocellulose membranes. The membranes were blocked with TBST buffer (10 mM Tris/HCl, pH 7.5, 150 mM NaCl, and $0.05\%$ Tween-20) supplemented with $5\%$ non-fat milk for 2 h and incubated with primary antibodies (Anti-GFP antibody, abcam, diluted at 1:1,000) in TBST buffer with $5\%$ BSA overnight at 4°C. Afterwards, the membranes were washed three times (10 min each) with TBST buffer and incubated with the secondary antibodies (Goat Anti-Mouse IgG H&L (HRP), abcam, dilution at 1:1,000) for 2 h. After washing three times with TBST buffer, the membranes were incubated with a chromogenic agent using the Enhanced HRP-DAB Chromogenic Substrate Kit (Boster).
## 2.7 Salt tolerance analysis of transgenic arabidopsis plants at the germination and seedling stage
Seeds of WT and WT35S::GmHXK2 transgenic Arabidopsis plants of homozygous T3 generation were sterilized using $75\%$ ethanol and $5\%$ sodium hypochlorite for 1 min and 10 min, respectively. The seeds were washed six to ten times in sterilized water and were sown on half-strength MS ($\frac{1}{2}$ MS) medium with 0 and 100 mM NaCl. They were then transferred to a culture room after 3 days of vernalization at 4°C. Finally, root lengths and fresh weight of the germinated seeds were measured after planting for 7 days to detect salt tolerance in transgenic Arabidopsis overexpressing GmHXK2.
To analyze salt tolerance of transgenic Arabidopsis plants at the seedlings stage, treatment of plant samples were as followed. After the seeds were sterilized, they were sown on MS medium after 3 days of vernalization at 4°C, then they were transferred to a culture room and grown for 9 days. Next, Arabidopsis seedlings were transplanted into $\frac{1}{2}$ MS medium containing 0 mM, 100 mM and 150 mM NaCl with or without 100 mM glucose (Glc). Finally, fresh and dry weight, length of the roots, malondialdehyde, chlorophyll and proline contents of the seedlings were measured after 6 days.
## 2.8 Determination of chlorophyll content
Chlorophyll was extracted using ethanol as solvent (Matschinsky et al., 2006). 100 mg of leaves was pulverized with 2 mL $95\%$ ethanol, and the sample supernatant was measured with a spectrophotometer UV-1800PC at 665 nm and 649 nm. The chlorophyll extraction solution was calculated using the following formula: Ca=13.95A665−6.8A649 Cb=24.96A6665−7.32A649 CT=Ca+Cb The chlorophyll content per unit fresh weight of leaf was calculated using the following formula: Chlorophyll content (mg g-1) = (CT×extraction solution volume×dilution ratio)/(fresh weight of leaves).
## 2.9 Determination of malondialdehyde (MDA) content
Fresh seedlings (0.1 g) were immediately homogenized with liquid nitrogen and were then mixed with 5 mL of $10\%$ trichloroacetic acid (TCA) and centrifuged at 4,000 rpm for 10 min. The supernatant (2 mL) and 2 mL $0.6\%$ thiobarbituric acid (TBA) were pipetted into a new tube. The mixture was incubated in a water bath at 100°C for 15 min and then cooled on ice and centrifuged at 5,000 rpm for 10 min. The absorbance of the supernatant was measured at 532 and 450 nm by spectrophotometer (Zhang et al., 2008). The MDA content was calculated using the formula: MDA (nmol g-1) = (6.45× 10−6×A532–0.56×10−6×A450)×V/W. V = volume of supernatant(L), W = weight of seedlings (g).
## 2.10 Determination of proline content
The proline content of Arabidopsis seedling was determined according to the method described by Jaemsaeng et al. ( Zhang et al., 2008). Seedlings (0.05 g) were homogenized in 5 mL of sulphosalicylic acid ($3\%$) and centrifuged for 10 min at 12,000 rpm. 2 mL of glacial acetic acid and 2 mL of acid ninhydrin were added to the supernatant (2.0 mL). The mixture was boiled in a water bath at 100°C for 30 min. After cooling, extraction was done with 4 mL of toluene. The absorbance was measured at 520 nm using toluene as a blank. The proline content (mg*g-1) = (Y × $\frac{5}{2}$)/W. Y = content of proline in 2 mL supernatant. W = Weight of seedlings (g).
## 2.11 Determination of superoxide dismutase (SOD)
Seedings (0.1 g) were homogenized with 1 mL phosphate buffer (100 mM, ph = 7.8) and centrifuged at 12000 rpm for 30 min at 4°C. Then 0.1 mL of supernatant and phosphate buffer were added to the reaction solution (50 mM, ph = 7.8 Na3PO4,130 mM MET, 750uM NBT, 100uM EDTA, 20uM riboflavin) respectively. The reaction solution in which phosphate buffer was added served as the light control. The reaction was placed under fluorescent light for 15 min and then terminated in the dark. The absorbance value was measured at 560 nm. The SOD was calculated using the formula: SOD(U)=(ODL-ODS)*V/(0.5*ODL*VS*T*M). ODL = Absorbance value of light control. ODS = Absorbance value of samples. V = Total volume of sample extracts (ml). VS = Volume of sample extract for determination (ml). T = Light reaction time (min). M = Weight of seedings(g) (Donahue et al., 1997).
## 2.12 Determination of electrolyte leakage (EL)
The conductivity of the sample (0.2 g) immersed in 20 mL of deionized water for 2 h at room temperature was measured as C1. Then the sample was boiled in a water bath for 15 min and cooled to room temperature. The conductivity measured was C2. Conductivity of deionized water as a blank control (C0). The electrolyte leakage (%)=(C1-C0)/(C2-C0) (Jungklang et al., 2017).
## 2.13 Construction of VIGS vectors and sprout infiltration for silenced plants
The specific fragments from CDS of GmHXK2 and GmPDS were amplified using primers. The PCR products and the virus vector pTRV2 were digested with XbaI and BamHI(TAKARA),respectively. The products were ligated to obtain pTRV2-HXK2 and pTRV2-PDS vectors. For VIGS experiment, plasmids of pTRV1, pTRV2 and pTRV2 recombinant vectors (pTRV2-PDS and pTRV2-HXK2) were transformed into A. tumefaciens strain GV3101 cells by using the freeze-thaw method. The empty plasmid pTRV2 was used as control (TRV:00). The Agrobacterium strains were inoculated into 50 mL of LB medium as above on a shaker at 180 rpm at 28°C for 12–16 h to an OD600 of 1.2–1.5. The Agrobacterium cells were centrifuged at 4000 g for 10 min at room temperature and washed twice, resuspended with the infiltration solution (10 mM MgCl2, 10 mM MES, and 200 μM acetosyringone) to a final OD600 of 0.8–1.0 and placed at room temperature in darkness for 3 h. The infiltration solution of the Agrobacterium strain containing pTRV1 was mixed with the infiltration solution of pTRV2 or Agrobacterium carrying the constructs in a 1:1 ratio (v/v) and 20–40 washed soybean seeds were added respectively (Zhao et al., 2020b). Soybean seeds that had been soaked for 24 h in darkness at room temperature were removed and planted in nutrient soil. The silencing efficiency of soybean seedlings was determined by qRT-PCR when the first true leaf was fully expanded.
## 2.14 Statistical analysis
The results of root length, fresh weights, and dry weights were from five independent experiments, and results of Chlorophyll, MDA, proline contents, EL and SOD were from three independent experiments. GraphPad Prism 5 was utilized for all statistical analysis. Data significance analysis was performed using Student’s t-test (Yang et al., 2022). Data are presented as mean ± SE. Single, double and three asterisks denote significant differences compared with the values of WT at $p \leq 0.05$, $p \leq 0.01$ and $p \leq 0.001$, respectively.
## 3.1 Identification of G. max HXK gene family
In total, 17 HXK genes were identified in the G. max genome, and they were designated as GmHXK1-17. The name, Ensembl Plants accession number, mRNA and CDS length of nucleotide sequence as well as the length of amino acid sequence, MW, PI, chromosome location, cTP, and number of TMHs, are summarized in Table 1. As depicted in Table 1, the length of the CDS varied from 471 to 1,812 bp, encoding 156 to 504 amino acids and corresponding to molecular weights ranging from 17.42 to 55.40 kDa. The theoretical PIs of these proteins ranged from 5.18 to 8.76. There were three genes on chromosomes 1, 3 genes on chromosomes 17, and 2 genes on chromosomes 5, 7, 8, and 11. GmHXK2, GmHXK10 and GmHXK4 were distributed on chromosomes 9, 12 and 14, respectively. GmHXK1, GmHXK2 and GmHXK11 contained no cTP or TMHs. GmHXK5, GmHXK6, GmHXK12–17 contained cTP and one TMH. GmHXK3 and GmHXK4 contained cTP but no TMHs.
**TABLE 1**
| Gene name | Gene ID | CDS Length (bp) | Amino acid sequence Length (aa) | MW(kDa) | PI | Chromosome location | cTP | Number of TMHs |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| GmHXK1 | GLYMA_17G182400 | 471 | 156 | 17.42 | 5.18 | 17 | none | 0 |
| GmHXK2 | GLYMA_09G144600 | 891 | 296 | 32.92 | 6.22 | 9 | none | 0 |
| GmHXK3 | GLYMA_17G257800 | 1812 | 500 | 53.39 | 5.48 | 17 | Yes | 0 |
| GmHXK4 | GLYMA_14G218800 | 1506 | 501 | 53.73 | 5.11 | 14 | Yes | 0 |
| GmHXK5 | GLYMA_05G110500 | 1473 | 490 | 53.66 | 6.26 | 5 | none | 1 |
| GmHXK6 | GLYMA_17G156200 | 1473 | 490 | 53.85 | 6.3 | 17 | none | 1 |
| GmHXK7 | GLYMA_01G226900 | 1497 | 498 | 54.39 | 8.76 | 1 | Yes | 1 |
| GmHXK8 | GLYMA_11G015800 | 1497 | 498 | 54.59 | 8.6 | 11 | Yes | 1 |
| GmHXK9 | GLYMA_11G095600 | 1515 | 504 | 55.4 | 6.66 | 11 | Yes | 1 |
| GmHXK10 | GLYMA_12G021700 | 1515 | 504 | 54.8 | 6.34 | 12 | Yes | 1 |
| GmHXK11 | GLYMA_07G015100 | 1218 | 405 | 44.26 | 5.66 | 7 | none | 0 |
| GmHXK12 | GLYMA_05G226600 | 1497 | 498 | 53.7 | 5.58 | 5 | none | 1 |
| GmHXK13 | GLYMA_01G007300 | 1491 | 496 | 53.85 | 6.56 | 1 | none | 1 |
| GmHXK14 | GLYMA_08G200600 | 1479 | 492 | 53.66 | 5.54 | 8 | none | 1 |
| GmHXK15 | GLYMA_08G033300 | 1497 | 498 | 53.61 | 5.65 | 8 | none | 1 |
| GmHXK16 | GLYMA_01G007200 | 1491 | 496 | 53.65 | 5.96 | 1 | none | 1 |
| GmHXK17 | GLYMA_07G124500 | 1497 | 498 | 53.64 | 5.95 | 7 | none | 1 |
## 3.2 Multiple alignment and phylogenetic analysis of the GmHXK genes
To better study the structural difference between GmHXKs and their possible functional differences, the amino acid sequences of G. max were aligned and analyzed (Figure 1). Conserved domain analysis showed that most GmHXKs were multidomain proteins, which contained two phosphate sites (I and II), two connect sites (I and II), one α-helix site, one adenosine binding site, and one sugar binding site. However, GmHXK1 contained just three conserved domains, α-helix site, adenosine binding site, and sugar binding site. Additionally, GmHXK2 contained phosphate site II, two connect sites (I and II), one α-helix site, one adenosine binding site, and one sugar binding site.
**FIGURE 1:** *Multiple alignment of HXK amino acid sequences in G. max. Identical and similar amino acid residues are highlighted in black and pink shades, respectively. Core sugar binding site, phosphate sites (I and II), adenosine binding site, connection sites (I and II), and specific α-helix site are underlined.*
To further reveal the evolutionary relationship of hexokinase family, the protein sequences of 56 hexokinases from five different plant species, including G. max, A. thaliana, S. lycopersicum, O. sativa and N. tobacum were used to construct phylogenetic tree using MEGA-X. As shown in Figure 2, all hexokinase genes were divided into three subfamilies (I–III). Most of the HXK genes of five different plant species were sorted into Cluster III. Cluster II contains fewer genes. It indicated the close relationship between the five plant species aforementioned. There were none GmHXKs in Cluster I, which contains only some hexokinase genes in S. lycopersicum.
**FIGURE 2:** *Phylogenetic analysis of the HXK gene family from G. max and other plant species. At, A. thaliana; Gm, G. max; Sl, S. lycopersicum; Os, O. sativa; Nt, N. tobacum. The amino acid sequences of HXK were aligned by Clustal X, and the tree was generated using the neighbor-joining method in MEGA-X with 1,000 bootstrap replicates.*
## 3.3 Conserved motifs and gene structure analysis of GmHXKs
Generally speaking, the conserved regions of proteins frequently distinguish them from other proteins and determine the basis of their functions. To reveal the structural diversity and functional characteristics of the GmHXK family members, their conserved motifs were analyzed by MEME and mapped in the phylogenetic tree. In total, 10 conserved motifs were identified (Figure 3B). The detailed sequences and conserved motifs are shown in Supplementary Table S2. The identified motifs ranged from 29 to 50 amino acids in length. Among them, motifs 3, 6 and 8 were found to encode the hexokinase_1 domain, while motifs 1, 2, four and 5 encode the hexokinase_2 domain. Nevertheless, the functions of motif 7, 9 and 10 are unknown. GmHXK7-10, 12, 14–17 were found to have all 10 motifs, while other GmHXK family members had only portion motifs (Figure 3B). GmHXK3 and GmHXK4 contained all motifs except motif 2, and GmHXK5 and GmHXK6 contained all motifs except motif 9.
**FIGURE 3:** *The conserved domain organization and exon-intron structure of GmHXKs genes. (A). Phylogenetic relationships of GmHXK genes. The GmHXK amino acid sequences were aligned by Clustal X and the tree was generated using the Neighbor-Joining method in MEGA-X with 1,000 bootstrap replicates. (B). The conserved domain analysis of GmHXK proteins using MEME. Different colors represent the different conserved motifs. (C). Exon–intron structures of GmHXK genes. Exons and introns are shown as yellow boxes and black lines, respectively.*
Different combinations of exons and introns can lead to diverse gene function. To explore the structure diversity of GmHXK genes, Gene Structure Display Server 2.0 was employed to analyze the distribution of exon-intron structure based on the corresponding genome and coding sequences (Figure 3C). The results showed that most GmHXK genes contained nine exons and eight introns. GmHXK2 and GmHXK5 contained 10 exons and nine introns. GmHXK1, contained only three exons and two introns.
## 3.4 Cis-element analysis of the GmHXK genes
The identification of the cis-regulatory elements in the promoter part of the gene is important for functional and regulatory studies. To investigate the cis-regulatory elements of the 17 GmHXK genes, we analyzed the sequence 2,000 bp upstream of the start codon ATG. The cis-elements of the GmHXK genes were classified into three categories, involved in plant growth and development, stress responses and hormone-induced response (Supplementary Figure S1). The plant growth and development category contained seed-specific regulation (RY-element) and meristem expression (CAT-box) cis-elements. In the stress-responsive category, the elements included dehydration-responsive (DRE), anaerobic induction (ARE and GC-motif), low-temperature-responsive (LTR) and MYB-binding sites involved in drought inducibility (MBS), as well as defense and stress responsiveness (TCA). Seven types of phytohormone responsive cis-elements were detected, including auxin-responsive (AuxRR-core), abscisic acid-responsive (ABRE), methyl jasmonate-responsive (CGTCA-motif), ethylene-responsive (ERE), gibberellin responsive (GARE-motif), salicylic acid-responsive (TCA-element) and heat shock, osmotic stress, low pH, nutrient starvation-responsive (STRE). It is worth noting that the cis-elements MYB and MYC associated with phytohormone and abiotic stress were present in 17 GmHXK genes.
## 3.5 The expression pattern of GmHXK gene family
We analyzed the expression patterns of 14 GmHXK family members in different tissues including young-leaf, flower, pod, pod shell, seed, root and nodule using data in soybase (Figure 4A). It showed that GmHXK1,5,6,13,14 was expressed at a very low level or not expressed in seven tissues above. The level of expression of GmHXK4,7,8,9,17 was not obvious in majority of the plant tissues except for that in a certain tissue. GmHXK3,12,15,16 were expressed obviously in most tissues.
**FIGURE 4:** *The expression pattern of GmHXK gene family. (A). Tissue-specific expression profile of GmHXK under normal condition. Expression of GmHXK in roots under salt stress (B) and drought stress (C). Data are presented as mean ± SE. Red indicates high expression levels blue indicates low expression levels.*
And we analyzed also the expression of GmHXKs under salt and drought stress after treatments for different times by qRT-PCR. By analyzing expression data after treatment with NaCl or drought stress for 0, 2,8,24,72 h, we found that the expression of GmHXK 2,3,6,9,13,15 under treatment with NaCl (Figure 4B) and GmHXK2,4,6,7,9,15 under drought treatment with drought stress (Figure 4C) were increased after treatments within 72 h. The expression of other GmHXKs under salt or drought stress was obvious at the initial stage of treatment but decreased over time within 72 h. Owing to the increase in expression of GmHXK2,6,9,15 under both salt and drought stress, we selected GmHXK2 to study further its function under the salt stress.
## 3.6 Molecular characteristics of GmHXK2
GmHXK2 gene (Gene ID: GLYMA_09G144600) fragment containing introns without stop codon, was isolated by PCR with primers using genome DNA as a template. Fragments of size 2,033 bp were detected (Figure 5A). Then, the PCR product was inserted into a pMD18-T vector and sequenced. Sequence analysis suggested that the fragment was identical to the known GmHXK2 gene. The fragments were cloned into the entry vector TOPO by TOPO cloning reaction and subsequently into gateway destination vector pMDC83. Products amplified by PCR using genome DNA from T3 transgenic Arabidopsis WT35S::GmHXK2 seedlings of expected size 1,357 bp were detected (Figure 5B). Our results show that the GmHXK2 gene was integrated into the genome of WT.
**FIGURE 5:** *GmHXK2 gene amplification and identification of transgenic Arabidopsis seedlings. (A). PCR amplification of GmHXK2 gene. Marker: DL 2000; (B). PCR identification of transgenic Arabidopsis seedlings. Marker: DL 2000 (CWBIO, China).*
## 3.7 Analysis on salt tolerance of GmHXK2-silenced plants
To further explore the role of GmHXK2 in plant salt tolerance, we constructed a TRV-VIGS vector by selecting specific sequence in the CDS region of the GmHXK2 gene and identified the efficiency in silencing of GmHXK2-silenced plants by qRT-PCR, which were $30\%$–$50\%$ of control plants (Supplementary Figure S2). The silenced plants showed different phenotype from the control. Margins of silenced plants in first true leaf were discolored and wilted (Figure 6B) compared with the control (TRV:00). The validity of silenced plants was further determined by the PDS phenotypes (Figure 6C). Finally, we selected the plants with the highest efficiency of silencing for the subsequent experiment on salt stress.
**FIGURE 6:** *Phenotypic analysis of GmHXK2-silencing soybeans under salt stress. The seedings of TRV:00 and TRV:HXK2 divided into two groups (control and salt treatment) were grown for 7 days on pot under normal condition, then the plants were treated with 0 or 200 mM NaCl for 5 days, respectively. Salt treatment group phenotype as shown in figure (A–C) Phenotypes of TRV:00, TRV:HXK2 and TRV:PDS.*
After treatment with salt stress, all leaves in soybean plants had been wilt, curled, and were yellow to some extent, and the leaves of GmHXK2-silenced plants were more severely damaged than control (Figure 6A).
To investigate the potential physiological mechanisms by which GmHXK2 enhances plant tolerance, the proline, chlorophyll content, EL and SOD in TRV:00 and TRV:HXK2 plants were measured under normal and salt stress conditions. Our results showed that the EL of TRV:HXK2 ($26.18\%$) was significantly higher than that of TRV:00 ($11.33\%$) (Figure 7B) under salt conditions. This indicated that TRV:HXK2 plants were damaged more seriously than the control plants. The content of proline, chlorophyll and SOD of TRV:HXK2 (0.60 mg/g, 3.06 mg/g and 12.61 U, respectively) were decreased compared with that of TRV:00 plants (1.11 mg/g, 4.31 mg/g and 16.80 U, respectively) (Figures 7A, C, D). These results suggested that capability of TRV:HXK2 plants in resisting salt stress and scavenging ROS generated intracellularly diminished compared with control plants.
**FIGURE 7:** *Physiological response of GmHXK2 silent plant under salt stress. Content of proline (A), EL (B), chlorophyll (C) and SOD (D) were measured in TRV:00 and TRV:HXK2 after treatment with 0 or 200 mM NaCl for 5-days. Data are presented as mean ± SE. Single asterisks denotes significant differences compared to the values of TRV:00 at p < 0.05; Student’s t-test.*
## 3.8 Expression of Salt-Responsive genes in GmHXK2-silenced soybean plants
The plasma membrane Na+/H+ antiporters GmSOS1 (salt overly sensitive) (Ma et al., 2020), the high-affinity potassium transporters (GmHKTs) (Sun et al., 2021), GmbZIP44 (Zhao et al., 2020a), and GmSALT3 as one of cation/H exchangers, which play a vital role in response to environmental salinity (Xu et al., 2022a), were known to be involved in the regulation of salt tolerance in soybean. So we investigated the expression of these Salt-*Responsive* genes to ascertain the relationship of GmHXK2 and Na+ homeostasis.
Under control conditions, expressions of both GmSALT3 (Figure 8A) and GmSOS1 (Figure 8D) were decreased in silenced plants compared with WT, while expression of GmbZIP44 (Figure 8B) and GmHKT1 (Figure 8C) were not significantly different. However, the expression of all four genes in GmHXK2-silenced plants under salt stress was decreased significantly than WT (Figure 8). These results suggested that silence of the GmHXK2 gene downregulated the expression of salt stress-related genes involved in Na+ homeostasis, implying that GmHXK2 had an important role in remaining homeostasis of Na+ and K+.
**FIGURE 8:** *GmHXK2 affect the expression of Salt-Responsive genes. (A−D). Expression levels of salt stress-related genes. Data are presented as mean ± SE. Single and double asterisks denote significant differences compared to the values of WT at p < 0.05 and p < 0.01; Student’s t-test.*
## 3.9 Subcellular localization and western blot analysis of transgenic arabidopsis plants
As shown in Figure 9A, signals of GmHXK2-GFP were distributed on cell wall and cell membrane, which revealed that GmHXK2 was predominantly localized on both vacuolar membrane and cell membrane. To examine GmHXK2 protein expression, transgenic Arabidopsis seedlings WT35S::GmHXK2 were further analyzed via immunoblotting with an antibody specific to GFP by Western blot. The results showed that T3-T6 transgenic lines expressed the integrated protein with an expected molecular mass of 59.92 kDa (Figure 9B), which corresponds to the fusion protein, while no proteins of this size were observed in that of WT seedlings.
**FIGURE 9:** *Subcellular localization and WB analysis of transgenic plants. (A). Subcellular localization of GmHXK2 in transgenic Arabidopsis root cells. (B). Expression of GmHXK2 in transgenic Arabidopsis detected by Western-blot analysis. (A). GmHXK2-GFP fusion protein; (B). GAPDH was used as internal control with molecular mass of 36 kDa (M: Marker; C: Wild type Arabidopsis; T1-T6: transgenic Arabidopsis plants.*
## 3.10 Ectopic expression of GmHXK2 promote the growth of arabidopsis
As shown in Figure 10A, the WT and WT35S::GmHXK2 transgenic Arabidopsis grew well in the control group. After 100 mM NaCl treatment, the growth of the Arabidopsis was inhibited, but the growth of WT was more suppressed than that of WT35S::GmHXK2 transgenic Arabidopsis.
**FIGURE 10:** *The phenotype (A), root length (B) and fresh weight (C) of the WT and WT35S::GmHXK2 transgenic Arabidopsis plants under salt stress at the germination stage. Seeds of WT and WT35S::GmHXK2 were planted in 1/2 MS medium with or without100 mM NaCl for 7 days. Data are presented as mean ± SE (n = 5, biological replicates). Single, double and three asterisks denote significant differences at p < 0.05, p < 0.01 and p < 0.001, respectively; Student’s t-test.*
Under control condition, the root length of WT35S::GmHXK2 was significantly higher than that of WT, and fresh weight of WT35S::GmHXK2 were increased by $47\%$ compared with WT. After NaCl treatment, the root length and fresh weight of Arabidopsis were decreased, and the root length and fresh weight of WT35S::GmHXK2 were significantly higher than that of WT. ( Figures 10B, C). Hence, the expression of GmHXK2 promoted the growth of Arabidopsis.
## 3.11 Ectopic expression of GmHXK2 enhance the salt resistance of arabidopsis at the seedling stage
To verify whether GmHXK2-expressing Arabidopsis contributes to salt tolerance, we observed the phenotype of transgenic Arabidopsis and WT after 6 days of NaCl and glucose treatments and then measured the physiology and biochemistry indexes. Under salt stress, the growth of Arabidopsis seedlings were obviously inhibited, the leaves of all plants gradually became yellow and shriveled in addition to roots becoming shorter. In addition, the growth of WT was more suppressed than that of WT35S::GmHXK2. After adding 100 mM glucose, the growth condition of seedlings was much better compared to those at 0, 100 and 150 mM NaCl treatments. ( Figure 11).
**FIGURE 11:** *The phenotype of WT and WT35S::GmHXK2 transgenic Arabidopsis plants under salt stress. Glc represents 100 mM glucose treatment.*
After NaCl treatment, the fresh and dry weight, root length and chlorophyll content decreased, though the MDA and proline contents increased (Figure 12). As shown in Figures 12A, B, the fresh weight and dry weight of WT35S::GmHXK2 transgenic Arabidopsis were higher than that of WT under 0, 100 and 150 mM NaCl treatment. After salt treatment, root length, chlorophyll and proline contents of WT35S::GmHXK2 seedlings were significantly increased compared to WT, but the MDA content of WT35S::GmHXK2 seedlings was much lower than that of WT (Figures 12C–F).
**FIGURE 12:** *The fresh weight (A), dry weight (B), root length (C), chlorophyll content (D), MDA (E) and proline content (F) of the WT and WT35 S:GmHXK2 transgenic Arabidopsis plants under salt stress. Data are presented as mean ± SE. Single, double and three asterisks denote significant differences compared to the values of WT at p < 0.05, p < 0.01 and p < 0.001, respectively; Student’s t-test.*
Exogenous glucose counteracted the effect on salt stress. The fresh weight and dry weight, root length, chlorophyll and proline contents of WT and WT35S::GmHXK2 were increased compared to those at the 0, 100 and 150 mM NaCl stress. And the application of glucose under salt treatment inhibited the increase of MDA content under salt stress. ( Figure 12).
These results revealed the expression of GmHXK2 could enhanced the salt tolerance of plants during the seedling stage. In addition, 100 mM exogenous glucose alleviated the inhibition of salt stress on the growth of Arabidopsis plants.
## 4 Discussion
Hexokinases are very important enzymes in the growth and development of plants (Xiao et al., 2000). They not only play key roles in sugar signaling, but also involve in responding to abiotic stress in plants (Kim et al., 2013; Li et al., 2017). The OsHXK10 gene in rice can regulate plant reproduction (Xu et al., 2008), the prunus HXK3 gene can promote tolerance to drought and salt stress (Perez-Diaz et al., 2021), expression of Arabidopsis AtHXK1 in tobacco guard cells can attenuate transpiration in plants and improve salt and drought tolerance (Lugassi et al., 2019). In *Malus domestica* Borkh., MdHXK1 interacts with the salt tolerance gene MdNHX1 to improve the salt tolerance of plants (Sun et al., 2018). Though HXK genes have been widely studied in plants (Dai et al., 1999; Guglielminetti et al., 2000; Moore et al., 2003; Claeyssen et al., 2013; Li et al., 2017), HXK gene family members of G. max have not been characterized nor have their molecular properties under salt abiotic stresses been clarified until now.
Previous researches suggested that HXK proteins contain some conserved domains, phosphate 1 and 2, sugar binding site, and adenosine binding site, which are important to plant HXKs and essential for their enzymatic functions (Bork et al., 1993; Katz et al., 2000). In this study, a total of 17 HXK genes were identified in G. max. Except for GmHXK1, phosphate site, connect site, α-helix site, adenosine binding site, and sugar binding site, were well conserved in other GmHXKs (Figure 1). It suggested that they had the ability to phosphorylate hexoses. According to the characteristics of structure in Type A, B and C hexokinases and their distribution on subcellular organelle, we hypothesized that GmHXK3, GmHXK4 and GmHXK7-10 belong to Type A hexokinases and the other GmHXKs are Type B hexokinases using the data on the prediction of 17 hexokinase proteins and their subcellular localization in soybean.
ABA is a classical plant hormone whose level inside the plant is regulated by external environmental stress (Roychoudhury et al., 2013). Plants respond to environmental stress through interaction of transcription factors with a handful of cis-regulatory elements (Saidi and Hajibarat, 2019; Wang et al., 2021b). GmHXK2 exhibited abscisic acid-responsive (ABRE), ethylene-responsive (ERE), and MYB elements, etc. ABRE is an eight bp cis-acting sequence present in the promoter region of most ABA-inducible or ABA-responsive genes. It has been shown that bZIP transcription factor genes bind to ABRE cis-acting elements to regulate stress-induced expression of related genes in many plants such as A. thaliana and soybean (Kang et al., 2002; Liao et al., 2008). MYB transcription factor family genes are widely distributed in plants, which act on MYB cis-elements that involve in many important physiological and biochemical processes, such as cell development, signal transduction, and stress response (Ambawat et al., 2013; Li et al., 2015). Ethylene response factor (ERF) plays an important role in plant stress tolerance by regulating gene expression through binding to the ERE element of stress response genes (Wang et al., 2016). Transgenic plants expressing the ERF gene were shown to have significantly increased tolerance to abiotic stresses such as drought and salt stress (Zhang et al., 2009; Rong et al., 2014). GmHXKs not only contains Cis-acting regulatory element involved in seed-specific regulation (RY-element), Cis-acting regulatory element essential for meristem expression (CAT-box) but also ABRE, ERE, MYB and MYC elements related with response to abiotic environmental stress, suggesting GmHXKs might be induced by different signal such as ABA and ethylene to take part in regulation of development and abiotic tolerance to environmental stress through being activated by MYB or MYC transcription factor.
VIGS is a very effective method for gene function analysis (Ramegowda et al., 2014). We obtained GmHXK2-silenced soybean plants by this method and investigated the growth and physiological parameters under salt stress. During the growth of plants, many physiological indicators are often used to verify the tolerance of plants (Du et al., 2018; Sun et al., 2019; Wang et al., 2019; Leng et al., 2021; Li et al., 2021). It has been demonstrated that the accumulation of proline under adversity conditions can help plants to resist abiotic stresses (Lehmann et al., 2010; Xu et al., 2022b). To a certain extent chlorophyll content can indicate the degree of plant tolerance to stress. The reactive oxygen species (ROS) are by-products produced by various metabolic pathways within plant cells. Plants produce a range of defensive substances to maintain the dynamic intracellular equilibrium and prevent from causing damage to the organism due to accumulation of ROS. SOD is an enzyme that scavenges ROS in plants, and its level reflects the plant’s tolerance to stress (Apel and Hirt, 2004; Mittler et al., 2004; Wang et al., 2021a). EL can indicate the degree of plant cell membrane damage, higher EL suggests a greater degree of cell damage (Jungklang et al., 2017). Based on the data on phenotype (Figure 5) and physiological indicators that we obtained (Figure 6), silence of GmHXK2 decreased the salt tolerance of the plants, we deduced that the GmHXK2 gene is contributed to salt tolerance in soybean plants under salt stress.
Under salt stress, excessive sodium (Na+) inhibits enzyme activity and disrupts potassium (K+) uptake, leading to decrease in ration of K+/Na+. Plants have evolved multiple physiological mechanisms to cope with external stresses (Zhao et al., 2020a). GmHXKT1, GmSOS1, and GmSALT3 are genes that maintain ion homeostasis in cells in soybean plants. SOS1 is the key gene in the SOS pathway that regulates Na+ concentration and is believed to play an important role in osmoregulation (Shi et al., 2002; Olias et al., 2009; Ma et al., 2014). HKT is one of Na+/K+ transporters, which protects plants from Na+ accumulation in photosynthetic organs to improve the salt tolerance of plants by maintaining the Na+/K+ balance under salt stress (Berthomieu et al., 2003; Ren et al., 2005; Plett et al., 2010). GmSALT3 encodes a protein of the cation/H+ exchanger family which has a role in sensing or coping with salinity (Guan et al., 2014). GmbZIP44, as stress-responsive protein in the ABA pathway, can binds to the ABRE cis-regulatory element of target genes to regulate downstream gene expression (Kang et al., 2002; Liao et al., 2008). In our findings, expression of GmHXK2 gene were increased gradually in roots after treatment with NaCl for 2 h–72 h (Figures 4B, C). And the expression GmHXKT1, GmSOS1, and GmSALT3 were downregulated in GmHXK2-silenced soybean plants after treatment with NaCl compared with WT (Figure 8). These results confirmed that the silence of GmHXK2 in soybean plants was correlated significantly with decrease in expression of gene involving in uptake, transport and homeostasis of Na+, which reflected the important role of GmHXK2 through regulating homeostasis between K+ and Na+ to improve salt tolerance of soybean plants.
Plant transcription factors can bind to specific cis-element through their conserved domain. For example, overexpression of ThMYB8 decreased ROS levels and maintained K+/Na+ homeostasis (Liu et al., 2021). TaPIMP1 can acts by bind to a few of MYB-binding sites to regulate of genes related with abiotic stress in response to ABA or salicylic acid signal (Zhang et al., 2012). According to our results, we supposed that in response to salt stress, MYB or MYC transcription factor might be induced by ABA or ethylene, afterwards they might bind to GmHXK2 and triggered target genes downstream such as some genes related with homeostasis of Na+ to regulate the salt tolerance of soybean plants. The deduction on the molecular mechanisms of GmHXK2 gene in salt tolerance need to be further confirmed by extensive experiments. Furthermore, Arabidopsis plants of ectopic expressing GmHXK2 had the longer roots, lower levels of MDA contents, and higher levels of proline and chlorophyll content under salt stress, specially at conditions of exogenous application with glucose, indicating that GmHXK2 might also sense and respond to glucose signal to participate in salt tolerance under salt stress.
## 5 Conclusion
In this work, we not only identified HXK gene family members but also obtained GmHXK2 transgenic Arabidopsis lines and GmHXK2-silenced soybean plants. Subsequent analysis found that overexpressing GmHXK2 in Arabidopsis plants improved their tolerance to salt stresses. Moreover, 100 mM of exogenous glucose can alleviate plant growth inhibition under salt stress. Conversely, GmHXK2-silenced soybean plants reduced the expression of salt tolerance genes, which lead to less tolerant to salt stress. Our results not only provided the theoretical foundation for further research on HXK gene family in G. max, but also they could provide important information for breeding stress-resistant G. max cultivars.
## Date 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
YG designed the research. SC and ZT performed most of the experiments. All authors performed data analyses and took part in writing the manuscript.
## 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
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## Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fgene.2023.1135290/full#supplementary-material
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|
---
title: Unconscious processing of prototype heuristics in scientific innovation problem-solving
authors:
- Yushi Ling
- Liu Tan
- Liyi Zhang
- Guikang Cao
journal: Frontiers in Psychology
year: 2023
pmcid: PMC9996053
doi: 10.3389/fpsyg.2023.1056045
license: CC BY 4.0
---
# Unconscious processing of prototype heuristics in scientific innovation problem-solving
## Abstract
Previously published studies on the effect of how different levels of unconsciousness (UC) and different loads of executive functions (EFs) affect insight problem solving are inconsistent. In a set of three experiments, we used scientific innovation problems (SIP) as insight metrics and distractor tasks to induce UC. Experiment 1 confirmed that, compared with conscious processing, unconscious processing is more conducive to obtaining prototype heuristics for correctly solving scientific innovation problems creatively. Furthermore, Experiment 2 found that different levels of unconscious processing, which were induced by different distractor tasks, made a different impact on high or low difficulty creative problem solving. Experiment 3 indicated that unconscious processing could improve prototype activation and the ability to use key heuristics information in prototype heuristics processing by improving working memory, inhibitory control, and shifting ability of EFs. Overall, the present results provide additional evidence for the role of consciousness levels in insight problem solving.
## The prototype heuristics theory in the scientific invention problems
Creativity is the ability to produce both novel and appropriate work (Sternberg and Lubart, 1991, 1996), and insight is an important topic in the research of creativity in psychology (Dietrich and Kanso, 2010; Tong et al., 2013). Sometimes, a flash of inspiration or intuition may trigger a critical moment of thought that leads to an “aha” moment and solves a problem, known as insight. For example, the well-known golden crown problem. That is, Archimedes, the famous Ancient Greek philosopher, was asked to estimate whether the golden crown was made from pure gold. He was very confused at the beginning, but a solution suddenly hit him during his bath time because he found that when he got into the bathtub, once the water has been drained from the bathtub, objects of the same weight and density should drain the same volume of water. He realized that this discovery could solve the golden crown problem. This anecdote is a good example of insight (Hao et al., 2013; Zhu et al., 2019).
To further illustrate, we can treat the phenomenon that the object drains water out of a bathtub as a prototype, realize the connection between that prototype and the golden crown problem, and then apply that prototype to that problem; this is a way to solve the insight problem, that is, the prototype heuristic (Zhang et al., 2004). During the prototype heuristic process, the activation of a semantic representation of a prototype that benefits insight problem-solving is known as prototype activation, and the application of the heuristic information implied by the prototype (such as principles, rules, and methods) leads to successfully solving the insight problem of creativity (Zhang et al., 2012).
Many recorded insights, such as the golden crown problem, were derived from scientists and inventors (Ovington et al., 2018), which may suggest that a large part of real-world epiphanies come from scientific inventions. Most published studies that concentrate on the cognitive mechanism of insight more frequently adopt the compound remote associate problem (RAT; Beaty et al., 2014) and the puzzle task (Wu, 2007), and they all have a common problem, that is, artificial materials and lack of ecological validity (Tong et al., 2013, 2015; Yang et al., 2016). To solve this problem, Zhu [2011] used scientific innovation problems (SIPs) as experimental materials and compiled the Scientific Innovation Problems Database. Unlike divergent thinking, convergent thinking, or analogical transfer problems, SIPs comprise knowledge-rich problems (Yang et al., 2022). In this experimental material, each SIP includes contextual information of the scientific innovation problem, a prototype associated with it, and a reference answer. For example, groundwater needs to be pumped to irrigate crops in arid areas, but it uses too much electricity and is expensive to drill wells. This makes it difficult to scale up in the vast arid areas of the west (contextual information). The question is how to use groundwater without electricity or drilling wells. The relevant prototype information is that trees use capillary action in their roots to transport underground water from their roots to leaves hundreds of feet above the ground. Researchers always combine the SIP with the “learning-testing” paradigm to explore the effect of the prototype heuristic. Participants need to learn the prototype information first; the specific operation is to present one or more prototypes to the participants for learning without limitation of time, and after the participants report the completion of learning, researchers present them with target problems to explore whether the participants could activate the previously learned prototype to help solve the current creative problem (Yan et al., 2011). This is known as the prototype heuristic paradigm, which considers knowledge-rich contexts and enables more ecologically valid investigations of creative problem-solving in the laboratory (Yang et al., 2022). Moreover, the Scientific Innovation Problems Database has also been widely used in research (Zhu et al., 2017, 2019).
## The influence of levels of consciousness on prototype heuristic and insight
Dijksterhuis and Meurs [2006] suggest that, compared with conscious thinking, unconscious thinking is more “liberal” and leads to “less obvious, less accessible, and more creative” ideas. Compared with non-insight problem-solving, insight problem-solving relies more on implicit, bottom-up, unconscious processes (Lebed and Korovkin, 2017; Stuyck et al., 2022). Simultaneously, the processing of the prototype heuristic includes unconscious thinking as well. For example, Cao et al. [ 2006] found that prototype activation has no difference between participants who implicitly or explicitly learned the prototype. Furthermore, they suggested that prototype activation could occur unconsciously and does not need conscious induction and summary. Hereafter, the process of matching various information of the prototype with the problem to find the solution to the problem is completed through conscious processing. A recent study by Xing et al. [ 2018] also showed that heuristics from prototypes probably involve an implicit, unconscious process.
Zhao [2018] identified that conscious and unconscious processing are both involved in the creative problem-solving process, meanwhile, Zhao also verified that the level of UC has deep and shallow processing by the sandwich masking and distractor task paradigms. The sandwich masking paradigm reduces or disappears the visibility of the target stimulus through the continuous and rapid presentation of the two stimuli, thus achieving unconscious-level processing. The distractor task is a task that causes the unconscious level processing of the target task through a task that occupies more cognitive resources. Sio and Ormerod [2009] used meta-analysis to assess the incubation effect of RAT tasks; their results also show that, compared with those difficult distractor tasks that consume more cognitive resources, the remote association of target words was observed faster in the easy distractor tasks with less cognitive resources. Overall, conscious and unconscious processing is involved in creative problem-solving, and different levels of UC will have different effects on creative problem-solving. Moreover, it could be argued that different levels of UC will also have different effects on prototype heuristics.
## Executive functions
As mentioned earlier, conscious thinking, which involves bottom-up processing, and insight problem-solving appear to be closely linked. However, EFs, which involve top-down processing, do not rely on instinct or intuition (Diamond, 2013). Furthermore, an important factor of insight problem-solving, such as progress monitoring theory (Knoblich et al., 1999) developed by modern cognitive psychology, is that effective insight problem-solving involves substantial loads on working memory (an EF process). Chrysikou [2019] further points out that the importance of cognitive control mechanisms for creative thinking is a consensus in the field of creative neuroscience.
EFs refer to a series of higher cognitive abilities of individual consciousness and effective control of thinking and behavior, which include inhibition, shifting, and updating (Miyake et al., 2000). Inhibition is the repression of automatic reactions in the cognitive process or content, which mainly prevents irrelevant information from entering and being stored in working memory. Shifting means individuals respond to new situations with appropriate reactions and maintain cognitive and behavioral flexibility. That is, when faced with multiple tasks competing for a cognitive resource, the control process of attentional switching in these tasks takes precedence. Updating is the process by which an individual continuously incorporates new information and discards irrelevant information to the current task to change the contents of working memory based on the information presented. These three sub-functions are related to each other, but they play different roles in complex cognitive processes and play an important role in insight problem-solving.
For example, four fluid reasoning tests, 13 working memory tasks, and an intensive range of insight tasks were used by Chuderski and Jastrzebski [2018] to verify the relationships among the three; they found a strong positive correlation between EFs and insight problem-solving of 0.795, which verified a strong link between the two of them. Moreover, they also found that the working memory capacity factor explained $51.8\%$ of insight variance, as well as $87.0\%$ of reasoning variance. Xing et al. [ 2018] also found positive correlations between EFs and insight problem-solving, and updating (an EF) significantly predicted insight performance. Cassotti et al. [ 2016] suggested that inhibitory control is a central process in creative problem-solving and idea generation from childhood through adulthood because developing a solution to a creative problem requires suppressing inappropriate thoughts. In addition, EEG research by Benedek et al. [ 2012] showed that inhibitory control resources were positively correlated with creative task scores (Benedek et al., 2012; Beaty et al., 2014).
To summarize, an antagonistic relationship between UC and EFs may exist; however, unconsciousness, EFs, and its three components play a role in promoting the performance of insight problem-solving. So, what are the true relationships among UC, EFs, and insight problem-solving? Previous research has established some models to explore the relationships among these three constructs. For example, the associative theory contends that UC promotes insight problem-solving (Mednick, 1962); however, this theory does not consider the role of EFs. Recent research by Stuyck et al. [ 2022] claimed that they proved the associative theory as they found that cognitive control did not influence the performance of insight problem-solving. In their research, the performance of insight problem-solving was measured by RAT grades with an accuracy of $91\%$–$94\%$. Of note, however, previous research has already provided evidence that when RAT difficulty was very high (all but one of 39 participants were able to solve no more than one problem out of nine), the promoting effect of unconscious processing on RAT performance could be observed, but when RAT difficulty was medium (correct answer rates were between $41\%$ and $59\%$ with 39 participants), the promoting effect disappeared (Zhong et al., 2008). The results of Zhong et al. [ 2008] may reveal that the research by Stuyck et al. [ 2022] is insufficient to conclude that cognitive control did not influence the performance of insight problem-solving. In addition to the association theory, some researchers claimed that only EFs could promote insight problem-solving, such as Chuderski and Jastrzebski [2018] who attributed their results to working memory playing a central role in insight problem-solving and “nothing special with special add-ons.” *Although this* view considers the role of EFs, it ignores the UC that already exists. Early in 2007, Schmeichel [2007] found that working memory tasks could deplete EFs. Therefore, the working memory task can also play the role of a distractor task, making the insight problem-solving processing into the unconscious thinking state. Thus, it is inappropriate to consider only the role of working memory. The view of associative processes and executive control both playing a role in insight problem-solving has also been proposed. To illustrate, Beaty et al. [ 2016] argued that the default network influences the generation of candidate ideas, but the control network can constrain and guide the process through top-down monitoring and executive control to meet the goals of a particular task. However, they also make it clear that the framework does not include cases of creative insight. Therefore, how EFs and UC play a role in insight problem-solving remains to be explored.
According to Beaty et al. [ 2016], in creative problem-solving, the default network influences the generation of candidate ideas before the constraint and guidance of executive control. Therefore, we believe that only after the UC induces the generation of ideas, will EFs play a role. Otherwise, the process of logical reasoning or functional fixation can only occur. On the other hand, if the EFs are not functioning, individuals may not be able to report correct opinions even with unconscious thinking. Therefore, we hypothesize that EFs mediate the relationship between UC and insight problem-solving.
## Current research
To sum up, in solving creative problems, sometimes unconscious processing may be better than conscious processing results. In addition, the effect of unconscious processing induced by a low cognitive load distractor task (low-distractor task) on creative problem-solving is greater than that of a high cognitive load distractor task (high-distractor task). Research has also shown that improvements in certain cognitive abilities in EF can also boost creative performance. However, although sufficient research exists on the relationships among UC, EFs, and insight problem-solving, no clear explanation has been determined. Therefore, we report three experiments to explore the relationships between UC and EFs as well as how they play a role in insight problem-solving.
To this end, the purpose of Experiment 1 was to verify whether unconscious processing is better than conscious processing in SIP solving and whether item difficulty will influence performance. Most researchers investigating UC have utilized the distractor task (Ding et al., 2019), mask priming paradigm (Huber-Huber and Ansorge, 2018; Silva et al., 2018), and dual-task paradigm (Lebed and Korovkin, 2017). In Experiment 1, a distractor task was used wherein participants were required to perform several comparison tasks in the process of problem-solving. Such concurrent cognitive control tasks would prevent cognitive control from playing a role in the main task (Huber-Huber and Ansorge, 2018); thus inducing the participants to engage in unconscious thinking. Based on the empirical results mentioned earlier, we predicted that participants who performed the distractor task performed better on insight problem-solving than participants who performed conscious thinking, especially when the problem was difficult.
Experiment 2 added different levels of consciousness. We speculate that the more difficult the distractor task, the more cognitive resources will be occupied, and the higher the level of UC. According to previous studies, too much occupation of cognitive resources will lead to poorer performance in SIP solving than less occupation.
A measure of EFs was introduced in Experiment 3 to further explain the results of Experiment 2. EFs are a higher-level cognitive ability used in careful research and goal realization (Cristofori et al., 2019). As an ability, researchers assume that it will not change in the short term. Therefore, many researchers have focused their attention on individual differences in EFs (Steward et al., 2018; Stolte et al., 2020; Tsai et al., 2021). However, Schmeichel [2007] believed that, similar to the ego depletion hypothesis (Baumeister et al., 1998), EFs have a depletable capacity. Their study found that, compared with the control group, participants who completed the distractor task showed decreased performance in the next EFs measuring. Moreover, participants who previously completed the inhibition task had a negative influence on the subsequent working memory updating task and vice versa (the previous working memory updating task had a negative impact on inhibition task performance). This suggests that the completion of tasks involving EFs impacts subsequent measures of EFs. Specifically, the previous task consumes a portion of the limited resources of EFs, thus reducing the amount of EFs available later. To test this idea, EFs were measured immediately after the distractor task in Experiment 3 to explore whether the distractor task affected EFs and whether the affected EFs would lead to different SIP-solving results. In addition, although the EFs measurement task itself also occupied cognitive resources, the three groups of subjects completed the same EFs measurement after the level of consciousness manipulation; therefore, theoretically, the occupied cognitive resources are equal and can be balanced.
## Experiment 1
Given the evidence that UC has been effective in creativity, especially in difficulty problem-solving, we verified whether, when SIP is used to test creativity, different levels of consciousness have different effects on it. We hypothesize that when the SIP is difficult, UC can promote prototype heuristics in solving problems more than consciousness.
## Participants
Seventy-eight participants (aged between 18 and 27 years, mean age = 21.48 years, SD = 1.68) from Southwest University were recruited. Participants were randomly assigned to the conscious condition ($$n = 38$$) and the unconscious condition ($$n = 40$$). After removing one subject who failed to complete all the experimental tasks, the final number of effective subjects was 77. All participants provided informed consent before participating and received some remuneration after the experiment. Experimental protocols for all three experiments were approved by the University’s local ethics committee.
Ninety participants (aged between 18 and 23 years, mean age = 19.67 years, SD = 1.29, 36 male participants) from Southwest University were recruited by advertising. Participants were randomly assigned to the conscious condition ($$n = 30$$), low-distractor task condition ($$n = 30$$), and high-distractor task condition ($$n = 30$$). All participants provided informed consent before participating and received some remuneration after the experiment.
Eighty-six participants (aged between 18 and 23 years, mean age = 19.67 years, SD = 1.43, male = 31) from Southwest University were recruited by advertising. Participants were randomly assigned to the conscious condition ($$n = 28$$), low-distractor task condition ($$n = 29$$), and high cognitive loads condition ($$n = 29$$). All participants provided informed consent before participating and received some remuneration after the experiment.
## Scientific innovation problem
We chose 84 SIPs from the Scientific Innovation Problems Database (Zhu, 2011) based on difficulty. Three students with psychology as a major were asked to rate the difficulty of the questions on a 7-point scale (1 = lowest difficulty and 7 = highest difficulty). The order of each question was presented randomly. The scorer reliability was 0.89. According to the scoring results, 20 questions were selected for the high-difficulty condition (MD = 4.70, SD = 0.52) and 20 questions for the low-difficulty condition (MD = 2.10, SD = 0.48).
Twenty-four SIPs were selected from the Scientific Innovation Problems Database (Zhu, 2011). Twelve of them were low difficulty ($M = 0.81$, SD = 0.05) and the others were high difficulty ($M = 0.57$, SD = 0.02). Importantly, we measured difficulty by heuristics rate.
Twenty-four high-difficulty ($M = 0.63$, SD = 0.05) SIPs were selected from the Scientific Innovation Problems Database (Zhu, 2011); and the difficulty was measured by heuristics rate.
## Distractor task
Several comparison tasks were adopted to induce UC. A random set of numbers appeared on both the left and right sides of the computer screen. The numbers were random integers between 10 and 99 and appeared for only 1 s. The participants were asked to quickly and accurately determine which side of the screen had a larger number and to respond accordingly. If the number on the left side of the screen was larger, the participant pressed “Q,” and if the number on the right side of the screen was larger, the participant pressed “P.” Participants were asked to perform a 3-min distractor task. This is because previous studies have found that when the distractor task lasts 3 min, it has the best effect on creativity compared to 1 or 5 min (Gilhooly, 2016).
Experiment 2 adopted the same distractor task as Experiment 1; the only difference was that different numeric types were used to induce high and low cognitive loads. As comparisons between fractions are more complicated compared with integer comparisons, fraction comparisons require a higher cognitive load to process. Therefore, we induced low cognitive loads by requiring participants to compare random two-digit numbers and induced high cognitive loads by requiring participants to make comparisons between random fractions, in which the numerator and denominator were both random integers between 10 and 99. A preliminary experiment was used to examine the cognitive load distinction between integer and fraction comparisons. As a result, the accuracy of fraction comparison tasks ($M = 0.73$, SD = 0.89) was significantly lower than the accuracy of integer comparison tasks ($M = 0.92$, SD = 0.45), reaction time ($M = 928.79$ ms, SD = 205.03) was significantly higher than that of integer comparison tasks ($M = 613.77$ ms, SD = 135.93), and the difficulty score ($M = 5.90$, SD = 0.89), which was subjectively assessed by participants, was significantly lower than the difficulty score of integer comparison tasks ($M = 1.33$, SD = 0.69). It could be argued that the integer and fraction comparison tasks have a reliable effect on distinguishing between low and high cognitive loads.
The distractor task in Experiment 3 was identical to Experiment 2.
## Procedure
The experiment was programmed using E-Prime 2.0 and consisted of four phases: problems presentation, prototypes learning, consciousness level manipulation, and answer.
In the problems presentation phase, the participants were presented with eight blocks. Each block had five trials that would randomly present a SIP on the computer screen (high-and low-difficulty problems were presented randomly too), resulting in 40 trials presenting 40 problems. The participants had 30 s to memorize it carefully. To eliminate participants answering questions based on personal experience, instead of learning from the prototype we provided, we also required the participants to judge whether they already knew the answer to the question due to personal life experience or education. Participants were asked to press the “F” key if they already knew the answer before participating in this study and the “J” key if not. The answer was eliminated in the data analysis phase if participants pressed the “F” key.
The prototypes learning phase used the same design as the problems presentation phase, but each trial was randomly presented with the prototype information corresponding to the problem in the first phase (but the problem itself was not presented), such as “When the nurse gives the injection, she can use a small needle to inject the medicine like squeezing toothpaste,” and participants did not have to press any key, then after 60 s, the screen will automatically display the next prototype until all prototypes corresponding to the problem are presented.
In the consciousness level manipulation phase, participants in the unconscious group were informed to complete a 3-min number comparison task; participants in the conscious group were instructed to recall the problems and prototypes they had seen before and to try to think of solutions to the problems.
In the answer phase, the participants began to answer the SIP with paper and pen.
The basic procedure in Experiment 2 was identical to Experiment 1, with two alterations. One was that in the problems presentation phase, the participants were presented with four blocks instead of eight, and each block had six trials that would randomly present a SIP on the computer screen, resulting in 24 trials presenting 24 problems. The other alteration was that in the consciousness level manipulation phase, participants in the low-distractor task condition were informed to complete a 3-min integer comparison task, participants in the h-distractor task condition were informed to complete a 3-min fraction comparison task, and the conscious condition participants were informed to recall the problems and prototypes they had seen before and to try to think of solutions to the problems.
The procedure was the same as in Experiment 2, but there was an extra phase before the answer phase. The new phase was the EFs measurement phase, which measured three dimensions in random order: working memory was measured by a two-back task, shifting by a shifting number task, and inhibition by the Stroop task. Participants had at most 3 min to complete each task. Note that the problems presentation and the prototypes learning phases presented different problems and prototypes from Experiment 2; in the current experiment, 24 high-difficulty problems were selected as materials.
## Data analysis
For the SIP, we rated the answers on a scale from 0 to 2, based on the criteria shown in previous studies (Yang et al., 2016). If the participant had recalled the correct prototype and correctly solved the problem, the score was 2; if the participant had only recalled the correct prototype but failed to solve the problem, the score was 1; and if the participant had failed to answer the question correctly, the score was 0. To assess how well the participants solved the problem, we computed two indices, one was the prototype activation rate, which refers to the number of questions with a non-zero individual score divided by the number of all questions after eliminating the questions with known answers. The other was the accuracy rate, which refers to the number of questions with an individual score of 2 divided by the number of all questions, after excluding questions to which the participant knew the answer. Hereafter, SPSS 22.0 was used for statistical analysis. Repeated measures ANOVA was performed for the prototype activation rate and problem-solving accuracy rate.
## Results
Three trained psychology majors were asked to rate participants’ SIP-solving scores according to the method we presented in the Data Analysis section earlier. The scorer reliability was 0.918. Descriptive statistics for the prototype activation rate and accuracy rate are provided in Table 1.
**Table 1**
| Unnamed: 0 | Conscious | Conscious.1 | Unconscious | Unconscious.1 |
| --- | --- | --- | --- | --- |
| | High difficulty | Low difficulty | High difficulty | Low difficulty |
| Prototype activation rate | 0.765 (0.11) | 0.893 (0.09) | 0.918 (0.06) | 0.922 (0.06) |
| Accuracy rate | 0.313 (0.12) | 0.598 (0.17) | 0.668 (0.13) | 0.718 (0.11) |
SPSS 22.0 was used for statistical analysis. Repeated measures ANOVA was performed for the prototype activation and problem-solving accuracy rates. Three trained psychology majors were asked to rate participants’ SIP-solving scores according to the method we presented in the Data Analysis section in Experiment 1. The scorer reliability was 0.848. Descriptive statistics for the prototype activation and the accuracy rates are provided in Table 2.
**Table 2**
| Unnamed: 0 | Conscious condition | Conscious condition.1 | Low cognition loads condition | Low cognition loads condition.1 | High cognition loads condition | High cognition loads condition.1 |
| --- | --- | --- | --- | --- | --- | --- |
| | High difficulty | Low difficulty | High difficulty | Low difficulty | Low difficulty | High difficulty |
| Prototype activation rate | 0.752 (0.158) | 0.806 (0.119) | 0.938 (0.076) | 0.927 (0.066) | 0.848 (0.086) | 0.905 (0.075) |
| Accuracy rate | 0.510 (0.138) | 0.474 (0.175) | 0.630 (0.808) | 0.658 (0.118) | 0.651 (0.102) | 0.575 (0.127) |
SPSS 22.0 was used for statistical analysis; relative mediation analyses were performed using the mediation package. To investigate the relations among the levels of consciousness, three sub-functions of EFs, two rates of prototype heuristics, and descriptive statistics are summarized in Table 3. Among them, the three sub-functions (inhibition, shifting, and updating) scores were calculated by the reaction time of the correct response to the task, in milliseconds. The correlation between each variable was analyzed and is presented in Table 4.
As can be seen from Table 4, there were significant positive correlations between all conditions. Hereafter, mediation analyses were performed. As independent variables are categorical variables, and intermediate variables and dependent variables were continuous variables in the current experiment, bootstrap relative mediation analysis was performed using the mediation package (Hayes and Preacher, 2014; Jie and Wen, 2017). The independent variable levels of consciousness were coded, with the conscious condition as the reference variable, the high-distractor task condition as dummy variable 1, and the low-distractor task condition as dummy variable 2. Bootstrap set random sampling to 5,000 times, with the prototype activation rate and the accuracy rate as dependent variables under a $95\%$ confidence interval. The global mediation analysis and relative mediation analysis were conducted with the three sub-functions of EFs as three parallel mediation variables. The results were as follows.
The total effect of the global mediation analysis with the prototype activation rate as the dependent variable was significant [F[4,81] = 43.5, $p \leq 0.001$], indicating that the two relative total effects are not 0. The total direct effect of the global mediation analysis with the prototype activation rate as the dependent variable was also significant [F[7,78] = 114.95, $p \leq 0.001$] and indicated that the two relative direct effects are not 0. The total effect of the global mediation analysis with the accuracy rate as the dependent variable was significant [F[4,81] = 11.96, $p \leq 0.001$], indicating that the two relative total effects are not 0. The total direct effect of the global mediation analysis with the accuracy rate as the dependent variable was also significant [F[7,78] = 13.31, $p \leq 0.001$], indicating that the two relative direct effects are not 0. Therefore, further relative mediation analysis had to be conducted.
## The prototype activation rate
A 2 × 2 repeated-measures ANOVA was performed to assess the effects of the within-subjects factor difficulty (high difficulty vs. low difficulty) and the between-subjects factor group (conscious vs. unconscious) on the prototype activation rate; age and gender of participants were used as covariables. A significant main effect of the group [F[1,76] = 32.732, $p \leq 0.001$, ηp2 = 0.304] revealed that the prototype activation rate of the conscious condition ($M = 0.827$, SD = 0.085) was significantly lower than the unconscious condition ($M = 0.920$, SD = 0.049). The main effect on difficulty was also significant [F[1,76] = 46.172, $p \leq 0.01$, ηp2 = 0.381]; the high-difficulty prototype activation rate ($M = 0.840$, SD = 0.116) was significantly lower than the low-difficulty prototype activation rate ($M = 0.907$, SD = 0.077). Moreover, the interaction between group and difficulty was significant, F[1,76] = 40.509, $p \leq 0.001$, ηp2 = 0.351.
As we found a significant interaction between group and difficulty, we followed up with a simple effect analysis. The outcomes showed that the prototype activation rate was not significantly different ($$p \leq 0.095$$) between the unconscious ($M = 0.922$, SD = 0.055) and conscious groups ($M = 0.893$, SD = 0.092) under the low difficulty condition. However, in the high-difficulty condition, the prototype activation rate of the unconscious group ($M = 0.918$, SD = 0.055) was significantly higher than the conscious group ($M = 0.765$, SD = 0.110; $p \leq 0.01$). In the conscious condition, the prototype activation rate of the high-difficulty task ($M = 0.765$, SD = 0.110) was significantly lower than the low-difficulty task ($M = 0.893$, SD = 0.092). However, there was no difference between the high-difficulty prototype activation ($M = 0.918$, SD = 0.055) and low-difficulty prototype activation rates ($M = 0.922$, SD = 0.055) in the unconscious group. The results are shown in Figure 1.
**Figure 1:** *The prototype activation rate of Experiment 1.*
A 2 × 3 repeated-measures ANOVA was performed to assess the effects of the within-subjects factor difficulty (high difficulty vs. low difficulty) and the between-subjects factor group (conscious vs. low-distractor task vs. h-distractor task) on prototype activation rate; age and gender of participants were used as covariables. A significant main effect of the group [F[2,85] = 26.552, $p \leq 0.001$, ηp2 = 0.358] revealed that the prototype activation rate of the low-distractor task condition ($M = 0.933$, SD = 0.050) was significantly higher than the high-distractor task condition ($M = 0.876$, SD = 0.061), and the prototype activation rate of the high-distractor task condition was significantly higher than that of the conscious condition ($M = 0.778$, SD = 0.125). The main effect on difficulty was not significant [F[2,85] = 0.015, $$p \leq 0.903$$, ηp2 < 0.001]. The interaction between groups and difficulty was significant, F[2,85] = 3.625, $$p \leq 0.031$$, ηp2 = 0.079.
Simple effect analysis showed that the outcomes indicated that the prototype activation rate in the conscious condition ($M = 0.805$, SD = 0.017) was significantly lower than the low-distractor task condition ($M = 0.928$, SD = 0.016) and high-distractor task condition ($M = 0.905$, SD = 0.016; $p \leq 0.001$). However, the prototype activation rate was not significantly different between the high-and low-distractor task conditions ($$p \leq 0.319$$). However, when the problems were highly difficult, the prototype activation rate in the conscious condition ($M = 0.750$, SD = 0.021) was significantly lower than in the low-distractor task condition ($M = 0.939$, SD = 0.020) and high-distractor task condition ($M = 0.848$, SD = 0.020; $p \leq 0.001$), and the prototype activation rate in the high-distractor task condition was significantly lower than the low-distractor task condition ($p \leq 0.001$). Simultaneously, in the conscious condition, the prototype activation rate in high-difficulty problems ($M = 0.750$, SD = 0.021) was significantly lower than that in low-difficulty problems ($M = 0.805$, SD = 0.017; $$p \leq 0.011$$). In the low-distractor task condition, the prototype activation rate in high-and low-difficulty problems was not significantly different ($$p \leq 0.582$$). In the high-distractor task condition, the prototype activation rate in high-difficulty problems ($M = 0.848$, SD = 0.020) was significantly lower than that in low-difficulty problems ($M = 0.905$, SD = 0.016; $$p \leq 0.006$$). A visual display is provided in Figure 3.
**Figure 3:** *The prototype activation rate of Experiment 2.*
## The accuracy rate
A 2 × 2 repeated measures ANOVA was performed with the accuracy rate as the dependent variable, difficulty (high vs. low difficulty) as the within-subject variable, group (conscious vs. unconscious) as the between-subjects variable, and age and gender as the covariables. The results showed that the main effect was highly significant, F[1,76] = 71.199, $p \leq 0.001$, ηp2 = 0.487. The accuracy rate of the conscious condition ($M = 0.455$, SD = 0.138) was significantly lower than the unconscious condition ($M = 0.693$, SD = 0.106). The main effect on difficulty was significant, F[1,76] = 193.755, $p \leq 0.001$, ηp2 = 0.721; the high difficulty accuracy rate ($M = 0.488$, SD = 0.216) was significantly lower than the low difficulty accuracy rate ($M = 0.657$, SD = 0.156). The interaction between group and difficulty was significant, F[1,76] = 94.566, $p \leq 0.001$, ηp2 = 0.558.
Further simple effect analysis showed that the accuracy rate of the unconscious group ($M = 0.718$, SD = 0.110) was significantly higher than the conscious group ($M = 0.598$, SD = 0.172; $p \leq 0.01$) under the low-difficulty condition, and the accuracy rate of the unconscious group ($M = 0.667$, SD = 0.125) was significantly higher than the conscious group ($M = 0.313$, SD = 0.121; $p \leq 0.01$) under the high-difficulty condition. In the unconscious processing group, the accuracy rate of the high-difficulty task ($M = 0.668$, SD = 0.125) was significantly lower than the low-difficulty task ($M = 0.598$, SD = 0.172; $$p \leq 0.004$$). Moreover, there was no difference between the high-difficulty ($M = 0.667$, SD = 0.125) and low-difficulty accuracy rates ($M = 0.598$, SD = 0.172). The results are shown in Figure 2.
**Figure 2:** *The accuracy rate of Experiment 1.*
A 2 × 3 repeated-measures ANOVA was performed to assess the effects of the within-subjects factor difficulty (high difficulty vs. low difficulty) and the between-subjects factor group (conscious vs. low-distractor task vs. high-distractor task) on the accuracy rate; age and gender of participants were used as covariables. A significant main effect of the group [F[2,85] = 15.109, $p \leq 0.001$, ηp2 = 0.258] revealed that the accuracy rate of the low-distractor task ($M = 0.644$, SD = 0.080) and high-distractor task conditions ($M = 0.613$, SD = 0.098) were significantly higher than that of the conscious condition ($M = 0.492$, SD = 0.149), but the accuracy rate of the high-distractor task and low-distractor task conditions were not differentiated. The main effect on difficulty was significant [F[2,85] = 5.217, $$p \leq 0.025$$, ηp2 = 0.057]. Moreover, the interaction between groups and difficulty was significant, F[2,85] = 6.092, $$p \leq 0.003$$, ηp2 = 0.123.
A simple effect analysis was conducted and the outcomes indicated that when SIPs were high-difficulty problems, the accuracy rate in the conscious condition ($M = 0.510$, SD = 0.138) was significantly lower than the low-distractor task ($M = 0.630$, SD = 0.808) and high-distractor task conditions ($M = 0.651$, SD = 0.102; $p \leq 0.001$), but the accuracy rate was not significantly different between the high-and low-distractor task conditions ($$p \leq 0.846$$). When SIP were low-difficulty problems, the accuracy rate in the conscious condition ($M = 0.474$, SD = 0.175) was significantly lower than the low-distractor task ($M = 0.658$, SD = 0.118; $p \leq 0.001$) and high-distractor task conditions ($M = 0.575$, SD = 0.127; $$p \leq 0.021$$), but the accuracy rate was not significantly different between the high-and low-distractor task conditions ($$p \leq 0.069$$). The accuracy rates of high-and low-difficulty problem-solving were not significantly different under the conscious and low-distractor task conditions ($$p \leq 0.10$$; $$p \leq 0.195$$), and a significant difference was found under the high-distractor task condition ($$p \leq 0.001$$). More specifically, the accuracy rate of high-difficulty problem-solving was significantly greater than that of low-difficulty problem-solving. A visual display is presented in Figure 4.
**Figure 4:** *The accuracy rate of Experiment 2.*
## Discussion
Taken together, these results showed that if we solve SIP consciously, two indices of prototype heuristics (the prototype activation and accuracy rates) can be quite different because of the different task difficulties. Otherwise, the effect of prototype heuristics is good regardless of the difficulty level of the task if unconscious processing is used. When the task difficulty was low, the prototype activation rate of the unconscious group was no different from that of the conscious group, but the accuracy rate was significantly higher than that of the conscious group. When the task difficulty was high, the prototype activation rate and accuracy rate of the unconscious group were significantly higher than that of the conscious group. This result is consistent with our hypothesis, that is, UC promotes SIP solving. According to previous research, the prototype heuristic is an automatic process (Zhu et al., 2019), specifically, there is a semantic similarity between the “need function” in problem representation and the “characteristic function” in prototype representation. When participants map the “characteristic function” to the “need function,” the problem will be solved, and such structural mapping is an automatic process (Zhang et al., 2012), also known as representation-connection (Zhu et al., 2019). In SIP solving, individuals need to find the prototype character that plays a key role in the current problem among the numerous prototype characters, which requires a wide range of information processing. Unconscious processing, with its powerful searching and associative abilities, can help individuals find corresponding archetypes and solve problems.
Both simple and difficult scientific inventions benefited from unconscious processing, which is somewhat different from previous studies. Zhong et al. [ 2008], using RAT as a creative task, showed that when the difficulty of the task was simple, there was no significant difference in the impact of conscious and unconscious thought on creative problem-solving, but when the difficulty of the task was medium, unconscious thought had a more prominent role in promoting creative problem-solving. In our study, UC was significantly more conducive to creative problem-solving than consciousness, regardless of whether the task was easy. This occurred presumably because different creative tasks were used. From the perspective of semantic processing, RAT requires the semantic processing of words, while SIP requires a semantic connection between sentences. It may even be that the low-difficulty SIP is more difficult than the RAT task, so it is more suitable for unconscious processing. Due to different creative tasks, the definition of difficulty may also be different. To illustrate, the difficulty of the materials in this experiment was subjectively assessed by three students majoring in psychology, while in the Scientific Innovation Problem Database, each question has a corresponding heuristic index. A heuristic index refers to the accuracy rate of solving problems obtained by the participant after learning the prototype minus the accuracy rate of solving problems without learning the prototype. Therefore, in Experiment 2, the heuristic index was used as the difficulty standard of SIP to further explore whether the facilitation of unconscious processing in creative problem-solving was related to difficulty.
Overall, the results provide strong support that distractor tasks can promote problem-solving after leading to the individual unconscious thought. It is worth further discussing that if the distractor task occupies too many cognitive resources, will the promotion effect of UC on creative problem-solving be weakened? To address this, we conducted Experiment 2.
In low-difficulty SIP solving, the prototype activation rate of the two unconscious groups was better than that of the conscious group, which partially supports the results of Experiment 1. Simultaneously, the prototype activation rate of the high-distractor task was significantly lower than that of the low-distractor task condition, which demonstrates that with an increase in cognitive load induced by the distractor task, the effect of unconscious thinking promoting creative problem-solving declined. This occurred presumably because although the participants were still unconsciously processing the SIPs when they were doing irrelevant tasks, the level of cognitive load of distractor tasks might affect the degree of involvement in the target and irrelevant tasks (Damian and Sherman, 2013). In prototype heuristics, to realize the connection between prototype information and SIPs, the individual needs to search out the corresponding information from all the currently learned prototypes to activate the prototype successfully. Previous research suggests that unconscious thinking made individuals conduct a wide range of searches, including information that may seem irrelevant to the problem (Ding et al., 2019). If the degree of involvement of the individual in the distractor task is too high, even though the activation of the prototype can be realized in unconscious processing, the individual may not be aware of it at the conscious level, thus reducing the individual’s activation rate in the prototype.
The accuracy rates were not significantly different between the three levels of consciousness. However, in the high-distractor task condition, the accuracy rate of high-difficulty problem-solving was significantly higher than the accuracy rate of low-difficulty problem-solving. These findings verified Zhong’s assumption, showing that conscious or unconscious thinking makes no difference in the promoting effect of problem-solving when the difficulty of problems is low; only high-difficulty problems could distinguish the promoting effect between conscious and unconscious thinking. According to Zhao [2018], unconscious processing could be divided into deep and shallow processing; deep processing could improve the accessibility of answers. Therefore, do the different levels of cognitive load induced by distractor tasks lead to changes in the depth of unconscious processing and further lead to differences in problem resolution rates? Furthermore, how does the depth of unconscious processing affect individual cognitive activities in problem-solving? To address this, we conducted Experiment 3.
The positive results of Experiment 3 supported our hypothesis and showed that, compared with the conscious condition, participants in the unconscious condition (low-distractor task and high-distractor task) had higher EFs. Moreover, participants who performed the low-distractor task also had higher EFs than participants who performed the high-distractor task, supporting the viewpoint that EFs can be depleted (Schmeichel, 2007). This means that previously conscious SIP solving occupies the largest amount of EFs resources, followed by the high-distractor task, and the low-distractor task occupies the least cognitive resources. Thus, conscious SIP solving occupies more cognitive resources than the distractor task. On the one hand, research has found that the correlation between insight and reasoning ability is as high as 0.920, but when the correlation between the two abilities is assumed to be 1, the model has a significant loss of fit, indicating that insight problem-solving and reasoning abilities highly overlap, although differently (Chuderski and Jastrzebski, 2018). Reasoning is an important ability that constitutes EFs (Chrysikou, 2019), so consciously solving insight problems will involve more EFs. On the other hand, Schmeichel [2007] also mentioned that distractor tasks (such as simple mathematical calculations) are achieved through automatic or regular cognitive processes that do not require a lot of EFs. Therefore, it is not surprising that SIP requires more EFs than distractor tasks.
Based on the above reasoning, after manipulating the level of consciousness, the rest of the EFs in the conscious condition was less than the high-distractor task and low-distractor task conditions, verifying that the EFs used in SIP in the conscious condition were less than the high-distractor task and low-distractor task conditions. The remaining EFs were positively correlated with the SIP-solving performance, which suggests that EFs contribute to unconscious SIP-solving. Specifically, when the prototype activation rate was used as the dependent variable, the three dimensions of EFs in the two distractor task groups had partial mediating effects compared with the control group, but the mediating effects of the three mediating variables in the high-distractor task condition were all smaller than those in the low-distractor task condition, which verified hypothesis b.
Of note, however, Korovkin et al. [ 2018] used the dual task to investigate the effect of different working memory systems’ load on insight problem-solving. They found that insight reorganization relies on fairly low levels of processing occurring in the working memory storage system, and the closer a person is to an insight solution, the more important the role of working memory in insight problem-solving becomes. This suggests that working memory is involved in insight problem-solving but at a very low level. Specifically, the difficulty of recalling memory content rather than the organization form affects an individual’s ability to make creative associations (Beaty et al., 2014). In the current experiment, the link between the prototype and the problem was already established at the unconscious level. To this end, bringing the connection to the conscious level requires very little updating ability, and the closer individuals get to the insight solution, the more important the role of updating working memory becomes. This reasoning also explains why updating has a significant mediating effect on the high and low cognitive load of prototype activation and a significant mediating effect on the low cognitive load of problem-solving, but not on the high cognitive load of problem-solving. This is because the prototype activation by working memory updating only needs to extract the key prototype, and the requirement of working memory updating is very small, but the problem solving of working memory updating needs to extract and problem solve the related characteristic of the prototype of the function and get the solution, which is more demanding on working memory updating. Therefore, working memory updating cannot be supported enough under a high cognitive load.
## Experiment 2
The results of Experiment 1 suggested that UC has a facilitatory effect on prototype heuristics. Will this phenomenon be affected by different cognitive loads induced by different difficulties of distractor tasks? We hypothesized that UC’s positive effect on prototype heuristics would be reduced when the distractor task becomes more difficult and consumes more cognitive resources.
## Experiment 3
The results of Experiments 1 and 2 suggested that, compared with consciousness, UC had a facilitatory effect on prototype heuristics under high-difficulty problem-solving conditions, and the size of the facilitation effect is related to the cognitive load induced by the distractor task. This occurred presumably because UC induced by a distractor task changes individual EFs and influences creative problem-solving. Thus, we only chose high-difficulty SIPs to further investigate the internal mechanism of the promotion effect of unconscious processing on prototype heuristics. The experiments reported here try to verify whether unconscious thinking promotes individual EF, which is conducive to creative problem-solving.
We hypothesized the following: a. compared with conscious processing, unconscious processing occupied fewer EFs, and compared with low-distractor task, high-distractor task depleted more EFs; b. residual EFs in the unconscious state can promote SIP solving, and EFs play a mediating role between UC and SIP solving.
## Executive functions measurement
The current study used a two-back task to examine the ability of individuals to update. During the task, random integer numbers between 0 and 9 were presented one at a time, and participants were asked to compare each number with the second number before it. If the two numbers are the same, a key response is required. If the two numbers are different, participants do not react. Each number was presented for only 1 s, requiring participants to react as quickly as possible. In data processing and analysis, we excluded the reaction time of the wrong reaction of the participants and then calculated all the reaction times of the correct trial.
The shifting number task examined the ability of individuals to shift. In each trial, a single letter and a single number were presented on the screen concurrently, the word color could be red or green, and stimuli would change color randomly. Participants responded to stimuli by pressing keys. If the word color was green, participants needed to respond to the parity of numbers; they were asked to press “F” in response to any odd number [1,3,5,7,9] and “J” in response to any even number [2,4,6,8]. If the word color was red, participants needed to decide whether the letter was a vowel or consonant; they were asked to press “F” in response to any vowels (A, E, I, O, U) and “J” in response to any consonants (G, K, M, R, etc.). After learning the rules in the practice phase, the participants entered the formal experiment. At the end of Experiment 3, data processing and analysis were performed on the total conversion response time of the shifting number task.
The Stroop task is used to examine individuals’ inhibition ability (Zhang et al., 2020). The task involved presenting a single-color word at the center of the screen; in the current experiment, one Chinese character was presented at a time, which was red, green, yellow, or blue and meant “red,” “green,” “yellow,” or “blue.” Sometimes the character and its color were the same, for example, the character “red” was red, and sometimes the character and its color were not the same, for example, the character “red” was green; every trial contained consistencies and inconsistencies. Participants were inquired to ignore the word and give a key-press in response to the color. The colors corresponded to the keys one by one (“D” for red, “F” for green, “J” for yellow, and “K” for blue). One block in the emotional Stroop task comprised 30 stimuli (i.e., trials). During each trial, each Chinese character remained until the participant responded or 2,000 ms passed, and after a 1,500 ms fixation was presented, the next stimulus appeared. Ten practice trials and a 3-min formal test were designed. The program gave feedback on correct or incorrect responses after the participant pressed the button during the practice trials, and there was no feedback during the formal tests irrespective of whether the response was correct or incorrect. At the end of Experiment 3, data processing and analysis were conducted by subtracting the response time of the inconsistent Stroop test from the response time of the consistent Stroop test.
## The prototype activation rate as the dependent variable
As shown in Figure 5, the relative mediation analysis, with the prototype activation rate as the dependent variable and levels of consciousness as the reference variable, showed that the working memory ability (updating) as the intermediate variable and the $95\%$ bootstrap confidence interval between the high-distractor task and the conscious conditions was [0.15, 0.28], excluding 0, indicating significant relative mediation effect (a11 = 0.14, b1 = 0.38, a11b1 = 0.053). The $95\%$ bootstrap confidence interval of the relative mediation analysis between the low-distractor task and conscious conditions was [0.19, 0.62], excluding 0, indicating a significant relative mediation effect (a12 = 1.03, b1 = 0.38, a12b1 = 0.39). These results suggest that high-and low-distractor tasks promote the updating ability of individuals and thus promote the ability of individuals to activate prototypes. However, the indirect mediating effect of the updating function was higher in the low-distractor task than in the high-distractor task condition.
**Figure 5:** *Mediation analysis with prototype activation rate as the dependent variable. ** indicates that the correlation is significant at the level of 0.01.*
With the prototype activation rate as the dependent variable, levels of consciousness as the reference variable, and shifting ability as the intermediate variable, the $95\%$ bootstrap confidence interval between the high-distractor task and conscious conditions was [0.36, 0.79], excluding 0, indicating a significant relative mediation effect (a21 = 1.14, b2 = 0.51, a21b2 = 0.58); and the $95\%$ bootstrap confidence interval between the low-distractor task and conscious conditions was [0.66, 1.17], excluding 0, indicating a significant relative mediation effect (a22 = 1.83, b2 = 0.51, a22b2 = 0.93). These results suggest that high-and low-distractor tasks promote the shifting ability of individuals and thus promote the ability of individuals to activate prototypes. However, the indirect mediating effect of the shifting function was higher in the low-distractor task than in the high-distractor task condition.
With the prototype activation rate as the dependent variable, levels of consciousness as the reference variable, and the inhibition ability as the intermediate variable, the $95\%$ bootstrap confidence interval between the high cognitive load condition and the conscious condition was [0.26, 0.97], excluding 0, indicating a significant relative mediation effect (a31 = 1.04, b3 = 0.56, a31b3 = 0.58); and the $95\%$ bootstrap confidence interval between the low-distractor task and conscious conditions was [0.51, 1.39], excluding 0, indicating a significant relative mediation effect (a32 = 1.65, b3 = 0.56, a32b3 = 0.92). These results suggest that high-and low-distractor tasks promote the inhibition ability of individuals, and thus promote the ability of individuals to activate prototypes. However, the indirect mediating effect of the inhibition function was higher in the low-distractor task than in the high-distractor task condition.
## The accuracy rate as the dependent variable
As shown in Figure 6, relative mediation analysis with the accuracy rate as the dependent variable, levels of consciousness as the reference variable, and working memory ability (updating) as the intermediate variable, showed that the $95\%$ bootstrap confidence interval between the high-distractor task and conscious conditions was [−0.16, 0.33], including 0, indicating no significant relative mediation effect; and the $95\%$ bootstrap confidence interval between the low-distractor task and conscious conditions was [0.18, 0.71], excluding 0, indicating a significant relative mediation effect (a12 = 1.04, b1 = 0.40, a12b1 = 0.42). These results suggest that, compared with the conscious condition, the high-distractor task condition does not promote the working memory ability of individuals, but low-distractor tasks promote individuals’ working memory ability and thus promote individuals’ ability to solve SIPs. In addition, the indirect mediating effect of the updating function was higher in the low-distractor task than in the high-distractor task condition.
**Figure 6:** *Mediation analysis with an accuracy rate as the dependent variable. ** indicates that the correlation is significant at the level of 0.01.*
The relative mediation analysis with the accuracy rate as the dependent variable, levels of consciousness as the reference variable, and the shifting ability as the intermediate variable showed that the $95\%$ bootstrap confidence interval between the high-distractor task and conscious conditions was [0.09, 0.82], excluding 0, indicating a significant relative mediation effect (a21 = 1.14, b2 = 0.37, a21b2 = 0.42); and the $95\%$ bootstrap confidence interval between the low-distractor task and conscious conditions was [0.15, 1.30], excluding 0, indicating a significant relative mediation effect (a22 = 1.83, b2 = 0.37, a22b2 = 0.68). These results suggest that, compared with the conscious condition, the high-and low-distractor task conditions promote the shifting ability of individuals, and thus promote individuals’ ability to solve SIPs. However, the indirect mediating effect of the shifting function was higher in the low-distractor task than in the high-distractor task condition.
The relative mediation analysis with the accuracy rate as the dependent variable, levels of consciousness as the reference variable, and the inhibition ability as the intermediate variable showed that the $95\%$ bootstrap confidence interval between the high-distractor task and conscious conditions was [0.17, 0.76], excluding 0, indicating a significant relative mediation effect (a31 = 1.04, b3 = 0.42, a31b3 = 0.44); and the $95\%$ bootstrap confidence interval between the low-distractor task and conscious conditions was [0.39, 1.10], excluding 0, indicating a significant relative mediation effect (a32 = 1.65, b3 = 0.42, a32b3 = 0.69). These results suggest that, compared with the conscious condition, the high-and low-distractor task conditions promote the inhibition ability of individuals, and thus promote individuals’ ability to solve SIPs. However, the indirect mediating effect of the inhibition function was higher in the low-distractor task than in the high-distractor task condition.
## General discussion
The current study performed three experiments to investigate the difference in the effect of prototype heuristics in SIP solving with different levels of consciousness and explore its internal mechanism. The results found that after learning prototypes, distractor tasks induced unconscious processing, and when solving scientific innovation problems creatively by unconscious thinking, especially when the difficulty of the problem increased, the facilitation effect of unconscious processing became more prominent. The effect of unconscious processing was also related to the cognitive load of distractor tasks. This is generally consistent with previous studies on the relationship between unconscious processing and creative problems. Based on previous research, this research also studied the relationships among UC, EFs, and SIP and found that three dimensions of EFs (working memory, shifting, and inhibition) mediated the relationship between the level of consciousness and SIP solving. However, what is the specific process and mechanism of this action?
First, it is worth thinking about whether the executive function is a trait or an ability because different perspectives can lead to the opposite result. When we think of the executive function as a trait, the researchers will treat the measured executive function scores as a general level of executive control, and participants who have high executive function scores will have more resources to complete any task. However, if we think of the executive function as an ability, then the resource depletion hypothesis (Schmeichel, 2007) tells us that prior tasks deplete our executive control, and the executive function scores measured in later tasks was the amount of executive control ability that the participant has left available for this measuring task, the lower these scores, the higher the level of executive control the subject used in the previous task. In our experiment, the results of Experiment 3 can only be explained by taking the executive function as a kind of ability, that is, the differences in the executive function of different groups are caused by the differences in the operations that induce different levels of consciousness previously, rather than the differences in the pre-existing traits of different groups. Moreover, the order of such differences has been reasonably explained in the discussion of Experiment 3. Therefore, this study also provides additional support for the conclusion that executive function is an ability.
In addition, according to the prototype heuristic theory, the insight of SIP includes at least two stages: prototype activation and obtaining heuristics from a prototype. Prototype activation is automatic and obtaining heuristics requires executive control (Cao et al., 2006), which implies a cooperative mode of UC and EFs in SIP solving. Similarly, Beaty et al. [ 2016] summarized brain imaging research on creative thinking and found that many studies have pointed out the important role of default network and episodic memory in creative cognition; they suggested that the default network influences the generation of candidate ideas, while executive control guides and monitors them. The results of Experiment 3 supported this view, that is, compared to the high-distractor task condition, although the low-distractor task condition had a larger set of available EFs resources that led to better performance on SIP, participants who only performed conscious SIP solving had the worst performance, despite all their EFs resources used to solve the SIP. This means that although EFs are important for SIP solving, the result of problem-solving will be very poor if there is no unconscious processing, and EFs and UC are both indispensable in difficult insight problem-solving.
While many studies documented the positive role of EFs in creativity, others provide evidence to the contrary. For example, Chuderski and Jastrzebski [2018] concluded that to date, researchers’ studies on working memory and insight problem-solving have reported highly inconsistent results, ranging from moderately positive to zero and even negative effects. Zhu et al. [ 2019] discussed brain structure and resting brain function in SIP solving; they reported that decreased response inhibition, as well as the automatic association of semantics, will support representation-connection in the insight process. This suggests that the decrease in inhibition ability promotes semantic linkage during insight problem-solving, and thus facilitates problem-solving. We suggest that a paradoxical result of the different roles of EFs in insight problem-solving is that the UC and EFs resources required are different at different stages of insight problem-solving. That is, with the unconscious processing of the problem situation, key information retrieval, and the formation of semantic links, excessive EFs will hinder the process. Contrastingly, if the solution to the problem has already been found in the unconscious state, too little executive control will make individuals unable to extract the results to the level of consciousness and report them, thus affecting the performance of the subjects.
Past research has outlined this process. For example, the role of UC is to generate ideas by searching for materials in episodic memory (Beaty et al., 2016), or generate “structural mapping” and “representation-connection” between prototypes and problems (Zhu et al., 2019), while before, during, and after unconscious action, different levels of EFs play different roles. For example, many researchers believe that working memory is important in the early stage of insight problem-solving, such as problem understanding and goal orientation (Chrysikou, 2019), and in the later stage, Korovkin et al. [ 2018] suggested that working memory is more important the closer it is to problem-solving. Simple creativity tasks were not affected by working memory loads (Stuyck et al., 2022); it can be inferred that working memory also plays a role in extracting thoughts or links from the unconscious to the conscious level. In addition, inhibition plays an important role in suppressing conventional thoughts that are not novel when the individual is in cognitive fixation (Camarda et al., 2018), but also blocks UC-dominated representation-connection (Zhu et al., 2019). This suggests a complex relationship between the negative role of inhibitory control in unconscious processes and the important role it plays in the top-down overcoming of functional fixity. Lu et al. [ 2017] found that task switching can enhance creativity by reducing cognitive fixation, suggesting the role of switching in fixation, similar to inhibition. Ding et al. [ 2019] found that subjects’ performance in the Creative Scientific Problem Finding Test, regardless of the field, had no significant difference after conscious and unconscious thinking. However, in the Creative Scientific Problem Finding Test of a specific field, conscious thinking is superior to unconscious thinking, which may reveal that the role of conscious thinking is to screen and control creative thoughts in a specific situation so that creativity can better meet the direction required by the question.
Finally, to further understand the role of UC and EFs in SIP solving, we propose a conjecture about this process based on the viewpoints of previous studies (see Figure 7). As can be seen from Figure 7, we divided the process of UC involving difficult insight problem-solving into three phases: prepared, problem-solving, and answer. Among them, the problem-solving phase was further divided into the first half dominated by UC and the second half dominated by EFs. In the preparation phase, working memory capacity and updating are used to learn and memorize insight problems (and prototype in our experiments), and EFs also help individuals form goal orientation. In the first half of the problem-solving phase, UC plays an important role that assesses its powerful search capabilities to retrieve questions and relevant prototypes and experiences, make new connections, and try to come up with answers. At this time, if EFs (such as inhibition) are too strong, it will cause certain damage to this part. In the second half of the problem-solving phase, working memory tries to extract related information to consciousness and to pick up the semantic links that had formed at the unconscious level; if the participants think they found the right solution, the method is reported (the answer phase), and if it is wrong, one can inhibit the wrong solution, suppress interference or irrelevant information, and use the ability to switch and overcome the fixation, think from a new perspective, and re-enter the cycle until a satisfactory answer is obtained and reported (the answer phase), or a satisfactory answer is not obtained and the problem is not solved (the answer phase).
**Figure 7:** *An insight prototype heuristic model of executive functions and unconsciousness. This model is only applicable to the problem-solving process of difficult insight problems with unconscious effects.*
Moreover, Yang et al. [ 2022] argued that it is unclear whether the state of creativity can have an impact on knowledge-rich creative problem-solving and whether interventions, that support analogical transfer in the heuristic prototype paradigm, can be used to improve knowledge-rich creative problem-solving. Current experimental manipulation and findings of this study provide definitive answers that, through certain distractor tasks, it is possible to improve knowledge-rich creative problem-solving, such as SIPs.
To summarize, we reported three experiments to explore the relationships among UC, EFs, and insight problem-solving, found that low cognitive load UC promotes prototype heuristics in SIPs, and proved more evidence for research in this area. To further understand the role of UC and EFs in SIP solving, we propose a conjecture about this process based on the viewpoints of previous studies.
## Limitations
The current study first explored the relationships among UC, EFs, and insight problem-solving and proposed a new conjecture. However, direct evidence of the internal mechanism is somewhat insufficient, and future research can further verify the fuzzy zone. Second, this study uses SIPs as the insight problem, which can only show that the EFs and UC collaboration mode are such in solving the SIPs. The conclusion should be cautiously generalized, and future research can use other insight paradigms for more exploration.
## 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 the Human Research Ethics Committee, Faculty of Psychology, Southwest University. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
YL provided ideas for the argumentation and wrote the submitted manuscript. LT contributed to the conception and design of the study and wrote the first draft of the manuscript. LZ reviewed and edited the manuscript. GC contributed to study design and to manuscript drafting and reviewing. All authors contributed to the article and approved the submitted version.
## Funding
This study was supported by Southwest University open access funding and Subject on Social Science of Chongqing Medical and Pharmaceutical College (ygz2022203).
## 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.
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|
---
title: Effect of intravenous thrombolysis on core growth rate in patients with acute
cerebral infarction
authors:
- Xueqi Wang
- Hao Zhang
- Qi Wang
- Gang Li
- Hao Shen
- Yaping Xiao
- Luran Xu
- Yuming Long
- Chen Chen
- Zhengyu Huang
- Yue Zhang
journal: Frontiers in Neurology
year: 2023
pmcid: PMC9996056
doi: 10.3389/fneur.2023.1096605
license: CC BY 4.0
---
# Effect of intravenous thrombolysis on core growth rate in patients with acute cerebral infarction
## Abstract
### Objective
This study aimed to investigate the effects of recombinant tissue plasminogen activator intravenous thrombolysis (IVT) on the core growth rate of acute ischemic stroke.
### Methods
Stroke patients with large vessel occlusion and non-recanalization from IVT treatment were retrospectively included in this study and divided into two groups: IVT and non-IVT. The core growth rate was estimated by the acute core volume on perfusion CT divided by the last known well time from stroke to CT perfusion. The primary endpoint was the core growth rate, the tissue outcome was 24 h-ASPECTS, and the clinical outcome was a 3-month modified Rankin score.
### Results
A total of 94 patients were included with 53 in the IVT group and 41 in the non-IVT group. There was no significant difference in age, gender, hypertension, diabetes, atrial fibrillation, acute NIHSS, and last known well time from stroke to CT perfusion acquisition between the two groups. The core growth rate in the IVT group was lower than that in the non-IVT group, which was statistically significant after multivariate adjustment (coefficient: −5.20, $95\%$ CI= [−9.85, −0.56], $$p \leq 0.028$$). There was a significant interaction between the IVT and the collateral index in predicting the core growth rate. The analysis was then stratified according to the collateral index, and the results suggested that IVT reduced the core growth rate more significantly after the worsening of collateral circulation (coefficient: 15.38, $95\%$ CI= [−26.25, −4.40], $$p \leq 0.007$$). The 3-month modified Rankin score and 24 h-ASPECTS were not statistically significant between the two groups.
### Conclusion
Intravenous thrombolysis reduces the core growth rate in patients with AIS, especially those with poor collateral status.
## 1. Introduction
Intravenous thrombolysis (IVT) is an established treatment for acute ischemic stroke (AIS), and it can be rapidly initiated after clinical assessment and cranial CT scan (1–3). However, IVT also has significant limitations, such as the patients' need to receive IV tPA within 4.5 h of the onset, and the recanalization rates are low in patients with large vascular occlusion (LVO), that is, a meta-analysis reported approximately $35\%$ for M1 MCA occlusions, $13\%$ for ICA occlusions, and $13\%$ for BA occlusions [4].
Since 2015, several clinical trials acknowledged the superiority of endovascular thrombectomy (EVT), which has a higher rate of recanalization of LVO and a longer treatment window (5–9). Therefore, the “bridge” therapy was proposed to use EVT to rescue patients with a lack of recanalization after IVT. Some clinical trials showed that early administration of alteplase can promote microvessel patency and that the rate of successful recanalization in bridge therapy was significantly higher than that in patients who only accept EVT [10, 11]. However, IVT may increase the risk of bleeding [12] and promote thromboplastic migration [13]. Hence, whether patients with LVO-AIS who arrive at the hospital within 4.5 h of the onset can benefit from the “bridge” therapy is a research hotspot [14, 15].
In patients with AIS, successful recanalization and core volume are the strongest predictors of outcome [16]. Before vessel recanalization, the infarct core increases linearly within 6 h of stroke onset [17, 18]. Assuming that the core at the time of stroke onset was 0, the infarction volume divided by the time from stroke onset to CTP can be used to estimate the speed of cerebral infarction progression. The core growth rate has been reported to be an independent predictor of clinical outcomes and is highly associated with the collateral status. Reducing the core growth rate may reduce the volume of core infarction and may have important therapeutic implications on AIS.
This study aimed to assess the efficacy of IVT on changing infarct core growth rate in patients with LVO-AIS who had not achieved recanalization.
## 2.1. General information
We conducted a retrospective cohort study that involved 94 AIS patients with LVO between January 2017 and March 2021 at the Shanghai East Hospital—Department of Neurology. All the patients were divided into two groups according to whether or not they received IVT.
## 2.2. Inclusion criteria
Patients were selected based on the following criteria: [1] All the patients met the diagnostic criteria of acute cerebral infarction due to LVO; according to the guidelines, the IVT group patients met the indications of IVT, and the contraindications of IVT were excluded; [2] the last known well time from stroke to completion of CTP was <6 h; [3] the ischemic core volume on the CTP was <70 ml, penumbra ≥10 ml, and radio > 1.2; and [4] the clinical information was complete.
## 2.3. Methods
The clinical data of patients included age, gender, and histories of hypertension, diabetes, heart issues, prior stroke, current smoking, and NIHSS recorded at the hospital arrival, as well as radiographic data. The DT collateral index was used to evaluate the collateral status [19].
The IVT group patients were given 0.9 mg/kg of rt-PA for thrombolysis: $10\%$ of the total dose was given intravenously for 1 min, while the remaining dose was injected intravenously 1 h later.
## 2.4. Imaging acquisition and post-processing
Baseline CT imaging included brain non-contrast CT, CTP, and CTA, obtained with different CT scanners (64, 128, 256, or 320 detectors, with Toshiba [Tokyo, Japan], Siemens [Munich, Germany], or GE [Cleveland, OH, USA] scanners). The axial coverage ranged from 80 to 160 mm.
The CTP data were processed by commercial software MIStar (Apollo Medical Imaging Technology, Melbourne, Vic, Australia). CTP parameters were generated by applying the mathematical algorithm of singular value decomposition with delay and dispersion correction [20, 21]. The following four CTP parameters were generated: cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), and delay time (DT). The penumbra and core volume were measured on acute CTP with dual threshold setting [22]: DT at the threshold of 3 s for whole ischemic lesion volume and CBF at the threshold setting of $30\%$ for acute core volume. The collateral index was defined by the ratio of DT >6 s/DT >2 s volume.
## 2.5. Calculation of the ischemic core growth rate
The core growth rate was determined using the baseline core volume divided by the time between the symptom onset and the CTP. It was assumed that the core volume was zero just prior to symptom onset and would grow in a near-linear pattern within 6 h of stroke onset [23]. This study calculated the core growth rate using the following approach: Core growth rate = Acute core volume on CTP / Last known well time from stroke to CTP [19, 24].
## 2.6. Statistical analysis
Data were statistically analyzed using the statistical software SPSS 25.0. The normality of the continuous variables was examined using the Kolmogorov–Smirnov test. When the normality assumption was not met, a non-parametric test was used. The categorical variables were compared by the chi-square test. Multi-factor linear regression was used to analyze predictors of ischemic core growth rate. Variables with a $p \leq 0.25$ were included in the regression model. In addition, collateral circulation was included in the model as a significant predictor. A scatter diagram was used to describe the interaction between IVT and DT collateral index in predicting the core growth rate. Then, the core growth rate of IVT vs. non-IVT patients was plotted across the collateral index (with a 0.100 increment). Within each collateral index category, the predictive power of IVT (vs. non-IVT) on the patient core growth rate was assessed by regression models.
## 2.7. Tissue outcomes
Alberta stroke program early CT score (ASPECTS) was used to evaluate the extent of anterior circulation infarction [25]. The posterior circulation stroke was assessed by pc-ASPECTS [26]. A normal CT scan has an ASPECTS value of 10 points. ΔASPECTS is defined as 24 h-ASPECTS minus baseline ASPECTS, which would be used to describe the change in brain tissue. Multi-factor logistic and linear regression was used to describe the relationship between intravenous therapy and clinical outcomes and tissue outcomes.
## 2.8. Patient outcomes
The primary endpoint was the core growth rate. The clinical outcome was the modified Rankin score (mRS) at three months. The good clinical outcome was defined by an mRS score of 0–2 vs. an mRS score of 3–6, and the poor clinical outcome was an mRS score of 5–6 vs. an mRS score of 0–4.
## 3. Results
The flow diagram of the study is given in Figure 1. A total of 94 patients met the inclusion criteria, of whom 53 patients received IVT and 41 patients were treated without IVT.
**Figure 1:** *A flow diagram of the study.*
The baseline characteristics of the patients were similar in the two groups (Table 1). The average age of the patient was 39–98 years, and 57 patients ($61\%$) were men. The median baseline NIHSS was 16 (IQR = 7). The median last known well time from stroke to CTP was 3.18 h (IQR, 2.27–3.98) in the IVT group and 3.4 h (IQR, 1.88–4.57) in the non-IVT group. There was no statistical difference between the two groups in age, gender, or AIS risk factors such as hypertension, diabetes mellitus, atrial fibrillation, previous history of stroke, or current smoking ($P \leq 0.05$). Neither infarct volume nor collateral index showed a significant difference between the two groups ($$p \leq 0.982$$ and $$p \leq 0.760$$). No between-group differences were found at baseline ASPECTS or ΔASPECTS ($$p \leq 0.698$$ and $$p \leq 0.682$$). Although the difference was not statistically significant, 24 h-ASPECTS in the IVT group was slightly higher (7 vs. 6).
**Table 1**
| Unnamed: 0 | Intravenous (n = 53) | Non-intravenous (n = 41) | P-value |
| --- | --- | --- | --- |
| Age, median (IQR) | 71 (63–83) | 71 (65–79) | 0.565 |
| Men, % (N) | 58.5 (31/53) | 63.4 (26/41) | 0.628 |
| Atrial fibrillation, % (N) | 43.4 (23/53) | 48.4 (20/41) | 0.603 |
| Hypertension, % (N) | 64.2 (34/53) | 46.3 (19/41) | 0.084 |
| Diabetes, % (N) | 26.4 (14/53) | 19.5 (8/41) | 0.433 |
| current smoking, % (N) | 20.8 (11/53) | 22.0 (9/41) | 0.888 |
| Prior Stroke, % (N) | 22.6 (12/53) | 34.1 (14/41) | 0.216 |
| Acute NIHSS score, median (IQR) | 15 (12–19) | 16 (12–19) | 0.472 |
| CBF <30, median (IQR) | 14.0 (4.0–30.5) | 18.0 (5.0–41.5) | 0.235 |
| Core growth, median (IQR) | 4.52 (1.35–11.16) | 5.91 (1.52–12.98) | 0.43 |
| Time from last-known-well to CTP acquisition (Hours), median (IQR) | 2.83 (2.27–3.98) | 3.98 (1.88–4.57) | 0.064 |
| DT > 6 s, median (IQR) | 35 (16.5–61.0) | 39 (13.5–55.5) | 0.982 |
| DT > 2 s, median (IQR) | 176.0 (111.5–228.5) | 203.0 (136.5–248.6) | 0.595 |
| Collateral index, median (IQR) | 0.17 (0.12–0.32) | 0.21 (0.12–0.31) | 0.76 |
| Baseline ASPECTS, median (IQR) | 8 (8–9) | 8 (7–9) | 0.698 |
| 24 h-ASPECTS, median (IQR) | 7 (6–8) | 6 (5.75–7) | 0.224 |
| ΔASPECTS, median (IQR) | −1 (−2–0) | −1 (−2–0) | 0.682 |
| In-hospital mortality, % (N) | 26.4% (14/53) | 26.8% (11/41) | 0.964 |
| 3-month modified Rankin score, median (IQR) | 5 (1–6) | 5 (2–6) | 0.521 |
| Good outcome rate, % (N) | 34.0% (18/53) | 26.8% (11/41) | 0.314 |
| Poor outcome rate, % (N) | 50.9% (27/53) | 56.1% (23/41) | 0.581 |
Delay time collateral index (coefficient: 30.65, $95\%$ CI= [14.37, 46.93], $p \leq 0.001$) and intravenous therapy (coefficient: −5.20, $95\%$ CI = [−9.85, −0.56], $$p \leq 0.028$$) showed significant differences. Intravenous may decrease the core growth rate of 5 ml/h for patients with stroke (Table 2).
**Table 2**
| Unnamed: 0 | Adjusted p-value | Coefficient |
| --- | --- | --- |
| Intravenous | 0.028 | −5.20 |
| DT collateral index | <0.001 | 30.65 |
| hypertension | 0.598 | −1.24 |
| Prior stroke | 0.700 | −0.99 |
Figure 2 depicts the scatter plots for the association between the DT collateral index and the core growth rate in two groups.
**Figure 2:** *The core growth rate in different collateral indexes for the patients in the two groups. The IVT and non-IVT groups intersected two times, which suggested a significant interaction between the IVT and the collateral index, indicating that both of them interfere significantly with the core growth rate. The line of the non-IVT group showed a major increase when the DT collateral index was 0.250.*
Delay time collateral index was classified into three categories based on the core growth rate of the IVT vs. the non-IVT group (Figure 2). For DT collateral index <0.100 and 0.100–0.250, there was no statistical significance in the effect of IVT on the core growth rate ($$p \leq 0.616$$ and $$p \leq 0.426$$). For DT collateral index >0.25, after adjusting for DT collateral index, hypertension, and prior stroke, the IVT showed a statistically significant result on the core growth rate (coefficient: 15.38, $95\%$ CI= [−26.25, −4.40], $$p \leq 0.007$$) (Table 3). In other words, for patients with poor collateral index, IVT may significantly decrease the core growth rate (Figures 3, 4).
Univariate and multivariate regression analyses were used to explore the association between intravenous therapy and clinical outcomes. There was no statistical difference in the 3-month modified Rankin score (Table 1). After adjusting for hypertension, prior stroke, and DT collateral index, both the good outcome (OR = 0.60, $95\%$ CI = [0.205, 1.760]) and poor outcome (OR = 0.83, $95\%$ CI = [0.355,1.953]) showed no significant predictive power between the two groups (Table 4). No significant between-group differences were detected in 3-month mortality (26.4 vs. $26.8\%$; OR=0.77, $95\%$ CI = [0.314, 1.886]).
**Table 4**
| Unnamed: 0 | P value | Adjusted p-value | Adjusted OR (95%CI) |
| --- | --- | --- | --- |
| 3-month good outcome | 0.459 | 0.353 | 0.600 (0.205,1.760) |
| 3-month poor outcome | 0.62 | 0.673 | 0.832 (0.355,1.953) |
| 3-month mortality | 0.647 | 0.566 | 0.769 (0.314,1.886) |
| In-hospital mortality | 0.979 | 0.603 | 0.768 (0.283,2.081) |
After multivariate adjustments, the differences in 24 h-ASPECTS (coefficient: 0.451, $95\%$ CI= [-0.281, 1.183], $$p \leq 0.224$$) and ΔASPECTS (coefficient: 0.183, $95\%$ CI = [−0.501, 0.868], $$p \leq 0.595$$) were not statistically significant (Table 5).
**Table 5**
| Unnamed: 0 | Adjusted p-value | Coefficient |
| --- | --- | --- |
| 24 h-ASPECTS | 0.224 | 0.451 |
| ΔASPECTS | 0.595 | 0.183 |
## 4. Discussion
In acute ischemic stroke (AIS) treatment, recanalization of the occluded vessel is crucial for a good clinical outcome [27]. Intravenous thrombolysis (IVT) with rt-PA is a conventional treatment for AIS. However, the low vascular recanalization rate led to many conflicting views [4, 28]. When clinical symptoms of patients with LVO-AIS did not significantly improve after receiving IVT, endovascular therapy was required to achieve recanalization. For such patients, the role of IVT remains controversial. This study showed that IVT may reduce the core growth rate in patients with AIS, even if the vessel did not achieve recanalization. Moreover, this effect was influenced by collateral circulation and was clearer in patients with poor collateral circulation.
Whether patients with AIS can benefit from the “bridge” therapy (BT) is a research hotspot. In a meta-analysis of 38 eligible observational studies, BT was associated with a higher likelihood of 3-month functional independence compared to direct mechanical thrombectomy [29]. Whether IVT will extend the total time of recanalization therapy remains controversial. In recent years, numerous studies showed that the average recanalization time was not statistically different between the “bridge” therapy and direct thrombectomy [14, 15, 29], indicating that IVT did not delay the time to recanalization. In addition, with the application of tenecteplase, the time difference between the two treatment modalities was further reduced [30]. This study demonstrated that IVT may reduce the core growth rate without vessel recanalization. Some studies also found that, in time-window patients who were transferred to an EVT capable center, the outcomes were better in patients who had previously received IVT [31]. Therefore, IVT is considered the first-line treatment for patients with AIS even with LVO and IVT is required in those patients as early as possible.
Infarct core volume is an independent predictor for the outcome of patients with AIS. The smaller the infarct core, the better the likelihood of clinical outcomes [32]. In recent years, several studies demonstrated that the infarct core grows linearly during the first 6 h of AIS [17, 18]. Wheeler suggested that the early stroke core growth curves exhibited a nearly linear growth during the first 8 h after symptom onset for patients with <$10\%$ reperfusion [23]. Therefore, the core growth rate in the first 6 h from the onset can predict the infarct core to some extent. In this study, all patients successfully completed the CTP examination in 6 h from last known well time. IVT was validated to reduce the infarct core growth rate in this study, and one possible mechanism is probably due to the reduction in thrombus volume by IVT [33, 34]. In addition, the rt-PA can act on the distal microvasculature and reduce microvenous thrombosis [35]. Thus, the blood supply in infarct areas can be improved. A retrospective study showed that the rate of successful recanalization was significantly higher in patients who received IVT before mechanical thrombectomy [11], and it might be implicated in those mechanisms. In this study, we found that IVT may reduce the infarct core growth rate, and patients with AIS who had a lower collateral circulation and underwent IVT exhibited slower infarct growth rates. The impact of collateral circulation on infarct core growth is well established [19]. Better collateral circulation indicates slower infarct growth, while the effect of IVT is not perfect. However, in patients who had poor collateral circulation, along with a decrease in the thrombosis volume and thrombus load in the local microcirculation, the blood flow might have improved more in the ischemic penumbra.
There was no statistical difference in a 3-month modified Rankin score between the two groups. The potential explanation offered might be that, although the rate of core growth was decreased in the IVT group, the clinical outcome was largely decided by the developed infarct core volume and the degree of recanalization. Several studies confirmed that collateral circulation is the factor that has the greatest impact on the growth of infarct core [36, 37]. Moreover, the factors associated with collateral circulation were age, smoking, hypertension [38], and the use of statins [39, 40]. All these factors had no statistical significance in this study, which may have resulted in no difference in collateral circulation between the two groups. All patients in this trial had LVO, and they accepted mechanical thrombectomy after the CTP examination, and the intraoperative recanalization levels have been shown to impact the prognosis of stroke [41]. Thus, the follow-up treatments might heavily influence the clinical outcome. It is difficult to assess the precise relationship between IVT with patient prognosis. Further studies may be needed to elucidate this relationship in a future study.
Although the 24 h-ASPECT in the IVT group was slightly higher than that of the non-IVT group, there was no significant difference in the tissue outcomes. Two considerations may have contributed to this result. On the one hand, despite this study finding that IVT may reduce the core growth rate in patients with LVO, the final infarct core was largely decided by the collateral circulation and the efficacy of endovascular therapy. On the other hand, the sample size in our study is relatively small, which needs to be expanded for a more in-depth research in future, and the effects of intravenous thrombolysis on histological changes would have likely been observed.
This retrospective single-center study has several limitations. First, the lack of randomized treatment allocation in this study was the main limitation. Second, there were no clear differences in baseline core growth rates between the two groups, which might be affected by the collateral circulation. We further adjusted it by the multivariate regression models and removed the effects of the collateral index, which showed positive results. Third, this study was conducted with a small sample size, which might have led to a certain degree of bias. Fourth, some of the patients had unwitnessed onset and the time of stroke onset was uncertain. Hence, the estimation of time from onset to CTP may be extended.
Although the difference in the long-term prognosis of the patients was not observed, the present study provided important new information about the benefit of IVT. “ *Time is* brain” in this study, and IVT successfully reduced the core growth rate, suggesting the underlying application in the treatment of acute cerebral infarction. For patients with AIS who arrive at the hospital within the time window, we suggest IVT in the absence of contraindications and expect more benefits from the “bridge” therapy. Further studies are required to confirm these conclusions.
## 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 the Shanghai East Hospital. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
YZ and ZH contributed to the conception and design of the present study. XW and HZ contributed to drafting a significant portion of the manuscript or figures. QW, HS, YX, and LX contributed to the acquisition and analysis of data. YL, CC, and GL helped perform the analysis with constructive discussions. 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.
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|
---
title: 'Sociodemographic and lifestyle factors and the risk of metabolic syndrome
in taxi drivers: A focus on street food'
authors:
- Machoene Derrick Sekgala
- Maretha Opperman
- Buhle Mpahleni
- Zandile June-Rose Mchiza
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC9996058
doi: 10.3389/fnut.2023.1112975
license: CC BY 4.0
---
# Sociodemographic and lifestyle factors and the risk of metabolic syndrome in taxi drivers: A focus on street food
## Abstract
### Background
In South Africa, similar to other populous countries, the taxi industry is an important form of transportation that contributes to the country's development. As a result, minibus taxi driving is an occupation characterized by strenuous activities such as long hours of driving, limited rest, and challenges related to securing passengers, among several others. Consequently, to combat stress, some commercial drivers resort to smoking, overeating unhealthy food sold at transportation interchange areas (i.e., taxi ranks), and participating in sedentary behaviors. Most of these activities are risk factors for metabolic syndrome (MetS).
### Aim
Therefore, this study aimed to investigate the sociodemographic and lifestyle factors that predispose South African taxi drivers who work in the Cape Town *Metropole area* to the risk of developing MetS.
### Methods
This cross-sectional study used a convenient sampling method that included 185 male minibus taxi drivers aged 20 years or above. The participants were interviewed using a validated questionnaire to gather information regarding their sociodemographic characteristics and lifestyle practices. They also underwent physical and metabolic assessments, and the International Diabetes Federation (IDF) criteria were used to diagnose people with MetS.
### Results
Overall, the mean age and driving experience of the taxi drivers were 40.0 years (SD: 10.7) and 9.1 years (SD: 7.4), respectively, with those with MetS being significantly older and having more driving experience than those without. Older participants were 3 and 2.9 times more likely to be diagnosed with MetS than the younger participants. Most taxi drivers ($70\%$) met the IDF diagnostic criteria for MetS. Smokers, those who spent more than 100 ZAR (USD 5.9) and those who spent less than 1.4 MET-minutes per week on physical activity were 1.96, 2.0, and 13.6 times more likely to suffer from MetS that those who were nonsmokers, those who spent less than 100 ZAR and those who spent <1.4 MET-minutes per week on physical activity. Consumption of alcohol and sugar-sweetened beverages (SSBs), as well as takeaway and fried foods, snacks, and sold by the SF vendors, increased the likelihood of developing MetS, abnormal HDL-C, TG, and hypertension, while avoiding takeaway and fried foods decreased this likelihood. Taxi drivers who also avoided consuming fresh fruits had abnormal HDL-C.
### Conclusion
These findings have significant public health implications, highlighting the need for South African policymakers to adopt a system-level approach to promote lifestyle changes among taxi drivers within the taxi industry. This can help reduce the health risks faced by these drivers and improve their overall health profile.
## Introduction
Several international epidemiological studies have found the prevalence of metabolic diseases to be high among occupational drivers compared to other professionals, such as industrial and office workers (1–3). For example, the majority of professional drivers are at an increased risk of hypertension, myocardial infarction, and hemorrhagic stroke [4]. Furthermore, most drivers are in the habit of eating large main meals and consuming snacks (often oily and fried) and fast-food items from street vendors between trips. In addition, many of them resort to alcohol and smoking to overcome stress. It follows logically that they may have an additional risk of developing metabolic diseases. According to Kurosaka et al. [ 5], taxi driving is characterized by poor eating habits, ongoing stress from driving, and exposure to various health hazards such as air pollution and a lack of physical activity.
In South Africa, taxi drivers and commuters are major consumers of street food (SF) since it is relatively cheap and easily accessible at taxi ranks and bus stations [6, 7]. According to Mchiza et al. [ 8] and Hill et al. [ 9], the food sold in the streets of Cape Town and surrounding areas seems to be a public health risk since it is energy-dense and high in saturated fat, trans fats, salt, and sugar. Taxi drivers working in these areas may be at risk of developing metabolic syndrome (MetS) as they have been identified to be among the $38\%$ of individuals who consume SF almost daily [6].
Good health is a basic constitutional right for all South African citizens [10]. The Occupational Health and Safety Act (OHSA) Section 12(C) [11] requires medical surveillance for all individuals who have high-risk occupations, such as taxi drivers. Similar to other countries, the taxi industry is an important form of transportation in South Africa, contributing to the country's development [12]. However, less focus has been given to this industry to ensure that its workers are in good health. To the best of our knowledge, there has never been any health intervention directed at improving the health condition of taxi drivers in South Africa. Substantiated evidence (13–18) suggests that a healthy lifestyle, including healthy eating and regular physical activity, can help to reduce weight, reduce blood pressure, and improve lipid disorders, including raising high-density lipoprotein cholesterol (HDL-C) and lowering triglycerides (TGs). Moreover, unhealthy eating habits and a sedentary lifestyle are known as modifiable risk factors for MetS among taxi drivers [19].
To our knowledge, to date, there are no data on lifestyle and SF consumption in relation to metabolic syndrome (MetS) among minibus taxi drivers in the Western Cape, South Africa. The current study is the first of its kind in South Africa since it investigated the understudied population of minibus taxi drivers, examining their biochemical parameters, sociodemographic characteristics, and lifestyle practices, with a particular focus on SF consumption and the association of these factors with MetS and its components. The results of this study provide valuable insights for further public health research in this neglected field. Moreover, it will contribute to developing targeted interventions to curb the escalation of MetS in adult male South Africans, especially those working in long-duration driving business.
## Study participants and sampling size
This cross-sectional study was conducted among 185 professional taxi drivers, who were recruited from taxi ranks in Bellville and Cape Town. They were at least 20 years old. This study used a convenient sampling method, and its aim was not to make generalizations about the entire population but rather to focus on taxi drivers who consume SF. These taxi ranks were chosen because they are the two major transport interchange hubs in the Cape Metropolitan Area in South Africa's Western Cape Province. Some of the data used in this study were used in a previous paper [20]. The detailed sample size selection, including the power sampling calculation for the current study, is presented elsewhere [20]. The participants of this study were full-time minibus taxi drivers, who had been working in this field for at least 1 year and consumed SF at least three times per week. They donated blood samples that were analyzed in a laboratory to diagnose the presence of MetS. We excluded taxi drivers who had a history of non-communicable diseases (NCDs) such as hypertension, kidney failure, hypo- or hyper- thyroidism, liver diseases, known cardiovascular diseases (CVDs), or diabetes mellitus since their eating habits might have been changed based on the advice given by their health practitioners.
## Data on sociodemographic and lifestyle practices
A previously validated and structured questionnaire developed and validated for use in South Africans aged 15 years and older, which was successfully used in the first South African National Health and Nutrition Examination Survey (SANHANES-1) [21], was administered by a trained researcher to collect data on sociodemographic characteristics (i.e., age, marital status, race, and education level) and lifestyle practices (i.e., physical activity levels, alcohol consumption, and cigarette smoking) from the taxi drivers via face-to-face interviews. Moreover, the duration of sleep, driving experience, and money spent on purchasing SF were assessed using a validated questionnaire used in the study by Hill et al. [ 6].
The International Physical Activity Questionnaire (IPAQ) [22] was also used to measure the level of physical activity (PA). The results were then based on the calculated physical activity levels (PAL) using the MET-minutes per week criteria. In this case, a sedentary lifestyle was regarded as PAL < 1.4 MET-minutes per week, with low being PAL between 1.4 and 1.69 MET-minutes per week, moderate being PAL between 1.7 and 1.9, and vigorous being PAL ≥ 2 [23].
## Frequency of consuming street food
The SANHANES-1 questionnaire [21] was also used to collect information regarding the frequency of consuming street food (FF). The FF list comprised processed meat (i.e., sausages, polony, and cold cuts, such as Viennas, Frankfurters, Russians, and salami); fast food/takeaway foods, including pizzas, fried chicken, fried fish, and burgers, that were packaged to take home; fried meat and fish dishes (i.e., chips, fried chicken, and fried fish) that were consumed on site; deep-fried snacks (i.e., fries/chips, vetkoek, samoosas, and doughnuts), fresh fruits (i.e., all kinds of fruits, excluding fruit juices and dried fruits), sugar-sweetened beverages (SSBs) (i.e., gas/fizzy and reconstituted cold drinks). Consumption frequency for each food item was measured as “none”, “every day”, “1–3 times per week”, and “4–6 times per week”.
## Anthropometric measurements
A nonelastic tape was used to measure the waist circumference (WC) at the narrowest point between the lower rib and the upper iliac crest. A cut-off point of ≥94 cm was used to determine abnormal WC levels in men [24].
## Blood pressure
After the participant had been seated for 5 min or longer, three blood pressure (BP) readings were taken from the right arm in a sitting position using an electronic Micronta monitoring kit [25]. Normal systolic BP (SBP) was regarded as a BP that was ≤ 130 mmHg or a diastolic BP (DBP) that was ≤ 85 mmHg [24].
## Biochemical parameters
The fasting blood glucose (FBG) was estimated using the capillary method with a glucometer (OneTouch®). To measure biochemical parameters, a venous fasting blood sample was obtained. The plasma lipid profile was used for MetS analysis. The concentration of triglycerides was assessed using the phosphoglycerides oxidase peroxidase method, while the HDL-C was analyzed using the colorimetric non-precipitation method. The IDF criterion was used to diagnose MetS [26]. According to the IDF definition, abdominal obesity (i.e., an abnormal WC reading) and two or more of the other four metabolic risk factors are required to diagnose MetS. The cutoff points for the five MetS risk factors are as follows: WC ≥94 cm for men; TG ≥ 1.7 mmol/l; SBP ≥ 130mmHg or DBP ≥ 85 mmHg; FBG ≥ 5.6 mmol/l; and HDL-C < 1.0 mmol/l.
## Statistical analysis
Descriptive statistics were used to describe the basic features such as the categories, distribution, and spread of metabolic status, dietary intake, and lifestyle practices using sociodemographic characteristics. In this case, data were analyzed using the analysis of variance (ANOVA) and the Kruskal–Wallis tests and presented as frequencies, means, medians, and standard deviations, depending on whether they were categorical or continuous. The associations between different variables were analyzed using the Chi-square test. A binary logistic regression analysis was conducted to examine the odds ratios (OR). Multivariate analyses using multiple logistic regression models, which incorporated all risk factors for MetS while adjusting for the effect of possible confounders such as age, employment status, marital status, ethnicity, physical activity, and monthly income, were also applied (AOR). $95\%$ confidence intervals (CIs) that did not overlap and p-values that were less than 0.05 indicated significant differences and associations between variable results. All data were analyzed using the statistical package for social sciences (SPSS version 28.0 for Windows; SPSS Inc., Chicago, IL, USA).
## Results
Table 1 presents the sociodemographic characteristics of the study participants based on their MetS status. Overall, the mean age of the participants was 40.0 years (SD: 10.7), with those suffering from MetS being significantly older than those without. There were $10.2\%$ more participants within the age group of 20–39 years. There was a significantly higher prevalence of participants with MetS in the older age group than in the younger age group (61 vs. $39\%$).
**Table 1**
| Unnamed: 0 | N = 185 | IDF MetS | IDF MetS.1 | P value |
| --- | --- | --- | --- | --- |
| | | No (n = 108) | Yes (n = 77) | |
| Age_(years) | Age_(years) | Age_(years) | Age_(years) | Age_(years) |
| M ± SD | 40.0 ± 10.7 | 37.3 ± 10.2 | 43.7 ± 10.3 | < 0.001 |
| N(%) | N(%) | N(%) | N(%) | N(%) |
| 20-39 | 102 (55.1) | 72 (66.7) | 30 (39.0) | |
| ≥40 | 83 (44.9) | 36 (33.3) | 47 (61.0) | < 0.001 |
| Ethnicity | Ethnicity | Ethnicity | Ethnicity | Ethnicity |
| N(%) | N(%) | N(%) | N(%) | N(%) |
| Black | 146 (78.9) | 85 (78.7) | 61 (79.2) | |
| Non-Black | 39 (21.1) | 23 (21.3) | 16 (20.8) | 0.932 |
| Level of Education N (%) | Level of Education N (%) | Level of Education N (%) | Level of Education N (%) | Level of Education N (%) |
| No schooling/primary education | 58 (31.4) | 35 (32.4) | 23 (29.9) | |
| Some high school/high education | 127 (68.6) | 73 (67.6) | 54 (70.1) | 0.714 |
| Marital status | Marital status | Marital status | Marital status | Marital status |
| N (%) | N (%) | N (%) | N (%) | N (%) |
| Single/separated/divorced | 97 (52.4) | 60 (55.6) | 37 (48.1) | |
| Married/living as married | 88 (47.6) | 48 (44.4) | 40 (51.9) | 0.314 |
| Driving experience (years) | Driving experience (years) | Driving experience (years) | Driving experience (years) | Driving experience (years) |
| M ± SD | 9.1 ± 7.4 | 7.2 ± 6.1 | 11.7 ± 8.4 | < 0.001 |
| N(%) | N(%) | N(%) | N(%) | N(%) |
| 1–7 | 103 (55.1) | 72 (66.7) | 31 (40.3) | |
| ≥8 | 82 (44.3) | 36 (33.3) | 45 (59.7) | < 0.001 |
While there were no other significant differences in sociodemographic characteristics in relation to MetS in this cohort, the mean driving experience of the participants was 9.1 years (SD: 7.4). In this case, the participants who presented with MetS had significantly higher driving experience compared to those without. There was also a significantly higher prevalence of participants with MetS who had a driving experience of 8 years or more compared to those with a driving experience of one to seven years (59.7 vs. $40.3\%$).
Table 2 shows that older participants were 3 and 2.9 times more likely to have MetS than younger participants. While the significant association of MetS with age was unavailable when the data were adjusted for lifestyle practices (i.e., cigarette smoking, alcohol consumption, sleeping duration, physical activity level, and money spent on SF each day), it was available for age after we removed the confounding effects of the other sociodemographic variables explored in the current analysis.
**Table 2**
| Unnamed: 0 | IDF Metabolic syndrome | IDF Metabolic syndrome.1 | IDF Metabolic syndrome.2 | IDF Metabolic syndrome.3 | IDF Metabolic syndrome.4 | IDF Metabolic syndrome.5 | IDF Metabolic syndrome.6 | IDF Metabolic syndrome.7 | IDF Metabolic syndrome.8 | IDF Metabolic syndrome.9 | IDF Metabolic syndrome.10 | IDF Metabolic syndrome.11 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | | | Model 1 | Model 1 | Model 1 | Model 2 | Model 2 | Model 2 | Model 3 | Model 3 | Model 3 |
| | Crude | 95% CI | P value | AOR | 95% CI | P value | AOR | 95% CI | P value | AOR | 95% CI | P value |
| Age (years) | Age (years) | Age (years) | Age (years) | Age (years) | Age (years) | Age (years) | Age (years) | Age (years) | Age (years) | Age (years) | Age (years) | Age (years) |
| 20–39 | 1 | | | 1 | | | 1 | | | 1 | | |
| ≥40 | 3.133 | 1.706–5.756 | < 0.001 | 0.541 | 0.217–1.351 | 0.188 | 0.589 | 0.225–1.539 | 0.280 | 2.955 | 1.2955–6.969 | 0.013 |
| Ethnicity | Ethnicity | Ethnicity | Ethnicity | Ethnicity | Ethnicity | Ethnicity | Ethnicity | Ethnicity | Ethnicity | Ethnicity | Ethnicity | Ethnicity |
| Black | 1 | | | 1 | | | 1 | | | 1 | | |
| Non-Black | 0.969 | 0.473–1.987 | 0.932 | 0.908 | 0.356–2.312 | 0.839 | 0.991 | 0.378–2.597 | 0.986 | 0.560 | 0.245–1.280 | 0.169 |
| Level of Education | Level of Education | Level of Education | Level of Education | Level of Education | Level of Education | Level of Education | Level of Education | Level of Education | Level of Education | Level of Education | Level of Education | Level of Education |
| No schooling/primary education | 1 | | | 1 | | | 1 | | | 1 | | |
| Some high school/high education | 1.126 | 0.598–2.120 | 0.714 | 2.880 | 1.212–6.847 | 0.017 | 2.676 | 1.103–6.506 | 0.030 | 2.004 | 0.940–4.273 | 0.072 |
| Marital status | Marital status | Marital status | Marital status | Marital status | Marital status | Marital status | Marital status | Marital status | Marital status | Marital status | Marital status | Marital status |
| Single/separated/divorced | 1 | | | 1 | | | 1 | | | 1 | | |
| Married/living as married | 1.351 | 0.752–2.429 | 0.314 | 0.557 | 0.254–1.219 | 0.143 | 0.639 | 0.282–1.450 | 0.284 | 0.893 | 0.449–1.778 | 0.748 |
When examining the components of MetS (Table 3), the overall mean values for WC, TG, HDL-C, SBP, DBP, and FBG were 99.1 (SD: 18.3), 1.3 (SD: 1.1), 1.1 (SD: 0.3), 133.4 (SD: 17.2), 84.8 (SD: 13.2), and 6.4 (SD: 3.5), respectively. We also observed that there were many participants with abnormal WC ($59.5\%$), HDL-C ($51.4\%$), and SBP ($58.4\%$). However, there were few participants with abnormal TG ($20.5\%$) and DBP ($43.2\%$). The participants with MetS had significantly higher abnormal WC ($64.5\%$ vs. $35.5\%$), TG (81.6 vs. $18.4\%$), HDL-C (61.1 vs. $38.9\%$), SBP (50.9 vs. $49.1\%$), DBP (67.5 vs. $32.5\%$), and FBG (64.1 vs. $35.9\%$) compared to those without.
**Table 3**
| Unnamed: 0 | Entire cohort (n = 185) | Entire cohort (n = 185).1 | IDF MetS | IDF MetS.1 | IDF MetS.2 | IDF MetS.3 | Unnamed: 7 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | Mean ±SD | n (%) | No MetS (77) | No MetS (77) | With MetS (108) | With MetS (108) | P value “between groups” |
| | | | Mean ±SD | n (%) | Mean ±SD | n (%) | |
| Waist circumference (cm) | 99.1 ± 18.3 | | 90.7 (14.5) | | 110.8(16.7) | | < 0.001 |
| Normal < 94 | | 75 (40.5) | | 69 (92.0) | | 6(8.0) | |
| Abnormal ≥94 | | 110 (59.5) | | 39 (35.5) | | 71(64.5) | |
| Triglycerides (mmol/l) | 1.3 ± 1.1 | | 1.0 (0.4) | | 1.9(1.5) | | < 0.001 |
| Normal < 1.7 | | 147 (79.5) | | 101 (68.7) | | 46(31.3) | |
| Abnormal ≥1.7 | | 38 (20.5) | | 7 (18.4) | | 31(81.6) | |
| HDL-C (mmol/l) | 1.1 ± 0.3 | | 1.2 (0.4) | | 1.0(0.3) | | < 0.001 |
| Normal ≥1.0 | | 90 (48.6) | | 71 (78.9) | | 19(21.1) | |
| Abnormal < 1.0 | | 95 (51.4) | | 37 (38.9) | | 58(61.1) | |
| Systolic blood pressure (mmHg) | 133.4 ± 17.2 | | 127 (13.3) | | 141(18.8) | | 0.002 |
| Normal < 130 | | 77 (41.6) | | 55 (71.4) | | 22(28.6) | |
| Abnormal ≥130 | | 108 (58.4) | | 53 (49.1) | | 55(50.9) | |
| Diastolic blood pressure (mmHg) | 84.8 ± 13.2 | | 79 (9.1) | | 92.7(13.9) | | < 0.001 |
| Normal < 85 | | 105 (56.8) | | 82 (78.1) | | 23(21.8) | |
| Abnormal ≥85 | | 80 (43.2) | | 26 (32.5) | | 54(67.5) | |
| Fasting blood Glucose (mmol/l) | 6.4 ± 3.5 | | 5.3 (1.1) | | 7.9(4.8) | | < 0.001 |
| Normal < 5.5 | | 93 (50.3) | | 75 (80.6) | | 18(19.4) | |
| Abnormal ≥5.5 | | 92 (49.7) | | 33 (35.9) | | 59(64.1) | |
Seventy-seven ($$n = 77$$) study participants met the IDF diagnostic criteria for MetS (i.e., had a clustering of 3 or more metabolic disorders), of which 46 had three [3] risk factors, 25 had four [4] risk factors, and 6 had five [5] risk factors. The distribution is shown in Table 4.
**Table 4**
| Number of metabolic disorders | n (%) |
| --- | --- |
| 0 | 22 (11.9) |
| 1 | 40 (21.6) |
| 2 | 46 (24.9) |
| 3 | 46 (24.9) |
| 4 | 25 (13.5) |
| 5 | 6 (3.2) |
Table 5 presents the lifestyle practices based on the outcomes of MetS. Overall, the participants smoked an average of almost 10 cigarettes (SD: 5.3) a day, slept an average of 6.1 h (SD: 1.1) each day, spent an average of ZARR 92.1 (exchange rate: ZARR 1 = United States Dollar [USD]$ 17.23) (SD: 36.7) on SF each day, and had an average PAL of 1.42 MET-minutes per week (SD: 0.14). While there were no significant differences regarding the average number of cigarettes smoked by the participants or the average amount of money spent on SF between those who had MetS and those without MetS.
**Table 5**
| Unnamed: 0 | Unnamed: 1 | IDF metabolic syndrome | IDF metabolic syndrome.1 | Unnamed: 4 |
| --- | --- | --- | --- | --- |
| | Entire cohort (n = 185) | No (n = 108) | Yes (n = 77) | P value |
| | n (%) | n (%) | n (%) | n (%) |
| Cigarettes smoking | Cigarettes smoking | Cigarettes smoking | Cigarettes smoking | Cigarettes smoking |
| Yes | 80 (43.2) | 54 (50.0) | 26 (33.8) | |
| No | 105 (56.8) | 54 (50.0) | 51 (66.2) | 0.028 |
| Average number of cigarettes smoked each day | Average number of cigarettes smoked each day | Average number of cigarettes smoked each day | Average number of cigarettes smoked each day | Average number of cigarettes smoked each day |
| M ± SD | 9.9 ± 5.3 | 9.3 ± 4.9 | 11.0 ± 5.9 | 0.179 |
| 1–5 | 24 (30.0) | 17 (31.5) | 7 (26.9) | 0.793 |
| 6-9 | 7 (8.8) | 4 (7.4) | 3 (11.5) | |
| ≥10 | 49 (61.3) | 33 (61.1) | 16 (61.5) | |
| Current alcohol drinking | Current alcohol drinking | Current alcohol drinking | Current alcohol drinking | Current alcohol drinking |
| Yes | 100 (54.1) | 55 (50.9) | 45 (58.4) | 0.312 |
| No | 85 (45.9) | 53 (49.1) | 32 (41.6) | |
| Frequency of alcoholic beverage consumption | Frequency of alcoholic beverage consumption | Frequency of alcoholic beverage consumption | Frequency of alcoholic beverage consumption | Frequency of alcoholic beverage consumption |
| Monthly or less | 52 (36.9) | 31 (36.9) | 24 (37.0) | 0.248 |
| 2–4 time a month | 51 (34.2) | 30 (35.7) | 21 (32.3) | |
| 2–3 times a week | 28 (18.8) | 12 (14.3) | 16 (24.6) | |
| 4 or more time a week | 15 (10.1) | 11 (13.1) | 4 (6.2) | |
| Number of alcoholic beverages consumed on a typical day | Number of alcoholic beverages consumed on a typical day | Number of alcoholic beverages consumed on a typical day | Number of alcoholic beverages consumed on a typical day | Number of alcoholic beverages consumed on a typical day |
| 1 or 2 | 9 (9.0) | 4 (7.3) | 5 (11.1) | 0.906 |
| 3 or 4 | 9 (9.0) | 4 (7.3) | 5 (11.1) | |
| 5 or 6 | 59 (59.0) | 34 (61.8) | 25 (55.6) | |
| 7,8 or 9 | 18 (18.0) | 10 (18.2) | 8 (17.8) | |
| 10 or more | 5 (5.0) | 2 (5.5) | 2 (4.4) | |
| Sleeping duration (hours) | Sleeping duration (hours) | Sleeping duration (hours) | Sleeping duration (hours) | Sleeping duration (hours) |
| M ± SD | 6.1 ± 1.1 | 6.1 ± 1.0 | 6.2 ± 1.2 | 0.624 |
| n (%) | | | | |
| < 6 | 112 (60.5) | 70 (64.8) | 42 (54.5) | |
| ≥7 | 73 (39.5) | 38 (35.2) | 35 (45.5) | 0.159 |
| Money spend on street food each day (ZAR) | Money spend on street food each day (ZAR) | Money spend on street food each day (ZAR) | Money spend on street food each day (ZAR) | Money spend on street food each day (ZAR) |
| M ± SD | 92.1 ± 36.7 | 88.6 ± 37.7 | 96.9 ± 34.9 | 0.056 |
| N (%) | | | | |
| < R99.00 | 96 (52.7) | 63 (60.0) | 33 (42.9) | |
| ≥R100.00 | 86 (47.5) | 42 (40.0) | 44 (57.1) | 0.022 |
| Physical activity level (MET-minutes per week) | Physical activity level (MET-minutes per week) | Physical activity level (MET-minutes per week) | Physical activity level (MET-minutes per week) | Physical activity level (MET-minutes per week) |
| N ± SD | 1.42 ± 0.14 | 1.35 ± 0.12 | 1.51 ± 0.10 | < 0.001 |
| Sedentary PAL | Sedentary PAL | Sedentary PAL | Sedentary PAL | Sedentary PAL |
| < 1.4 | 94 (50.8) | 33 (30.6) | 61 (79.2) | |
| Low PAL | Low PAL | Low PAL | Low PAL | Low PAL |
| 1.4-1.69 | 86 (46.5) | 75 (69.4) | 11 (14.3) | |
| Moderate PAL | | | | < 0.001 |
| 1.70-1.99 | 5 (2.7) | - | 5 (6.5) | |
| Vigorous PAL | Vigorous PAL | Vigorous PAL | Vigorous PAL | Vigorous PAL |
| ≥2 | - | - | - | |
In terms of participant lifestyle distribution based on the MetS status, while there were no significant differences between participants with and without MetS for lifestyle practices such as alcohol consumption and sleeping duration, there were significantly higher number of nonsmokers who were positive for MetS (those who gave an affirmative response for smoking) and those who were negative for MetS (those who gave a negative response for smoking). There were also significantly more participants with MetS who spent ZARR 100 or more than those who spent less than 100 ZAR ($57.1\%$ vs. $42.9\%$, $$p \leq 0.022$$). Finally, there were significantly more sedentary participants with MetS compared to those with low and moderate PAL ($79.2\%$ vs. $14.3\%$ and $6.5\%$).
According to Table 6, smokers, those who spent ZARR 100 or more and those who spent < 1.4 MET-minute/week were 1.96, 2.0, and 13.6 times significantly more likely to suffer from MetS compared to those who did not smoke, those who spent less than ZARR 100, and those who spent 1.4 or more MET-minute/week. While the increased significant likelihood of MetS for sedentary activity remained, even after removing the confounding effects of sociodemographic characteristics and other lifestyle practices explored in the current study, the likelihood of smoking and the amount spent on SF disappeared. It is also important to note that removing the confounding effects of the other lifestyle facts of the participants resulted in an increased significant likelihood for developing MetS by 2.2 and 2.1 times for those who consumed alcohol and those who slept 7 h or more, respectively.
**Table 6**
| Unnamed: 0 | IDF—metabolic syndrome | IDF—metabolic syndrome.1 | IDF—metabolic syndrome.2 | IDF—metabolic syndrome.3 | IDF—metabolic syndrome.4 | IDF—metabolic syndrome.5 | IDF—metabolic syndrome.6 | IDF—metabolic syndrome.7 | IDF—metabolic syndrome.8 | IDF—metabolic syndrome.9 | IDF—metabolic syndrome.10 | IDF—metabolic syndrome.11 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | Crude | Crude | Crude | Model 1 | Model 1 | Model 1 | Model 2 | Model 2 | Model 2 | Model 3 | Model 3 | Model 3 |
| | OR | 95% CI | P value | AOR | 95% CI | P value | AOR | 95% CI | P value | AOR | 95% CI | P value |
| Current cigarettes smoking | Current cigarettes smoking | Current cigarettes smoking | Current cigarettes smoking | Current cigarettes smoking | Current cigarettes smoking | Current cigarettes smoking | Current cigarettes smoking | Current cigarettes smoking | Current cigarettes smoking | Current cigarettes smoking | Current cigarettes smoking | Current cigarettes smoking |
| No | 1 | 1.072–3.590 | 0.029 | 1 | 0.301–1.063 | 0.077 | 1 | 0.318–1.142 | 0.120 | 1 | 0.270–1.208 | 0.143 |
| Yes | 1.962 | | | 0.566 | | | 0.602 | | | 0.571 | | |
| Alcohol consumption | Alcohol consumption | Alcohol consumption | Alcohol consumption | Alcohol consumption | Alcohol consumption | Alcohol consumption | Alcohol consumption | Alcohol consumption | Alcohol consumption | Alcohol consumption | Alcohol consumption | Alcohol consumption |
| No | 1 | | | 1 | | | 1 | | | 1 | | |
| Yes | 1.355 | 0.751–2.444 | 0.312 | 1.776 | 0.935–3.374 | 0.079 | 1.706 | 0.886–3.287 | 0.110 | 2.191 | 1.021–4.699 | 0.044 |
| Sleeping duration | Sleeping duration | Sleeping duration | Sleeping duration | Sleeping duration | Sleeping duration | Sleeping duration | Sleeping duration | Sleeping duration | Sleeping duration | Sleeping duration | Sleeping duration | Sleeping duration |
| < 6 | 1 | | | 1 | | | 1 | | | 1 | | |
| ≥7 | 1.535 | 0.844–2.791 | 0.160 | 1.558 | 0.832–2.915 | 0.166 | 1.497 | 0.786–2.851 | 0.220 | 2.107 | 0.977–4.521 | 0.057 |
| Money spends on street food each day (ZAR) | Money spends on street food each day (ZAR) | Money spends on street food each day (ZAR) | Money spends on street food each day (ZAR) | Money spends on street food each day (ZAR) | Money spends on street food each day (ZAR) | Money spends on street food each day (ZAR) | Money spends on street food each day (ZAR) | Money spends on street food each day (ZAR) | Money spends on street food each day (ZAR) | Money spends on street food each day (ZAR) | Money spends on street food each day (ZAR) | Money spends on street food each day (ZAR) |
| < R99.00 | 1 | | | 1 | | | 1 | | | 1 | | |
| ≥R100.00 | 2.000 | 1.101–3.633 | 0.023 | 1.225 | 0.619–2.423 | 0.560 | 1.400 | 0.691–2.838 | 0.350 | 1.157 | 0.548–2.441 | 0.702 |
| PAL (MET–minutes per week) | PAL (MET–minutes per week) | PAL (MET–minutes per week) | PAL (MET–minutes per week) | PAL (MET–minutes per week) | PAL (MET–minutes per week) | PAL (MET–minutes per week) | PAL (MET–minutes per week) | PAL (MET–minutes per week) | PAL (MET–minutes per week) | PAL (MET–minutes per week) | PAL (MET–minutes per week) | PAL (MET–minutes per week) |
| Active (low, moderate and vigorous) PAL≥1.4 | 1 | 6.388–29.109 | < 0.001 | 1 | 5.449–29.354 | < 0.001 | 1 | 5.769–32.780 | < 0.001 | 1 | 6.769–36.891 | < 0.001 |
| Sedentary (< 1.4) | 13.636 | | | 12.647 | | | 13.751 | | | 15.802 | | |
The frequency of SF consumption in relation to the likelihood of developing MetS and its components was also analyzed and is presented in Supplementary Table 1. Approximately $40.0\%$ of the entire population consumed processed meat (sausages, polony, and cold cuts Viennas, Frankfurters, Russians, and salami) at least 1–3 times a week, with a significantly higher proportion ($44.4\%$) of them experiencing abnormal BP compared to those with normal BP ($37.5\%$). Similar results were also observed for the participants who consumed takeaway foods. Moreover, a higher proportion of participants with MetS and hypertension consumed fried food and snacks (i.e., chips, vetkoek, fried chicken, fried fish) compared to those who did not consume these foods. The daily consumption of deep-fried foods was also associated with an abnormal WC.
As illustrated in Table 7, consuming processed meat daily increases the risk of abnormal HDL-C by 3.7 times, while avoiding processed meat reduces hypertension. Further, avoiding takeout reduced the likelihood of developing MetS by $68.2\%$ and abnormal TG by $25\%$. Daily takeout meal consumption increased hypertension risk by 3.1 times, even after adjusting for age and sociodemographic charactristics. The daily consumption of fried meat and fish increased the likelihood of developing MetS, abnormal WC, and hypertension by 2.1, 2.2, and 2.3 times, respectively. The association remained unchanged, even after removing the confounding effects of age, ethnicity, money spent on buying these foods, sociodemographic characteristic, and unhealthy lifestyle practices.
**Table 7**
| Unnamed: 0 | MetS | MetS.1 | Abnormal WC | Abnormal WC.1 | Abnormal FBG | Abnormal FBG.1 | Abnormal HDL–C | Abnormal HDL–C.1 | Hypertension | Hypertension.1 | Abnormal triglyceride | Abnormal triglyceride.1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Frequency of food consumption | OR | 95% CI | OR | 95% CI | OR | 95% CI | OR | 95% CI | OR | 95% CI | OR | 95% CI |
| Processed meat (sausages, polony, and cold cuts Viennas, Frankfurters, Russians, salami) | Processed meat (sausages, polony, and cold cuts Viennas, Frankfurters, Russians, salami) | Processed meat (sausages, polony, and cold cuts Viennas, Frankfurters, Russians, salami) | Processed meat (sausages, polony, and cold cuts Viennas, Frankfurters, Russians, salami) | Processed meat (sausages, polony, and cold cuts Viennas, Frankfurters, Russians, salami) | Processed meat (sausages, polony, and cold cuts Viennas, Frankfurters, Russians, salami) | Processed meat (sausages, polony, and cold cuts Viennas, Frankfurters, Russians, salami) | Processed meat (sausages, polony, and cold cuts Viennas, Frankfurters, Russians, salami) | Processed meat (sausages, polony, and cold cuts Viennas, Frankfurters, Russians, salami) | Processed meat (sausages, polony, and cold cuts Viennas, Frankfurters, Russians, salami) | Processed meat (sausages, polony, and cold cuts Viennas, Frankfurters, Russians, salami) | Processed meat (sausages, polony, and cold cuts Viennas, Frankfurters, Russians, salami) | Processed meat (sausages, polony, and cold cuts Viennas, Frankfurters, Russians, salami) |
| | 0.963 | 0.416–2.227 | 1.024 | 0.477–2.199 | 0.842 | 0.390–1.819 | 1.056 | 0.477–2.376 | 0.413* | 0.182–0.937 | 1.165 | 0.459–2.957 |
| Every day | 1.143 | 0.318–4.109 | 0.594$ | 0.208–1.702 | 0.605$ | 0.205–1.783 | 3.667*#°@$∧ | 4.881–15.264 | 1.353 | 0.461–3.975 | 0.879 | 0.200–3.859 |
| 1–3 times last week | 0.830 | 0.366–1.882 | 1.007 | 0.481–2.107 | 0.756 | 0.357–1.599 | 0.850 | 0.388–1.860 | 0.831 | 0.390–1.768 | 0.563 | 0.210–1.506 |
| 4–6 times last week | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | |
| Takeaway food (pizza, burgers, chicken and fish parcels) | Takeaway food (pizza, burgers, chicken and fish parcels) | Takeaway food (pizza, burgers, chicken and fish parcels) | Takeaway food (pizza, burgers, chicken and fish parcels) | Takeaway food (pizza, burgers, chicken and fish parcels) | Takeaway food (pizza, burgers, chicken and fish parcels) | Takeaway food (pizza, burgers, chicken and fish parcels) | Takeaway food (pizza, burgers, chicken and fish parcels) | Takeaway food (pizza, burgers, chicken and fish parcels) | Takeaway food (pizza, burgers, chicken and fish parcels) | Takeaway food (pizza, burgers, chicken and fish parcels) | Takeaway food (pizza, burgers, chicken and fish parcels) | Takeaway food (pizza, burgers, chicken and fish parcels) |
| | 0.318* | 0.115–0.878 | 1.618 | 0.266–9.852 | 0.865 | 0.163–4.602 | 1.250 | 0.173–9.019 | 0.767 | 0.075–7.860 | 0.750*°@$ | 1.574–67.602 |
| Every day | 1.227 | 0.263–5.734 | 1.014 | 0.448–2.298 | 1.300 | 0.583–2.899 | 0.833 | 0.338–2.052 | 3.097*#$ | 1.123–8.544 | 3.055 | 0.680–13.729 |
| 1–3 times last week | 0.738 | 0.275–1.981 | 0.518 | 0.177–1.514 | 1.615 | 0.538–4.853 | 0.917 | 0.276–3.040 | 1.533 | 0.402–5.841 | 1.853 | 0.277–12.389 |
| 4–6 times last week | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | |
| Fried meat and fish dishes (chips, vetkoek, fried chicken, fried fish) | Fried meat and fish dishes (chips, vetkoek, fried chicken, fried fish) | Fried meat and fish dishes (chips, vetkoek, fried chicken, fried fish) | Fried meat and fish dishes (chips, vetkoek, fried chicken, fried fish) | Fried meat and fish dishes (chips, vetkoek, fried chicken, fried fish) | Fried meat and fish dishes (chips, vetkoek, fried chicken, fried fish) | Fried meat and fish dishes (chips, vetkoek, fried chicken, fried fish) | Fried meat and fish dishes (chips, vetkoek, fried chicken, fried fish) | Fried meat and fish dishes (chips, vetkoek, fried chicken, fried fish) | Fried meat and fish dishes (chips, vetkoek, fried chicken, fried fish) | Fried meat and fish dishes (chips, vetkoek, fried chicken, fried fish) | Fried meat and fish dishes (chips, vetkoek, fried chicken, fried fish) | Fried meat and fish dishes (chips, vetkoek, fried chicken, fried fish) |
| | 1.778 | 0.236–13.405 | 1.200$ | 0.471–3.056 | 1.059 | 0.406–2.762 | 1.128 | 0.393–3.236 | 1.310 | 0.465–3.684 | 2.000 | 0.533–7.508 |
| Every day | 2.051*#°@$∧ | 1.757–5.558 | 2.223*#°@∧ | 1.007–4.904 | 1.310 | 0.595–2.882 | 1.974 | 0.851–4.580 | 2.278* | 1.977–5.312 | 2.082 | 0.696–6.227 |
| 1–3 times last week | 1.697 | 0.447–6.439 | 1.079 | 0.850 | 1.116 | 0.501–2.486 | 2.538*#°@$∧ | 1.089–5.918 | 0.776 | 0.314–1.916 | 1.358 | 0.437–4.228 |
| 4–6 times last week | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | |
| Fried snacks (vetkoek, samoosas, doughnuts) | Fried snacks (vetkoek, samoosas, doughnuts) | Fried snacks (vetkoek, samoosas, doughnuts) | Fried snacks (vetkoek, samoosas, doughnuts) | Fried snacks (vetkoek, samoosas, doughnuts) | Fried snacks (vetkoek, samoosas, doughnuts) | Fried snacks (vetkoek, samoosas, doughnuts) | Fried snacks (vetkoek, samoosas, doughnuts) | Fried snacks (vetkoek, samoosas, doughnuts) | Fried snacks (vetkoek, samoosas, doughnuts) | Fried snacks (vetkoek, samoosas, doughnuts) | Fried snacks (vetkoek, samoosas, doughnuts) | Fried snacks (vetkoek, samoosas, doughnuts) |
| | 1.786 | 0.550–5.802 | 0.457 | 0.191–1.096 | 1.315 | 0.551–3.138 | 1.875 | 0.678–5.182 | 0.483 | 0.180–1.301 | 0.520 | 0.139–1.945 |
| Every day | 3.772*°@ | 1.479–9.616 | 1.760#° | 0.693–4.470 | 1.004 | 0.431–2.342 | 2.333*# | 2.880–6.188 | 2.194* | 1.927–5.191 | 1.354 | 0.459–3.998 |
| 1–3 times last week | 1.875 | 0.730–4.816 | 0.656 | 0.320–1.342 | 0.994 | 0.494–2.001 | 2.035 | 1.898–4.611 | 0.842 | 0.403–1.703 | 0.780 | 0.303–2.006 |
| 4–6 times last week | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | |
| Packaged snacks (chips/crisps, mazimba) | Packaged snacks (chips/crisps, mazimba) | Packaged snacks (chips/crisps, mazimba) | Packaged snacks (chips/crisps, mazimba) | Packaged snacks (chips/crisps, mazimba) | Packaged snacks (chips/crisps, mazimba) | Packaged snacks (chips/crisps, mazimba) | Packaged snacks (chips/crisps, mazimba) | Packaged snacks (chips/crisps, mazimba) | Packaged snacks (chips/crisps, mazimba) | Packaged snacks (chips/crisps, mazimba) | Packaged snacks (chips/crisps, mazimba) | Packaged snacks (chips/crisps, mazimba) |
| | 3.036 | 0.593–15.547 | 0.606 | 0.186–1.977 | 0.643 | 0.201–2.052 | 3.354 | 0.716–18.637 | 0.381 | 0.117–1.241 | 3.056 | 0.351–26.593 |
| every day | 3.125 | 0.474–20.583 | 0.273 | 0.063–1.178 | 1.111 | 0.262–4.719 | 8.000*#°@$∧ | 1.215–52.693 | 0.889 | 0.216–3.662 | 3.300 | 0.294–37.103 |
| 1–3 times last week | 4.113*°@∧ | 1.862–19.626 | 0.739 | 0.244–2.237 | 0.705 | 0.239–2.086 | 6.538$ | 1.373–31.132 | 0.660 | 0.226–1.926 | 3.075 | 0.379–24.933 |
| 4–6 times last week | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | |
| Sugar Sweetened beverages (gas/fizzy cold drink and reconstituted) | Sugar Sweetened beverages (gas/fizzy cold drink and reconstituted) | Sugar Sweetened beverages (gas/fizzy cold drink and reconstituted) | Sugar Sweetened beverages (gas/fizzy cold drink and reconstituted) | Sugar Sweetened beverages (gas/fizzy cold drink and reconstituted) | Sugar Sweetened beverages (gas/fizzy cold drink and reconstituted) | Sugar Sweetened beverages (gas/fizzy cold drink and reconstituted) | Sugar Sweetened beverages (gas/fizzy cold drink and reconstituted) | Sugar Sweetened beverages (gas/fizzy cold drink and reconstituted) | Sugar Sweetened beverages (gas/fizzy cold drink and reconstituted) | Sugar Sweetened beverages (gas/fizzy cold drink and reconstituted) | Sugar Sweetened beverages (gas/fizzy cold drink and reconstituted) | Sugar Sweetened beverages (gas/fizzy cold drink and reconstituted) |
| | 0.545 | 0.133–2.236 | 0.541 | 0.186–1.577 | 0.923 | 0.317–2.685 | 0.426 | 0.127–1.427 | 0.500 | 0.127–1.965 | 0.967 | 0.229–4.087 |
| Every day | 1.660* | 1.796–3.461 | 1.164 | 0.608–2.228 | 1.620 | 0.850–3.088 | 0.833 | 0.416–1.668 | 1.691 | 0.848–3.372 | 0.767 | 0.324–1.812 |
| 1–3 times last week | 1.889 | 0.772–4.619 | 0.854 | 0.383–1.904 | 0.997 | 0.444–2.235 | 1.193 | 0.502–2.833 | 1.442 | 0.610–3.409 | 1.364 | 0.507–3.669 |
| 4–6 times last week | 1 | | 1 | | 1 | | 1 | | 1 | | 1 | |
Moreover, the consumption of these foods 1–3 times a week increased the likelihood of developing abnormal HDL-C by 2.5 times, and this interaction also remained unchanged, even after removing all the confounding effects. The daily consumption of fried snacks also increased the likelihood of developing MetS, abnormal WC, abnormal HDL-C, and hypertension by 3.8, 1.7, 2.3, and 1.9 times, respectively. The consumption of packaged snacks such as crisps and amazimba (Niknaks Maize Snack) every day also increased the likelihood of abnormal HDL-C by eight times. Moreover, consuming these snacks 1–3 times a week increased the likelihood of developing MetS by 4.1 times. These interactions remained unchanged even after removing the confounding effects such as age, ethnicity, money spent on these foods, and sociodemographic status, and unhealthy lifestyle practices. The daily consumption of SSB increased the likelihood of developing MetS by 1.6 times. However, this interaction disappeared after removing the confounding effects such as age, ethnicity, money spent on these foods, sociodemographic characteristics, and unhealthy lifestyle practices.
## Discussion
The current study aimed to investigate the risk factors for MetS among the male minibus taxi drivers working in Cape Town and the surrounding areas. The majority of the taxi drivers had abnormal levels of WC, HDL-C, SBP, and FBG. Approximately $70\%$ of the taxi drivers had clusters of three or more of these health issues. These results are corroborated by both national and international literature that show that individuals who are in the long-duration driving occupation, including taxi drivers, have a high likelihood of developing metabolic disorders compared to other professionals such as industrial and office workers (1–3, 27–30). In addition, these studies also identified age, driving duration, and driving experience as factors that accelerate the onset of these metabolic diseases (31–34). More importantly, Hildrum et al. [ 35] long argued that this condition strongly increases with age, regardless of any algorithm used to measure MetS. Indeed, in the current study, the mean age of the minibus taxi drivers was 40 years, with older participants having more driving experience compared to their younger counterparts. Even though the current analysis did not demonstrate a significant relationship between driving experience and MetS, it showed that old age increased the likelihood of developing MetS by up to 2.95 times.
Moreover, like in the current study, the majority of occupational drivers involved in other studies [28, 36] reported sleeping hours that are less than the recommended 6 h of sleep each day [37]. This may be due to these drivers' long and irregular shift hours [28, 31]. Unlike the aforementioned international researchers who reported sleep duration and its quality as the determinants of MetS, in the current study, the association between sleeping duration and MetS was significant. However, it is important to note that the majority of the minibus taxi drivers participating in the current research also reported long working hours such that their daily shifts started as early as 5 am most days and sometimes ended after 10 pm. They cite reasons such as the need to secure passengers who start work early and those who knock off late in the evenings from work because of their long working hours.
In the current study, despite no significant differences observed in the number of cigarettes smoked by those who had MetS and those without MetS, on average, the minibus taxi drivers smoked almost 10 cigarettes each day, and smoking increased their likelihood of developing MetS by up to two times. However, this interaction disappeared after we removed the confounding effects, such as sociodemography and other lifestyle practices investigated in the current study. Therefore, this suggests that factors such as age, ethnicity, the number of cigarettes smoked, and so on moderate propensity of smoking and the likelihood of developing MetS. Additionally, while some of the literature [32, 33] could not establish a relationship between smoking and the likelihood of developing MetS among occupational drivers, Appiah et al. [ 38] found that non-users of tobacco are less likely to suffer from MetS and its components. Mohebbi et al. [ 31] also showed that smokers are more likely to suffer from with MetS than nonsmokers. It is also important to explain the differences in the results regarding smokers between the current study and a recent study by Mabetwa et al. [ 32], which was also conducted for South African taxi drivers. Mabetwa et al. 's [32] study was conducted in the Gauteng province, while the current study was conducted in the Western Cape province. According to Statistics South Africa [39], Cape Town has the highest concentration of male smokers in South Africa. Additionally, it is commonly reported that smokers often smoke in public places, increasing the likelihood of exposure to secondhand smoke for nonsmokers. Therefore, we observed a high prevalence of MetS among nonsmokers in the current study. Moreover, it is important to highlight that the prevalence of smokers in the current study was higher than that of smokers reported in Mabetwa's study (43 vs. $30\%$).
In the current study, we also found results suggesting that minibus taxi drivers with a sedentary lifestyle had a 13-fold increased risk of developing MetS. This relationship remained strong even after removing the confounding effects such as sociodemography and other lifestyle factors investigated in the current study. This study indicates that physical activity has an independent and significant impact on metabolic health independently, regardless of other social determinants of health. These results are corroborated by substantiated evidence from international studies[40, 41] suggesting a significant negative correlation between physical activity and the likelihood of developing MetS among bus and taxi drivers. Moreover, Chen et al. [ 1] showed that sedentary occupations, including taxi driving, increase the risk of developing MetS. Several international studies have shown the dose–response relationship between physical activity and metabolic outcomes (13–18). According to Myers et al. [ 42], most active individuals generally have a low risk of developing metabolic diseases. Additionally, the aforementioned studies found that even meeting the minimal physical activity requirements outlined in the health guidelines [14] (i.e., at least 150 min per week of moderate-intensity activity or 75 min per week of vigorous activities) has significant benefits for reducing metabolic risk. However, we also have to acknowledge that, in our analysis, even though the participants who suffered from MetS had a higher PAL than those without MetS, their activity levels were still within the low PAL range (i.e., they were within the range of 1.4 and 1.7 Met-minutes per week). Thus, the average 1.51 MET-min per week dosage they obtained could not improve their metabolic health. We also must acknowledge that other studies could not find a significant association between physical activity and MetS [32, 38]. The reason for this is currently unknown and needs further investigation.
Other interesting results from the current research were that the type and quality of food and beverages consumed by minibus taxi drivers impacted their metabolic health. For instance, when the confounding effects of other lifestyle factors were removed from the current study, alcohol consumption increased the risk of MetS by up to two times. Even though we did not measure the exact amount of alcohol consumed by the minibus taxi drivers participating in our research, the majority reported that they consumed alcoholic beverages that ranged from 5 to 9 standard drinks most days. This is a cause for concern given that studies by Hernández-Rubio et al. [ 43] and Fan et al. [ 44] found that heavy drinking is independently associated with reduced kidney function and metabolic risk factors such as impaired fasting glucose/diabetes mellitus, abdominal obesity, arterial stiffness and plaque buildup, hypertension, and dyslipidemia. In the current analysis, we also found that the consumption of takeaway foods, fried foods, and snacks such as crisps and SSB sold by the SF vendors increased the likelihood of developing MetS, abnormal HDL-C, TG, and hypertension. We also found that avoiding takeaway and fried foods decreased the likelihood of MetS.
International research by Kim and Je [45] corroborates our finding in that individuals with MetS generally consume large quantities of processed meat (such as sausages, polony, and cold cuts such as Viennas, Frankfurters, Russians, and salami). Furthermore, the aforementioned study also found that participants in the highest category of total meat, red meat, and processed meat consumption had an increased risk of developing MetS by approximately 14, 33, and $35\%$, respectively, compared to those in the lowest consumption category of these foods. A meta-analysis of studies [46, 47] revealed a strong correlation between the consumption of red meat and the likelihood of developing MetS after excluding studies from Asia. For instance, Pan et al. [ 48] found that even a slight increase in the daily consumption of red and processed meat had a $14\%$ and $32\%$ increase in the likelihood of type 2 diabetes mellitus, respectively. Abete et al. [ 49] also found high rates of mortality due to metabolic disorders in populations with high consumption of processed meat.
Some potential mechanisms have been explained to indicate the association between processed meat consumption and the likelihood of developing MetS. Among these are the findings that total and saturated fat contained in processed meat increase the risk of MetS through increased body fat centralization, hyperinsulinemia, and hyperglycemia, which are important components of MetS [50]. According to Abete et al. [ 49], the aforementioned mechanism is mediated by nitrosamines. This chemical is toxic to pancreatic cells formed from the nitrates used as preservatives in processed meat. Additionally, these compounds cause insulin resistance.
Moreover, Marku et al. [ 51] argue that because iron is a strong pro-oxidant, it causes oxidative stress, which can harm tissues such as pancreatic beta cells. Furthermore, the aforementioned researchers argue that high iron levels may inhibit glucose metabolism and reduce pancreatic insulin synthesis and secretion. Based on the literature, we must also acknowledge that high levels of inflammatory mediators, such as C-reactive protein, in people who consume a high amount of red and processed meat could be another reason for the increased risk of MetS. Because C-reactive proteins also increase blood pressure [52], this could explain the association we found in the current research between the consumption of processed meat and hypertension. Griep et al. [ 53] reported similar results that suggest high consumption of processed meat is positively associated with the risk of hypertension. Another possible explanation for our findings may be those given by Micha et al. [ 54], who suggest that the high sodium content of processed meat results in elevated blood pressure.
Our current study further found the association between MetS risk and high consumption of fried food bought from street vendors and consumed on-site (i.e., fries/chips, vetkoek, fried chicken, and fried fish, to be specific). Our results were unsurprising given that the food sold on South African streets, including at the transport interchange areas where we recruited our participants, is not healthy. Additionally, Mchiza et al. [ 8] and Flores et al. [ 55] showed that, besides fruits and vegetables, most of the SF sold by vendors are not healthy as they are deep-fried, which is associated with cardiovascular risks. However, we must acknowledge that not all researchers have found associations between fried food and the risk of MetS. For instance, upon investigating a Mediterranean cohort of young Asian adults, Sayon-Orea et al. [ 56] and Kang and Kim [57] found no association between MetS and the frequency of consuming fried foods. The differences between the results of our study and those of the aforementioned Asian studies could be based on the type of food groups included in the current study and the two Asian studies; among the four groups of fried food included in the Asian studies were fried vegetables, fried fish, and fried seaweed. Therefore, we must always be cognizant of the literature that associates plant foods and fish with preventing metabolic diseases [58, 59]. In the current study, on the other hand, the four groups of fried food were deep-fried potato chips (or French fries), vetkoek (a cake of deep-fried dough that is stuffed inside), fried chicken, and fried fish. In this case, fried vegetables and seaweeds impact health differently than fried potatoes and fried starch. Finally, the frying mechanisms in these studies were also different. In Asia, pan frying is mostly preferred, while deep frying is favored in South Africa, and these cooking methods have also been shown to impact health differently [60, 61].
This study's results found a statistically significant association between fast-food consumption and MetS risk. These results are consistent with those of Bahadoran et al. [ 62], where they showed evidence that regular fast-food consumption has a detrimental effect on general health and can increase the risk of obesity, insulin resistance, and other metabolic abnormalities. Several mechanisms have been proposed to explain the negative effects fast foods have on health outcomes. One such mechanism is that fast foods are energy-dense, thus modulating the weight gain process [63]. Indeed, Mchiza et al. [ 8] showed that most fast foods sold in the streets of South Africa are energy dense and have an energy density that is almost two times the recommended energy for a healthy meal. Moreover, the mean total energy of these meals is estimated to be approximately 158–163 kcal per 100 g of food, with the total fat percent of beef hamburgers, chips, chicken hamburgers, and hot dogs being reported to be about 35.8 ± 10.7, 35.8 ± 8.7, 23.0 ± 5.1, and 34.0 ± $13.5\%$, respectively, with most of this fat being saturated fat [64].
The current study also showed that consuming packaged snacks (chips/crisps and mazimba) 1–3 times a day was associated with an increased risk of developing MetS. In agreement with this study's results, a significant relationship was also shown between dyslipidemia and the frequency of consumption of hydrogenated fat, fast foods, cheese puffs, and crisps in both urban and rural areas of Iran [65].
The current analysis also showed that the consumption of SSBs increased the risk of MetS by up to 1.8 times. Consistent with this study's are a few international studies [66, 67] that reported that SSB intake has significant effects on MetS risk. Moreover, a study conducted on 596 young adult South Africans by Seloka et al. [ 68] also showed that high consumption of SSBs increases the risk of high FBG in men. This is not a surprise since Deshpande et al. [ 67] have long shown that sweetened beverages disrupt the hormones involved in regulating energy balance and the satiety center within the human limbic system, which may lead to overeating and result in an increase in positive energy balance in the body. Therefore, the results are body weight gain and an increase in WC. It is also important to note that the SSBs that were included in our study consisted of cold drinks and reconstituted gas/fizzy drinks. Overconsumption of fructose and sucrose from these SSBs has been shown to stimulate and initiate lipid production in the liver, resulting in higher serum triglyceride and cholesterol levels, visceral fat accumulation, and plaque buildup [69]. Moreover, glucose in SSBs has a higher glycaemic index, which can cause high blood glucose spikes and may lead to glucose intolerance, insulin resistance, and an increase in inflammatory biomarkers [70].
Finally, in the current study, we found that avoiding the consumption of fresh fruits increased the likelihood of developing abnormal HDL-C. Although we could not specify the type, color, and amount of fruit we referred to in our research, we could attribute these significant interactions to the fiber and antioxidants that fresh fruit and vegetables have, which are compounds that have been shown to mitigate metabolic risks [71]. Although several epidemiological studies have evaluated the association between fruit and vegetable consumption and the risk of MetS, the results remain controversial. For instance, some studies have emphasized fruits' and vegetables' roles in mitigating metabolic disease risk or eliminating the disease entirely (72–74), while others have shown the opposite or no association at all with disease downregulation [75]. However, a meta-analysis of international studies by Tian et al. [ 76] cleared up the controversy by showing that, when data from these studies were combined, high fruit and vegetable consumers were $13\%$ and $24\%$ less likely to have MetS, respectively. This meta-analysis of 78 studies further investigated the relationship between the consumption of fruits, vegetables, and MetS risk. When these researchers stratified these interactions by continent, the inverse association of fruit and vegetable consumption was observed to be OR: 0·86 (0·77, 0·96) and OR: 0·86 (0·80, 0·92), respectively, with the risk of MetS remaining significant in Asia. Based on these results, they concluded that people should consume more fruits and vegetables to reduce the risk of metabolic diseases. However, we should be cognizant of the amount, type, and quality of fruit and vegetables that bring about this kind of health benefit. Studies by Nguyen et al. [ 77] and Sharma et al. [ 78] suggest that plant foods high in fiber, such as brown and white rice, have greater metabolic health benefits. Numerous substantiated pieces of evidence suggest that the consumption of good fatty acids can prevent MetS risk and its components (79–83). Sekgala et al. [ 81], in their recent research, eloquently indicated that substituting SFA for PUFA significantly decreases the likelihood of elevated BP by $7\%$.
To end the aforementioned arguments, it is also important to highlight that, unlike many studies conducted in South African populations with financial constraints, the current study was based on a population that could afford to procure food. Hence, the majority spent more than ZARR 100 on buying SF. A hundred ZAR and more a day is way above the recommended amount (ZARR 40 per day) per person recommended as enough budget to spend on healthy food each day. Abraham et al. [ 84] have long suggested that, on average, for an adult South African man, a healthier diet costs ZARR 17.3 (which is about USD$ 1) per day. In the current research, the minibus taxi drivers who spent more than ZARR 100 on SF had two times greater risk of MetS than their counterparts who spent ZARR 99 or less. This adverse outcome of MetS could be attributed to the unhealthy food options readily available near transportation hubs. While this relationship was lifestyle and sociodemography dependent, the amount spent on SF was not found to be the mediator/moderator of the type of foods purchased and consumed by the minibus taxi drivers included in the current study.
## Limitation
Despite the notable and important results of the current study outlined above, there are a number of limitations to this study that need to be considered. First, the study was cross-sectional. Hence, causal inferences cannot be drawn from this study's results. Second, the results of the current study focused only on South African male taxi drivers. Therefore, they can only be generalized to occupational drivers in the long-duration driving business but not to the general population. Finally, even though most of the major confounders have been taken into account in most of the studies, there is still a chance of unmeasured and residual confounding factors impacting in the current study. The confounders that were taken into account in the current study were also different from those in the other international studies that have been used to corroborate/contrast this study's results. Hence, notable differences were observed.
## Conclusion
In the current study, we have shown the significant determinants of MetS and its components among South *African minibus* taxi drivers who presented with abnormal levels of WC, HDL-C, SBP, and FBG, of whom $70\%$ were diagnosed to have MetS according to the IDF diagnostic criteria. Among these important determinants of MetS, we showed that sociodemographic factors such as age and high experience in taxi driving are significantly associated with MetS risk and its components. Moreover, lifestyle factors such as fewer sleeping hours, smoking many cigarettes each day, alcohol and SSB consumption, spending a lot of money on SF, and being sedentary impacted the minibus taxi drivers' metabolic health. More importantly, the consumption of fried food, processed foods, and commercially packaged snacks like crisps, obtained as takeaways, increased the likelihood of minibus taxi drivers developing MetS and its components. However, avoiding the consumption of takeaway and fried foods reduces the risk of MetS. Finally, avoiding the consumption of fruit increased MetS risk. These results have significant public health implications, as policymakers need to adopt evidence-based strategies to encourage a healthy lifestyle among South African men, especially minibus taxi drivers.
## 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.
## Ethics statement
The studies involving human participants were reviewed and approved by Ms. Patricia Josias Research Ethics Committee Officer University of the Western Cape. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
MS and ZM: conceptualization and funding acquisition. MS: formal data analysis, methodology, and writing–original draft. ZM and MO: supervision, writing, review, and editing. BM: biochemical analysis. All authors contributed to the article and approved its 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/fnut.2023.1112975/full#supplementary-material
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|
---
title: Sarcopenia, obesity, and their association with selected behavioral factors
in active older adults
authors:
- Kaja Teraž
- Miloš Kalc
- Manca Peskar
- Saša Pišot
- Boštjan Šimunič
- Rado Pišot
- Primož Pori
journal: Frontiers in Physiology
year: 2023
pmcid: PMC9996059
doi: 10.3389/fphys.2023.1129034
license: CC BY 4.0
---
# Sarcopenia, obesity, and their association with selected behavioral factors in active older adults
## Abstract
Introduction: The number of obese people in the world is increasing, as is the number of sarcopenic people among the older adults. Although both states are concerning, they can be positively influenced by selected behavioral factors such as adequate nutrition and physical activity. We were interested in the prevalence of sarcopenic obesity in active older people and the influence of behavioral factors on this phenomenon.
Methods: The study included 38 older adults (21 women) with a mean age of 75.3 ± 5.0 years. Sarcopenic parameters were determined with different tests: Handgrip Test, Chair Stand Test, Gait Speed, Timed Up and Go Test, and Short Physical Performance Battery. Body composition was measured by dual-energy x-ray absorptiometry. Physical activity level was measured using accelerometers, and nutritional status was assessed using the Mini-Nutritional Assessment and MEDLIFE Index questionnaire.
Results: Of all included active participants (the average number of steps per day was 8,916 ± 3,543), $47.4\%$ of them were obese. Of all included women, $52.4\%$ were obese. Sarcopenic obesity was found in three ($7.9\%$) participants. Nutritional status correlated with strength of lower extremities and physical performance tests (gait speed, Timed Up and Go Test and Short Physical performance battery). Higher number of steps per day positively correlates with physical performance.
Discussion: Interestingly, we did not find any correlation between the main obesity parameter such as percent body fat or body mass index (and thus sarcopenic obesity) and any of the selected behavioral factors (physical activity, sedentary behavior, or dietary habits). In conclusion, reaching the recommended levels of physical activity in older adults may not be sufficient to prevent the occurrence of obesity and sarcopenic obesity.
## 1 Introduction
The population is ageing, but there is little evidence that living longer life is accompanied by healthier ageing (Beard et al., 2016). As the population ages, the number of diseases directly related to behavioral factors increases (Kruger et al., 2009), these include obesity and degenerative and involuntary skeletal muscle disorders, also known as sarcopenia. In Europe, $21.2\%$ of older adults aged 65–74 years were obese in 2017 (European Commission. Statistical Office of the European Union. 2020), this number rises to $22.3\%$ in 2019 (European Union, 2022). Similar is expected to be true for sarcopenia as well; it is expected the increase of sarcopenia from $11.1\%$ in 2016 to $12.9\%$ in 2045 (Ethgen et al., 2017). Moreover, sarcopenia and obesity often coexist and the prevalence of both independent mortality factors (obesity and sarcopenia) has increased in older adults in recent years (World Health Organization, 2000; Cruz-Jentoft et al., 2010; Cruz-Jentoft et al., 2014). The combination of them leads to a specific condition named sarcopenic obesity (Roubenoff, 2004). Namely, a decline in muscle mass and a gain in fat mass (both visceral fat and intramuscular fat) often coincide and can lead to sarcopenic obesity (SO) (Gallagher et al., 2000). According to the European Working group on Sarcopenia in Older People 2 (EWGOSP2) (Cruz-Jentoft Alfonso et al., 2019), the definition of sarcopenic obesity is a condition of a reduced lean body mass in the context of excess adiposity. The lack of consensus on its definition persisted till this year when The European Society for Clinical Nutrition and Metabolism (ESPEN) and the European Association for the Study of Obesity (EASO) reached a consensus on a definition (Donini et al., 2022).
Among the most important factors that can contribute to the faster progression of sarcopenia, obesity and sarcopenic obesity are behavioral factors, e.g., inadequate dietary habits and insufficient physical activity (PA) with higher levels of sedentary behavior. The beneficial effect of PA in general for the prevention of sarcopenia has been already established (Steffl et al., 2017). In addition, the positive effects of adequate nutrition on muscle mass maintenance have been repeatedly reported in the literature (Marcos-Pardo et al., 2020). In addition, with proper exercise habits and optimal nutrition, we can have a positive impact on the occurrence of obesity and morbid obesity (Marcos-Pardo et al., 2020; Zhuang et al., 2022). The Mediterranean diet is an example of a dietary pattern that illustrates the relationship between dietary quality and healthy ageing (Milte and McNaughton, 2016). The Mediterranean diet and lifestyle, includes a diet high in nutrients, whole grains, vegetables, fruits, fish, olive oil, and herbs combined with a certain lifestyle such as regular exercise, social interaction, and so on. Health benefits of the Mediterranean diet and Mediterranean lifestyle have been already well established; adherence to a Mediterranean lifestyle and thus also diet is associated with a more than $50\%$ lower rate of all-cause and cause-specific mortality (Diolintzi et al., 2019), while higher adherence to the Mediterranean diet is specifically associated with lower odds of sarcopenia among older adults (Hashemi et al., 2015). An important part of the Mediterranean lifestyle is also regular physical activity. Current recommendations for healthy older adults encourage at least 150 min of aerobic physical activity of moderate-to-vigorous intensity per week to maintain functional abilities (Bull et al., 2020). In addition, muscle and bone strengthening activities that activate major muscle groups should be performed at least two times a week (Tremblay et al., 2011; Bull et al., 2020).
As far as we know, no study to this day investigated the association of sarcopenia and obesity in active older adulty in connection with behavioral factors such as physical activity and nutritional assessment. The aim of this study was to evaluate the presence of sarcopenia and obesity parameters in selected active older adults and to determine whether behavioral factors such as the amount of physical activity, sedentary behavior, nutritional status, and Mediterranean lifestyle show an association with it.
## 2.1 Participants
The current study is a cross-sectional analysis of active older adults, aged above 65 years, who previously participated in the Physical Activity and Nutrition for Great Aging (PANGeA) mass measurements study in 2013 and were again invited for follow-up measurements in 2021. For the purpose of this study, we were interested in 52 active older adults, aged between 65 and 85 (22 men and 30 women, mean age: 68.4 ± 5.6 years), who participated in follow-up measurements in 2021. Participants were from Slovenian coastal area (Koper and surroundings) which applies to the Mediterranean (Adriatic) part of Slovenia. Participants were invited to the measurements via mail and additionally via phone call. Measurements included anthropometric and body composition measurements, motor tests, and different questionnaires such as information about socio-demographic data, information on health status, drug therapy and The Montreal Cognitive Assessment (MoCA) (Nasreddine et al., 2005) screening tool for cognitive impairment was used to obtain a measure of general cognitive functioning.
The study was conducted in accordance with the Declaration of Helsinki and approved by the National Ethical Committee of the Slovenian Ministry of Health (ethical approval No. 0120-$\frac{76}{2021}$/6) and confirmed by the ZRS Koper Scientific Council No. 0624–$\frac{77}{21.}$ Moreover, the clinical trial protocol was registered on ClinicalTrials.gov, Identifier: NCT04899531. Informed consent was obtained from all participants involved in the study.
## 2.2.1 Anthropometric characteristics and body composition
During the measurement of anthropometric characteristics and body composition, participants wore only light underwear and no shoes. Body mass (BM) was measured to the nearest 0.1 kg using a hand scale (Seca 709, Hamburg, Germany), and height was measured to the nearest 0.5 cm using a standardized wall-mounted height board. Body mass index (BMI) was calculated as the participant’s body mass in kilograms divided by the square of body height in meters.
Body composition (total mass, appendicular fat, skeletal muscle mass, and bone mass) was measured with a dual-x-ray absorptiometry (DXA) (Lunar Prodigy; GE Medicals, Madison WI). The DXA was calibrated daily with a standard phantom.
## 2.2.2 Sarcopenia parameters
Sarcopenia parameters were assessed using two tests of physical performance suitable for the age group and DXA analysis as described above. A representative spectrum of the target group’s physical abilities was thus obtained. All cut-off points for sarcopenia parameters [skeletal muscle mass, hand grip strength, Chair stand test, gait speed, Timed-Up and Go test (TUG) and Short physical performance battery (SPPB)] were identified using algorithm of the EWGSOP2 (Cruz-Jentoft Alfonso et al., 2019).
## 2.2.2.1 Muscle strength
Skeletal muscle strength was evaluated by two tests: The hand-grip test for evaluating muscle strength of the upper extremities and Chair stand test for evaluating muscle strength of lower extremities. The hand-grip test was evaluated with a hand dynamometer (Yamar, Patterson Medical, United Kingdom). The participant performed the test with dominant hand in a seating position with the elbow flexed at 90° and positioned on the side, but not against, the trunk. The hand was positioned firmly on the dynamometer with the thumb pointing up. The average of three trails measured in kilograms was considered for further analysis. Lower extremity muscle strength was measured using the Chair stand test. It was performed in accordance with established protocol (Ng et al., 2015). Shortly, participants were instructed to stand up, from a seated position, and sit down as quickly as possible. After five repetitions, the administrator noted the time required. Performance impairment on the Chair stand was defined using the EWGSOP210 cut-off >15 s.
## 2.2.2.2 Muscle mass
Appendicular skeletal muscle mass (ASM) was evaluated using DXA (see Anthropometric characteristics and body composition section). Moreover, the absolute level of ASM was adjusted for body height squared (ASM/height2) to obtain skeletal muscle index (SMI) (Shepherd, 2016).
## 2.2.2.3 Physical performance
Physical performance was evaluated by determining gait speed, Timed up and go test (TUG), and the Short Physical Performance Battery (SPPB). To evaluate gait speed the participant walked for 1 min and 30 s in their usual pace between 4 m long system, from one side to another. Slow gait speed was defined using EWGSOP2(Cruz-Jentoft Alfonso et al., 2019) reference values of ≤0.8 m/s. The sum of mobility, balance, and ability to perform activities of daily living was measured with a validated TUG test (Beauchet et al., 2011). The time it took participants to stand up from a chair, walk a distance of 3 m, turn around, return to the chair, and sit down again (in seconds) was measured. The time was measured with a hand-held stopwatch (Podsiadlo and Richardson, 1991). Slow TUG performance was defined using EWGSOP2 (Cruz-Jentoft Alfonso et al., 2019) reference value of ≥20 s. The SPPB is an assessment that includes five tests for lower limb function including balance, strength, gait and endurance and it is used for characterization of lower extremity function (Guralnik et al., 1994). Three individual measures of physical performance include gait speed, balance test and Chair stand test. The aim of the balance tests was to stand for 10 s with the feet together in side-by-side, semi-tandem, and tandem positions and unaided, with the test progressing in difficulty after successful completion. Gait speed was assessed as previously described (see “gait speed”), and the Chair stand test was performed as described above (see “Chair stand test”). The total SPPB score ranging from 0 to 12; higher scores indicate better performance (Kwon et al., 2009).
## 2.2.3 Physical activity level and sedentary behavior
The amount and intensity of physical activity and sedentary behaviour was assessed using an Actigraphy GT3x accelerometer (Actigraph, Pensacola, FL, United States). We obtained data on the amount of moderate to vigorous physical activity (MVPA), light physical activity (LPA), and sedentary behavior (SB). Participants were instructed to wear an accelerometer around their waist for seven consecutive days (five weekdays and two weekend days) during all waking hours. Participants were instructed to put on the accelerometer in the morning after waking and to wear it until bedtime. The exception was water activities (e.g., showering, swimming), in which case they had to remove the accelerometer and put it back on, after the activity. Each day, they recorded when the accelerometer was installed and when it was stored in the attached logbook. The recorded data was then used to calculate the valid minutes the accelerometer was worn. Accelerometers were preprogrammed to save data in 10 s epochs and attached with an elastic strap on their right hip. Data were processed using standard methods; raw data were converted to counts per minute (cpm), which reflects the acceleration and hence the intensity of PA, data of 20 min of consecutive zeros were removed. Inclusion criteria for data validation were at least 10 h (>600 min) (Choi et al., 2011; Hart et al., 2011) of wearing time for a valid day and at least 5 days for a valid record, including one weekend day (Mâsse et al., 2005; Trost, Mciver, and Pate, 2005). From a valid record, an overall physical activity on cpm was calculated. The higher the cpm, the higher intensity of movement measured. PA intensity is typically categorized based on metabolic rate (RMR). Each valid minute of wearing time was divided into three intensity categories. Cut-off points were determined by converting vector accelerometer magnitude from cpm and corresponded to previously standardized MET values: SB (<1.5 MET in supine or seated position), LPA (1.5–2.99 MET), and MVPA (>3 MET). We used cut-off that are typical for older adults. Time spent in MVPA was determined based on the established accelerometer count cut-point of >1,041 cpm.
## 2.2.4 Nutritional status and mediterranean lifestyle
Nutrition status was evaluated using Mini Nutritional Assessment (MNA) (Guigoz and Vellas, 1997). The questionnaire is an internationally validated tool to assess the risk of malnutrition and nutritional status. The questionnaire can be scored from 0 to 30 points, interpreted as well nourished (≥24 points), at risk of malnutrition (17–23.5 points), or undernourished (<17 points).
The Mediterranean lifestyle was assessed using The Mediterranean lifestyle (MEDLIFE) index (Sotos-Prieto et al., 2015). The MEDLIFE index consists of 28 questions divided into three groups. The groups include food consumption, traditional dietary habits, and physical activity, rest and social contacts. The participant can score 1 or 0 points for each question, giving a maximum total of 28 points for the entire questionnaire. The more points, the more one adheres to the Mediterranean lifestyle.
## 2.2.5 Obesity
Following the example of the study by Bahat et al. [ 2020] and Bahat [2022] obesity was defined by two different methods; if a participant had a fat percentage (FM) above the 60th percentile [the Zoico method (Zoico et al., 2004)] or BMI of a participant was of ≥30 kg/m2 [the WHO definition of obesity (World Health Organization 2010)]. The obesity cut-off according to Zoico’s definition was $29.1\%$ FM for men and $35.8\%$ FM for women.
## 2.2.6 Sarcopenia
Sarcopenia was defined in three stages: probable sarcopenia, sarcopenia and severe sarcopenia. Probable sarcopenia was confirmed if low muscle strength was identified (grip strength lower than 27 kg for men and 16 kg for women and/or chair stand test was completed in more than 15 s for five rises), sarcopenia was confirmed if low muscle strength was noted in addition to SMI lower than 7.0 kg/m2 for men and 5.5 kg/m2 for women was identified, severe sarcopenia was confirmed when participants were diagnosed with low muscle strength and low physical performance measured with gait speed and/or TUG and/or SPPB (gait speed cut-off ≤0.8 m/s, TUG cut-off ≥20 s, SPPB cut-off ≤8 point score).
## 2.2.7 Sarcopenic obesity
As proposed by Donini et al. [ 2022], we established a two-step process for defining sarcopenic obesity: screening and diagnosis. The screening process involved identifying participants who had increased BMI (≥30 kg/m2) and were 70 of age or older, along different clinical symptoms or health risk factors, listed by Donini et al. [ 2022]. Once screening was confirmed, the diagnosis followed: sarcopenic obesity was diagnosed when obesity and sarcopenia coexisted in the participant.
## 2.3 Statistical analysis
All anthropometric characteristics, and sarcopenic parameters are described using mean and standard deviation (SD) The normality of the distribution was checked statistically (the Shapiro-Wilk test) and then confirmed graphically (histogram and QQ plots), which was implemented to evaluate their normal distribution. Comparison of selected sarcopenia and obesity parameters between sexes were made with independent-samples t-test. The correlation between sarcopenia and obesity parameters (lower extremities strength, BMI, gait speed, TUG and SPPB) and selected behavioral variables was evaluated using Pearson’s r correlation coefficient. Due to differences between sexes, non-parametric Spearman’s rank correlation coefficients was used for SMI, handgrip strength, FM (%) and selected behavioral variables. Multiple regression analysis was used to analyze all sarcopenia obesity parameters; hand grip strength, lower extremities strength, SMI, FM (%), BMI, gait speed, TUG, and SPPB. The covariates included in the models were age, amount of MVPA and SB, number of steps per day, adherence to the Mediterranean lifestyle, and nutritional status. All statistical analyses were performed by IBM SPSS Statistics 22 (SAS Institute, Cary, NC, United States), with a significance level set at $p \leq .05.$ G*Power (Faul et al., 2009) was used for effect size calculation, using Cohen’s d (α = 0.05, power = 0.95).
## 3 Results
Of the 52 enrolled participants, 38 were included in the analysis, and 14 were excluded due to refusal to wear an accelerometer ($$n = 5$$) or did not meet the inclusion criteria for data validation of accelerometer ($$n = 3$$), six of participants did not perform body composition measurements due to difficulty lying on their back. Socio-demographic characteristics of the sample are presented in Table 1. Application of the chosen definition of sarcopenia, obesity, and sarcopenic obesity indicated that a total of three participants were sarcopenic (two women), 18 participants were obese ($61.1\%$ were women) and three participants had sarcopenic obesity (two women).
**TABLE 1**
| Unnamed: 0 | Total n = 38 | Men n = 17 | Women n = 21 |
| --- | --- | --- | --- |
| Age (y) | 75.3 ± 5.2 | 74.4 ± 4.9 | 76.1 ± 5.2 |
| Education | | | |
| Primary | — | — | — |
| Secondary, n (%) | 18 (47.4) | 8 (47.1) | 10 (47.6) |
| Collage/University, n (%) | 20 (52.6) | 9 (52.9) | 11 (52.4) |
| Marital status | | | |
| Married, n (%) | 25 (65.8) | 14 (82.4) | 11 (52.4) |
| Other, n (%) | 13 (34.2) | 3 (17.6) | 10 (47.6) |
| Status of living | | | |
| Alone, n (%) | 13 (34.2) | 1 (5.9) | 12 (57.1) |
| In company, n (%) | 25 (65.8) | 16 (94.1) | 9 (42.9) |
| Number of medications | 1.8 ± 1.7 | 2.1 ± 1.6 | 1.6 ± 1.8 |
| Number of comorbidities | 3.5 ± 2.2 | 3.1 ± 1.8 | 3.7 ± 2.4 |
| MoCA | 25.2 ± 3.2 | 24.8 ± 3.1 | 25.6 ± 3.2 |
| Sarcopenia, n (%) | 3 (7.9) | 1 (5.9) | 2 (9.5) |
| Obesity, n (%) | 18 (47.4) | 7 (41.2) | 11 (52.4) |
| Sarcopenic obesity, n (%) | 3 (7.9) | 1 (5.9) | 2 (9.5) |
There was a statistical difference between women and men (Table 2) in body mass ($t = 4.452$; $p \leq .001$; Cohen’s $d = 1.47$), body heights ($t = 9.868$; $p \leq .001$; Cohen’s $d = 3.18$), fat-free mass expressed in kilograms ($t = 9.696$; $p \leq .001$; Cohen’s $d = 3.11$) and in percentages ($t = 3.082$; $$p \leq .004$$; Cohen’s $d = 0.97$), fat mass (t = −3.407; $$p \leq .002$$; Cohen’s $d = 1.08$), SMI ($t = 4.930$; $p \leq .001$; Cohen’s $d = 1.56$) and hand grip strength ($t = 7.890$; $p \leq .001$; Cohen’s $d = 2.65$). We did not find any statistical difference in selected behavioral factors (MVPA, SB, steps, MNA, and MEDLIFE index) between the sexes.
**TABLE 2**
| Unnamed: 0 | Total | Men | Women | p (Cohen d) |
| --- | --- | --- | --- | --- |
| N | 38 | 17 | 21 | N.A. |
| Body mass (kg) | 72.4 ± 15.2 | 82.3 ± 10.6 | 64.4 ± 13.6 | <.001 (1.47) |
| Body height (cm) | 167.2 ± 9.1 | 175.7 ± 5.1 | 160.4 ± 4.5 | <.001 (3.18) |
| Body mass index (kg/m2) | 25.7 ± 4.2 | 26.6 ± 2.8 | 25.0 ± 5.1 | .247 |
| Fat free mass (kg) | 49.2 ± 10.4 | 58.9 ± 6.4 | 41.3 ± 4.8 | <.001 (3.11) |
| Fat free mass (%) | 68.3 ± 7.4 | 71.9 ± 4.9 | 65.5 ± 7.9 | .004 (0.97) |
| Fat mass (%) | 31.8 ± 7.1 | 28.1 ± 4.4 | 34.8 ± 7.6 | .002 (1.08) |
| Skeletal muscle index (kg/m2) | 7.1 ± 1.2 | 7.9 ± 0.9 | 6.5 ± 0.9 | <.001 (1.56) |
| Hand grip strength (kg) | 32.8 ± 12.1 | 43.6 ± 9.0 | 24.1 ± 5.2 | <.001 (2.65) |
| Chair Stand test (sec) | 9.6 ± 2.8 | 10.1 ± 2.7 | 9.3 ± 2.9 | .369 |
| Gait Speed (m/s) | 1.1 ± 0.2 | 1.1 ± 0.3 | 1.1 ± 0.2 | .635 |
| TUG (sec) | 6.7 ± 1.4 | 6.8 ± 1.7 | 6.6 ± 1.0 | .613 |
| SPPB | 11.4 ± 0.9 | 11.4 ± 0.8 | 11.5 ± 1.1 | .834 |
| AWT/day | 913.7 ± 102.4 | 936.3 ± 97.9 | 895.3 ± 104.6 | .224 |
| MVPA (min/day) | 98.0 ± 44.6 | 95.6 ± 39.6 | 99.9 ± 49.1 | .769 |
| SB (min/day) | 646.8 ± 87.2 | 673.7 ± 84.4 | 625.1 ± 85.2 | .336 |
| Steps/day | 8,916 ± 3,543 | 9,007 ± 3,700 | 8,842 ± 3,500 | .889 |
| MNA | 27.4 ± 1.9 | 27.6 ± 1.9 | 27.2 ± 1.9 | .581 |
| MEDLIFE | 18.2 ± 3.3 | 18.0 ± 3.7 | 18.3 ± 3.0 | .794 |
Table 3 shows that handgrip strength and SMI are moderately related to age (negative correlation). Lower extremity strength was moderately related to the nutritional status (both negatively correlated). In the physical performance tests, all selected parameters showed a relationship with selected behavioral variables; gait speed had a moderate relationship with the number of steps and nutritional status (positive correlation), TUG had a moderate relationship MVPA and number of steps (both negative correlation) and nutritional status (positive correlation), SPPB had a moderate positive correlation with nutritional status.
**TABLE 3**
| Variables | HGS* | CHT | SMI* | FM* | BMI | GS | TUG | SPPB |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Age (y) | −.351 (.031) | .136 (.416) | −.324 (.047) | .212 (.202) | −.051 (.762) | −.231 (.163) | .239 (.148) | −.264 (.110) |
| MVPA (min) | .155 (.354) | −.161 (.333) | .028 (.868) | −.123 (.461) | −.182 (.275) | .161 (.334) | −.371 (.022) | −.034 (.840) |
| SB (min) | −.002 (.989) | .045 (.786) | .157 (.346) | −.175 (.294) | .100 (.550) | −.056 (.739) | .065 (.697) | .039 (.816) |
| Steps | .207 (.257) | −.313 (.056) | −.001 (.995) | .058 (.754) | −.221 (.182) | .333 (.041) | −.391 (.015) | .153 (.360) |
| MNA | .141 (.398) | −.441 (.006) | .243 (.142) | .131 (.434) | .099 (.553) | .352 (.030) | .454 (.004) | .379 (.019) |
| MEDLIFE index | −.183 (.272) | .081 (.630) | −.209 (.208) | .050 (.767) | −.132 (.428) | −.224 (.175) | −.053 (.754) | .119 (.477) |
Multiple regression was run to predict different sarcopenia and obesity parameters such as age, physical activity, number of daily steps and nutritional status (Table 4).
**TABLE 4**
| Unnamed: 0 | B | β | β.1 | SE | p | Partial r | VIF |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Hand grip strength | R = .367; F (1,36) = 5.612; p = .023 | R = .367; F (1,36) = 5.612; p = .023 | R = .367; F (1,36) = 5.612; p = .023 | R = .367; F (1,36) = 5.612; p = .023 | R = .367; F (1,36) = 5.612; p = .023 | R = .367; F (1,36) = 5.612; p = .023 | R = .367; F (1,36) = 5.612; p = .023 |
| Constant | 99.188 | | | 28.095 | .001 | | |
| Age | −.882 | −.367 | −.367 | .372 | .023 | −.367 | 1.000 |
| Chair stand test | R = .441; F (1,36) = 8.715; p = .006 | R = .441; F (1,36) = 8.715; p = .006 | R = .441; F (1,36) = 8.715; p = .006 | R = .441; F (1,36) = 8.715; p = .006 | R = .441; F (1,36) = 8.715; p = .006 | R = .441; F (1,36) = 8.715; p = .006 | R = .441; F (1,36) = 8.715; p = .006 |
| Constant | 27.708 | | | 6.134 | <.001 | | |
| MNA | −.660 | −.441 | −.441 | .224 | .006 | −.441 | 1.000 |
| SMI | R = .311; F (1,36) = 3.863; p = .057 | R = .311; F (1,36) = 3.863; p = .057 | R = .311; F (1,36) = 3.863; p = .057 | R = .311; F (1,36) = 3.863; p = .057 | R = .311; F (1,36) = 3.863; p = .057 | R = .311; F (1,36) = 3.863; p = .057 | R = .311; F (1,36) = 3.863; p = .057 |
| Constant | 12.496 | | | 2.743 | <.001 | | |
| Age | −.071 | −.311 | −.311 | .036 | .057 | .311 | 1.000 |
| Gait speed | R = .542; F (2,35) = 7.267; p = .002 | R = .542; F (2,35) = 7.267; p = .002 | R = .542; F (2,35) = 7.267; p = .002 | R = .542; F (2,35) = 7.267; p = .002 | R = .542; F (2,35) = 7.267; p = .002 | R = .542; F (2,35) = 7.267; p = .002 | R = .542; F (2,35) = 7.267; p = .002 |
| Const | −.580 | | | .496 | .250 | | |
| Steps | 2.646E-5 | .420 | .420 | .009E-3 | .005 | .440 | 1.042 |
| MNA | .052 | .436 | .436 | .017 | .006 | .453 | 1.042 |
| TUG | R = .677; F (4,33) = 6.963; p < .001 | R = .677; F (4,33) = 6.963; p < .001 | R = .677; F (4,33) = 6.963; p < .001 | R = .677; F (4,33) = 6.963; p < .001 | R = .677; F (4,33) = 6.963; p < .001 | R = .677; F (4,33) = 6.963; p < .001 | R = .677; F (4,33) = 6.963; p < .001 |
| Const | 21.983 | | | 4.843 | <.001 | | |
| Age | −.026 | −.098 | −.098 | .041 | .525 | −.111 | 1.409 |
| MVPA | −.004 | −.138 | −.138 | .009 | .626 | −.085 | 4.4778 |
| Steps | −.166E-3 | −.431 | −.431 | .109E-3 | .138 | −.256 | 4.890 |
| MNA | −.417 | −.575 | −.575 | .098 | <.001 | −.597 | 1.105 |
| SPPB | R = .379; F (1,36) = 6.034; p = .019 | R = .379; F (1,36) = 6.034; p = .019 | R = .379; F (1,36) = 6.034; p = .019 | R = .379; F (1,36) = 6.034; p = .019 | R = .379; F (1,36) = 6.034; p = .019 | R = .379; F (1,36) = 6.034; p = .019 | R = .379; F (1,36) = 6.034; p = .019 |
| Const | 6.358 | | | 2.076 | .004 | | |
| MNA | .186 | .379 | .379 | .076 | .019 | .379 | 1.000 |
The results indicated that the gait speed model explained $29.3\%$ of the variance and was a significant predictor of gait speed F [2,35] = 7.267, $$p \leq .002.$$ Both MNA and number of steps per day contributed significantly to the model ($B = .052$, $$p \leq .005$$; $B = 2.646$E-5, $$p \leq .006$$). The model for TUG explained $45.8\%$ of the variance and was a significant predictor of TUG F [4,33] = 6.963, $$p \leq .001.$$ Only MNA contributed significantly to the model (B = −.417, $p \leq .001$).
## 4 Discussion
The aim of this study was to determine the presence of sarcopenia and obesity parameters in selected active older adults and whether behavioural factors such as the amount of physical activity, sedentary behaviour, nutritional status, and Mediterranean lifestyle are associated with them. Our results show that three participants in our sample had sarcopenia (two of them were women), 18 of participants were obese (11 of them were women), and three of participants had sarcopenic obesity (two of them were women). Although we found a higher prevalence of obesity in women ($61.1\%$ of all obese participants were women), we found no differences between the selected behavioural factors (exercise and diet). Further analyses were therefore not performed separately by sex. The presence of sarcopenia is consistent with previously published literature about prevalence of sarcopenia (Keller, 2019; Smith et al., 2020; von Haehling et al., 2010). Interestingly, the percentage of obese individuals was relatively high despite having enrolled active older adults who reported being physically active on a regular basis. Despite the previously established association between a higher percentage of body fat and lower perceived quality of life in older adults (Giovannini et al., 2019), our participants reported an active, healthy lifestyle, as already reported in published study (Teraž et al., 2022). Because the definition of sarcopenic obesity has not been precisely defined in recent years, the prevalence of sarcopenic obesity in the literature varies widely (from $2.1\%$ to $12\%$) (Baumgartner, 2006; Stenholm et al., 2008; Bahat, 2022). The most commonly used definition in the past, that was published by Baumgartner [2006], states that a high amount of fat mass, typically expressed as percent body fat greater than $27\%$ in men and $38\%$ in women, and the skeletal muscle index (skeletal mass normalized by the square of the participant’s body height in meters) of less than −1 or −2 SD of a sex-specific mean of young reference group are classified as sarcopenic obesity. Other studies showed (e.g., Newman et al., 2005) that this definition can underestimate sarcopenia in overweight and obese participants. Moreover, there is a wide range of diagnostic tools, criteria, and cut-off values for sarcopenia, obesity and sarcopenic obesity, which can lead to inconsistent results and reporting of prevalence. This is also reflected in the wide range of reported prevalence of sarcopenic obesity, which ranges from $4.4\%$ to $17\%$ in men and $3\%$ to $14\%$ in women (Zamboni et al., 2008). ESPEN and EASO launched an initiative to adopt a definition of SO, which is the co-existence of obesity and sarcopenia (Donini et al., 2022), but the evaluation of individuals with suspected SO was performed on two levels, screening, and diagnosis. Eighteen participants ($47.4\%$) who took part in our study had increased BMI [which is the first condition of screening (Donini et al., 2022)]. Furthermore, three of them had surrogate indicators of sarcopenia such as age above 70 years in combination with various clinical symptoms or health risk factors. In all three we confirmed the diagnosis of SO with skeletal muscle functional parameter tests. Interestingly, all included participants had obesity in addition to sarcopenia and were therefore recorded as having SO. Majority of obese participants were women ($61.1\%$), two of them had SO, confirming observations previously noted by Oh et al. [ 2015] but no other studies (Batsis et al., 2015; Perna et al., 2017; Du et al., 2019). Because an official definition of sarcopenic obesity has been proposed in the year 2022, the actual prevalence of sarcopenic obesity must await studies that categorize sarcopenic obesity according to the same system. In this regard, it would be useful to review the studies already conducted to determine the prevalence of SO according to the new proposed definition.
Furthermore, we analysed the association between behavioural variables, namely, PA, SB, steps, nutritional status, and parameters that are typical for sarcopenic obesity. When we ran a multiple linear regression model, we found that the nutritional status was the only factor that made a significant contribution to the model of TUG, the model of SPPB and the model of Chair stand test. On the other hand, to the gait speed model contributed two factors, number of steps per day and nutritional status of the individual. Our participants walked an average of 8,916 ± 3,543 steps per day, which according to the literature, puts them in a active older population (Tudor-Locke et al., 2011). We already know that the high number of daily steps has advantages to the individual’s health (Tudor-Locke and Bassett, 2004; Tudor-Locke et al., 2011). Some studies suggest that nutritional status affects gait speed in older adults (White et al., 2012; Asp et al., 2017). As Mendes et al. [ 2018] previously stated, we also found that nutritional status positively influences individual gait speed. However, knowing that gait speed is a commonly used indicator of physical performance in older adults, this is an important finding that may contribute to the overall management of older adults.
Interestingly, we found no association between sedentary time and selected parameters of sarcopenia and obesity. Donini et al. [ 2022] established that a sedentary behaviour may play a relevant role in the incidence of sarcopenia and obesity. On average, our participants wore the accelerometer for 15.2 h per day (we can assume that this is the time they were awake on average per day), of which approximately 10.8 h per day were spent sitting, and that is more than recommended (Bull et al., 2020). Smith et al. [ 2020] have shown that older adults who have more than 4 h/day SB, are at higher risk for sarcopenia (one of the conditions for sarcopenic obesity). Moreover, it is already known, a higher amount of PA at low intensity and a lower proportion of SB can have a positive effect on BMI (Bann et al., 2015). Moreover, total PA with a higher proportion of MVPA may protect against sarcopenia and have a positive effect on body composition (Rosique-Esteban et al., 2019; Marcos-Pardo et al., 2020). However, to our surprise, none of the selected behavioural factors (physical activity or inactivity and eating habits) correlated with the fat percentage or body mass index of the participants (two factors used to determine SO). Despite adherence to recommended daily step count (>7,000), $47.4\%$ of participants were obese. Again, this could be due to the prolonged sedentary time or the eating habits of the participants. Adherence to the Mediterranean lifestyle has been shown to have a positive effect on the defence against excess body mass (Mendez et al., 2006; Beunza et al., 2010) and thus on excess fat mass and BMI. Although our participants were well nourished (the mean of MNA was 27.4) and adherence to the Mediterranean lifestyle was high, the reasons for the relatively high percentage of fat mass could be inadequate dietary intake. The dietary questionnaires included in the study did not analyse individual macronutrients; MNA is designed especially to assess malnutrition (Vellas, Garry, and Guigoz, 1999) and it has already been established that it is not useful to predict sarcopenia (Lengelé et al., 2021). Although our participants scored high on the MNA, we were unable to assess the extent to which they consumed nutrients that contribute to weight gain and increased fat mass, such as fatty meats, processed meat, full -fat dairy products, baked products, soft drinks, baked products and canned and processed foods (Shlisky et al., 2017). All mentioned leads to another possible explanation, namely, excessive energy intake, as a positive energy balance also influences obesity (Trouwborst et al., 2018).
Although regular, adequate physical activity and a reduction in sedentary behaviour are of high importance for health, problems such as obesity, sarcopenia, and sarcopenic obesity need to be addressed in more complex ways. The important strength of our study is objectively measured PA and SB on active older adults, who are underrepresented in the literature. Moreover, our findings suggest that the recommended level of physical activity for older adults may not be sufficient to counteract obesity and sarcopenic obesity and that the problem is more complex. These conclusions warrant further investigation because sarcopenia, obesity, and sarcopenic obesity are all significant health problems.
## 4.1 Limitations of the study
Some limitations should be addressed. The prevalence of sarcopenia and sarcopenic obesity determined in this study is comparable to other published studies. However, the “true” prevalence of sarcopenic obesity depends on the definition that used. The use of a standardized definition will provide a more realistic picture of the prevalence of sarcopenic obesity in future publications. Second, the sample is too small to draw firm conclusions, but it provides an indication of what needs to be explored further. Further insight into the dietary habits of active older people would be needed to assess what types of foods predominate in the diet of the selected sample and may be influencing the increased fat mass.
## Data availability statement
The raw data presented in this study will be available on request from the corresponding author, without undue reservation.
## Ethics statement
The study was conducted in accordance with the Declaration of Helsinki and approved by the National Ethical Committee of the Slovenian Ministry of Health (ethical approval No. 0120-$\frac{76}{2021}$/6) and confirmed by the ZRS Koper Scientific Council No. 0624–$\frac{77}{21.}$ Informed consent was obtained from all participants involved in the study.
## Author contributions
KT, RP, and PP contributed to the conception and the design of the study, KT, SP, BŠ, and MP collected the data. KT performed the statistical analysis and wrote the first draft of the manuscript. MK and KT wrote sections of the manuscript. SP, BŠ, MK, MP, RP, and PP review and edited the manuscript. All authors have read and agreed to the published version of the manuscript.
## 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
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|
---
title: Nomogram-based prediction of portal vein system thrombosis formation after
splenectomy in patients with hepatolenticular degeneration
authors:
- Zhou Zheng
- Qingsheng Yu
- Hui Peng
- Long Huang
- Wanzong Zhang
- Yi Shen
- Hui Feng
- Wenshan Jing
- Qi Zhang
journal: Frontiers in Medicine
year: 2023
pmcid: PMC9996067
doi: 10.3389/fmed.2023.1103223
license: CC BY 4.0
---
# Nomogram-based prediction of portal vein system thrombosis formation after splenectomy in patients with hepatolenticular degeneration
## Abstract
### Objective
Splenectomy is a vital treatment method for hypersplenism with portal hypertension. However, portal venous system thrombosis (PVST) is a serious problem after splenectomy. Therefore, constructing an effective visual risk prediction model is important for preventing, diagnosing, and treating early PVST in hepatolenticular degeneration (HLD) surgical patients.
### Methods
Between January 2016 and December 2021, 309 HLD patients were selected. The data were split into a development set (215 cases from January 2016 to December 2019) and a validation set (94 cases from January 2019 to December 2021). Patients’ clinical characteristics and laboratory examinations were obtained from electronic medical record system, and PVST was diagnosed using Doppler ultrasound. Univariate and multivariate logistic regression analyses were used to establish the prediction model by variables filtered by LASSO regression, and a nomogram was drawn. The area under the curve (AUC) of receiver operating characteristic (ROC) curve and Hosmer–Lemeshow goodness-of-fit test were used to evaluate the differentiation and calibration of the model. Clinical net benefit was evaluated by using decision curve analysis (DCA). The 36-month survival of PVST was studied as well.
### Results
Seven predictive variables were screened out using LASSO regression analysis, including grade, POD14D-dimer (Postoperative day 14 D-dimer), POD7PLT (Postoperative day 7 platelet), PVD (portal vein diameter), PVV (portal vein velocity), PVF (portal vein flow), and SVD (splenic vein diameter). Multivariate logistic regression analysis revealed that all seven predictive variables had predictive values ($P \leq 0.05$). According to the prediction variables, the diagnosis model and predictive nomogram of PVST cases were constructed. The AUC under the ROC curve obtained from the prediction model was 0.812 ($95\%$ CI: 0.756–0.869) in the development set and 0.839 ($95\%$ CI: 0.756–0.921) in the validation set. Hosmer–Lemeshow goodness-of-fit test fitted well ($$P \leq 0.858$$ for development set; $$P \leq 0.137$$ for validation set). The nomogram model was found to be clinically useful by DCA. The 36-month survival rate of three sites of PVST was significantly different from that of one ($$P \leq 0.047$$) and two sites ($$P \leq 0.023$$).
### Conclusion
The proposed nomogram-based prediction model can predict postoperative PVST. Meanwhile, an earlier intervention should be performed on three sites of PVST.
## Introduction
Hepatolenticular degeneration (HLD) is a copper metabolic disorder caused by mutations in ATP7B gene encoding copper transporting ATPase [1]. ATP7B protein is mainly expressed in the liver. The mutation of ATP7B gene leads to copper transport dysfunction and results in excessive deposition of copper in the liver, which further alters mitochondrial function in hepatic cells and causes damage to the lipid, protein, DNA, RNA, and other molecules, ultimately resulting in liver injury and liver steatosis. Copper deposition can also activate hepatic stellate cells and accelerate the progression of liver fibrosis [2]. Statistically, there are $\frac{1}{90}$ carriers of ATP7B mutation worldwide, and HLD prevalence is about $\frac{0.25}{10}$,000∼$\frac{4}{10}$,000 [3, 4]. The estimated prevalence of HLD in Europe has increased from 5 to 142 per million in the last 50 years. The prevalence of HLD in South *Korea is* 38.7 per million. In Japan, it is estimated to be $\frac{1.21}{10}$,000 to $\frac{1.96}{10}$,000 based on the mutation of ATP7B [5, 6]. In a recent study, 1,533,370 people were investigated in Anhui Province, China, and nine cases of HLD were found, with an estimated incidence of 17.93 per million [7]. The liver was found to be the most frequently involved and one of the earliest affected organs in the HLD population (onset may occur as young as 2 years old), and its main consequences are hepatitis, cirrhosis, and even liver failure [8].
However, about 35–$45\%$ of diagnosed HLD patients already had cirrhosis, whether the primary manifestation was liver injury, neuropsychiatric damage, or asymptomatic cases (9–11). Other major clinical features included malaise, loss of appetite, esophageal and gastric varicose veins, and even ascites, splenomegaly, as well as hypersplenism. Currently, the mainstays of HLD treatment are drugs, including penicillamine, sodium dimercaptopropyl sulfonate, and other complexing agents. However, these lifelong copper-repellent drugs have myelosuppression, decreasing peripheral blood cells. Besides, the increased destruction caused by splenomegaly and hypersplenism would also lead to the dilemma of the treatment [12]. In 1993, Professor Yang Renmin and his colleagues at the HLD Diagnosis and Treatment Center of our hospital achieved a satisfactory effect during the therapy of HLD patients with splenomegaly and hypersplenism using strong anti-copper treatments before and after splenectomy [1]. However, postoperative portal venous system thrombosis (PVST) formation is one of the most common complications, and the characteristic clinical manifestations are upper gastrointestinal bleeding, small intestinal ischemic necrosis, intractable ascites, jaundice, hepatic encephalopathy, and other serious damage that influence the survival and prognosis of the patients [13].
Therefore, early ultrasound examination plays a vital role in PVST prevention and treatment, which is conducive to reducing the mortality and disability rate caused by liver function deterioration and small intestine necrosis, etc. On the one hand, our study is focused on constructing a simple and effective risk model to screen out independent influencing factors of PVST and predict the risk of PVST in HLD patients after splenectomy. On the other hand, it is focused on analyzing the number of PVST sites to benefit the patients’ survival. Collectively, all these findings aim at early diagnosis, treatment, and improvement of prognosis in HLD patients after surgery.
## Study population and study design
A total of 309 HLD patients were selected from the Department of General Surgery of the First Affiliated Hospital of Anhui University of Chinese Medicine from January 2016 to December 2021. The patients’ data were divided into two parts: the patients from January 2016 to December 2019 were used as the development set (215 cases), and the others from January 2019 to December 2021 were used as the validation set (94 cases). The diagnostic criteria were determined as follows: [1] HLD: (i) extraspinal symptoms and neuropsychiatric symptoms; (ii) liver damage with elevated serum transaminase and positive corneal of Kayser–Fleischer ring; (iii) serum ceruloplasmin < 200 mg/L and 24 h output volume of urine copper > 100 μg; and (iv) characteristic changes in liver and brain imaging [14]; [2] PVST: Portal vein thrombosis (trunk and intrahepatic branches), mesenteric vein thrombosis, and splenic vein thrombosis were demonstrated by hyperechoic or isoechoic filling in the cavity by Doppler ultrasound/CT/MRI, and venous diameter may be dilated in the acute phase [15].
[3] Splenomegaly grade: (i) mild degree (I): the lower boundary of the spleen was less than 3 cm below the costal arch when deep inspiration; (ii) moderate degree (II): the lower boundary of the spleen was 3 cm beyond the costal margin, but did not exceed the umbilical level and median abdominal line; (iii) severe degree (III): The lower boundary of the spleen extends beyond the umbilical level or the median abdominal line (giant spleen) [16].
[4] Hypersplenism grade: (i) mild: white blood cells (WBC) (3.0–4.0) × 109, red blood cells (RBC) (2.5–3.5) × 1012, platelet (PLT) (7.0–10.0) × 109; (ii) moderate: WBC (2.0–3.0) × 109, RBC (1.5–2.5) × 1012, PLT (5.0–7.0) × 109; (iii) severe: WBC < 2.0 × 109, RBC < 1.5 × 1012, PLT < 5.0 × 109 [16].
The inclusion criteria were set as follows: [1] Splenomegaly and hypersplenism: WBC < (3.0–4.0) × 109/L; RBC < (2.5–3.5) × 1012/L; PLT < (7.0–10.0) × 109/L; [2] HLD patients diagnosed with cirrhosis and portal hypertension who underwent splenectomy according to clinical, B-ultrasound, CT, or MRI examination; [3] Bone marrow puncture demonstrated signs of myelodysplasia;
[4] The Child–Pugh class is A or B.
The exclusion criteria were set as follows: [1] Cirrhosis complicated with blood system diseases and immune system diseases; [2] Patients who had alcoholic hepatitis, schistosomiasis hepatitis and other types of liver disease that induce splenomegaly and hypersplenism; [3] Patients with cardiac and renal insufficiency or complicated gastrointestinal tumor;
[4] Patients who underwent transjugular intrahepatic portal shunt (TIPS) or partial splenic embolization (PSE) before.
## Treatment
Patients with liver dysfunction were given liver protection treatment, vitamin supplementation, and anemia correction before surgery, especially for Child–Pugh class C, which should be adjusted to class B. Open splenectomy was used in all cases. The splenic colonic and gastric splenic ligaments were separated, and the splenic artery was fully exposed and ligated under unambiguous vision. Next, the second- and third-grade branch vessels of the spleen were dissected and ligated one by one. After that, the splenic ligament was dissociated sharply, and the spleen was completely removed. For patients with pericardia varices, the esophageal branches, lateral branches, and inferior diaphragmatic branches about 5 cm in the lower esophagus were ligated and severed. Finally, drainage tubes were placed, followed by layer and layer suture. Postoperative anti-infection, liver protection and other support treatments were given.
## The collection of clinical features and laboratory indicators
The included demographic features were age, sex, and Child–Pugh classification (scores of 5–6, 7–9, and 10–15 for classes A, B, and C, respectively). Method of surgery, whether anatomic splenectomy was performed, BMI at admission: BMI = weight (kg)/height (m2), diabetes mellitus, hypertension, smoking history (one or more cigarettes daily for 6 months), duration of surgery, intraoperative bleeding and splenomegaly grade. Patients fasted for 8–12 h, and 5 mL of venous blood was extracted at 8 am before the procedure. Automatic hemacytometer (XN-9000, Sysmex Corporation, Japan) was used to detect the level of WBC, RBC, hemoglobin (HGB), and hematocrit (HCT) by counter method. Activated partial thromboplastin time (APTT), prothrombin time (PT), and fibrinogen (FIG) were determined using the coagulation method, and D-dimer was determined using immunoturbidimetry. Automatic biochemical analyzer (7600-010, Hitachi, Ltd. Japan) was employed to detect the level of alanine transaminase (ALT), while aspartate transaminase (AST) was determined using the rate method. Albumin (ALB) concentration was measured using bromocresol green method, while total bilirubin (TBIL) was measured using the diazo method. Blood urea nitrogen (BUN) level was ascertained using HMMPS method, and serum creatinine (CER) was determined using the uric acid method. The dynamics of blood flow [portal vein diameter (PVD), splenic vein diameter (SVD), portal vein flow (PVF), and portal vein velocity PVV)] were determined using color Doppler ultrasound (ACUSON Antares, Siemens AG, Germany) with 5.0 MHz wide-screen concave array probe.
## Statistical analysis
Statistical Package for Social Sciences 26.0 (SPSS Inc, Chicago, USA) and R (Version 4.1.3) were used to analyze and plot the data. Normal data were expressed in terms of mean ± standard deviation, while the median (quartile) [M (P25, P75)] was adopted for the non-normal data. Both groups used the independent T-test or rank-sum test (Mann–Whitney U). Categorical data were expressed as percentages, and the chi-squared test was selected for intergroup comparison.
The least absolute shrinkage and selection operator (LASSO) was used to screen primary data using R “glmnet” package in development set. Single- and multi-factor logistic regression analyses were performed to filter the influence factors of PVST, and regression equations were constructed for meaningful variables. A nomogram was created using R packages “regplot,” and the differentiation of nomogram was determined by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve using the “pROC” package. Calibration was performed using Hosemer–Lemeshow with “ResourceSelection” package, and decision curve analysis (DCA) obtained by R “rmda” package was used to evaluate the clinical utility of the nomogram model. The external data were endorsed by the validation set. The endpoint was defined as the time from the date of surgery to the date of death; otherwise, it was defined as censored data. Kaplan–Meier calculation was performed by R “survminer” and “survival” packages and compared with log-rank test, and data were plotted by “ggplot” packages.
## The principle of splenectomy and portal vein system thrombosis
The long-term pathological changes of hepatolenticular cirrhosis, such as higher hepatic vascular resistance, higher visceral hemodynamics, and relatively lower splenic vascular resistance, cause spleen enlargement and hyperactivity. It results in an increase in the splenic artery diameter and blood flow velocity and a decrease in blood cell count. Currently, bone marrow suppressive copper-repellent drugs (such as penicillamine and 2,3-dimercaptopropanesulfonic sodium) exacerbate blood cell depletion, increasing the risk of bleeding and infection and making long-term copper removers impossible (Figure 1A).
**FIGURE 1:** *The status of the portal vein system thrombosis before and after operation in HLD patients. (A) Before operation; (B) after operation; DMPS, 2,3-Dimercaptopropanesulfonic Sodium; LHA, left hepatic artery; RHA, right hepatic artery; PHA, proper hepatic artery; CHA, common hepatic artery; GDA, gastroduodenal artery; CA, celiac artery; LGA, left gastric artery; SA, splenic artery; SMA, superior mesenteric artery; PV, portal vein; SMV, superior mesenteric vein; IMV, inferior mesenteric vein; PVT, portal venous thrombosis; SVT, splenic vein thrombosis; SMVT, superior mesenteric vein thrombosis; IMVT, inferior mesenteric vein thrombosis.*
After splenectomy, blood cell count rises rapidly, especially platelets. It makes blood appear hypercoagulable and consequently increases the risk of thrombosis. Moreover, since portal venous blood flow decline rapidly, the reduction of portal vein velocity and diameter and the vortex formation of the splenic vein stump also lead to thrombosis. However, the thickened hepatic artery increases blood flow and improves liver function afterward. The copper-repellent drugs can be carried out for a life-long time due to the recovery of blood cell count (Figure 1B).
## Demographic characteristics
Figure 2 reveals that 309 patients were included in this research, including 215 in the development set and 94 in the validation set. Age, gender, Child–Pugh classification, surgical method, anatomic splenectomy, BMI, diabetes, hypertension, smoking history, operation time, intraoperative bleeding, splenomegaly grade, levels of WBC, RBC, HGB, PLT, HCT, APTT, PT, FIG, D-dimer, ALT, AST, ALB, TBIL, BUN, CER, PVD, SVD, PVV, and PVF revealed no statistical significance between two groups ($P \leq 0.05$), as evident from Table 1 and Supplementary materials 1, 2.
**FIGURE 2:** *Flow chart of selection and analysis of development set and validation set of HLD patients.* TABLE_PLACEHOLDER:TABLE 1
## The screening variables of LASSO
Based on the demography, biochemical, and imaging examination of patients in the development set, seven non-zero coefficient predictors were screened out between 33 variables using LASSO regression analysis (Figure 3). A vertical line was drawn at the λ minimum value (λ = 0.024) and λ minimum value of 1 SE (λ = 0.055), respectively. When log (λ) = –2.905, seven non-zero coefficient predictive variables were screened, for which LASSO regression constituted the most appropriate penalized linear model with a shrinkage penalty. The predictive variables for screening included splenomegaly grade, POD14D-dimer, POD7PLT, preoperative indicators of PVD, SVD, PVV, and PVF.
**FIGURE 3:** *LASSO logistic regression model for clinical feature selection. (A) Distribution diagram of the model was drawn for logarithmic (lambda) sequences under different penalty coefficients. (B) 10-fold cross-validation: The first vertical line is the minimum error, and the second line is the cross-validation error of 1-time minimum standard deviation.*
## The construction of prediction model
Taking PVST occurrence in HLD patients as the dependent variable (non-PVST = 0, PVST = 1), univariate and multivariate logistic regressions were used to establish the clinical prediction model by the seven selected variables of LASSO regression analysis. The results indicated that splenomegaly grade, POD14D-dimer, POD7PLT, preoperative indicators of PVD, SVD, PVV, and PVF were the influencing factors of PVST in HLD patients ($P \leq 0.05$). The formula was as follows: Logit $$P \leq 0.862$$ + 1.263 Splenomegaly grade (Moderate vs. Mild) + 1.704 Splenomegaly grade (Severe vs. Mild) + 0.137 POD14D-dimer (mg/L) + 0.016 POD7PLT (× 109/L) + 0.159 PVD + 0.188 SVD–0.311 PVV- 0.010 PVF [AIC (min): 242.05], as displayed in Table 2. The nomogram was drawn based on the predicted variables (Figure 4).
## Validation of prediction models
The prediction model was validated based on the differentiation and calibration of the model. The differentiation of the prediction model was evaluated by drawing the ROC curve with AUC calculations to predict PVST occurrence in HLD patients following the procedure. The AUC results of the development set (0.812, $95\%$ CI: 0.756–0.869, Figure 5A) and the validation set (0.839, $95\%$ CI: 0.756–0.921, Figure 5B) indicated that the prediction model had good discriminant ability. Meanwhile, the Hosmer–Lemeshow goodness-of-fit test (development set $$P \leq 0.856$$, Figure 6A; validation set $$P \leq 0.137$$, Figure 6B) revealed that the predicted probability of the model is consistent with the actual probability, which demonstrates that it has a good calibration degree. Collectively, nomogram models displayed better predicting ability.
**FIGURE 5:** *Discrimination of ROC curves in the development (A) and validation (B) sets. The prediction probability was calculated based on the development set model, and the discrimination of the model was evaluated by ROC curves under development and validation sets. Logit P = 0.862 + 1.263 Splenomegaly grade (Moderate vs. Mild) + 1.704 Splenomegaly grade (Severe vs. Mild) + 0.137 POD14D-dimer (mg/L) + 0.016 POD7PLT (× 109/L) + 0.159 PVD + 0.188 SVD-0.311 PVV- 0.010 PVF. Probability of prediction = exp (Logit P)/[1 + exp (Logit P)].* **FIGURE 6:** *Nomogram calibration curve to predict the risk of PVST in HLD patients in the development (A) and validation (B) sets. The calibrations of development and validation sets are verified based on Hosmer–Lemeshow goodness-of-fit test, where X-axis represents the probability of nomogram prediction of PVST, and Y-axis represents the actual incidence of PVST.*
## The clinical decision curve
Decision curve analysis was used to evaluate the clinical validity of the prediction model. The DCA of the postoperative PVST risk nomogram of HLD patients is revealed in Figure 7. The results demonstrated that the development set in the range of 15–$99\%$ and validation set in the range of 18–$96\%$ had better net benefits, which were higher than those of the prediction model with all measures taken (gray slash line) and no measures taken (gray horizontal line) for undiagnosed PVST; the implementation of intervention in this range is more favorable.
**FIGURE 7:** *Nomogram decision curve analysis for PVST prediction in HLD patients in the development (A) and validation (B) sets. The Y-axis of development and validation sets represents the net benefit, calculated as the true incidence of PVST minus the false-positive rate (true positive rate), the red and blue lines represent the nomogram prediction probability of the development and validation sets, and the gray line represents the extremes of intervention and non-intervention.*
## Survival of portal venous system thrombosis after the operation
Portal venous system thrombosis after Wilson’s disease surgery was classified into portal vein thrombosis (left and right branches and main trunk), splenic vein thrombosis, and mesenteric thrombosis. PVST group was stratified into three groups (one-, two-, and three-site thrombus groups), and others were part of non-PVST group. The study of survival of postoperative patients at 36 months suggested that the death of the three groups did not exceed the median survival time, and the 36-month cumulative survival rate was 76.3, 82.5, 60.0, and $75.8\%$, respectively. There was no significant difference in survival rates between the one- and two-site thrombus groups (log-rank = 0.222; $$P \leq 0.640$$). A statistical difference could be highlighted between the one- versus three-site and two- versus three-site thrombus groups (log-rank = 3.928; $$P \leq 0.023$$, log-rank = 5.146; $$P \leq 0.047$$). There was obvious difference between the PVST and the non-PVST groups (log-rank = 5.337; $$P \leq 0.021$$), suggesting a need for early and positive intervention when PVST occurs, especially at three sites (Figure 8 and Supplementary materials 3–6).
**FIGURE 8:** *36-month survival analysis of the patients with different numbers of sites of thrombosis. PVST patients represented portal vein thrombosis (left and right branches and main trunk) or/and splenic vein thrombosis or/and mesenteric thrombosis. One site represents portal vein thrombosis (left and right branches and main trunk) or splenic vein thrombosis, or mesenteric thrombosis. Two sites: portal vein thrombosis (left and right branches and main trunk) and splenic vein thrombosis; portal vein thrombosis (left and right branches and main trunk) and mesenteric thrombosis; splenic vein thrombosis and mesenteric thrombosis. Three sites: portal vein thrombosis (left and right branches and main trunk) and splenic vein thrombosis + mesenteric thrombosis. Survival of patients with PVST after surgery: (A) One site versus two sites. (B) Two sites versus three sites. (C) One site versus three sites. (D) PVST versus non-PVST.*
## Discussion
Portal hypertension of cirrhosis is a potentially deadly complication of chronic liver disease. Currently, surgical treatments, including TIPS, PSE, splenectomy, and liver transplantation, are the main curative treatments for these populations (17–20). However, in the context of portal hypertension caused by HLD liver disease, the operative treatment aims to solve leukopenia and thrombocytopenia caused by hypersplenism to meet the life-long copper removal treatment; thus, TIPS surgery is unrecommended. The management strategy for updated guidelines for Wilson’s disease 2022 is primarily liver transplantation [12]. This does not mean that a liver transplant should be performed once portal hypertension occurs [21]. Studies have demonstrated that the mortality of portal hypertension hemorrhage is positively correlated with the severity of primary liver disease (Child–Pugh class A: < $10\%$, class B: 5–$20\%$, class C: > $50\%$). Notably, the one-year survival rate of patients with Child–Pugh class A after variceal hemorrhage alone was equal to or even better than that of liver transplantation recipients [22, 23]. Owing to adverse factors such as the shortage of liver donors, perplexing operation, high cost, complications related to liver transplantation, life-long immunosuppressive therapy, and recurrence of primary disease, liver transplantation is regarded as an alternative therapy for liver failure (end-stage liver disease) (24–26); Studies have evidenced that splenic embolism could improve leukocytes and platelets in the short term, but the long-term effect remains ambiguous. Moreover, the presence of extensive adhesion around the spleen greatly did not increase the surgical difficulty but was reported to harm patients and added to the treatment costs [27]. Such an approach has only been utilized for class C cases, currently. Since HLD is an inherited metabolic disease, anti-copper therapies are essential for long-term survival. Currently, bone marrow suppression of copper-removal drugs, coupled with hypersplenism, can aggravate thrombocytopenia and leukopenia [28]. Therefore, the main purpose of splenectomy is to restore normal blood cells to meet and ensure life-long copper-removal treatment.
However, the risk of PVST will increase due to the rapid recovery of blood cells (especially platelets), as well as the large splenic vein detachment and the formation of a local “whirlpool” following splenectomy. Some studies have indicated that the incidence of PVST in patients with portal hypertension under a natural state is about 0.6–$2.1\%$. Postoperatively, it increased to 18.9–$57.0\%$ [29, 30]. The presence of PVST not only aggravates liver damage and induces intestinal necrosis but also causes varicose hemorrhage again. Therefore, it is very important to predict the risk of PVST following splenectomy for HLD patients with cirrhosis and take effective interventions timely for these high-risk patients. As one of the intuitively displayed graphs in mathematical models, a nomogram can predict specific end points by combining multiple influencing factors. In addition, the nomogram has become a reliable and convenient tool for quantifying risk factors due to its ability to provide personalized assessments, thus facilitating disease management and clinical decision-making [31]. Our study analyzed the clinical characteristics, hematology, and imaging examinations of PVST patients after splenectomy for HLD combined with cirrhosis and portal hypertension. Using LASSO, univariate, and multivariate regression analyses, we finally found that splenomegaly grade, POD14D-Dimer, POD7-PLT, and preoperative indexes of PVD, SVD, PVF, and PVV were the influencing factors of PVST, and then, a nomogram was constructed. A recent study has suggested that the greater the degree of spleen enlargement in patients with portal hypertension, the more likely the formation of PVST after splenectomy [32]. Pietrabissa et al. noted that spleen weight was the only significant factor predictive of postoperative thrombosis. The combination of splenomegaly and an elevated preoperative platelet count was associated with a $75\%$ incidence of this complication [33]. Péré et al. reported that when the spleen weight was estimated to be greater than 500 g by CT on admission, active reexamination should be performed 5 days after surgery to exclude PVST formation [34]. Our study depicted that the incidence of PVST after splenectomy is associated with splenomegaly grade ($$P \leq 0.002$$). The larger the spleen, the more severe the reduction of blood flow through the splenic vein into the portal vein system. Owing to an apparent descending of portal vein pressure after operation in the short term, a sudden decrease of blood flow into the portal vein system may appear, and thus, the incidence of PVST is likely to occur.
The postoperative imbalances of the blood coagulation system and the rise of platelet are also high-risk factors of PVST. D-dimer is a degradation product of cross-linked fibrin by factor XIII, which is a marker of activated coagulation and fibrinolysis. For patients with secondary fibrinolysis, the level of D-dimer is elevated [35]. A recent study reported no statistical difference in D-dimer level between the patients in the thrombus group and the non-thrombus group preoperatively. However, after splenectomy, the levels of D-dimer displayed a significant difference between the two groups, and the specificity and sensitivity of predicting PVST were 76.9 and $83.5\%$, respectively, indicating that D-dimer can be used as a monitoring indicator of PVST formation [36]. D-dimer was a risk factor for PVST 14 days after surgery in our study ($$P \leq 0.021$$; OR = 1.147; CI: 1.021–1.288). Thrombocytopenia in patients with cirrhosis has historically been attributed to hypersplenism because of portal hypertension. Post-splenectomy reactive thrombocytosis may occur in a short time due to resolution of hypersplenism. A previous study has shown that platelets in most postoperative patients begin to increase within 24 h, reaching a peak at 1–2 weeks, and return to normal after 4 weeks [37]. These findings agree with our study that a significant difference in PLT level was observed between the two groups 7 days after the operation ($P \leq 0.001$). Multifactorial analysis suggested that PLT was a risk factor for PVST formation 7 days after the operation ($$P \leq 0.002$$; OR = 1.016; CI: 1.006–1.027). The reason may be that post-splenectomy platelet count rises rapidly in the short term, with a high blood coagulation state, especially for the large-volume-motivated new platelets releasing more vascular active substances (P-selection), thereby enhancing platelet adhesion and aggregation. Consequently, the possibility of thrombosis formation will be increased because it can release more precursor substances of thrombosis, such as alkane. The blood clot size was positively correlated with plasma P-selection level in two previous studies [29, 38]. However, in clinical practice, not all PLT increases lead to PVST formation after surgery. We speculated that PLT level is not the only factor that affects postoperative thrombosis, and its importance warrants further investigation.
Postoperative hemodynamic alternations were considered the most important factor related to PVST formation. In the stage of HLD cirrhosis, the obstruction and resistance of portal vein blood flow are elevated, resulting in the increased portal and splenic vein pressure, compensatory splenic vein widening, and splenic congestive enlargement. The enlarged spleen would increase portal venous blood flow [39]. After splenectomy, the splenic venous blood flow can be reduced by about 20–$40\%$, which causes blood stasis. In addition, the formation of a local “vortex” after splenic vein dissection can also slow down the blood flow and lead to thrombosis [40]. On the other hand, since vascular intima smooth muscle hyperplasia, muscle fiber thickening, and inflammatory cell infiltration had existed under the long-term hypertension of splenic and portal vein, the initiation of the coagulation system could occur [41]. Senzolo et al. remarked that the remaining splenic vein formed a larger blind end following ligation of the widened splenic vein. The hematic stasis in it was more likely to promote thrombosis, which could then spread to the portal vein and superior mesenteric vein [42]. The same results were obtained in this study: preoperative PVD, SVD, PVV, and PVF were the influencing factors of PVST ($$P \leq 0.023$$, OR = 1.172, CI: 1.022–1.345; $$P \leq 0.013$$, OR = 1.207, CI: 1.040–1.401; $$P \leq 0.001$$, OR = 0.733, CI: 0.608–0.883; $$P \leq 0.041$$, OR = 0.990, CI: 0.981–0.999).
Survival analysis between PVST and non-PVST groups depicted no significant difference at 36 months of follow-up from a retrospective study by Dong et al. [ 43]. According to Shengjing classification of PVST. Song et al. found that the higher the thrombosis level, the wider the range of thrombosis involvement. During the follow-up process, subgroup analysis revealed that the overall survival rate of type I and II groups was statistically different from that of type III, while no difference was found between type I and type II groups [44]. In our study, we analyzed cumulative survival at 36 months in each group according to the number of thrombotic sites. The results showed no differences between PVST and non-PVST groups ($$P \leq 0.021$$). Subgroup analysis demonstrated no difference between the one- and two-site thrombus groups ($$P \leq 0.64$$). Compared with the three-site thrombosis group, the difference between one site and two sites was statistically significant ($$P \leq 0.023$$; $$P \leq 0.047$$). All these findings suggest that for HLD patients with portal hypertension, compared to patients without venous thrombosis or limited local thrombosis after splenectomy, the wider range of PVST requires close attention and early intervention. Timely detection, severity evaluation, and treatment such as anti-coagulant intervention can contribute to the recovery of liver function and reduce fatal complications, ultimately benefiting long-term or even life-long anti-copper therapy for HLD patients.
Compared with the traditional diagnostic model, seven influencing factors of PVST were screened in this study. Finally, the established model was verified internally and externally. The AUC of the development and the validation sets were 0.812 and 0.839, respectively, while the calibration degree ($$P \leq 0.519$$; $$P \leq 0.137$$) indicated the robustness of the model. The DCA plots reveal a good favorable clinical net benefit of the nomogram. However, there are some non-negligible limitations in the current study. First, since this study was retrospective and external validation was conducted in different time periods, a large sample and multi-center were required. Second, the molecular mechanism of portal vein thrombosis remains unknown and needs further study in animal and cell experiments. Third, it can be considered that continuous variables can be classified and transformed, and then the optimal scale regression method can be used for screening, which is more convenient for the clinical application of the nomogram prediction model.
## Conclusion
Based on the nomogram, we have established a risk prediction model for PVST after splenectomy in HLD patients combined with cirrhosis and portal hypertension. Splenomegaly grade, POD14D-dimer, POD7PLT, preoperative indexes of PVD, SVD, PVV, and PVF were the influencing factors of PVST formation. The ROC, calibration, differentiation, and DCA of development and validation sets show that the established model has good prediction ability, aiding clinicians in diagnosis and decision-making. On the other hand, we carried out a stratified analysis of the number of PVST ranges after surgery. Patients with extensive PVST should maintain high vigilance. Effective prevention and treatment measures should be taken to reduce the risk of postoperative death by guaranteeing long-term copper removal for HLD patients.
## Data availability statement
The original contributions presented in this study are included in this article/Supplementary material, further inquiries can be directed to the corresponding author.
## Ethics statement
This studies involving human participants were reviewed and approved by the Ethics Committee of The First Affiliated Hospital of Anhui University of Chinese Medicine (Batch number: 2019AH-32), and Clinical trial Registration Number: ChiCTR2000034137. The study was carried out in accordance with the ethical principles laid down in the Declaration of Helsinki. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.
## Author contributions
QY contributed to the conceptual design of the study. HP, WZ, HF, and WJ contributed to data collection and analysis. LH, YS, and QZ were responsible for the treatments of the patients. ZZ interpreted and drafted the manuscript. All authors have read and approved this 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/fmed.2023.1103223/full#supplementary-material
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|
---
title: Association between magnesium intake and the risk of anemia among adults in
the United States
authors:
- Jungao Huang
- Jing Xu
- Ping Ye
- Xiaoqin Xin
journal: Frontiers in Nutrition
year: 2023
pmcid: PMC9996106
doi: 10.3389/fnut.2023.1046749
license: CC BY 4.0
---
# Association between magnesium intake and the risk of anemia among adults in the United States
## Abstract
### Background
Magnesium deficiency is related to an increased risk of anemia, but epidemiological evidence supporting this association remains scarce. The purpose of the present survey was to evaluate the relationship between dietary magnesium intake and the risk of anemia.
### Methods
In total, 13,423 participants aged 20–80 years were enrolled using data from the National Health and Nutrition Examination Survey 2011–2016. Magnesium consumption was evaluated using 24 h dietary recalls. *Multivariable* generalized linear models were developed to demonstrate the association between dietary magnesium intake and the prevalence of anemia.
### Results
An inverse association between dietary magnesium intake and the risk of anemia was detected based on a full adjustment model. We evaluated magnesium intake as a categorical variable (five quartiles). Compared with the lowest value, the highest multivariate adjusted odds ratio ($95\%$ confidence interval) for anemia was 0.64 (0.46–0.89). Stratified analyses revealed a reverse relationship between magnesium intake and anemia in women. However, no significant association was observed in men (pfor trend = 0.376). A similar reverse association was found among the older group (aged ≥60 years).
### Conclusion
Magnesium deficiency is closely related to a higher rate of anemia occurrence, especially among women and older Americans. Further larger-scale prospective studies are required to confirm these conclusions.
## 1. Introduction
Anemia threatens public health worldwide. The prevalence of anemia nearly doubled (4.0 to $7.1\%$) from 2003–2004 to 2011–2012 [1]. Anemia is associated with an increased sequence of adverse effects, including cardiovascular disease, low quality of life, morbidity, and mortality (2–4). Anemia reflects the decreased oxygen-carrying capacity of the blood, which may lead to fatigue, cardiovascular complications, and impaired body function (5–7). Moreover, anemia has been shown to increase hospitalization rates, especially in older adults [8].
Magnesium plays a crucial role in the functioning and sustainment of the body [9]. The imbalance of magnesium homeostasis can lead to modification of the cell membrane and increased oxidative stress [10]. Magnesium deficiency often leads to inflammation through the activation of the nuclear factor kappa B (NF-kB) pathway in immune cells and in the pathogenesis of many chronic disorders, including congestive heart failure, type 2 diabetes and hypertension [11, 12]. In recent decades, a few studies have indicated that magnesium is involved in the regulation of cell replication, differentiation, and apoptosis (13–15). Magnesium is important for the hematopoietic system [16]. In the United States, dietary magnesium intake is often below the recommended dietary intake, and $28\%$ of women develop anemia during pregnancy due to magnesium deficiency [17]. A cross-sectional retrospective study by Zeynep et al. identified a positive relationship between magnesium deficiency and anemia among individuals with chronic kidney disease [18]. Moreover, Cinar et al. reported that magnesium supplementation increases hemoglobin levels in athletes [19].
Although these studies have reported that magnesium deficiency has a potentially modifiable association with anemia, they have mostly focused on specific populations. Data on the relationship between dietary magnesium intake and anemia in the general population are limited. Therefore, we explored the association of magnesium intake with anemia in adults, as well as possible effects of age and sex, using data from the National Health and Nutrition Examination Survey (NHANES) database between 2011 and 2016.
## 2.1. Study population
Data were collected from three continuous survey cycles (between 2011 and 2016) of the NHANES, which was a nationally representative survey conducted by the Centers for Disease Control and Prevention (CDC) and the National Center for Health Statistics (NCHS) to estimate health and nutrition in the US population. The NHANES contains demographic, nutritional, and medical examination information of civilian noninstitutionalized people in the US. Written informed consent was provided by all participants, and the survey protocol was supported by the Institutional Review Board of the NCHS [20].
Data of 14,754 individuals aged 20–80 years from 2011 to 2016 were extracted. Pregnant or lactating females ($$n = 124$$) and participants with unreliable or missing magnesium intake data, hemoglobin data, and important confounders were further excluded ($$n = 1$$,207). Ultimately, 13,423 participants were included in the analysis (Figure 1).
**Figure 1:** *Flowchart of recruitment in our study.*
## 2.2. Nutrient intake assessments
Nutritional information including total dietary energy, vitamin D, calcium, magnesium, protein, and fiber intakes was collected via the first 24 h dietary recall interview, which was performed at the Mobile Examination Center (MEC). Data collection by 24-h recall interview is the most common method used to determine dietary intake in large-scale surveys and has been used in the NHANES for many years, based on expert consensus [21]. Details of the dietary interview have been described in the Dietary Interviewers Procedure Manuals [22]. The dietary interview information included food species, consumption frequency, duration, and quantity. Information on dietary intake is detailed in the NHANES.1
## 2.3. Anemia assessment
Anemia was described as a hemoglobin concentration < 120 g/L for women and < 130 g/L for men [23].
## 2.4. Other covariates
Covariates included age, sex, race/ethnicity, educational experience, smoking status, physical activity level, and body mass index (BMI). Dietary information included total energy, protein, fiber, magnesium, calcium, and Vitamin D intakes. Race was classified into the following categories: Mexican American, non-Hispanic Black, non-Hispanic White, Other Hispanic, and Other Race. Educational background was categorized into “less than a high school diploma,” “graduated from high school,” or “education beyond high school” category. Poverty income ratio (PIR) was calculated as the federal poverty level divided by the family income and defined as a value <1 or ≥ 1. BMI was estimated as weight in kilograms divided by height in meters squared and categorized into <25.0, 25.0 to <30.0, and ≥ 30.0 kg/m2. The participants were classified as current smokers, former smokers, and never smokers according to their smoking status. Physical activity was categorized into three strata: light activity, moderate activity, and vigorous activity. Dietary data, including total dietary energy, vitamin D, calcium, magnesium, protein, and fiber intakes, were acquired from a 24-h dietary recall interview.
## 2.5. Statistical analysis
Statistical analysis was conducted using the statistical software package R2 and Empower Stats (X&Y Solutions, Inc., Boston, MA, United States).3 Categorical variables and continuous variables were evaluated using chi-square tests and t-tests, respectively. Generalized linear models were developed to explore the connection between magnesium intake and anemia. First, we used univariable logistic regression to identify factors linked to anemia. Second, multivariate logistic regression models were applied. In the crude model, we made no adjustments. Model 1 was adjusted for sex, age and race. Model 2 was adjusted for all confounders listed in Table 1, including age; sex; race; PIR value; educational level; BMI; smoking status; physical activity level; and dietary energy, protein, fiber, vitamin D, calcium, and magnesium intakes. Third, a series of sensitivity analyses were conducted to identify the robustness of the results. Magnesium intake was divided into five quartiles to test the p for trends, and the lowest quartile was considered the reference. Finally, to explore the robustness of our results, analyses were stratified by sex and age as shown in Tables 2, 3. The results were considered statistically significant at $p \leq 0.05.$
## 3. Results
Sociodemographic characteristics and possible confounding factors grouped by anemia status are shown in Table 1. In total, 13,423 individuals were included in this sample, and 1,476 participants ($11\%$) were defined as having anemia. Compared with participants without anemia, those with anemia were more likely to be female, older, non-Hispanic black, individuals with a lower daily dietary intake (magnesium, calcium, vitamin D, energy, fiber, and protein), those with a lower family income, and individuals with obesity.
The multivariate logistic regression analysis results are displayed in Table 4. We determined an inversely proportional association between dietary magnesium intake (log2 transformation) and the risk of anemia in the crude model (OR, 0.66; $95\%$ CI, 0.61–0.7). In Model 1, we adjusted for age, sex, and race. The OR ($95\%$ CI) was 0.78 (0.72–0.84). After adjusting for all possible confounders, the result was compatible with that of Model 1 (OR, 0.78; $95\%$ CI, 0.68–0.91). We divided magnesium intake into five quartiles. The p for trends was robust irrespective of the three different model analyses (crude model: $p \leq 0.001$, Model 1: $p \leq 0.001$, Model 2: $$p \leq 0.035$$).
**Table 4**
| Magnesium intake (mg/d) | Event (%) | Crude model | Crude model.1 | Model 1 | Model 1.1 | Model 2 | Model 2.1 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Magnesium intake (mg/d) | Event (%) | OR (95% CI) | P value | OR (95% CI) | P value | OR (95% CI) | P value |
| Magnesium (log2 transform) | 1,476 (11) | 0.66 (0.61~0.7) | <0.001 | 0.78 (0.72~0.84) | <0.001 | 0.78 (0.68~0.91) | 0.002 |
| Q1 (<179) | 434 (29.40) | 1(Ref) | | 1(Ref) | | 1(Ref) | |
| Q2 (179–239) | 301 (20.39) | 0.65 (0.56~0.76) | <0.001 | 0.72 (0.61~0.84) | <0.001 | 0.76 (0.63~0.92) | 0.008 |
| Q3 (239–304) | 297 (20.12) | 0.63 (0.54~0.74) | <0.001 | 0.76 (0.64~0.9) | 0.002 | 0.83 (0.67~1.02) | 0.097 |
| Q4 (304–395) | 258 (17.48) | 0.55 (0.46~0.64) | <0.001 | 0.71 (0.6~0.84) | <0.001 | 0.78 (0.61~1) | 0.066 |
| Q5 (>395) | 186 (12.60) | 0.38 (0.32~0.45) | <0.001 | 0.56 (0.47~0.68) | <0.001 | 0.64 (0.46~0.89) | 0.011 |
| P for trend | | | <0.001 | | <0.001 | | 0.035 |
Stratified analyses for dietary magnesium intake by sex were conducted (Table 2). Among female participants, magnesium consumption was inversely associated with anemia (p for trend = 0.046). Nevertheless, this correlation did not show a significant difference in males (p for trend = 0.376).
We further conducted age-stratified analyses to evaluate the association of magnesium intake with anemia (Table 3). After adjusting for all confounders, an inverse relationship between dietary magnesium intake and the risk of anemia was statistically remarkable among the older group (age ≥ 60 years [p for trend = 0.005]). However, there was no statistically significance in the other two groups (age < 60 years). To ensure the robustness of our findings, a subgroup analysis was conducted to evaluate the potential interaction between magnesium intake and anemia (Table 5). After adjustment for age, sex, race, PIR value, educational level, BMI, smoking status, physical activity level, and dietary intake of energy, protein, fiber, vitamin D, and calcium, our smoking status had an interactive effect on the relationship between daily magnesium intake and anemia (p for interaction = 0.012). Non-interactive effect was calculated in other subgroup (p for interaction <0.05).
**Table 5**
| Subgroup | n.total | n.event_% | Crude OR 95CI | Crude P value | Adj OR 95CI | Adj P value | P for interaction |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Gender | | | | | | | 0.427 |
| Female | 6745.0 | 932 (13.8) | 0.73 (0.67 ~ 0.81) | <0.001 | 0.71 (0.58 ~ 0.86) | 0.001 | |
| Male | 6678.0 | 544 (8.1) | 0.65 (0.58 ~ 0.72) | <0.001 | 0.9 (0.7 ~ 1.15) | 0.401 | |
| Age | | | | | | | 0.57 |
| 20–44 | 4482.0 | 333 (7.4) | 0.64 (0.56 ~ 0.74) | <0.001 | 0.88 (0.65 ~ 1.19) | 0.405 | |
| 45–59 | 4458.0 | 445 (10) | 0.69 (0.6 ~ 0.79) | <0.001 | 0.75 (0.58 ~ 0.98) | 0.037 | |
| ≥60 | 4483.0 | 698 (15.6) | 0.68 (0.61 ~ 0.76) | <0.001 | 0.77 (0.61 ~ 0.98) | 0.036 | |
| Race | | | | | | | 0.168 |
| Mexican American | 1842.0 | 193 (10.5) | 0.81 (0.66 ~ 1) | 0.052 | 1.04 (0.65 ~ 1.67) | 0.869 | |
| Non-Hispanic Black | 2938.0 | 633 (21.5) | 0.66 (0.58 ~ 0.74) | <0.001 | 0.71 (0.57 ~ 0.9) | 0.004 | |
| Non-Hispanic White | 5380.0 | 359 (6.7) | 0.75 (0.65 ~ 0.87) | <0.001 | 1.03 (0.76 ~ 1.39) | 0.848 | |
| Other Hispanic | 1431.0 | 128 (8.9) | 0.63 (0.49 ~ 0.81) | <0.001 | 0.58 (0.36 ~ 0.96) | 0.032 | |
| Other race | 1832.0 | 163 (8.9) | 0.72 (0.57 ~ 0.9) | 0.005 | 0.74 (0.46 ~ 1.19) | 0.218 | |
| Poverty-income ratio | | | | | | | 0.233 |
| PIR < 1 | 2750.0 | 359 (13.1) | 0.74 (0.65 ~ 0.85) | <0.001 | 0.92 (0.7 ~ 1.22) | 0.577 | |
| PIR ≥ 1 | 9621.0 | 982 (10.2) | 0.62 (0.57 ~ 0.68) | <0.001 | 0.73 (0.61 ~ 0.88) | 0.001 | |
| Education level | | | | | | | 0.197 |
| College education or above | 2908.0 | 399 (13.7) | 0.69 (0.61 ~ 0.8) | <0.001 | 0.98 (0.73 ~ 1.32) | 0.886 | |
| Graduated from high school | 2967.0 | 323 (10.9) | 0.74 (0.63 ~ 0.86) | <0.001 | 0.67 (0.48 ~ 0.92) | 0.013 | |
| < High school | 7545.0 | 754 (10) | 0.62 (0.56 ~ 0.69) | <0.001 | 0.77 (0.62 ~ 0.94) | 0.013 | |
| BMI | | | | | | | 0.051 |
| <25 | 3809.0 | 413 (10.8) | 0.67 (0.59 ~ 0.77) | <0.001 | 0.77 (0.58 ~ 1.01) | 0.057 | |
| 25–30 | 4316.0 | 429 (9.9) | 0.58 (0.51 ~ 0.67) | <0.001 | 0.67 (0.5 ~ 0.88) | 0.004 | |
| ≥30 | 5181.0 | 597 (11.5) | 0.71 (0.64 ~ 0.8) | <0.001 | 0.89 (0.7 ~ 1.13) | 0.333 | |
| Smoking status | | | | | | | 0.012 |
| Never smoker | 7572.0 | 913 (12.1) | 0.6 (0.54 ~ 0.66) | <0.001 | 0.73 (0.6 ~ 0.89) | 0.002 | |
| Former smoker | 3199.0 | 372 (11.6) | 0.61 (0.53 ~ 0.7) | <0.001 | 0.77 (0.57 ~ 1.03) | 0.08 | |
| Current smoker | 2639.0 | 190 (7.2) | 0.87 (0.73 ~ 1.05) | 0.153 | 1.07 (0.75 ~ 1.53) | 0.716 | |
| Physical activity, n (%) | | | | | | | 0.237 |
| Light work activity | 8042.0 | 1,005 (12.5) | 0.67 (0.62 ~ 0.73) | <0.001 | 0.79 (0.66 ~ 0.95) | 0.01 | |
| Moderate work activity | 2781.0 | 297 (10.7) | 0.61 (0.51 ~ 0.72) | <0.001 | 0.79 (0.56 ~ 1.11) | 0.174 | |
| Vigorous work activity | 2598.0 | 174 (6.7) | 0.74 (0.6 ~ 0.91) | 0.004 | 0.91 (0.59 ~ 1.4) | 0.654 | |
## 4. Discussion
In the present cross-sectional survey, an inverse association between dietary magnesium intake and anemia was found among US adults, utilizing data from three continued NHANES cycles. In the sex-stratified analysis, an inverse association was found in females, whereas no significant difference was observed in males. Furthermore, we noticed a similar relationship between dietary magnesium intake and the risk of anemia among older participants (age ≥ 60 years). To the best of our knowledge, this is the first and largest sociodemographic investigation to reveal the relationship between magnesium intake and the prevalence of anemia in a general population.
The recommended daily allowance for magnesium intake in US adults is 420 mg for males and 320 mg for females. However, our data showed that daily magnesium intake value was 239 mg in the anemia group, which is significantly lower than recommended daily allowance. Inadequate magnesium intake has been a growing concern in recent years. Magnesium deficiency has been partially attributed to unhealthy dietary pattern such as the consumption of so-called “Western diet” [24, 25]. Clinical magnesium deficiency or magnesium deficiency patients can be found in internal medicine. Magnesium deficiency has been associated with a number of diseases, including atherosclerosis [26], diabetes [27], hypertension [28], myocardial infarction [29], and calculi [30]. Nuts, fresh vegetables, and integral grains are the major sources of magnesium. Moreover, with the exception of milk, the concentration of magnesium in dairy products is very low [24]. Therefore, the consumption of foods rich in magnesium may decrease the risk of certain diseases.
A limited number of studies have quantified the relationship between magnesium intake and anemia. A similar study found an inverse correlation between magnesium intake and anemia depending on ferritin levels among 8,511 Chinese adults. However, this association was not significant with serum ferritin levels <15 ng/mL [25]. A cross-sectional domestic review of 2,849 Chinese adults aged 20 years or older reported that sufficient magnesium and iron intakes were positively correlated with hemoglobin levels and negatively linked to the prevalence of anemia [31]. Another similar study involving 2,401 individuals aged 60 years or older in China showed that adopting a modern dietary pattern (magnesium consumption) is an appropriate strategy for preventing anemia in older Chinese people [32]. In addition, an investigation indicated that low levels of magnesium and serum ferritin were linked to a higher risk of anemia among 180 pregnant women from Khartoum, Sudan [33]. Our study extended these findings in a much larger cohort ($$n = 13$$,423) and different subgroups.
Although the precise potential mechanism of this association between magnesium and anemia remains unclarified, several possible mechanisms may explain our results. Magnesium is considered an important coenzyme for glutathione peroxidase, which is involved in the synthesis of hemoglobin [34, 35]. Furthermore, animal experiments have shown that magnesium deficiency can cause microcytic anemia, damage the membranes of red blood cells, and reduce the osmotic fragility of erythrocytes in rats (36–38). Magnesium deficiency reduces erythrocyte energy metabolism and hemoglobin synthesis, leading to anemia [25]. Moreover, chronic magnesium deficiency may promote the release of inflammatory compounds [39]. Moreover, a study reported that anemia is more prevalent in individuals with hemodialysis who suffer from decreasing erythropoietin (EPO) concentrations; nevertheless, increased serum magnesium level appears to reduce the risk of anemia by enhancing EPO response [40]. In addition, higher concentrations of magnesium may promote the driving of HIF-1α (hypoxia-inducible factor) expression, which is mediated by reactive oxygen species (ROS) via the NF-κB signaling pathway, in which HIFs are considered an important factor in the process of hemoglobin production [41, 42]. A supposed mechanism is that magnesium deficiency may alter macrophage and iron homeostasis through the NF-κB pathway, which may indirectly impair the membranes and accelerate the aging of and damage to RBCs [43].
This study had several advantages. [ 1] To our knowledge, this was the first study to investigate the relationship between dietary magnesium intake and anemia using a nationally representative sample of US adults. [ 2] This was the largest investigation exploring this association, which may ensure statistical efficiency. [ 3] We controlled and adjusted for more potential confounders. [ 4] A sensitivity analysis stratified by sex and age was performed to explore potential special populations. However, our study had certain limitations. First, in consideration of the characteristics of cross-sectional studies, the temporal sequence of this relationship could not be assessed. Second, biochemical parameters, serum magnesium, and the type of anemia information were not available in the database. In addition, multiple 24-h dietary recalls could not signify long-term magnesium status. Finally, our study was vulnerable to unmeasured confounders. Therefore, more large sample prospective studies are needed to further probe the mechanisms of the relationship between magnesium and anemia.
In conclusion, magnesium deficiency is positively associated with a higher rate of anemia occurrence, especially among females and older populations. Healthy and adequate dietary magnesium intake should be promoted.
## 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 the Institutional Review Board of the NCHS. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
JH drafted the manuscript. JX and PY collected the clinical data. XX conceived the study. All authors read and approved the final manuscript.
## 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.
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---
title: Sotatercept analog improves cardiopulmonary remodeling and pulmonary hypertension
in experimental left heart failure
authors:
- Sachindra R. Joshi
- Elif Karaca Atabay
- Jun Liu
- Yan Ding
- Steven D. Briscoe
- Mark J. Alexander
- Patrick Andre
- Ravindra Kumar
- Gang Li
journal: Frontiers in Cardiovascular Medicine
year: 2023
pmcid: PMC9996114
doi: 10.3389/fcvm.2023.1064290
license: CC BY 4.0
---
# Sotatercept analog improves cardiopulmonary remodeling and pulmonary hypertension in experimental left heart failure
## Abstract
Pulmonary hypertension due to left heart disease (PH-LHD) is the most frequent manifestation of PH but lacks any approved treatment. Activin receptor type IIA-Fc fusion protein (ActRIIA-Fc) was found previously to be efficacious in experimental and human pulmonary arterial hypertension (PAH). Here we tested the hypothesis that ActRIIA-Fc improves pulmonary vascular remodeling and alleviates PH in models of PH-LHD, specifically in subtypes of heart failure with reduced ejection fraction (PH-HFrEF) and preserved ejection fraction (PH-HFpEF). Treatment with murine ActRIIA-Fc reduced cardiac remodeling and improved cardiac function in two mouse models of left heart disease without PH, confirming that this inhibitor of activin-class ligand signaling can exert cardioprotective effects in heart failure. In a mouse model of PH-HFrEF with prolonged pressure overload caused by transverse aortic constriction, ActRIIA-Fc treatment significantly reduced pulmonary vascular remodeling, pulmonary fibrosis, and pulmonary hypertension while exerting beneficial structural, functional, and histological effects on both the left and right heart. Additionally, in an obese ZSF1-SU5416 rat model of PH-HFpEF with metabolic dysregulation, therapeutic treatment with ActRIIA-Fc normalized SMAD3 overactivation in pulmonary vascular and perivascular cells, reversed pathologic pulmonary vascular and cardiac remodeling, improved pulmonary and cardiac fibrosis, alleviated PH, and produced marked functional improvements in both cardiac ventricles. Studies in vitro revealed that treatment with ActRIIA-Fc prevents an abnormal, glucose-induced, activin-mediated, migratory phenotype in human pulmonary artery smooth muscle cells, providing a mechanism by which ActRIIA-Fc could exert therapeutic effects in experimental PH-HFpEF with metabolic dysregulation. Our results demonstrate that ActRIIA-Fc broadly corrects cardiopulmonary structure and function in experimental PH-LHD, including models of PH-HFrEF and PH-HFpEF, leading to alleviation of PH under diverse pathophysiological conditions. These findings highlight the important pathogenic contributions of activin-class ligands in multiple forms of experimental PH and support ongoing clinical evaluation of human ActRIIA-Fc (sotatercept) in patients with PH-HFpEF.
## Graphical Abstract
In Group 2 pulmonary hypertension (PH), left heart failure can cause retrograde elevation of blood pressure that leads to dysfunction of the pulmonary arterial system and subsequent right heart failure. Activin receptor type IIA-Fc fusion protein (ActRIIA-Fc) improves cardiopulmonary remodeling and function in models of Group 2 PH through mutually reinforcing, beneficial actions at multiple sites.
## Introduction
Pulmonary hypertension (PH) accompanies a spectrum of common and rare diseases [1]. The most prevalent form, PH associated with left heart disease (PH-LHD, Group 2 PH), is caused by left heart failure, including subtypes with reduced ejection fraction (PH-HFrEF) or preserved ejection fraction (PH-HFpEF) [2, 3]. In both subtypes of PH-LHD, functional correlates of pulmonary vascular remodeling predict higher morbidity and mortality than in its absence [4, 5]. Cardiac dysfunction associated with HFrEF and HFpEF can be treated with a range of efficacious therapies [6], including the recently-approved empagliflozin [7], but there are no approved treatments for either type of PH-LHD [2]. Diverse treatments approved for pulmonary arterial hypertension (PAH, Group 1 PH) have so far been found ineffective in PH-LHD [8].
A key pathogenic factor in PH-LHD is progressive pulmonary vascular remodeling. In left heart disease, elevated pressure can be transmitted in a retrograde manner from the left atrium to the right ventricle (RV) independent of secondary pulmonary vascular remodeling [9]. This results in a PH-LHD subtype termed passive, or isolated, post-capillary PH (IpcPH-LHD) [2, 10]. A second subgroup of more severely affected patients with precapillary vascular remodeling and combined post- and precapillary PH (CpcPH-LHD) displays pulmonary pathophysiology resembling that in PAH (Group 1 PH) [2, 5, 9, 10]. In this case, PH impairs RV function and in the absence of effective treatments eventually causes death by right heart failure. Although features of metabolic syndrome such as higher body mass index, elevated hemoglobin A1c levels, and diabetes are considered risk factors for PH-HFpEF (11–13), the potential influence of metabolic syndrome on pulmonary vascular remodeling in left heart disease is largely unexplored.
Activin receptor signaling is implicated in left heart failure and PAH vascular pathology, raising the possibility that this pathway contributes to PH-LHD pathogenesis. Imbalanced signaling by the transforming growth factor-β (TGF-β) superfamily contributes extensively to pathologic vascular remodeling in PAH, with overactive, pro-proliferative SMAD$\frac{2}{3}$ signaling occurring along with deficient, antiproliferative SMAD$\frac{1}{5}$/8 signaling [14]. The activin-class ligands activin A, growth differentiation factor 8 (GDF8), and GDF11—prominent activators of SMAD$\frac{2}{3}$-pathway signaling—are conspicuously upregulated in small pulmonary arteries in both experimental and human PAH [15]. Importantly, sequestration of activin-class ligands with an Fc-fusion protein incorporating the extracellular domain of activin receptor type IIA (ActRIIA-Fc) exerts antiproliferative and inflammation-suppressing effects in the lung vasculature, reverses pulmonary vascular remodeling, and reduces PH in experimental PAH, thus exhibiting disease-modifying activity not observed with vasodilator-based therapy [15, 16]. Additional evidence implicates activin receptor signaling in pathologic RV remodeling associated with experimental PAH [16] and left heart failure associated with models of systemic pressure overload, aging, ischemia, and acute ischemia-reperfusion injury (17–20). Therapeutic use of ActRIIA-Fc (sotatercept) provides clinically meaningful improvement in patients with PAH, even in those receiving multiple background therapies [21], underscoring the strong pathogenic roles for activin receptor signaling in PAH progression.
Based on these extensive clinical and preclinical observations, we hypothesized that sequestration of activin-class ligands with ActRIIA-Fc will alleviate experimental PH-LHD. Our results in models of PH-HFrEF and PH-HFpEF are the first to indicate that activin receptor signaling plays a critical role in pulmonary vascular remodeling in experimental PH-LHD. The ability of ActRIIA-Fc to reverse pathologic remodeling in the left heart, right heart, and pulmonary vasculature in experimental PH-LHD suggests that multi-ligand sequestration with this agent could be a promising therapeutic approach to treat Group 2 PH.
## Animal studies
All animal experiments were approved by the Institutional Animal Care and Use Committee at Acceleron Pharma Inc., a subsidiary of Merck & Co., Inc., Rahway, NJ, USA and performed in accordance with the guidelines from the NIH Guide for the Care and Use of Laboratory Animals. Male C57BL/6 mice (10 weeks old, Jackson Laboratory) were used for TAC and MI models as described [18, 22], and male Balb/c mice (10 weeks old, Jackson Laboratory) were used for the prolonged TAC model to establish PH. Male obese ZSF1 rats (8 and 23 weeks old) and their lean littermates (Charles River, Wilmington, MA, USA) were used for the PH-HFpEF study. PH was established by a single subcutaneous injection of a vascular endothelial growth factor receptor antagonist, Sugen 5416 (SU5416, 100 mg/kg; Cayman), suspended in CMC buffer ($0.5\%$ sodium carboxymethyl cellulose, $0.4\%$ polysorbate 80, $0.9\%$ sodium chloride, and $0.9\%$ benzyl alcohol) [23, 24]. Animals were euthanized in all experiments by heart and lung removal en bloc according to AVMA guidelines.
## Hemodynamic measurements
Animals were anesthetized with 3–$4\%$ isoflurane and placed on controlled heating pads. Right ventricular systolic pressure (RVSP) was measured by advancing a 2F curve tip pressure transducer catheter (SPR-513, Millar Instruments) into the right ventricle (RV) via the right jugular vein under 1.5–$2\%$ isoflurane anesthesia. En-bloc heart and lungs were collected, and lungs were perfused with physiological saline via the right ventricular outflow tract to flush blood cells from the pulmonary circulation. RV hypertrophy was assessed by calculating Fulton’s index, the weight ratio of the RV free wall to the combined left ventricle (LV) + septum [RV/(LV + S)].
## Histopathology and immunohistochemistry
After perfusion, the right bronchus was ligated and the right lung lobes were dissected and snap frozen for biochemical analysis. The left lung lobe was inflated with a formalin-agarose mixture [$0.5\%$ w/v low melting agarose in $1\%$ neutral buffered formalin (NBF)] at a constant pressure of 20 cm H2O. The inflated lungs were fixed in $10\%$ NBF for 48 h [16]. The left lung lobe was blocked, embedded in paraffin, and sectioned. Formalin-fixed, paraffin-embedded lung sections were stained with hematoxylin and eosin (H&E) and Masson’s trichome for histological analysis. Immunohistochemical staining was performed using antibodies against phospho-SMAD3 (pSMAD3) (cat# ab52903, Abcam), activin A (cat# PA5-47004, ThermoFisher), and GDF11 (cat# NBP2-57399, Novus). Dual immunofluorescence staining was performed using combinations of antibodies against pSMAD3 (cat# ab52903, Abcam) and smooth muscle α-actin (cat# A5228, Sigma) or CD31 (cat# ab182981, Abcam). DAPI (4′,6-diamidino-2-phenylindole) was used to identify cell nuclei.
## Morphological analyses
Wall thickness of pulmonary arteries was measured in H&E-stained sections with HALO software. Wall thickness was determined by the formula [outer diameter (OD) – inner diameter (ID)]/OD × $100\%$ as described previously [25]. Approximately 30 vessels with OD < 100 μm were randomly selected, and the outer and inner diameters of the vessels were annotated to calculate wall thickness.
## Echocardiography
Animals were anesthetized with 3–$4\%$ isoflurane and maintained at 1.5–$2\%$ isoflurane during echocardiography. A Vevo 3100 imaging system with MX201 scanhead (VisualSonics, Toronto, ON, Canada) was used to perform echocardiography for measurement of pulmonary. Pulmonary artery acceleration time (PAAT), tricuspid annular plane systolic excursion (TAPSE), isovolumic relaxation time (IVRT), and mitral inflow velocity (E) and mitral annular velocity (E’), from which the E/E’ ratio was derived. Briefly, rats were placed supine on a heated platform and allowed to breathe spontaneously. The right ventricular outflow tract was visualized using a modified parasternal long axis view. PAAT was measured as pulmonary artery blood flow time from start to peak velocity from the pulse wave doppler tracings recorded in the lumen of the main pulmonary artery distal to the pulmonary valve. TAPSE was obtained from the apical four-chamber view directing the M-mode doppler beam through the lateral annulus of the tricuspid valve plane. E, the maximal transmittal flow velocity during early inflow, was recorded using the apical four chamber view. E’, the peak mitral annular velocity during early filling, was obtained by tissue doppler imaging by placing the pulsed waved doppler at the septal corner of the mitral annulus. Using the recordings of transmitral flow velocities, we measured IVRT from the time interval between aortic valve closure and mitral valve opening. For each parameter, measurements from three individual heartbeats per animal were collected and averaged.
## Cell culture
Cardiomyocytes derived from human induced pluripotent stem cells (iPSCs, ATCC-ACS-1021) were cultured as instructed. For cardiomyocyte differentiation in vitro, a STEMdiff ventricular cardiomyocyte differentiation kit (STEMCELL Technologies, cat# 05010) was used according to the manufacturer’s instructions. Cardiomyocytes were cultured for 15 days in maintenance medium (STEMCELL Technologies, cat# 05020) to promote maturation. To induce hypertrophy, cardiomyocytes were treated with 10 nM of endothelin 1 (ET-1, Sigma-Aldrich, cat#E7764) for 24 h. Human pulmonary artery smooth muscle cells (PASMCs) were purchased (ATCC, cat# PCS-100-023) and cultured in vascular cell basal medium (ATCC, cat# PCS-100-030) supplemented with a vascular smooth muscle cell growth kit (ATCC, cat# PCS-100-042) in $5\%$ CO2 at 37°C. Cells from passages 4–7 were used for experiments. Sotagliflozin (Selleckchem, cat# S8103) was used as described for each experiment.
## Western blotting
Cells were lysed in RIPA buffer (Sigma, cat# R0278) on ice for 20 min and centrifuged at 15,000 rpm for 15 min at 4°C. Supernatants were quantified by BCA (Thermo Fisher, cat# A53225), and 30–40 μg of protein per sample was used for gel electrophoresis on a 4–$15\%$ gel (Bio-Rad, cat# 4568085) and transferred onto nitrocellulose membranes at 250 mA for 90 min. Tissue samples were pulverized and homogenized in RIPA buffer with small pulses for 3 min at 4°C. Homogenates were kept on ice for 20 min, and protein quantification was performed by BCA using 20–30 μg of protein per sample. Blots were probed with antibodies against SGLT1 (Cell Signaling Technologies, cat# 5042), pERK$\frac{1}{2}$ (Cell Signaling Technologies, cat# 9101S), ERK (Cell Signaling Technologies, cat# 4695S), pJNK (Cell Signaling Technologies, cat# 4668S), JNK (Cell Signaling Technologies, cat# 9252S), and GLUT1 (Cell Signaling Technologies, cat# 12939S).
## Real time RT-PCR
Pulverized samples (10–20 mg) were homogenized in QIAzol Lysis Reagent, and total RNA extraction was performed with an RNeasy Plus Mini Kit (Qiagen, cat# 74034) according to the manufacturer’s instructions. RNA concentration was measured with a NanoDrop spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA), and 0.5–1 μM of RNA was used for cDNA syntheses. Taqman probes (Thermo Fisher Scientific) were used for quantitative real-time RT-PCR (qRT-PCR). At least four technical replicates were used for each group.
## Cellular migration assay
Human PASMCs (5 × 104) were seeded in a Boyden chamber (Sigma-Aldrich, cat# ECM508) in vascular cell basal medium supplemented with a vascular smooth muscle cell growth kit and incubated overnight. The next day, medium in the lower chamber was replaced with basal medium alone or medium containing 25 mM glucose while the upper chamber contained only basal medium. Inhibitors were added to both chambers, but activin A was added only to the lower chamber. Experimental values were measured at the end of a 72 h incubation period.
## Assay for activin A
Human PASMCs (1.5 × 105) were seeded in 24-well plates in vascular cell basal medium supplemented with a vascular smooth muscle cell growth kit. When cells reached 60–$70\%$ confluency, they were incubated with glucose-deficient medium for 6 h. Medium was then collected (defined as time zero), and cells were treated for 24 or 48 h with medium containing either 5 or 25 mM glucose. Medium samples were collected at each time point, and activin A concentrations in samples were measured with an ELISA kit (R&D Systems, cat# DAC00B) according to the manufacturer’s instructions.
## Statistical analysis
Data are presented as means ± standard error of the mean (SEM). Comparisons between groups were analyzed using either Student’s t-test or ANOVA with Dunnett’s or Tukey’s post-hoc test for multiple comparisons. Differences were considered significant at $P \leq 0.05.$
## ActRIIA-Fc improves cardiac structure, function, and histology and reduces cardiomyocyte injury markers in models of left heart failure
We evaluated ActRIIA-Fc effects in mouse models of left heart failure caused by sustained pressure overload or myocardial infarction because activin receptor signaling has been implicated in multiple forms of left heart disease (17–20). LV pressure overload induced in mice by transverse aortic constriction (TAC) for 3 weeks led to detrimental changes in cardiac structure, function, and histology (Figure 1). Treatment with a murine form of ActRIIA-Fc (RAP-011) exerted significant cardioprotective effects on these parameters when compared with vehicle treatment (Figure 1). This result is consistent with previously reported beneficial effects of activin receptor-like kinase 4 (ALK4) haploinsufficiency on cardiac hypertrophy, dysfunction, and fibrosis in a similar mouse model of LV pressure overload [17]. Treatment with RAP-011 also produced beneficial effects in a mouse model of myocardial infarction (Supplementary Figure 1), consistent with previously reported benefits of ActRIIB ligand inhibition under comparable conditions [20]. These results confirm cardioprotective effects of ActRIIA-Fc similar to those observed with other methods of activin-signaling inhibition in experimental heart failure.
**FIGURE 1:** *ActRIIA-Fc exerts structural, functional, and anti-fibrotic cardioprotective effects in a transverse aortic constriction (TAC) mouse model of left heart failure caused by sustained pressure overload. (A) Experimental approach used to assess effects of ActRIIA-Fc (RAP-011). Wild-type mice were subjected to TAC and treated twice weekly with RAP-011 (R011, 10 mg/kg, s.c.) or vehicle (veh, phosphate-buffered saline, PBS) for 3 weeks starting one day post-surgery. (B) Heart weight normalized to body weight (HW/BW), (C) fractional shortening, (D) ejection fraction, (E) myocardial performance index (MPI), (F) LV developed pressure (LVDP), and (G) peak rates of LV pressure rise (dP/dtmax) and decline (-dP/dtmin). Data are means ± SEM (n = 10–15 mice per group for day 21). (H) Representative images of LV sections stained with Masson’s trichrome blue to detect fibrosis (scale bar, 100 μm), and (I) quantification of percentage area occupied by fibrotic tissue. Data are means ± SEM (n = 10–15 mice per group). Analysis by one-way ANOVA and Dunnett’s post-hoc test (*P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001).*
To determine whether ActRIIA-Fc exerts cardioprotective effects through regulation of cardiomyocyte signaling, we evaluated effects of a human form of ActRIIA-Fc (ACE-011) on injury markers in cultured cardiomyocytes derived from human induced pluripotent stem cells (iPSCs) [26]. Treatment with endothelin-1 (ET-1) induced cellular injury in these cardiomyocytes, including increased NPPB expression and a shift in the relative abundance of myosin heavy chain isoforms from α to β (increased MYH7:MYH6 ratio) (Supplementary Figure 2). Co-treatment with ACE-011 significantly limited ET-1–induced NPPB expression and partially normalized the MYH7:MYH6 ratio in these cells (Supplementary Figure 2). Together, the above results indicate that ActRIIA-Fc exerts cardioprotective effects in multiple mouse models of left heart failure, consistent with previous studies of activin signaling pathway inhibition, and imply that activin-class ligands act directly on cardiomyocytes to drive pathogenic cellular activity in these contexts.
## ActRIIA-Fc improves cardiopulmonary function and alleviates PH in a mouse model of PH-HFrEF
To determine whether ActRIIA-Fc alleviates PH caused by LHD, we evaluated effects of RAP-011 treatment in a TAC-PH mouse model of PH-HFrEF in which prolonged TAC causes PH (Figure 2A) [27]. Compared with sham controls, this extended TAC intervention produced elevated pressures in the left atrium and induced PH (Figures 2B–D), hemodynamic effects associated with pathologic pulmonary vascular remodeling (Figures 2E, F) and pulmonary fibrosis (Figures 2G, H). Treatment with RAP-011 beginning 2 weeks after the start of TAC significantly improved each of these cardiopulmonary parameters as compared with vehicle treatment (Figures 2B–H). These results indicate that RAP-011 alleviates PH arising from sustained LV pressure overload.
**FIGURE 2:** *ActRIIA-Fc improves pulmonary vascular remodeling and reduces PH in a TAC-PH mouse model. (A) Experimental approach used to assess effects of ActRIIA-Fc (RAP-011). Wild-type mice were subjected to TAC and treated twice weekly with RAP-011 (R011, 10 mg/kg, s.c.) or vehicle (veh, PBS) for 4 weeks starting 2 weeks post-surgery to promote development of PH in the untreated state. (B) Left atrial pressure, (C) mean pulmonary arterial pressure (mPAP), and (D) RV systolic pressure (RVSP). (E) Images of representative lung sections stained with hematoxylin and eosin showing degree of vascular remodeling. Scale bar, 50 μm. (F) Quantification of vessel wall thickness as a percentage of vessel outer diameter. (G) Images of lung sections stained with Masson’s trichrome to detect fibrosis. Scale bar, 50 μm. (H) Quantification of fibrotic tissue area. Data are means ± SEM. Analysis by one-way ANOVA and Dunnett’s post-hoc test (**P < 0.01, ***P < 0.001, ****P < 0.0001).*
We then investigated whether alleviation of PH in this model by treatment with RAP-011 was accompanied by beneficial effects on heart structure. Treatment with RAP-011 biweekly for 6 weeks beginning 2 weeks after TAC onset provided significant protection against adverse cardiac remodeling when compared with vehicle-treated mice (Figure 3). Specifically, RAP-011 treatment reduced cardiac hypertrophy (Figures 3A–C), improved LV contractility (Figures 3D, E) and LV diastolic function (Figures 3F, G), and reduced LV fibrosis (Figures 3H, I) compared with these measures in vehicle-treated controls. RAP-011 treatment also conferred significant protection against RV remodeling in this model (Supplementary Figure 3). Together, these findings establish that treatment with ActRIIA-Fc alleviates PH and protects against pathologic cardiopulmonary remodeling in this model of PH-HFrEF.
**FIGURE 3:** *ActRIIA-Fc reduces left ventricle (LV) remodeling and improves left heart function in the TAC-PH mouse model. (A) Heart weight normalized to body weight (HW/BW), (B) interventricular septum thickness in diastole (IVSd), (C) LV mass (LVM), (D) fractional shortening, (E) LV ejection fraction, (F) mitral inflow velocity (E) and mitral annular velocity (E’) ratio (E/E’), and (G) isovolumic relaxation time (IVRT). Data are means ± SEM (n = 10–15 mice per group for day 42). (H) Representative images of LV sections stained with Masson’s trichrome blue to detect fibrosis (scale bar, 50 μm) and (I) quantification of percentage area occupied by fibrotic tissue. Data are means ± SEM (n = 10–15 mice per group). Analysis by one-way ANOVA and Dunnett’s post-hoc test (*P < 0.05, ***P < 0.001, ****P < 0.0001). RAP-011 (R011), vehicle (veh, PBS).*
## ActRIIA-Fc reduces pulmonary remodeling and improves cardiopulmonary function in the obese ZSF1-Su rat model of PH-HFpEF
We next tested whether therapeutic treatment with ActRIIA-Fc would exert favorable cardiopulmonary effects in a rat model of PH-HFpEF. In this two-hit model [23], obese ZSF1 rats develop PH-HFpEF after a single high dose of SU5416 (Su), an inhibitor of vascular endothelial growth factor receptor-2 used to induce pulmonary endothelial injury. Treatment with SU5416 alone did not induce pulmonary hypertension or diastolic dysfunction in lean rats (Supplementary Figure 4), while ZSF1 rats in the absence of SU5416 exhibited diastolic dysfunction without pulmonary hypertension (Supplementary Figure 4). Obese ZSF1-Su rats have been shown to recapitulate hemodynamic features and clinical outcomes of patients with PH-HFpEF, notably including CpcPH-HFpEF. As one aspect of our evaluation in this model, we compared ActRIIA-Fc effects with those of sildenafil, a representative phosphodiesterase type 5 (PDE5) inhibitor, because therapeutic agents in this class have been assessed in patients with HFpEF [28, 29]. Preclinically, sildenafil therapy produces modest improvements in systemic hypertension and LV stiffness in obese ZSF1 rats without PH [30], which model disease features observed in some patients with HFpEF.
To mimic a clinical stage in which disease has progressed substantially before onset of treatment, we initiated treatment with either RAP-011 or vehicle 6 weeks after administration of SU5416, at which time RV dysfunction is prominent, and continued treatment biweekly for 8 weeks (Figure 4A). As determined by right heart catheterization and echocardiography, obese ZSF1-Su rats treated with vehicle exhibited significantly increased RV systolic pressure (RVSP) and reduced pulmonary artery acceleration time (PAAT) (Figure 4B and Supplementary Figure 5). Therapeutic treatment with RAP-011 fully reversed these changes to values observed in lean controls, whereas sildenafil treatment improved RVSP by $19\%$ and PAAT by $36\%$. In addition, treatment with RAP-011, but not sildenafil, fully normalized RV hypertrophy (Fulton index) and tricuspid annular plane systolic excursion (TAPSE) (Figure 4B and Supplementary Figure 5). Histologic analysis confirmed the presence of pulmonary vascular remodeling in obese ZSF1-Su rats, as described previously. Therapeutic treatment with RAP-011 restored pulmonary vessel structure to conditions observed in lean controls (Figures 4C, D). Finally, elevated levels of pulmonary fibrosis present in obese ZSF1-Su rats were partially reversed by therapeutic treatment with RAP-011 (Figures 4E, F). Together, these results indicate that therapeutic treatment with ActRIIA-Fc reduces fibrosis, reverses RV and pulmonary vascular remodeling, improves RV function, and alleviates PH in the obese ZSF1-Su model of PH-HFpEF.
**FIGURE 4:** *Therapeutic treatment with ActRIIA-Fc improves pulmonary remodeling and cardiopulmonary function in the obese ZSF1-Su rat model of PH-HFpEF. (A) Experimental approach used to evaluate therapeutic effects of ActRIIA-Fc (RAP-011, R011) in an obese ZSF1-Su rat model of PH-HFpEF. Rats were treated at 8 weeks of age with a single dose of SU5416 (100 mg/kg, s.c). After allowing 6 weeks for development of PH, rats were treated with RAP-011 (10 mg/kg, s.c., twice weekly), sildenafil (30 mg/kg, p.o., twice daily), or vehicle (Veh, PBS) for 8 weeks. (B) Effects of RAP-011 (R011) or sildenafil (Sild) on RV systolic pressure (RVSP) and Fulton index [RV/(LV + S)], a measure of RV hypertrophy. (C) Images of representative lung sections stained with hematoxylin and eosin, with insets showing degree of vascular remodeling. Scale bar, 200 μm. (D) Vessel wall thickness as a percentage of vessel outer diameter. (E) Images of lung sections stained with Masson’s trichrome to detect fibrosis. Scale bar, 50 μm. (F) Quantification of fibrotic tissue area. Data are means ± SEM. Analysis by one-way ANOVA and Tukey’s (B) or Dunnett’s (D, F)
post-hoc test. *P < 0.05; ***P < 0.001; ****P < 0.0001. Lean, lean control rats.*
## ActRIIA-Fc improves remodeling and function of both left and right heart in obese ZSF1-Su rats
Based on the beneficial cardiac effects of ActRIIA-Fc observed in the above experiments, we investigated cardiac effects of ActRIIA-Fc treatment more extensively in the PH-HFpEF model. As before, we initiated treatment with either ActRIIA-Fc (RAP-011) or vehicle 6 weeks after administration of SU5416 and continued treatment biweekly for 8 weeks (weeks 14–22, Figure 4A).
As determined by right heart catheterization and echocardiography, LV and RV dysfunction and abnormal remodeling were prominent in diseased rats before treatment initiation at week 14 (Figures 5A–C). Follow-up echocardiographic analysis at week 22 revealed further disease progression of all evaluated parameters in vehicle-treated, obese ZSF1-Su rats, compared with assessments made either at baseline (week 14) or in lean controls (week 22) (Figures 5A–C). Therapeutic treatment of obese ZSF1-Su rats with RAP-011 normalized LV mass and produced significant improvements in two measures of LV diastolic dysfunction, E/E’ [the ratio of mitral inflow velocity (E) to mitral annular velocity (E’)] and isovolumic relaxation time (IVRT) (Figure 5A). RAP-011 treatment also significantly improved RV remodeling [RV free-wall thickness (RVFWT)] and RV function [RV fractional area change (RVFAC) and TAPSE] (Figure 5B), which confirmed the result for TAPSE above (Supplementary Figure 5). In addition, RAP-011 treatment in obese ZSF1-Su rats significantly improved the myocardial performance index (MPI), a measure of global cardiac function (Figure 5C). Finally, RAP-011 treatment significantly reduced fibrosis in both the LV and RV compared with vehicle-treated controls (Figure 5D and Supplementary Figure 6). These results provide compelling evidence that beneficial effects of ActRIIA-Fc treatment on PH are accompanied by reversal of pathologic cardiac remodeling as well as functional improvements in both ventricles in this model of PH-HFpEF.
**FIGURE 5:** *ActRIIA-Fc improves remodeling and function of both left and right heart in obese ZSF1-Su rats. Effects of ActRIIA-Fc (RAP-011, R011) on (A) left heart parameters LV mass, mitral inflow velocity (E) and mitral annular velocity (E’) ratio (E/E’), and isovolumic relaxation time (IVRT); (B) right heart parameters RV free-wall thickness (RVFWT), tricuspid annular plane systolic excursion (TAPSE), and RV fractional area change (RVFAC); (C) myocardial performance index (MPI); and (D) quantification of fibrosis in LV and RV. Data are means ± SEM (n = 5–13 rats per group). Analysis by one-way ANOVA and Dunnett’s post-hoc test. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.*
## ActRIIA-Fc inhibits canonical and non-canonical TGF-β superfamily signaling in the lungs of obese ZSF1-Su rats
We next investigated signaling pathways whose activity is influenced by ActRIIA-Fc in the lungs of obese ZSF1-Su rats. Compared with lean controls, obese ZSF1-Su rats displayed elevated activation of SMAD3 in pulmonary perivascular cells as determined by immunostaining for phospho-SMAD3 (pSMAD3) (Figures 6A, B). In obese ZSF1-Su rats, therapeutic treatment with RAP-011 normalized pSMAD3 immunostaining in these perivascular cells (Figures 6A, B), which were shown separately by dual immunofluorescence staining to include vascular smooth muscle cells and endothelial cells (Supplementary Figures 7, 8). Consistent with disease-related overactivation of SMAD3 in the pulmonary vasculature, perivascular regions of ZSF-Su rat lung were found to contain increased immunostaining for activin A and GDF11 (Supplementary Figure 9), prominent activators of SMAD$\frac{2}{3}$ signaling previously implicated in PAH [15]. Obese ZSF1-Su rats treated with vehicle exhibited elevated activation of the p38 mitogen-activated protein kinase (MAPK) and c-Jun N-terminal kinase (JNK) pathways in lung as determined by protein blot analysis of phospho–extracellular-signal-regulated kinase (pERK) and pJNK levels in whole lung lysate (Figure 6C). In obese ZSF1-Su rats, RAP-011 treatment reduced pERK and pJNK levels, whereas total ERK and total JNK expression remained unchanged (Figure 6C). These results indicate that lung tissue in obese ZSF1-Su rats displays elevated activation of pathways associated with TGF-β superfamily canonical (pSMAD3) and non-canonical (pERK and pJNK) signaling and that therapeutic treatment with ActRIIA-Fc inhibits the activation of both types.
**FIGURE 6:** *ActRIIA-Fc inhibits canonical and non-canonical signaling associated with the TGF-β superfamily pathway in the lung of obese ZSF1-Su rats. (A) Representative images of lung sections immunostained for phospho-Smad3 (pSmad3) in obese ZSF1-Su rats and lean controls after treatment with ActRIIA-Fc (RAP-011, R011) or vehicle (veh, PBS) as in Figure 1A. Scale bar, 50 μm. (B) Percentage of pSmad3-positive cells in lungs based on assessment of 30 high-magnification fields per rat. (C) Lung homogenates immunoblotted for phospho–extracellular-signal-regulated kinase (pERK), total ERK, phospho-c-Jun N-terminal kinase (pJNK), total JNK, and β-actin. Data are means ± SEM. Analysis by one-way ANOVA and Dunnett’s post-hoc test. *P < 0.05.*
## ActRIIA-Fc inhibits glucose-induced release of activin and migration of human PASMCs mediated by the SGLT pathway
To explore mechanisms by which ActRIIA-Fc improves pulmonary vascular remodeling in obese ZSF1-Su rats, we examined the activity of hPASMCs in vitro under elevated glucose conditions to model metabolic comorbidities observed in patients with PH-HFpEF. We observed that glucose treatment produced a significant, time-dependent increase in activin A release by hPASMCs, with pronounced increases in extracellular activin A concentrations occurring at 24 h of exposure and increases of more than 10-fold at 48 h (Figure 7A). Sotagliflozin, a dual inhibitor of sodium-glucose cotransporters (SGLT), significantly reduced activin A release by hPASMCs under elevated glucose conditions (Figure 7B), implicating this transporter type in glucose-induced release of activin A. Treatment of cultured hPASMCs with ACE-011 completely blocked glucose-induced release of activin A (Figure 7C).
**FIGURE 7:** *ActRIIA-Fc inhibits glucose-induced release of activin A and migration by human pulmonary artery smooth muscle cells (PASMCs) mediated through the SGLT pathway. (A) Glucose-induced activin A release by hPASMCs. (B) Effect of SGLT inhibitor (SGLTi) sotagliflozin on glucose-induced release of activin A by hPASMCs. (C) Effect of human ActRIIA-Fc (ACE-011) on glucose-induced release of activin A by hPASMCs. (D) ACE-011 inhibits activin A-induced migration of hPASMCs. (E) Effects of SGLTi or ACE-011 on glucose-induced migration of hPASMCs. N or NG, normal glucose concentration (5 mM); H or HG, high glucose concentration (25 mM). Data are means ± SEM. Analysis by one-way ANOVA and Dunnett’s post-hoc test. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. (F) Effect of RAP-011 (R011) on dysregulated expression of glucose transporters in lung of obese ZSF1-Su rats. Lean, lean control rats; veh, vehicle (PBS). (G) Schematic overview of proposed mechanisms by which ActRIIA-Fc normalizes aberrant PASMC activity induced by high glucose levels in the obese ZSF1-Su rat. For clarity, activin A and its receptors are depicted as monomeric, and the non-covalent complex between mature activin A and its prodomain is not shown. Yellow circles identify phosphorylated proteins and dashed lines indicate mechanisms not yet delineated. I, activin receptor-like kinase 4 or 7; II, ActRIIA or ActRIIB; SGLT, sodium-glucose cotransporter; SGLTi, SGLT inhibitor.*
We next investigated migratory activity of hPASMCs in vitro using a Boyden chamber migration assay. Treatment with activin A significantly stimulated migration of hPASMCs, and this migratory effect was blocked completely by co-treatment with ACE-011 (Figure 7D). Treatment with elevated glucose levels increased migration of hPASMCs beyond rates observed with normal physiologic glucose levels, and this effect was completely blocked by either SGLT inhibitor or ACE-011 (Figure 7E). These results implicate a novel mechanism contributing to aberrant vascular remodeling in which glucose-induced release of activin A by PASMCs triggers their transition to an abnormal migratory phenotype, thereby promoting maladaptive pulmonary vascular modeling in the context of metabolic disorder.
Finally, we explored whether SGLT is similarly dysregulated in lungs of obese ZSF1-Su rats in vivo. Compared with lean controls, obese ZSF1-Su rats treated with vehicle exhibited increased pulmonary expression of SGLT1 protein and reduced expression of glucose transporter GLUT1 (Figure 7F). Treatment of obese ZSF1-Su rats with RAP-011 normalized SGLT1 protein expression in lung tissue without detectable changes in GLUT1 protein expression (Figure 7F). As summarized schematically in Figure 7G, our concordant findings from in vitro and in vivo models implicate SGLT, activin A, and activin receptor-mediated signaling—both canonical and non-canonical—as key components in a PASMC-mediated process by which hyperglycemia promotes aberrant pulmonary vascular remodeling and PH in PH-HFpEF. These results also provide mechanistic insight into ActRIIA-Fc alleviation of PH associated with metabolic comorbidities in this preclinical model of HFpEF.
## Discussion
There is major unmet need for patients with PH-LHD. PH is highly prevalent in patients with LHD and is associated with a worse prognosis than LHD without PH [2, 5]. Elevated left atrial pressure due to LHD causes pulmonary venous congestion and increased hydrostatic pressure in pulmonary capillaries, thereby leading to IpcPH-LHD and CpcPH-LHD [2]. Despite shared features in the pathophysiology of CpcPH-LHD and PAH [11], including pre-capillary pulmonary vascular remodeling, vasodilators used to treat PAH are ineffective in patients with PH-LHD and can even be harmful [8, 31]. In the present study, we find that therapeutic treatment with ActRIIA-Fc exerts reverse remodeling effects in the pulmonary vasculature, left heart, and right heart in rats with experimental CpcPH-HFpEF, whereas the vasodilator sildenafil does not, consistent with previous clinical results [32]. Here we also find that ActRIIA-Fc exerts protective cardiopulmonary effects in an established model of a second major type of PH-LHD, CpcPH-HFrEF.
Pulmonary vascular remodeling is a key pathologic feature in patients with PH and is associated with diminished pulmonary arterial compliance, impaired RV–pulmonary arterial coupling, reduced diffusing capacity of the lungs, abnormal RV remodeling, and higher patient morbidity or mortality [2, 9, 10, 33]. We recently identified unbalanced signaling by the SMAD pathways—with important contributions by activin-class ligands—in pathogenic vascular remodeling in lung that underlies human and experimental PAH [15]. In the present study, we similarly found evidence of increased expression of activin A and GDF11 in the pulmonary vasculature in two experimental models of PH-LHD. Several cell types are implicated in pulmonary vascular remodeling, including endothelial cells, vascular SMCs (VSMCs), adventitial fibroblasts, and immune cells [15, 16, 34]. Here we focused our analysis in vitro on pulmonary VSMCs because they are a nearly universal component of vascular remodeling [35] and contribute to progressive pre-capillary remodeling in CpcPH-HFpEF [3, 9], as confirmed in our histological assessment of obese ZSF1-Su rats. Unlike terminally differentiated skeletal or cardiac muscle cells, VSMCs retain remarkable plasticity in adulthood, which enables vessel growth and adaptive remodeling but also contributes broadly to cardiovascular pathologies [36].
The lung has historically not been considered an organ prominently afflicted by glucose dysregulation in diabetes [37]. However, our studies using hPASMCs reveal an activin-mediated mechanism by which hyperglycemia causes an abnormal phenotypic shift in hPASMCs that could contribute to pulmonary vascular remodeling and PH in experimental PH-HFpEF. Despite the resemblance between CpcPH-HFpEF and PAH with regard to vascular remodeling, the glucose-associated aspect of this mechanism differs from other mechanistic elements identified so far for activin signaling and pulmonary vascular remodeling in the context of experimental PAH [15, 16, 38].
In our model, activin A produced by hPASMCs acts in an autocrine or paracrine manner to mediate a glucose-inducible shift in these cells to an abnormal migratory phenotype (Figure 7). We speculate that activin A secreted by PASMCs in vivo could potentially also affect the activity of other cell types in the pulmonary vascular microenvironment. Our results indicate that glucose induction of activin A release by hPASMCs is partly mediated by SGLT. Additionally, our results indicate that SGLT1 protein is upregulated in the lungs of obese ZSF1-Su rats in the setting of metabolic syndrome, and ActRIIA-Fc treatment normalizes these SGLT1 levels. Thus, beneficial effects of ActRIIA-Fc treatment on PASMCs under these conditions arise not only from sequestration of extracellular activin A and potentially other activin-class ligands—with consequent inhibition of canonical and non-canonical activin receptor-mediated signaling—but also from downregulation of SGLT1 protein by an indirect mechanism yet to be determined. Although many studies have demonstrated adverse effects of hyperglycemia on VSMC or endothelial cell function [39], surprisingly few have examined the severity of PH in patients with diabetes, especially for PH types other than PAH [40]. However, clinical studies have identified activin A as a prominent marker of cardiovascular pathology, including increased arterial intima–media thickness, in the context of metabolic syndrome (41–44).
In the present study, ActRIIA-Fc also exerted protective cardiopulmonary effects in experimental PH-HFrEF. As underscored here by the beneficial effects of ActRIIA-Fc in models of left heart disease without PH, activin receptor signaling has been implicated previously as an important contributor to pathologic cardiac remodeling in multiple types of left heart failure. This aspect of ActRIIA-Fc activity resembles cardioprotective effects of ALK4 haplodeficiency and effects of other activin receptor pathway inhibitors (ActRIIB-Fc and a dual-specific antibody against ActRIIA and ActRIIB) in models of left ventricular failure associated with aging or systemic pressure overload [17, 18]. The roles of activin pathway signaling in cardiac muscle, especially by activin A and GDF11, have been controversial [18]. However, recent studies consistently implicate activin receptor–mediated SMAD$\frac{2}{3}$ signaling as a partially compensatory pathway that becomes maladaptive in heart failure and ischemia-reperfusion injury (17–20). Elevated levels of circulating activin A in patients support a role for this ligand in abnormal myocardial remodeling, diabetic cardiomyopathy, HFrEF, and HFpEF (42, 44–46). Interestingly, activin A, GDF8, and GDF11 induce similar but non-identical pathologic profiles in left ventricular cardiomyocytes [18]. We speculate that multi-ligand inhibition might therefore advantageously prevent overlapping as well as distinct activities of activin-class ligands, which could be mutually reinforcing in the therapeutic treatment of cardiovascular disease.
Our results obtained with complementary experimental models display robust concordance and support the translatability of our findings. In this study, we focused on pulmonary VSMCs due in part to their important roles in vascular muscularization and remodeling. However, evidence strongly suggests that other pulmonary vascular cell types contribute to PH and are regulated extensively by activin pathway signaling (14–16, 38). It will therefore be important to investigate the contributions of these signaling mechanisms to dysregulated endothelial cell, fibroblast, and immune cell activities implicated in PH-HFpEF. Immune cells and inflammatory processes play major roles in PH (14, 47–49), and recent evidence indicates that ActRIIA-Fc suppresses inflammation as one component of its multi-factorial mechanism of action in experimental PAH [16]. An ongoing phase 2 study is evaluating sotatercept, a human analog of ActRIIA-Fc, in patients with CpcPH-HFpEF.
## Data availability statement
The original contributions presented in this study are included in this article/Supplementary material, further inquiries can be directed to the corresponding author.
## Ethics statement
The animal study was reviewed and approved by the Institutional Animal Care and Use Committee at Acceleron Pharma Inc., a subsidiary of Merck & Co., Inc., Rahway, NJ, USA and performed in accordance with the guidelines from the NIH Guide for the Care and Use of Laboratory Animals.
## Author contributions
SJ, EA, and GL planned the research. SJ and JL performed in vivo experiments. EA, JL, and YD performed in vitro experiments. SJ, EA, JL, YD, and GL analyzed data. PA, RK, and GL provided guidance on experimental designs and data analysis. SJ, EA, SB, MA, and GL wrote the manuscript, which was reviewed and approved for submission by all authors.
## Conflict of interest
All authors were employed by Acceleron Pharma Inc., a subsidiary of Merck & Co., Inc., Rahway, NJ, USA.
## 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.2023.1064290/full#supplementary-material
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|
---
title: Wave reflection quantification analysis and personalized flow wave estimation
based on the central aortic pressure waveform
authors:
- Hongming Sun
- Yang Yao
- Wenyan Liu
- Shuran Zhou
- Shuo Du
- Junyi Tan
- Yin Yu
- Lisheng Xu
- Alberto Avolio
journal: Frontiers in Physiology
year: 2023
pmcid: PMC9996124
doi: 10.3389/fphys.2023.1097879
license: CC BY 4.0
---
# Wave reflection quantification analysis and personalized flow wave estimation based on the central aortic pressure waveform
## Abstract
Pulse wave reflections reflect cardiac afterload and perfusion, which yield valid indicators for monitoring cardiovascular status. Accurate quantification of pressure wave reflections requires the measurement of aortic flow wave. However, direct flow measurement involves extra equipment and well-trained operator. In this study, the personalized aortic flow waveform was estimated from the individual central aortic pressure waveform (CAPW) based on pressure-flow relations. The separated forward and backward pressure waves were used to calculate wave reflection indices such as reflection index (RI) and reflection magnitude (RM), as well as the central aortic pulse transit time (PTT). The effectiveness and feasibility of the method were validated by a set of clinical data (13 participants) and the Nektar1D Pulse Wave Database (4,374 subjects). The performance of the proposed personalized flow waveform method was compared with the traditional triangular flow waveform method and the recently proposed lognormal flow waveform method by statistical analyses. Results show that the root mean square error calculated by the personalized flow waveform approach is smaller than that of the typical triangular and lognormal flow methods, and the correlation coefficient with the measured flow waveform is higher. The estimated personalized flow waveform based on the characteristics of the CAPW can estimate wave reflection indices more accurately than the other two methods. The proposed personalized flow waveform method can be potentially used as a convenient alternative for the measurement of aortic flow waveform.
## 1 Introduction
The central aortic pressure waveform (CAPW) contains information on the cardiovascular system and thus can be used to evaluate the cardiovascular system status and to predict and diagnose cardiovascular diseases (CVDs) (Suleman et al., 2017; Vallée et al., 2018; Sequi-Dominguez et al., 2020; Flores Geronimo et al., 2021). Central aortic pressure, unlike peripheral arterial pressure, is the blood pressure at the root of the ascending aorta, which is directly connected with the left ventricle (Pini et al., 2008). Hence, CAPW can more directly reflect the load on the left ventricle, coronary arteries, and cerebral vessels and more accurately predict the occurrence of cardiovascular events and damage of target organs in comparison with the peripheral arterial pressure waveform (Roman et al., 2007; McEniery et al., 2008; Zócalo and Bia, 2022). The separation analysis of CAPW can be used to predict cardiovascular events such as all-cause mortality and left ventricular failure (Manisty et al., 2010), which is more clinically significant.
When the heart pumps blood, the aortic valve opens, and the pressure in the aorta rises rapidly, resulting in pressure and flow waves called forward waves. Forward waves will undergo wave reflections at sites of impedance mismatch (vessel diameter reduction, vessel bifurcation or change in wall stiffness) during the propagation from the aorta to the distal segments, generating backward waves, and propagating back to the proximal segment (Westerhof et al., 1972; Yao et al., 2022). When the left ventricle contracts, blood flows through the aortic valve into the aorta. After the aortic valve closes, the ventricle enters diastole, when blood perfuses the heart through the coronary arteries. A small amount of diastolic blood occasionally flows backwards into the left ventricle (Thubrikar et al., 1979). The pressure and flow waveforms are formed by the superposition of backward and forward components. Pulse wave propagation and reflection are related to arteriosclerosis and also affect the hemodynamic characteristics of the cardiovascular system (Sofogianni and Tziomalos, 2019). In pulse wave analysis, pulse wave reflection indices can be derived from the decomposition of CAPW to quantify the degree of pulse wave reflections (Townsend et al., 2015). Based on the pressure-flow relations, the CAPW can be decomposed into backward (Pb) and forward (Pf) waves (Westerhof et al., 1972). The amplitude characteristics and time delay of Pf and Pb can effectively reflect the reflection and propagation time of the pulse wave from the aorta to the distal segments and branches, and the magnitude of the CAPW reflections affects cardiac afterload and perfusion (Davis et al., 2009; Laurent and Boutouyrie, 2020). More accurate wave reflection measurements can be obtained from Pf and Pb, mainly including the aortic pulse transit time (PTT), reflection index (RI), and reflection magnitude (RM). PTT can be calculated from the time delay between Pf and Pb, a valuable indicator for assessing arterial stiffness (Qasem and Avolio, 2008). RM, the ratio of Pb and Pf amplitudes, is an independent predictor of risk and can predict heart failure (Westerhof et al., 2006; Zamani et al., 2014). RI and RM contain physiological information about CAPW and are important indices that quantify pulse wave reflection. These metrics are not affected by timing of wave reflection and usually be used to access left ventricle afterload, which has clear physiological significance (Wang et al., 2010; Zamani et al., 2016).
Flow waveforms are essential for the decomposition and analysis of pulse waves. Clinically, the aortic flow velocity can usually be obtained directly and non-invasively by ultrasonic detection or magnetic resonance imaging (MRI). Combined with the cross-sectional area of blood vessels, the blood flow waveform can finally be calculated (Rivera et al., 2020; Stortz et al., 2020). Although this method is feasible and accurate, the operation is considered inconvenient because it requires specific types of equipment and skilled operators. Consequently, some proposed approaches use the CAPW morphology to generate an aortic flow waveform with an assumed triangular shape (Westerhof et al., 2006; Butlin and Qasem, 2016). In these methods, the wave separation analysis matches the start, peak, and end points of the triangular flow waveform with the foot, inflection, and dicrotic notch points of the CAPW using the time and amplitude characteristics of the CAPW. The triangular flow wave was first proposed in a proof-of-principle study to quantify aortic wave reflections from pressure alone by Westerhof et al. ( Westerhof et al., 2006). This straightforward technique was utilized by the SphygmoCor MM3/CvMS system (AtCor Medical, Sydney, Australia) for the non-invasive acquisition of aortic flow (Ding et al., 2013; Carlsen et al., 2016; Yu et al., 2018). Later, they made improvements in the waveform decomposition of the CAPW by utilizing the triangular flow waveform as a novel way for determining the aortic pulse wave velocity (Qasem and Avolio, 2008). Although triangular flow waveform has been applied in several commercially available systems, this method poorly approximates the measured flow waveform, resulting in some errors in the decomposition of the CAPW.
Kip et al. demonstrated that in the participants of the Asklepios population study, the results for RM and aortic PTT based on the triangular flow waveform approximation method differed significantly from the values obtained from measured pressure and flow information (Verbeke et al., 2005; Rietzschel et al., 2007; Kips et al., 2009). In the Asklepios population study (Rietzschel et al., 2007), the measured flow waveforms were averaged and normalized to obtain more physiological aortic flow waveforms. The experimental results have demonstrated that the average flow method can evaluate RM better than triangular flow. However, there is still a significant deviation between the approximate and the actual values. This physiological flow method has been used to assess wave reflection indices in the multi-ethnic study of atherosclerosis (Zamani et al., 2015; Zamani et al., 2016). In this research, the pressure measured non-invasively by applanation tonometry at the common carotid artery was used as a substitute for central aortic pressure. Consequently, the difference persists and influences the experimental results.
Recently, Shenouda et al. proposed a new personalized physiological flow waveform method based on the CAPW morphology (Shenouda et al., 2021). The physiological flow waveform is more accurate than the triangle flow waveform for determining RM and Pb in the elderly. However, they did not examine children, healthy middle-aged individuals, or clinical populations such as cardiac disease patients. The sample set included only 49 young (18–42 years) and 29 older (51–77 years) adults. More recently, a novel lognormal flow wave method for separating the CAPW was proposed by Hao et al. ( Hao et al., 2022). This study demonstrated that the lognormal flow wave improves CAPW separation analysis results both in time and frequency domains. Nevertheless, the lognormal flow waveform method must be compared in different populations and not limited to healthy and young participants. For the data set validated in this paper, there is still a gap between the estimated and the measured flow waveforms. In addition, the definition of variance σ of the lognormal function needs to be clarified, and how to determine the specific value is not well described. When accurate flow is inconvenient to measure, better non-invasive estimation of aortic flow is still needed to improve the results of pulse wave separation of the CAPW.
This research aims to propose a novel method to approximate the actual flow waveform with a personalized flow waveform and to examine the feasibility to decompose the CAPW and quantify wave reflection. We use the relationship between pressure and flow to separate and analyze the CAPW with triangular, lognormal, and personalized flow waveform methods, respectively, to explore the accuracy of the three methods in wave reflections. Based on the simulated pulse wave dataset and clinical data, the accuracy of the personalized flow wave method is further compared with the other two methods in deducing the reflection indices of RI, RM, and PTT.
## 2.1 Data collection
In this study, we used two datasets to verify the feasibility and validity of the proposed method.
## 2.1.1 Nektar1D PWDB
The first dataset is the publicly accessible database (Nektar1D Pulse Wave Database, Nektar1D-PWDB), published by Alastruey et al. at King’s College London, United Kingdom, based on the Nektar1D model. This model used the Nektar1D non-linear one-dimensional flow model, which has been fully clinically validated and used in several studies to simulate the hemodynamic characteristics of the human arterial tree, to ensure the validity of hemodynamic parameters of the 1D model and the generated data (Matthys et al., 2007; Alastruey et al., 2011; Xiao et al., 2014; Willemet et al., 2015). For more detailed information on this database, see the study by Charlton et al. ( Charlton et al., 2019).
The database contains the arterial pulse waves from 4,374 virtual subjects, ranging from 25 to 75 years, at a sampling frequency of 500 Hz. A total of 537 out of the 4,374 subjects exhibited blood pressures outside of healthy norms (virtual subjects with abnormal blood pressure; without CVD), and 3,837 subjects are physiologically plausible. Table 1 contains basic population and hemodynamic statistics. SBP and DBP of the radial artery and central aortic are 95 mmHg–168 mmHg and 48 mmHg–87 mmHg, as shown in Figure 1.
## 2.1.2 Clinical data
In this section, we used clinical data to further validate the performance of personalized flow waves. There were 13 healthy participants in the study, seven male and six female, aged from 24 to 33 years old. The basic information of participants is summarized in Table 1. The Research Ethics Committee approved this study of Northeastern University (NO. NEU-EC-2021B022S), China, and all participants gave informed consent.
Each participant sat quietly and relaxed for 10 min in a quiet room before measuring their brachial systolic (SBP) and diastolic (DBP) blood pressures with the Yuwell Mercury sphygmomanometer (measurement accuracy of 2 mmHg). The pressure waveforms of the radial artery were measured non-invasively with the SphygmoCor device at a sampling rate of 128 Hz. In the SphygmoCor device, the corresponding CAPW was reconstructed using a generalized radial-to-aortic transfer function. *The* generalized transfer function (GTF) is the most widely used method to estimate the CAPW (Sharman et al., 2006), which is obtained by simultaneous measurement of aortic and peripheral pressure (Karamanoglu et al., 1993) to obtain the corresponding function between peripheral arterial pressure and central arterial pressure, then collecting new test samples, and validating the peripheral arterial pressure waveform signal by the trained transfer function to estimate the corresponding CAPW(Cameron et al., 1998; Payne et al., 2007). The corresponding CAPW is estimated by verifying the signal of the peripheral arterial pressure waveform with the trained transfer function. The flow velocity and diameter waveforms of the aortic root were concurrently captured and smoothed by a GE Vivid E95 US system. Flow waveforms were calculated by multiplying flow velocity waveforms with the aorta’s cross-sectional area (π× (diameter/2) 2). In the study of Zhou et al., the specifics of data collection are presented (Zhou et al., 2022).
## 2.2 Wave separation analysis and wave reflection
In the time domain, features can be calculated from the timing and amplitude of several fiducial points. The starting point of the pulse wave indicates the beginning of a pulse cycle and the end of the previous one. The time of the inflection point marks the arrival of the Pb (O'Rourke and Yaginuma, 1984). The notch is caused by aortic valve closure and blood reflux, representing the transition between the systolic and diastolic phases (Hartmann et al., 2019). The pulse wave systolic period is the duration between the starting point and the dicrotic notch point of the pulse wave, followed by the pulse wave diastolic period. Usually, the local maxima of the second derivative of the pulse waveforms are utilized to extract inflection points and dicrotic notch points (as in Figure 2 (Vlachopoulos et al., 2011)).
**FIGURE 2:** *The CAPW feature points extraction. The red point represents the inflection point of the pulse wave (the moment when the Pb is generated), while the black point represents the dicrotic notch point (the end of the systolic phase or the beginning of the diastolic phase).*
For some participants (e.g., those with severe atherosclerosis), the inflection point of the aortic pulse wave is difficult or even impossible to extract. In order to make this pulse wave decomposition method more practical, it has been proposed to use $30\%$ of the systolic time as the location of the inflection point (Miyashita et al., 1994; Westerhof et al., 2006). In this paper, for pulse wave with inconspicuous inflection point, $30\%$ of ejection time (ET) is used as the location of the inflection point to calculate the relevant features of pulse wave decomposition. The beginning of the pulse wave systole indicates the time of aortic valve opening and the start of ejection, and the notch time of the pulse wave is the time of aortic valve closure and the end of ejection. ET represents the systolic time of the pulse wave, which is determined by subtracting the beginning time from the end time of aortic flow (as in Figure 3).
**FIGURE 3:** *To facilitate wave separation analysis, the 30% ET is used as the location of the inflection point of the pulse wave.*
In the arterial system, both aortic pressure and flow waveforms consist of forward waves (Pf, Qf) and backward waves (Pb, Qb). The CAPW mainly comprises forward and lower limb reflection waves (Westerhof et al., 1972). As shown in Figure 4, CAPW equals the sum of the Pf and Pb; and the flow wave equals the difference between the Qf and Qb, (as shown in Eq. 1, 2). P=Pf+Pb [1] Q=Qf+Qb [2]
**FIGURE 4:** *(A) CAPW and (B) flow waveform. The CAPW is decomposed into Pf and Pb, from which RM and RI can be calculated.*
The basic principle of pulse wave decomposition is as follows (Westerhof et al., 1972): Pf=P+Zc×Q2 [3] Pb=P−Zc×Q2 [4] where, Q = U*A represents aortic flow; U is the flow velocity; A is blood vessels cross-sectional area; *Zc is* the characteristic impedance.
Since the pulse waveform is not affected by the Pb in the early systolic phase, Zc equals the ratio of blood pressure to flow (Li, 1986; Khir et al., 2001), and Zc can also be calculated by high-frequency input impedance (Murgo et al., 1981; Miyashita et al., 1994). The input impedance (Zin) is defined as follows: Zinw=Pw/Qw [5] where P(w) and Q(w) are pressure and flow frequency components.
RI is the amplitude ratio of Pb to the sum of Pb and Pf, and the amplitude ratio of Pb to *Pf is* RM (Hametner et al., 2013). RM and RI are defined as follows: RM=PbPf [6] RI=PbPb+Pf [7] PTT can be determined by pulse wave decomposition, an important index to assess arterial stiffness in the young and old (Qasem and Avolio, 2008). PTT can be calculated as half the time difference between Pf and Pb (Tfb), as in Eq. 8. PTT=Tfb/2 [8] Qasem and Avolio calculated the cross-correlation coefficient of Pf and Pb to determine Tfb (Qasem and Avolio, 2008). The time of maximum cross-correlation coefficient is the Tfb between Pf and Pb (as in Figure 5).
**FIGURE 5:** *Calculation of Tfb: cross-correlation between Pf and Pb.*
## 2.2.1 Triangular and lognormal flow waveform
By measuring aortic flow velocities with Doppler ultrasound or magnetic resonance imaging (MRI) and combining them with the cross-sectional area of the aortic valves, the aortic flow can be calculated (Wang et al., 2010; Zamani et al., 2016). However, this requires specific medical equipment and skilled operators.
The triangular flow method is used in the SphygmoCor MM3/CVMS device, which is well clinically validated and certified by the Food and Drug Administration (FDA) and is frequently used as a non-invasive testing standard to validate other devices (Zuo et al., 2010; Ott et al., 2012; Laugesen et al., 2014). SphygmoCor MM3/CVMS system uses triangles to approximate the central aortic flow waveforms (Rivera et al., 2020). Specifically, as shown in Figure 6A, the systolic flow is approximated as a triangle, and the base of the triangle represents the total systolic ET. The peak of the triangle corresponds to the inflection point (timing and amplitude) of the CAPW. Furthermore, the beginning and ending points of the triangular flow waveform coincide with the CAPW foot and dicrotic notch points, respectively. Westerhof et al. have shown that it is feasible to construct the aortic flow waveform by a triangular wave (Westerhof et al., 2006).
**FIGURE 6:** *(A) The start, peak, and end of the triangle flow waveform correspond in time and amplitude to the foot, inflection point, and dicrotic notch point of the CAPW, respectively (B) Lognormal function approximation flow waveform.*
As with the triangular flow waveform, there is a specific relationship between the characteristic points of the lognormal flow waveform and the characteristic points of CAPW. As shown in Figure 6B, the start, peak, and end points of the lognormal flow waveform correspond to the foot, the inflection point, and the dicrotic notch point of the CAPW, respectively (Plamondon et al., 2013; Hao et al., 2022).
## 2.2.2 Personalized flow waveform construction
The waveform of aortic flow can be estimated using a triangular wave. However, the Pf and Pb obtained directly using the triangular wave instead of the flow wave are not smooth and sometimes produce large Pb before the reflection point. The triangular flow waveform would also underestimate the degree of concavity of the flow waveforms. The lognormal approximate flow waveform has the same result, especially in early systole (as in Figure 8). Based on the above facts, we attempted to construct a flow waveform based on the characteristics of CAPW and explore the method’s generalizability.
In early systole (before the inflection point), the CAPW is linear with flow waveform because wave reflections are almost unaffected by the Pb (Hughes et al., 2020). The Pf propagates from the proximal to the distal end, and at the end of the contraction, the pressure-flow waves encounter a high impedance location for continuous decay. At the end of systole, it is proposed to use the Hermite interpolation function to fit the flow waveforms during this period.
The Hermite interpolation function is a standard method for solving predictive problems in mathematical modeling, which can effectively solve the problem such as insufficient waveform data of aortic flow (Lorentz, 2000). Three points are required to satisfy the Hermitian interpolation function condition. Using segmented Hermite interpolation to obtain a smooth and continuous curve on the interval a,b. On node a≤x0<x1<⋯<xn≤b,hi=xi−xi−1i=1,2,⋯,n, the function value and derivative value of the given node are as follows: yi=fxi,yi′=f′xi,$i = 0$,1,⋯,n [9] A piecewise cubic interpolation polynomial H3x is constructed on a,b, which satisfies the following interpolation conditions: H3xi=yi,H3′xi=yi′,$i = 0$,1,⋯,n [10] H3x on the interval xi−1,xi is the cubic Hermite interpolation polynomial of fx with xi−1,xi as nodes. H3x=1hi2[1+2x−xi−1hix−xi2yi−1+1−2x−xihix−xi−12yi+x−xi−1x−xi2yi−1′+x−xi−12x−xiyi′ [11] where x∈xi−1,xi $i = 1$,2,⋯,n.
The process of constructing the personalized flow waveform based on CAPW features is divided into three steps.1) The first part is the same as the CAPW before the inflection point.2) We used the piecewise cubic Hermitian interpolation function at the end-systole to obtain the second part of the estimated flow waveform. Two points, a and b (see Figure 7), can be readily obtained, but a third point is still needed to perform the Hermite function operation. The third point was identified as c, because the magnitude of MAP and the time of SBP in CAPW are between a and b (Li et al., 2021; Parittotokkaporn et al., 2021), respectively. We combine the magnitudes of MAP and SBP and the time of SBP to obtain c for participating in the Hermitian interpolation calculation. The average value of arterial blood pressure during a cardiac cycle is called mean arterial pressure (MAP). MAP can be calculated by Eq. 12 (Papaioannou et al., 2016). MAP=∫TCAPWt dtT [12] Where T represents a cardiac cycle. SBP and DBP are systolic and diastolic blood pressure, respectively. In the arterial system, the maximum peak and foot amplitudes of CAPW are SBP and DBP (as in Figure 7 (Avolio et al., 2009)), respectively.3) The rest of the flow waveform is set to 0.
**FIGURE 7:** *Personalized flow waveform constructed based on the feature points of CAPW.*
The waveforms of personalized flow, measured flow, triangular flow, and lognormal flow approximation are shown in Figure 8. The comparison of different flow waveforms reveals a prominent peak in the triangular estimated flow waveform, which has a considerable discrepancy with the measured flow waveform. In contrast, the estimated personalized flow waveform is closer in shape to the measured flow waveform. Additionally, there are also some variations between the lognormal flow waveform and the measured flow waveform, particularly in the initial part.
**FIGURE 8:** *Comparison and contrast of flow waveforms obtained by Hermite interpolation function estimation, measurement, triangular estimation, and lognormal approximation.*
To further verify the viability of the proposed personalized flow wave, the three wave reflection indices RM, RI, and PTT of wave separation analysis are quantitatively compared based on triangular flow waveform, lognormal flow wave approximation, and personalized flow waveform, respectively (Table 2). We investigated the correlation and consistency of calculated RM, RI, and PTT on the Nektar1D PWDB dataset and clinical data using linear regression analysis (r-values) and Bland-*Altman analysis* (see Figures 10–15), respectively.
**TABLE 2**
| Database | Variable | Wave reflection indices and biases (RMSE) | Wave reflection indices and biases (RMSE).1 | Wave reflection indices and biases (RMSE).2 | Wave reflection indices and biases (RMSE).3 |
| --- | --- | --- | --- | --- | --- |
| Database | Variable | Measured flow | Personalized flow and |Measured-Personalized| | Lognormal flow and |Measured-Lognormal| | Triangular flow and |Measured-Triangular| |
| Nektar1D PWDB (n = 4,374) | Q (mL/s) | 2.83 ± 5.62 | 3.04 ± 4.98 | 3.12 ± 5.20 | 4.22 ± 6.28 |
| Nektar1D PWDB (n = 4,374) | Q (mL/s) | — | 0.89 | 0.92 | 2.33 |
| Nektar1D PWDB (n = 4,374) | Pf amplitude (mmHg) | 21.95 ± 8.49 | 22.58 ± 7.99 | 22.85 ± 9.47 | 20.8 ± 9.41 |
| Nektar1D PWDB (n = 4,374) | Pf amplitude (mmHg) | — | 1.39 | 2.01 | 2.38 |
| Nektar1D PWDB (n = 4,374) | Pb amplitude (mmHg) | 15.8 ± 6.81 | 15.02 ± 5.94 | 14.99 ± 5.98 | 16.06 ± 7.07 |
| Nektar1D PWDB (n = 4,374) | Pb amplitude (mmHg) | — | 0.39 | 1.19 | 1.24 |
| Nektar1D PWDB (n = 4,374) | RM (%) | 71.49 ± 9.55 | 73.84 ± 4.14 | 66.15 ± 3.66 | 63.88 ± 9.73 |
| Nektar1D PWDB (n = 4,374) | RM (%) | — | 5.88 | 9.06 | 10.17 |
| Nektar1D PWDB (n = 4,374) | RI (%) | 41.5 ± 3.4 | 42.27 ± 1.57 | 39.78 ± 1.34 | 38.94 ± 3.33 |
| Nektar1D PWDB (n = 4,374) | RI (%) | — | 1.95 | 3.09 | 3.47 |
| Nektar1D PWDB (n = 4,374) | PTT (ms) | 34.9 ± 13.1 | 37.9 ± 14.3 | 28.1 ± 15.9 | 23.7 ± 21.4 |
| Nektar1D PWDB (n = 4,374) | PTT (ms) | — | 1.21 | 1.23 | 1.52 |
| Clinical data (n = 13) | Q (mL/s) | 5.52 ± 8.07 | 5.41 ± 8.12 | 5.10 ± 8.00 | 4.43 ± 6.92 |
| Clinical data (n = 13) | Q (mL/s) | — | 2.15 | 3.20 | 2.84 |
| Clinical data (n = 13) | Pf amplitude (mmHg) | 20.36 ± 5.16 | 21.39 ± 5.7 | 23.91 ± 7.41 | 35.14 ± 14.9 |
| Clinical data (n = 13) | Pf amplitude (mmHg) | — | 3.29 | 4.16 | 7.35 |
| Clinical data (n = 13) | Pb amplitude (mmHg) | 9.93 ± 2.9 | 10.19 ± 2.8 | 10.89 ± 2.9 | 11.79 ± 3.4 |
| Clinical data (n = 13) | Pb amplitude (mmHg) | — | 1.37 | 1.59 | 2.15 |
| Clinical data (n = 13) | RM (%) | 88.41 ± 2.62 | 87.69 ± 2.76 | 87.61 ± 3.6 | 83.34 ± 6.66 |
| Clinical data (n = 13) | RM (%) | — | 1.62 | 2.25 | 3.76 |
| Clinical data (n = 13) | RI (%) | 48.04 ± 1.16 | 48.03 ± 1.55 | 48.07 ± 1.47 | 46.46 ± 1.94 |
| Clinical data (n = 13) | RI (%) | — | 0.70 | 0.93 | 2.26 |
| Clinical data (n = 13) | PTT (ms) | 75.4 ± 15.9 | 79.5 ± 15 | 80.8 ± 18.7 | 80.4 ± 15.8 |
| Clinical data (n = 13) | PTT (ms) | — | 0.97 | 1.13 | 1.86 |
## 2.3 Evaluation and statistical analysis
In the experiment, we employed the root mean square error (RMSE) to quantitatively evaluate the deviation between measured and estimated flow waveform signals. Differences between wave reflection indices of the estimated and measured aortic flow waveforms were analyzed by two-tailed paired t tests (IBM SPSS Statistics, version-26) and reported as mean ± standard deviation (Mean ± SD) or $95\%$ CI where appropriate. Linear regression and Pearson correlation coefficients were used to analyze the correlations between estimated and measured and aortic flow waveforms. Bland-Altman plots were constructed to assess the agreement between estimated and measured aortic flow waveforms. A p-value of 0.01 or less is regarded as statistically significant.
## 3.1 Waveform analysis of Pf and Pb
In order to analyze the performance of the flow waveform estimation using the personalized flow method, the results are compared with the typical triangular flow method and lognormal flow wave approximation. Figure 9 shows an example of the Pf and Pb decomposed by four flow waves for CAPW, respectively. The results of CAPW separation show that both Pf and Pb have different degrees of triangular wave traces when separated by the triangular flow waveform. As shown in Figure 9C, the Pb decomposed by the triangular flow waveform appears as a sharp peak at its foot, like the triangular flow wave’s triangular apex. However, this does not occur using personalized and lognormal flow waves, as shown in Figure 9B,D. Neither Pf nor Pb calculated by the measured flow wave in a practical situation exhibit traces of a triangle (Figure 9A). And there are no triangular features at the feet of Pf and Pb. Therefore, the decomposition of CAPW using a personalized flow wave is better than the triangular flow wave analysis. The personalized flow wave performs well in estimating the morphology of Pf and Pb, which is closer to the reference flow wave (Figure 9B).
**FIGURE 9:** *Comparison of Pf and Pb decomposed from different flow waves: (A) results of waveform separation based on measured flow wave; (B) results of waveform separation based on personalized flow wave; (C) results of waveform separation based on triangular flow wave; and (D) results of waveform separation based on lognormal flow wave approximation.*
## 3.2 Performance evaluation of wave reflection indices
The corresponding correlation graphs and Bland-Altman plots for comparing measured and estimated flow CAPW reflection indices using three flow wave methods as shown in Figures 10–15.
**FIGURE 10:** *Correlation graphs and Bland-Altman plots of RM calculated by three flow waveforms (A) and (D) Results of the personalized flow wave (Nektar1D PWDB, 4,374 subjects); (B) and (E) Results of the triangular flow wave (Nektar1D PWDB, 4,374 subjects); (C) and (F) Results of the lognormal flow wave (Nektar1D PWDB, 4,374 subjects). RMm and RMe are measured and estimated RM, respectively. Difference: RMe - RMm; Average: (RMe + RMm)/2.* **FIGURE 11:** *Correlation graphs and Bland-Altman plots of RM calculated by three flow waveforms (A) and (D) Results of the personalized flow wave (Clinical data, 13 participants); (B) and (E) Results of the triangular flow wave (Clinical data, 13 participants); (C) and (F) Results of the lognormal flow wave (Clinical data, 13 participants). RMm and RMe are measured and estimated RM, respectively. Difference: RMe - RMm; Average: (RMe + RMm)/2.* **FIGURE 12:** *Correlation graphs and Bland-Altman plots of RI calculated by three flow waveforms. (A) and (D) Results of the personalized flow wave (Nektar1D PWDB, 4,374 subjects); (B) and (E) Results of the triangular flow wave (Nektar1D PWDB, 4,374 subjects); (C) and (F) Results of the lognormal flow wave (Nektar1D PWDB, 4,374 subjects). RIm and RIe are measured and estimated RI, respectively. Difference: RIe - RIm; Average: (RIe + RIm)/2.* **FIGURE 13:** *Correlation graphs and Bland-Altman plots of RI calculated by three flow waveforms. (A) and (D) Results of the personalized flow wave (Clinical data, 13 participants); (B) and (E) Results of the triangular flow wave (Clinical data, 13 participants); (C) and (F) Results of the lognormal flow wave (Clinical data, 13 participants). RIm and RIe are measured and estimated RI, respectively. Difference: RIe - RIm; Average: (RIe + RIm)/2.* **FIGURE 14:** *Correlation graphs and Bland-Altman plots of PTT calculated by three flow waveforms (A) and (D) Results of the personalized flow wave (Nektar1D PWDB, 4,374 subjects); (B) and (E) Results of the triangular flow wave (Nektar1D PWDB, 4,374 subjects); (C) and (F) Results of the lognormal flow wave (Nektar1D PWDB, 4,374 subjects). PTTm and PTTe are measured and estimated PTT, respectively. Difference: PTTe - PTTm; Average: (PTTe + PTTm)/2.* **FIGURE 15:** *Correlation graphs and Bland-Altman plots of PTT calculated by three flow waveforms (A) and (D) Results of the personalized flow wave (Clinical data, 13 participants); (B) and (E) Results of the triangular flow wave (Clinical data, 13 participants); (C) and (F) Results of the lognormal flow wave (Clinical data, 13 participants). PTTm and PTTe are measured and estimated PTT, respectively. Difference: PTTe - PTTm; Average: (PTTe + PTTm)/2.*
The equation of the linear regression obtained between the measured and estimated RM using the personalized flow method based on Nektar1D PWDB is $y = 0.99$x + 0.06 ($r = 0.97$, $p \leq 0.001$) as shown in Figure 10A; The corresponding equations obtained using the triangular flow approach and lognormal flow approximation (see Figure 10B,C) are $y = 0.34$x + 0.39 ($r = 0.79$, $p \leq 0.001$) and $y = 0.28$x +0.46 ($r = 0.73$, $p \leq 0.001$), respectively. A comparison (mean ± SD, 0.05 ± 0.03) between the measured and estimated RM using the personalized flow method based on Nektar1D PWDB is shown in Figure 10D. The same comparison using the triangular flow approach and lognormal flow approximation (mean ± SD, −0.08 ± 0.07 and −0.05 ± 0.07) is shown in Figure 10E,F, respectively. The linear regression and Bland-Altman plots of RM calculated by three flow waveforms (Clinical data, 13 participants) are shown in Figure 11. The regression equations (panels A, B and C) are $y = 0.90$x+0.08 ($r = 0.85$, $p \leq 0.001$), for the personalized flow wave method; $y = 0.81$x+0.11 ($r = 0.32$, $$p \leq 0.28$$) for the triangular flow wave approach; and $y = 1.09$x-0.08 ($r = 0.79$, $$p \leq 0.0013$$) for the lognormal flow wave approximation algorithm. The corresponding Bland-Altman plots (panels D, E and F) and their mean differences (± SD) for the personalized flow wave, triangular flow wave and lognormal flow wave methods respectively are (− 0.01 ± 0.15), (−0.05 ± 0.61) and (−0.01 ± 0.26).
The equation of the linear regression obtained between the measured and estimated RI using the personalized flow method based on Nektar1D PWDB is $y = 0.95$x + 0.04 ($r = 0.97$, $p \leq 0.001$) as shown in Figure 12A; The corresponding equations obtained using the triangular flow method and lognormal flow approximation (see Figure 12B,C) are $y = 0.37$x + 0.24 ($r = 0.80$, $p \leq 0.001$) and $y = 0.29$x + 0.28 ($r = 0.74$, $p \leq 0.001$), respectively. A comparison (mean ± SD, 0.02 ± 0.01) between the measured and estimated RI using the personalized flow method based on Nektar1D PWDB is shown in Figure 12D. The same comparison using the triangular flow method and lognormal flow approximation (mean ± SD, −0.03 ± 0.03 and −0.02 ± 0.03) is shown in Figure 12E,F, respectively. The linear regression and Bland-Altman plots of RI calculated by three flow waveforms (Clinical data, 13 participants) are shown in Figure 13. The regression equations (panels A, B and C) are $y = 1.20$x-0.09 ($r = 0.90$, $p \leq 0.001$), for the personalized flow wave method; $y = 0.84$x+0.06 ($r = 0.50$, $$p \leq 0.08$$) for the triangular flow wave approach; and $y = 0.95$x+0.02 ($r = 0.75$, $$p \leq 0.0029$$) for the lognormal flow wave approximation algorithm. The corresponding Bland-Altman plots (panels D, E and F) and their mean differences (± SD) for the personalized flow wave, triangular flow wave and lognormal flow wave methods respectively are (0 ± 0.01), (−0.02 ± 0.02) and (0 ± 0.01).
The equation of the linear regression obtained between the measured and estimated PTT using the personalized flow method based on Nektar1D PWDB is $y = 1.03$x - 0.01 ($r = 0.94$, $p \leq 0.001$) as shown in Figure 14A; The corresponding equations obtained using the triangular flow method and lognormal flow approximation (see Figure 14B,C) are $y = 1.19$x ($r = 0.73$, $p \leq 0.001$) and $y = 0.93$x ($r = 0.77$, $p \leq 0.001$), respectively. A comparison (mean ± SD, -0.01 ± 0.01 s) between the measured and estimated PTT using the personalized flow method based on Nektar1D PWDB is shown in Figure 14D. The same comparison using the triangular flow method and lognormal flow approximation (mean ± SD, 0 ± 0.02 s and −0.01 ± 0.01 s) is shown in Figure 14E,F, respectively. The linear regression and Bland-Altman plots of PTT calculated by three flow waveforms (Clinical data, 13 participants) are shown in Figure 15. The regression equations (panels A, B and C) are $y = 0.78$x+0.02 ($r = 0.83$, $p \leq 0.001$), for the personalized flow wave method; $y = 0.31$x+0.06 ($r = 0.31$, $$p \leq 0.3$$) for the triangular flow wave approach; and $y = 0.98$x+0.01 ($r = 0.83$, $p \leq 0.001$) for the lognormal flow wave approximation algorithm. The corresponding Bland-Altman plots (panels D, E and F) and their mean differences (± SD) for the personalized flow wave, triangular flow wave and lognormal flow wave methods respectively are (0 ± 0.01 s), (0.01 ± 0.02 s) and (0.01 ± 0.01 s).
The coefficient of determination between the measured and estimated RM using the personalized flow method based on two datasets are 0.94 and 0.72, and the results of using the triangular flow method are 0.62 and 0.10. The results of using the lognormal flow wave approximation are 0.53 and 0.62. The coefficient of determination between the measured and estimated RI using the personalized flow method based on two datasets are 0.94 and 0.81, and the results of using the triangular flow method are 0.64 and 0.25. The results of using the lognormal flow wave approximation are 0.55 and 0.56. The coefficient of determination between the measured and estimated PTT using the personalized flow method based on two datasets are 0.88 and 0.69, and the results of using the triangular flow method are 0.53 and 0.09. The results of using the lognormal flow wave approximation are 0.59 and 0.69. Therefore, the correlation of the reflection indices calculated by the personalized flow method is more robust than that of the triangular flow method and lognormal flow wave approximation (Figures 10–15). The results of personalized flow waveform method are the closest to one compared to the other methods, thus indicating a very good one to one correspondence. The personalized flow approximates the measured flow and gives better estimates of RM, RI, and PTT. The quantitative and objective comparison of the three flow wave methods is summarized in Table 2. To further strengthen the validity of the proposed method in obtaining the flow waveform from CAPW, we also calculated the RMSE between the actual known flow and the approximated flow using three methods (i.e., personalized flow, lognormal flow, and triangular flow). The proposed personalized flow method gave the smallest values (as shown in Table 2). The small errors indicate that the personalized flow wave shape is a good approximation for applying waveform analysis and improves wave separation analysis results compared to the other two methods.
## 4 Discussion
In this study, we applied a personalized wave to estimate the aortic flow waveform in two data sets (Nektar1D PWDB and Clinical data) to investigate the feasibility of CAPW separation. Moreover, the CAPW reflection indices calculated using the personalized estimated flow waveform were compared with the results derived from the traditional triangular flow wave and the recently proposed lognormal flow wave approximation method. The CAPW was decomposed into Pf and Pb using pressure-flow relations, and wave reflections were quantitatively and qualitatively analyzed. By experimental analysis, the correlation and consistency of the wave reflection indices calculated based on the personalized and measured flow waves are higher than the other two methods (Figures 10–15). From the perspective of RI, RM, and PTT, the RMSE between the personalized flow waveform and measured flow waveform are smaller than the difference between the other two methods (Table 2). Moreover, the shape of the personalized estimation flow wave is better than that of the triangle and lognormal flow waves (see Figure 8).
Also, the Pf and Pb of the CAPW decomposition by personalized flow waveforms are closer to the actual results. The errors of the amplitudes of Pf and Pb decomposed by the personalized estimated flow wave and CAPW are smaller (Table 2). The waveform of personalized flow is more consistent with the actual flow waveform compared with the lognormal and triangular flow waveform (Figure 8). Moreover, the biases between wave reflection indices calculated by decomposing CAPW with the measured and personalized flow are smaller. Furthermore, the Pf and Pb of the CAPW decomposition by personalized flow waveform are closer to the actual results in amplitude and waveform morphology than the other two methods (Nektar1D PWDB; RMSEs = 1.39 and 0.39, Table 2 and Clinical data; RMSEs = 3.29 and 1.37; Table 2). Using a triangle to estimate the flow waveform will lead to spikes, and also Pf and Pb calculated by triangle flow waves will also appear as spikes (see Figure 8). This will not happen in the measured flow, and the personalized flow is more reliable.
Through linear regression equation and Bland-Altman diagram analysis, RM, RI, and PTT obtained from personalized flow waveform are highly correlated with RM, RI, and PTT obtained from the measured flow (Figures 10–15). These show that the wave reflection indices can be calculated by the personalized estimated flow wave when the real flow wave is not convenient to measure. As shown in Figures 10–15, Bland–Altman plots generally revealed smaller biases and narrower $95\%$ LOA (Limits of agreement) for the personalized flow waveform, compared with the triangular and lognormal flow waveform approximation. Wave reflection indices derived using the truly measured flow waveform and estimated flow waveforms using three methods are reported in Table 2. Based on the comparison of the results between the Nektar1D PWDB and clinical data, the Pearson correlation coefficients between the personalized flow wave, lognormal flow, triangular flow wave, and the measured flow wave indicate that the accuracy of the personalized flow wave is higher. It was notable that over the pulse wave reflection indices, the biases of RM, RI, and PTT were lower for the personalized flow waveform than the triangular and lognormal flow waveform in most cases, thus confirming the superior performance of the personalized flow method. In addition, compared with the triangle flow wave, the personalized flow wave is more consistent with the measured flow wave in terms of RI, RM, and PTT. Besides, the personalized flow wave method does not require complex statistical calculations like the lognormal approximation, nor does it need to establish a variance value in advance.
The clinical data used for validation in this paper are limited to young, healthy participants only, which is one of the limitations of this study. There was no vascular or cardiac disease model included in the 1-D model when generating the virtual subjects. The 1-D database also only represents healthy subjects to the limitation. The proposed method should be validated in different populations (i.e., patients with heart disease) further to investigate the generalizability of the personalized flow waveform method. In addition, it is feasible that PTT is estimated only by calculating the time delay of Pf and Pb, but there is no comparison and correlation analysis with the measured carotid-femoral PTT and aortic pulse wave velocity. In order to better evaluate arterial stiffness, a comparison is necessary. The reliability of using the $30\%$ ET as a surrogate of inflection point has not been rigorously proven, but it has just been used as a rule of thumb in previous studies. Typically, some degree of flow regurgitation occurs when the aortic valve closes, i.e., the actual aortic flow is negative at end-systole (shown in Figure 8). As with the triangular and the lognormal flow waves, the proposed personalized flow wave ignores this by setting the diastolic flow to 0 (Westerhof et al., 2006; Hao et al., 2022). Although the personalized flow wave improves the results of wave reflection and wave separation analysis compared to the other two methods, it is still necessary to further strengthen this research to implement this typical feature of aortic flow waveform. Furthermore, in early-systole, the flow peak obtained by the proposed personalized flow method is closer to the measured flow peak than the other methods, and occurs later in time compared to the measured waveform. Also, during the late-systolic part of the personalized flow waveform, the waveform overestimates the measured waveform (see Figure 8). There are still errors between the approximate personalized flow waveform and the measured flow waveform. Future research should focus on the three feature points (a, b, and c) involved in the Hermite interpolation operation in order to construct a flow wave that is more consistent with the measurement.
## 5 Conclusion
In this paper, a novel method of approximate estimation of flow waves based on the characteristics of the CAPW is proposed, and the feasibility of personalized flow separation in CAPW is evaluated. The results indicate that the personalized flow wave method generates more accurate aortic flow waveform. Experiments on Nektar1D PWDB and clinical data verify the feasibility of the proposed method. The personalized flow wave estimated by our proposed method is more consistent with the measured flow wave when used to calculate RM, RI, and PTT, compared to the triangle estimation and lognormal approximation. Pf and Pb decomposed from CAPW using personalized flow wave method have more accurate shapes and amplitudes than the other two methods. The personalized flow wave method improves CAPW separation results both in accuracy and reliability.
## 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.
## Ethics statement
The studies involving human participants were reviewed and approved by the Research Ethics Committee of Northeastern University (NO. NEU-EC-2021B022S), China. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
HS: Conceptualization of this study, investigation, validation, visualization, writing–original draft preparation. YaY: Methodology of this study, writing–proof reading. WL: Conceptualization of this study, data curation, resources, software. SZ: Data curation, software. SD: Formal analysis, visualization, software. JT: Methodology, data analysis. YY: Investigation, visualization. AA: Writing–proof reading. LX: Supervision, writing–review and editing.
## Conflict of interest
Author LX was employed by Neusoft Research of Intelligent Healthcare Technology, Co. Ltd.
The remaining 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.
## Abbreviations
CAPW, Central aortic pressure waveform; CVD, Cardiovascular disease; DBP, Diastolic blood pressure; ET, Ejection time; FDA, Food and Drug Administration; MAP, Mean arterial pressure; Pb, Backward pressure wave; Pf, Forward pressure wave; PTT, Pulse transit time; Qb, Back flow wave; Qf, Forward flow wave; RI, Reflection index; RM, Reflection magnitude; RMSE, Root mean square error; SBP, Systolic blood pressure; Tfb, The time difference between Pf and Pb; Zc, Characteristic impedance; Zin, Input impedance.
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|
---
title: 'Global research trends between gut microbiota and lung cancer from 2011 to
2022: A bibliometric and visualization analysis'
authors:
- Haitao Chen
- Yuebiao Lai
- Chenxiao Ye
- Changhong Wu
- Jiali Zhang
- Zewei Zhang
- Qinghua Yao
journal: Frontiers in Oncology
year: 2023
pmcid: PMC9996130
doi: 10.3389/fonc.2023.1137576
license: CC BY 4.0
---
# Global research trends between gut microbiota and lung cancer from 2011 to 2022: A bibliometric and visualization analysis
## Abstract
### Background
An increasing number of studies have found that the gut microbiota was related to the occurrence and development of lung cancer. Nonetheless, publication trends and research hotspots in this field remain unknown. The study aimed to perform a bibliometric analysis to systematically identify publication trends and research hotspots in the field of gut microbiota and lung cancer research within a 12-year panorama.
### Methods
Publications related to the gut microbiota and lung cancer between 1 January 2011 and 25 October 2022 were retrieved from the Web of Science Core Collection (WoSCC) database. The online analytic tool of the WoSCC was used to analyze various bibliometric parameters. The bibliometrics website, CiteSpace, and VOSviewer were used to identify research trends and hotspots.
### Results
A total of 375 publications related to the gut microbiota and lung cancer were extracted from WoSCC and identified for analysis. The number of annual publications has grown rapidly since 2018 and reached a peak in 2022. China was the most prolific country in this field, with 120 publications, followed by the United States [114], with the highest H-index of 31. Additionally, France ranked the highest with an average of 133 citations, while the leading institution and journal were the Unicancer and the International Journal of Molecular Sciences, respectively. Interestingly, Routy Bertrand was the most prolific author and also the most cited author in terms of H-index and citations. Reference and keyword burst detection indicated that the research hotspots mainly included 1) the gut microbiota directly affects the efficacy of immunotherapy for lung cancer, 2) the application of different gut bacteria on lung cancer, and 3) the mechanism of the gut microbiota on lung cancer.
### Conclusion
The findings of this study revealed the general publication trends and evolving research hotspots in the field of gut microbiota and lung cancer at a global level. The research hotspots focused on the clinical application of the gut microbiota combined with immunotherapy in lung cancer and its mechanism. The findings of this study provide new perspectives on the field, which may shed light on a beneficial impact on further etiological studies, diagnosis, and treatment for lung cancer.
## Introduction
Globally, lung cancer is currently the leading malignancy in terms of incidence and mortality among cancers, while the annual incidence rate increased continuously [1]. Epidemiological surveys demonstrated that there were about 2.1 million new cases of lung cancer in 2018, with an estimated 1.8 million deaths, accounting for nearly one-fifth ($18.4\%$) of cancer deaths [2, 3]. Hence, lung cancer has seriously affected people’s quality of life and posed a certain socioeconomic burden; hence, the prevention and treatment of lung cancer are paramount.
An increasing number of studies have found that the development of many diseases was related to dysbiosis of the gut microbiota, including but not limited to inflammatory bowel disease [4], atrial fibrillation [5], and Parkinson’s disease [6]. Notably, numerous previous studies identified that the change in the gut microbiota was associated with lung cancer progression (7–9). Dysbiosis of the gut microbiota may promote the occurrence and development of lung cancer by regulating metabolic pathways, suppressing immune cell function, producing pro-inflammatory factors, and promoting immune escape (10–12). Therefore, the gut microbiota plays a crucial role in the progression of lung cancer. Meanwhile, these studies also suggested that the gut microbiota is a potential marker and therapeutic target for lung cancer.
As a well-established method for analyzing publication information, bibliometric analysis has been widely used in different research areas (13–16), specifically the identification of bibliometric relevant parameters, such as core scholars/institutions/countries and their collaborative associations, keyword co-occurrence, and bursts analysis, which can significantly contribute to revealing the current status, hotspots, and research trends over time in given research fields [17]. To the best of our knowledge, although the number of annual publications continues to grow rapidly, no in-depth bibliometric analysis has been conducted in this field. Therefore, this study aims to systematically reveal the research trends and hotspots in the field of gut microbiota and lung cancer over the past 12 years by using bibliometric analysis for the first time, providing a new perspective for future research in this field.
## Data source and search strategy
Web of Science Core Collection (WoSCC) is widely used for visualization and quantitative analyses, which is the most authoritative citation-based database with the function of a powerful index (18–20). All data were retrieved from the WoSCC in this study, with the timespan from 1 January 2011 to 25 October 2022.
The search strategy employed was as follows: TS=(((intestin) OR (gastrointestin) OR (gut) OR (gastro-intestin)) AND ((Microbiot) OR (Microbiome) OR (Flora) OR (Microflora) OR (Bacteria) OR (antibiotic) OR (probiotic) OR (prebiotic) OR (dysbiosis))) AND (lung) AND ((cancer) OR (tumor) OR (tumour) OR (carcinoma) OR (neoplasm)). Two authors (HT Chen and YB Lai) completed the data extraction on 26 October 2022 to reduce the deviation caused by data extraction. Any disagreement was resolved by discussion or by seeking the assistance of a third author (QH Yao). Additionally, only articles or review articles were included, while there were no strict language restrictions.
## Data collection and bibliometric analysis
Different file formats were downloaded from the Web of Science website and exported for analysis. Analysis metrics mainly included the annual number of publications, number of total citations, average citations per publication (CPP), country, institution, journal, keywords, authors, Hirsch index (H-index) [21], the impact factor (IF; 2021), and the category quartile. Additionally, the bibliometrics website (http://bibliometric.com/) was used to generate a visual cooperation map of the countries/regions, herein identifying the countries that cooperate most closely with each other [22, 23].
CiteSpace (Version 5.6.R3) was regarded as an excellent visualization tool invented by Professor Chaomei Chen, which was widely used for countries, journals, dual-map analysis, institutions, cited references, and timeline view and for detecting the keywords with strong citation bursts [24, 25]. The parameters of CiteSpace were set as follows: time slicing was from 2011 to 2022, years per slice [1]; selected one node type at a time; and selection criteria were the top 50 objects. VOSviewer (Version 1.6.18) was used to generate the visual map of co-authorship of authors, citation of authors, citation of references, and co-occurrence of keywords [26, 27]. Additionally, the keywords that appear at least 10 times were used to analyze the hotspots in this field.
## Result
A total of 396 publications related to the gut microbiota and lung cancer were extracted from WoSCC, with the timespan from 2011 to 2022. Excluding publications that were non-articles or reviews, such as meeting abstracts ($$n = 23$$), 375 publications were ultimately included for scientometric and visual analyses. The specific publication screening flowchart is shown in Figure 1.
**Figure 1:** *Flowchart of the study strategy.*
## Global trends of publication outputs and citations
The characteristics of 375 publications are demonstrated in Figure 2. As shown in Figures 2A, B, the number of annual publications was small in the first 7 years, with no more than 10 publication outputs per year, accounting for $11.47\%$ of 375. However, the number of annual publications has grown rapidly since 2018 ($$n = 29$$, accounting for $7.78\%$ of 375) and reached a peak in 2022 ($$n = 93$$, accounting for $24.93\%$ of 375), which indicated that the researchers had invested more interest and outputs in this field in recent years. There were some fluctuations in the value of the H-index over the past 12 years, and the highest year was 2019 (Figure 2C). Additionally, to the search date, all publications were cited 10,770 times, with a CPP of 28.72 and an H-index of 50. The top 5 cited years were identified, including 2021, with the number of annual citations 3,166 times, followed by 2022 (2,893 times), 2020 (2,143 times), 2019 (1,113 times), and 2018 (637 times) (Figure 2D).
**Figure 2:** *(A) Number of annual publications. (B) Annual percentage of the published publications. (C) Annual H-index of the publications. (D) Number of annual citations.*
## Contributions of top 10 productive countries/regions
All 375 publications were distributed covering 60 countries/regions. The world map based on the top 10 countries in terms of the number of publications issued related to the gut microbiota and lung cancer is shown in Figure 3. The different colors on the map represent the total number of publications, with the darker red color representing a higher number of publications. China was the most prolific country in this field, with 120 publications, followed by the United States [114], Italy [30], France [25], and Japan [23], while the United States had the highest H-index [31] (Figure 4A). When the country citations were analyzed, the United States, with 5,779 citations, had the highest total number of citations, followed by France [3,225], China [2,095], Canada [895], and the United Kingdom [895] (Figure 4B). As shown in Figure 4B, among the top 5 countries in CPP, France ranked the highest with an average of 133 citations, followed by the United States (50.69), Canada (49.72), the United Kingdom (47.11), and Japan (27.43). In addition, the number of annual publications had increased significantly in China, the United States, and France since 2018. China surpassed the United States as the country with the most publications in this field post-2020 (Figure 4C). Furthermore, the visual map of international collaboration between the different countries was also captured. The United States maintains the strongest partnerships with other countries in this field, working most closely with China, followed by Germany, Australia, France, and Singapore (Figure 4D). However, cooperative relationships among other countries were fragile.
**Figure 3:** *World map based on the total publications of the top 10 countries/regions. The different colors in the map represent the total number of publications, with the darker red color representing the higher number of publications.* **Figure 4:** *Top 10 productive countries related to the gut microbiota and lung cancer from 2011 to 2022. (A) The number of publications and H-index. (B) The total number of citations and average citations per publication. (C) The average annual number of national publications. (D) An international collaboration between countries. The countries were labeled using different colors, and the links represent international collaborations.*
## Analysis of the leading institutions and journals
A total of 941 institutions were active in this field, and the top 10 institutions in terms of publications are listed in Table 1; most of them were from France, followed by the United States and China. The most prolific institution was Unicancer, with 16 papers, followed by Udice French Research Universities (with 15 papers), Harvard University (with 15 papers), and Institut National De La Santé Et De La Recherche Médicale Inserm (with 14 papers). Harvard University had the highest H-index [11], while Gustave Roussy occupied the highest contribution of CPP (272.00).
**Table 1**
| Rank | Institutions | Counts | % of 373 | Citations | CPP | H-index | Location |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 1 | Unicancer | 16 | 4.29 | 3145 | 196.56 | 10 | France |
| 2 | Udice French Research Universities | 15 | 4.02 | 3135 | 209.0 | 10 | France |
| 3 | Harvard University | 15 | 4.02 | 459 | 30.6 | 11 | USA |
| 4 | Institut National De La Santé Et De La Recherche Médicale Inserm | 14 | 3.75 | 3047 | 217.64 | 9 | France |
| 5 | Universite Paris Saclay | 12 | 3.22 | 3005 | 250.42 | 8 | France |
| 6 | Harvard Medical School | 12 | 3.22 | 315 | 26.25 | 8 | USA |
| 7 | Gustave Roussy | 11 | 2.95 | 2992 | 272.0 | 7 | France |
| 8 | National Institutes of Health NIH USA | 11 | 2.95 | 581 | 52.82 | 7 | USA |
| 9 | Chinese Academy of Sciences | 11 | 2.95 | 435 | 39.55 | 8 | China |
| 10 | Shanghai Jiao Tong University | 10 | 2.68 | 405 | 40.5 | 6 | China |
The retrieved publications in this study were published in 225 journals, and the top 3 most productive journals were the International Journal of Molecular Sciences ($4.02\%$ of 373, with 15 papers), Frontiers in Immunology ($3.75\%$ of 373, with 14 papers), and Cancers ($2.68\%$ of 373, with 10 papers). Notably, as shown in Table 2, the journal of Frontiers in Immunology had both the highest IF (8.79) and the CPP (27.64), which indicated that it was considered the most pivotal journal in this field. The link between citing and cited journals is demonstrated in Figure 5, and the main citation paths were identified, including 1) molecular, biology, and immunology-molecular, biology, and genetics ($z = 5.16$, $f = 5$,776), and 2) medicine, medical, and clinical-molecular, biology, and genetics ($z = 4.35$, $f = 4$,924).
## Analysis of authors and co-authorship of authors
A total of 345 authors have contributed to this field. Routy Bertrand ranked first as the most prolific author with nine publications, followed closely by Zitvogel Laurence (seven publications), Derosa Lisa (six publications), and Richard Corentin (six publications). Meanwhile, Routy Bertrand had the highest H-index of 8 (Table 3). VOSviewer (Version 1.6.18) was used to analyze the co-cited authors identified from all publications in this study (Figure 6A). The top 10 co-cited authors are listed in Table 3. Routy Bertrand ranked first, with 668 citations, followed by Zitvogel Laurence (581 citations), Sears Cynthia L (141 citations), Richard Corentin (99 citations), and Jun Chen (85 citations). Additionally, the co-authorship map of authors, which indicated the authors that cooperate in this field over the past years, was generated, and the collaboration of 21 authors is shown in Figure 6B.
## Analysis of cited references
The visualization map of cited references consisted of 169 nodes and 381 links. Among them, the top 10 cited references in terms of citation frequency are shown in Figure 7A. Notably, the basic characteristics of these top 10 highest cited references are shown in Table 4. The top 3 publications with the highest citations were all published in Science (IF: 63.832), entitled “Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors” by Routy Bertrand, which was the most cited reference with 2,339 citations [28], followed by “Commensal Bifidobacterium promotes antitumor immunity and facilitates anti-PD-L1 efficacy” with 2,069 citations [29] and “Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients” with 2,010 citations [30]. Interestingly, the aforementioned publications are all dedicated to the correlation between the gut microbiome and the anti-tumor efficacy of PD-1, revealing that the gut microbiota could improve anti-tumor efficacy by modulating the tumor response to checkpoint blockade immunotherapy. Furthermore, the timeline view cluster of co-cited references related to the gut microbiota and lung cancer was generated by CiteSpace (Figure 7B), while the log-likelihood rate (LLR) was used to identify the distribution of hotspots from publications in the nine clusters. Specifically, the cluster with warmer colors and larger nodes contained more recent publications, indicating that the cluster was the hotspot in this field in recent years. Hence, as shown in Figure 7B, cluster #1 (antibiotics) has become a continuing hotspot of research in this field, with mapping of the impact of antibiotics on anti-tumor efficacy attracting great attention. In addition, cluster #0 (gut), cluster #3 (carcinogenesis), cluster #4 (bronchopulmonary dysplasia), and cluster #8 (neoadjuvant) indicated the hotspots in this field during the past latest years. To verify the credibility of the clusters, the structural characteristics of the cited reference clusters, especially silhouette, were established (Table 5). To the best of our knowledge, a silhouette value greater than 0.7 was generally considered very credible for the cluster [31]. As shown in Table 5, the biggest cluster was “gut” with a size of 28, while cluster #6 (host genetics) ranked first in terms of silhouette. Additionally, cluster #1 (antibiotics) had the highest LLR, which contained 20 references with a rate of 11.02, followed by “biomarker” (8.16), “immune-related adverse events” (7.43), “fibrosis” (7.23), and “carcinogenesis” (6.37).
**Figure 7:** *(A) CiteSpace visualization map of cited references in the field of gut microbiota and lung cancer from 2011 to 2022. The nodes represent cited references, and the lines between the nodes represent cited-reference relationships. (B) The timeline view clusters of co-cited references and their cluster labels via CiteSpace. The cluster with warmer colors and larger nodes contained more publications, indicating that this clustering issue was the hotspot in this field.* TABLE_PLACEHOLDER:Table 4 TABLE_PLACEHOLDER:Table 5
## Analysis of keywords and co-occurrence clusters and burst
A total of 145 keywords were identified as occurring more than five times, which could be classified as five clusters (Figure 8A). Keywords, such as “gut microbiome” and “lung cancer”, were excluded. Meanwhile, “immunotherapy”, “inflammation”, “antibiotics”, “efficacy”, and “immune checkpoint inhibitors” were identified to a better perspective. Notably, the 64 keywords with a frequency of no less than 10 times since 2019 were identified for further analysis. As shown in Figure 8B, the more yellow-colored dots were the most recent keywords to appear, indicating that these were the latest research trends in this field, mainly including immunotherapy, immune checkpoint inhibitors, dysbiosis, survival, efficacy, and non-small cell lung cancer. Furthermore, a network map to visualize the clusters of keywords related to the gut microbiota and lung cancer from 2011 to 2022 was also constructed. According to Figure 8C, cluster #0 labeled “immunotherapy” was the largest cluster, followed by “corticosteroids” (cluster #1), “tumor microenvironment” (cluster #2), “intestinal microflora” (cluster #3), “butyrate” (cluster #4), “meta-analysis” (cluster #5), “mycobacterium tuberculosis” (cluster #6), and “lactobacillus” (cluster #7). The top 25 keywords with the strongest citation bursts in this field from 2018 were spotlighted, which was attributed to a significant increase in the number of publications since 2018. Among them, the top 5 burst strength keywords were defined, including “PD-1 blockade”, with the highest burst strength of 2.1152, followed by “pembrolizumab” (1.838), “receptor” (1.838), “cystic fibrosis” (1.838), and “commensal bacteria” (1.6909) (Figure 8D).
**Figure 8:** *(A) Visualization of keyword co-occurrence analysis. The size of nodes indicates the frequency of occurrences of the keywords. The lines between the nodes represent their co-occurrence in the same publication. The shorter the distance between two nodes, the larger the number of co-occurrence of the two keywords. (B) Visualization map of the 64 keywords with a frequency of no less than 10 times since 2019 generated by using VOSviewer (blue, earlier; yellow, later). (C) The cluster of keywords related to the gut microbiota and lung cancer from 2011 to 2022. The different colors mean different clusters. (D) CiteSpace visualization map of the top 20 keywords with the strongest citation bursts from 2011 to 2022.*
## Discussion
Currently, bibliometric analysis is increasingly used to detect the status and trends in a particular field. To date, bibliometric analyses of the gut microbiota and lung cancer have not been reported. Hence, this is the first study to prospect the trends of the impact of the gut microbiota on lung cancer, which may help provide an instructive perspective for future research.
## General information
The current analysis demonstrated a significant increase in the number of annual publications and citations in the field of gut microbiota and lung cancer, especially in 2018, reaching the highest in 2022, indicating that this field has received sustained attention from researchers in recent years. The highest H-index and the number of annual citations were in 2019 and 2021, respectively. Therefore, publications retrieved in this field from 2019 deserved further in-depth mining. Additionally, the H-index for the last 2 years was lower, contributing to the fact that 2021 and 2022 were very close to the time of data extraction for this study (25; October 2022).
A total of 60 countries/regions, 941 institutions, and 345 authors contributed to this field. Internationally, China ranked first in terms of the total number of publications outputs, which reflected the fact on the increasing number of scholars in China who are dedicated to this field. However, the CPP and H-index of China were lower than those of other countries, such as the United States, indicating that while the total of publications has increased in China, there was still a lack of high-quality articles. Notably, the United States remained the prominent academic driver in this field, with high academic status, as confirmed by the highest CPP and H-index. Moreover, according to the visual map of international collaboration, the United States sustained a close cooperative relationship with many countries engaged in this field, including China, Germany, and France.
Interestingly, half of the top 10 institutions were from France, indicating that France has emerged as a major center for research in this field. Except for China, the rest of the top 10 institutions were from developed countries, which manifested that significant lagging in developing countries existed in this field. Therefore, Chinese institutions should actively maintain and benefit from close cooperation with affiliated institutions from developed countries to increase their international influence in future research strategies. Among the analysis of authors and co-cited authors, Routy Bertrand from Canada was the most active scholar and the most co-cited author, indicating that his research has had an important impact on the development of this discipline. Specifically, Routy Bertrand mainly focused on the effects of the gut microbiota on immune checkpoint inhibitors and carried out several clinical studies on the gut microbiota in the treatment of lung cancer [28, 32], confirming that the gut microbiota has an important therapeutic role in lung cancer.
## Evolution of research hotspots and frontiers
The research hotspots and frontiers in the field of gut microbiota for lung cancer were clarified by analyzing a combination of the highest cited references, keyword co-occurrence, clusters, and burst, which mainly included the gut microbiota directly affected the efficacy of immunotherapy for lung cancer. Immunotherapy is a crucial therapy in the comprehensive treatment of lung cancer. Recently, several immune check inhibitors (ICIs), such as anti-PD-1/PD-L1/CTLA-4, have been widely applied in lung cancer, which can effectively improve progression-free survival in patients with lung cancer [29, 33, 34]. Furthermore, the gut microbiota improves the effects of anti-tumor by modulating the tumor response to checkpoint blockade immunotherapy [35]. Notably, increasing evidence seemed to suggest that the efficacy of immunotherapy is influenced by relevant immune checkpoints, such as PD-1/PD-L1/TMB, which may be closely associated with alterations in the gut microbiota, including Bifidobacterium longum, Collinsella aerofaciens, and *Enterococcus faecium* [28, 36]. Therefore, regulating the gut microbiota may be an important method to upgrade the efficacy of immunotherapy in lung cancer patients. Moreover, multiple basic studies have reported that the gut microbiota affected the therapeutic effect of chemo-radiotherapy in lung cancer [37, 38], suggesting that modulating the gut microbiota was an important way to improve chemo-radiotherapy sensitivity. However, only two clinical trials of immunotherapy combined with probiotics for lung cancer were searched, with the registration number NCT04699721/NCT05094167. Thus, randomized, controlled clinical trials on the combination of regulatory microbiota (including probiotics and fecal microbiota transplantation) and anti-tumor therapies (such as chemotherapy, radiotherapy, and immunotherapy) with a fixed and standardized research protocol and strict quality control are urgently warranted.
Then, the application of different gut bacteria in lung cancer was another emerging topic identified by analyzing the keyword clusters, wherein the Lactobacillus obtained the most attention. Studies have demonstrated that Lactobacillus, one of the most widely studied probiotics in lung cancer, can significantly inhibit metastasis of tumor cells to the lung, thereby improving the prognosis of patients with lung cancer [39, 40]. A previous study by Valentino Le Noci et al. suggested that an increase in the gene encoding the joining chain (J chain) of immunoglobulins was observed by Lactobacillus aerosolization, while high levels of J-chain mRNA strongly associated with a good prognosis for patients with lung adenocarcinoma [41]. In addition, intravenous and intradermal injections of Lactobacillus casei significantly increased the anti-tumor activity in lung cancer model mice [42]. However, uncertainty about the efficacy of *Lactobacillus is* one of the preeminent reasons limiting clinical application. Therefore, to address this issue, the combination of Lactobacillus with other probiotics, such as Bifidobacterium, may provide more stable and excellent efficacy, which is helpful for guiding new research directions of different genera in the treatment of lung cancer in the future.
The final topic was the hotspots and trends of the mechanism of the gut microbiota on lung cancer progression. The gut microbiota may be involved in the occurrence and development of lung cancer through the following potential ways. First, changes in diversity and abundance, which are labeled dysbiosis, may be associated with the occurrence of lung cancer [43]. Studies revealed that there were significant differences in the gut microbiota beta diversity between patients with lung cancer and healthy controls, mainly manifested by the increased abundance of Enterococcus and the decrease in the levels of the phylum Actinobacteria and genus Bifidobacterium [44, 45]. Notably, these gut microbiota may be potential biomarkers of lung cancer, which would provide clues in the assessment of lung cancer progression and the effective development of targeted therapy [46]. Second, gut microbiota dysbiosis promoted the release of inflammatory mediators, along with the release of multiple toxins that promote the production of free radicals, which contributed to the occurrence of lung cancer [47]. Recently, the regulation of metabolites mediated by the gut microbiota on lung cancer progression was a hotspot in this field. Several studies have found that butyrate-producing bacteria decreased significantly in lung cancer patients, while promoting the levels of butyrate mediated by the gut microbiota played anti-inflammatory, anti-tumor proliferation, and metastasis roles (48–50). Hence, the gut microorganism-mediated metabolic network pathway is the key mechanism to revealing lung cancer and the gut microbiota.
## Limitations
Despite the strict principles of bibliometric analysis being adhered to in this study, there are some unavoidable limitations. First, only articles and reviews published within a specific period of time in the WoSCC were included, thereby potentially leading to publication bias in the study results. However, the search timespan of this study was sufficiently long for the findings, which can reflect the research trends in this field of interest. Second, a series of strict principles were identified in the early stages of publication retrieval, while two individuals were selected to review the publications initially searched so that many non-compliant documents were eliminated. However, bias in the selection of publications was difficult to avoid. Notwithstanding, we believe that our findings provide a relatively comprehensive overview of the gut microbiota and lung cancer.
## Conclusion
This bibliometric study provides an overview of the major research hotspots and frontiers in the field of gut microbiota and lung cancer, which provided potential collaborators and institutions, and hot topics. Specifically, the number of publications in this field has grown rapidly over the past decade. China has the largest number of publications in this field, while the United States and France have a greater influence. It is necessary to further strengthen cooperation among countries, especially emerging countries. The clinical and mechanism research of the gut microbiota in immunotherapy of lung cancer is currently a hotspot in this field. Although many studies have confirmed that the modulation of the gut microbiota can treat lung cancer and improve anti-tumor efficacy, there are few clinical trials in this field, especially in lung cancer patients with radio-chemotherapy. Therefore, more in-depth studies on the clinical efficacy and safety examination of the gut microbiota in lung cancer patients are needed. The findings of this study provide new perspectives on the field, which may shed light on a beneficial impact on further etiological studies, diagnosis, and treatment of lung cancer.
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Author contributions
HC, YL, and QY designed the study. HC, YL, and CY were responsible for data collection. HC, YL, CY, CW, and JZ were responsible for the investigation and construction of figures and tables. HC, and YL drafted the manuscript. QY revised and approved the final version of the manuscript.
## 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.
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|
---
title: 'Effect of replacing television viewing with different intensities of physical
activity on COVID-19 mortality risk: Short communication from UK Biobank'
authors:
- Malik Hamrouni
- Nicolette Bishop
journal: Scandinavian Journal of Public Health
year: 2023
pmcid: PMC9996135
doi: 10.1177/14034948231158441
license: CC BY 4.0
---
# Effect of replacing television viewing with different intensities of physical activity on COVID-19 mortality risk: Short communication from UK Biobank
## Abstract
### Aims:
This study aimed to examine the theoretical effects of replacing television (TV) viewing with different intensities of physical activity on COVID-19 mortality risk using isotemporal substitution models.
### Methods:
The analytical sample was composed of 359,756 UK Biobank participants. TV viewing and physical activity were assessed by self-report. Logistic regressions adjusted for covariates were used to model the effects of substituting an hour a day of TV viewing with an hour of walking, moderate-intensity physical activity (MPA) or vigorous-intensity physical activity (VPA) on COVID-19 mortality risk.
### Results:
From 16 March 2020 to 12 November 2021, there were 879 COVID-19 deaths in the analytical sample. Substituting an hour a day of TV viewing with an hour of walking was associated with a $17\%$ lower risk of COVID-19 mortality (odds ratio (OR)=0.83, $95\%$ confidence interval (CI) 0.74–0.92). In sex-stratified analyses, the same substitution was associated with a lower risk in both men (OR=0.85, $95\%$ CI 0.74–0.96) and women (OR=0.78, $95\%$ CI 0.65–0.95). However, replacing an hour a day of TV viewing with an hour of MPA was only associated with a lower risk in women (OR=0.80, $95\%$ CI 0.65–0.98).
### Conclusions:
Replacing TV viewing with walking was associated with a significant reduction in COVID-19 mortality risk. Public health authorities should consider promoting the replacement of TV viewing with walking as a protective strategy against COVID-19 mortality.
## Introduction
Physical inactivity and a high television (TV) viewing time have been shown to be risk factors for severe disease and mortality from COVID-19 [1–3]. Whilst the available research suggests that increasing physical activity and reducing TV viewing time may confer a protective effect against COVID-19, studies so far have only demonstrated the effect of a particular behaviour without considering that a change in said behaviour may displace other behaviours [4].
Isotemporal substitution models allow for the assessment of the theoretical effect on health outcomes of replacing one behaviour with another in an equal time-exchange manner [5]. As highlighted by Mekary et al., this accounts for the confounding effect of other relevant behaviours when assessing the role of a specific behaviour and enables better application of findings to public health recommendations [4].
Therefore, using data provided by UK Biobank, isotemporal substitution models were used to explore the theoretical effect of replacing TV viewing with walking, moderate-intensity physical activity (MPA) and vigorous-intensity physical activity (VPA) on the risk of COVID-19 mortality.
## Methods
All data were provided by UK Biobank, a cohort of >500,000 individuals recruited from the general population, aged 37–73 years at the time of baseline assessment (2006–2010) [6]. Exposure variables and covariates were assessed at baseline. COVID-19 mortality data were obtained via linkage of the cohort with NHS Digital. The analytical period for this study was 16 March 2020–12 November 2021. UK Biobank obtained ethical approval from the North West Multi-centre Research Ethics Committee. All participants gave written informed consent.
Baseline age was calculated from date of birth, and NHS records were used to determine sex. Height and weight were used to calculate body mass index (BMI; kg/m2). A touchscreen questionnaire was completed by participants to determine ethnicity, tobacco smoking and alcohol intake frequency, and number of cancer and non-cancer illnesses. The Townsend Index was used as a proxy for material deprivation and was determined from each participant’s postcode at recruitment. The Townsend *Index is* an area-level deprivation metric calculated based on non-home ownership, non-car ownership, unemployment and overcrowding within any geographical area [7]. The Townsend Index has been shown to be strongly correlated with measures of deprivation calculated at the individual level and to be similarly predictive of health [8]. Self-reported physical activity (for walking, MPA and VPA) was assessed using questions adapted from the International Physical Activity Questionnaire (IPAQ) short form [9]. The questions captured the frequency (days per week) and duration (minutes per day) of walking, MPA and VPA on a typical day. This information was then used to calculate hours per day spent doing each behaviour. All physical activity data processing was carried out in line with IPAQ guidelines [10]. To obtain TV viewing time, participants were asked how many hours they spend on a typical day watching TV. Values between 16 and 24 hours per day were recoded as 16 hours per day to minimise the influence of possible spurious outliers (i.e. winsorizing), in line with previous UK Biobank studies [11,12]. Mortality from COVID-19 was defined as the presence of ICD-10 code U071 (virus identified in laboratory testing) or U072 (clinical or epidemiological diagnosis) as a primary or contributary cause on the death certificate.
Isotemporal substitution models were used to examine the effect of replacing an hour a day of TV viewing with an hour of walking, MPA or VPA on COVID-19 mortality risk. These models included walking time, MPA time, VPA time and the total time in all behaviours (i.e. walking+MPA+VPA+TV viewing) [13]. By removing TV viewing time from these models and including total time in all behaviours, the output from these models represents the effect of replacing an hour a day of TV viewing with the same amount of time of another behaviour [13]. The aforementioned analyses were conducted across the whole sample and then stratified by sex. Performing sex-stratified analyses was determined a priori based on previous research suggesting there may be sex-based differences in the protective association of physical activity against COVID-19 [14]. However, we also tested whether there were significant interaction effects of sex with our exposure variables. Potential confounders included baseline age, sex, ethnicity, tobacco smoking and alcohol intake frequency; Townsend Index; and number of cancer and non-cancer illnesses. In line with Rowlands et al., BMI was not adjusted for in the main analysis, as it may be on the causal pathway for the protective effect of physical activity against COVID-19 [15]. However, a sensitivity analysis was performed whereby all models were further adjusted for BMI. Results are reported as odds ratios (ORs) with $95\%$ confidence intervals (CI). Statistical significance was accepted at $p \leq 0.05.$ All statistical analyses were performed using R (The R Foundation for Statistical Computing, R version 4.2.0, Vienna, Austria).
## Results
The final analytical sample was composed of 359,756 individuals after removing those who were lost to follow-up, those who died before 16 March 2020 and those with missing data. There were 879 COVID-19 deaths over the analytical period (16 March 2020 to 12 November 2021). Participant demographics for the full sample and stratified by sex are presented in the Table I. A participant flow diagram is shown in Figure 1. The number of participants with missing data for each variable can be found in the Supplemental Material (Supplemental Table SI). A notable number of participants had missing data for either walking, MPA or VPA ($23\%$). Participant demographics for those who did and did not die from COVID-19 are presented in the Supplemental Material (Supplemental Table SII).
The isotemporal substitution models are shown in Table II. For the full sample, the isotemporal substitution models demonstrated that replacing an hour a day of TV viewing with an hour of walking was associated with a significantly lower risk of COVID-19 mortality. Sex-stratified analyses were performed, although no statistically significant interactions for sex with our exposure variables were observed. In both men and women, replacing an hour a day of TV viewing with an hour of walking was associated with a significantly lower risk of COVID-19 mortality. However, replacing an hour a day of TV viewing with an hour of MPA was only associated with a significantly lower risk in women.
**Table II.**
| Unnamed: 0 | Walking | MPA | VPA |
| --- | --- | --- | --- |
| Full sample | 0.83 (0.74–0.92) | 0.90 (0.81–1.01) | 0.86 (0.70–1.06) |
| Men | 0.85 (0.74–0.96) | 0.96 (0.84–1.09) | 0.83 (0.65–1.05) |
| Women | 0.78 (0.65–0.95) | 0.80 (0.65–0.98) | 0.94 (0.62–1.42) |
Including BMI into the regression models rendered several associations no longer significant (Supplemental Table SIII). However, replacing an hour a day of TV viewing with walking was still associated with a significantly lower risk of COVID-19 mortality when analysing the full sample.
## Discussion
The isotemporal substitution models used in this study are the first to model the effect of replacing an hour a day of TV viewing with different physical activities (walking, MPA and VPA) on COVID-19 mortality risk. Across the full sample and the sex-stratified analyses, substituting an hour a day of TV viewing with walking was associated with a lower risk of COVID-19 mortality. However, replacing an hour a day of TV viewing with MPA was only associated with a lower risk in women.
When analysing the full sample, differential changes in COVID-19 mortality risk were found when replacing an hour a day of TV viewing with walking compared to MPA and VPA. The stronger associations observed for walking compared to MPA and VPA – which persisted even after adjustment for BMI in the sensitivity analysis – are interesting, given that some studies suggest that higher-intensity physical activity may confer greater health benefits [16,17]. However, this is not always the case. Using isotemporal substitution models to assess how replacing TV viewing with walking, MPA and VPA may modify the risk of developing specific cancers, Hunter et al. observed several instances in which substituting with walking but not MPA or VPA was associated with a lower risk (e.g. lung cancer) [13]. Similarly, Stamatakis et al. found that in individuals with a high sitting time (>6 hours a day) replacing an hour a day of sitting with walking but not MPA was associated with a lower risk of all-cause mortality [18].
Although there were no statistically significant interaction effects for sex, replacing an hour a day of TV viewing with MPA was associated with a significantly lower risk of COVID-19 mortality in women but not in men. The potentially stronger protective association of physical activity in women observed herein is consistent with previous findings from Rowlands et al., who found stronger relationships for all of the analysed physical activity metrics in women compared to men in relation to risk of severe COVID-19 [14]. Given that men are at disproportionately higher risk of severe COVID-19 [19], the ability for modifiable lifestyle factors such as physical activity to mitigate risk may be reduced. The current findings highlight the importance of not solely including sex as a confounder in studies examining the role of physical activity, as doing so may mask potential sex-based differences regarding the protective associations of physical activity.
The current study has several limitations. The analysed physical behaviours were measured by self-report, making them subject to recall and social desirability bias. The progression through walking to MPA to VPA reflects increases in activity intensity. However, the intensities within each activity domain are not necessarily homogenous. Indeed, research has shown that brisk walking may confer greater health benefits than slower walking [20,21]. Future studies using isotemporal substitution with stratification by time spent at different walking intensities (as done by Mekary et al. [ 4]) may help to elucidate further the protective association of walking against COVID-19. Furthermore, the UK Biobank population is healthier than the general population [22], meaning that the analysed sample is not representative. However, this likely does not affect the identification of risk factors for diseases [23]. As this is an observational study, causal associations cannot be inferred from the results, and the potential for confounding from unmeasured covariates cannot be dismissed. Although we adjusted for the Townsend Index (an area-level deprivation metric which has been shown to be strongly correlated with individual-level deprivation metrics) [8], future research may nevertheless benefit from further adjustment for individual-level data (e.g. occupation/employment status, income and education), in addition to exploring/comparing the extent to which area- and individual-level deprivation may modify the effect of TV viewing and physical activity on COVID-19 mortality risk. Another limitation of this study is that we observed a high number of participants with missing physical activity data, which may reduce statistical power, introduce selection bias and reduce the representativeness of the sample [24]. When interpreting the findings, one should also consider that a much greater number of participants did not partake in any MPA or VPA compared to walking ($14\%$ and $37\%$ of the analytical sample did not perform any MPA and VPA, respectively, compared to $2\%$ not performing any walking). This may have hindered our ability to detect significant protective associations for replacing TV viewing with MPA and VPA and may explain the markedly wider confidence intervals observed for VPA compared to walking.
In conclusion, the key finding of this short communication was that replacing TV viewing with walking was associated with a significant reduction in risk of COVID-19 mortality. Although future research investigating mechanisms is required to determine the biological plausibility of the present findings, the results are encouraging, especially considering that walking is a more convenient physical activity to partake in compared to MPA and VPA for most individuals. Based on the current results, public health authorities should consider promoting the replacement of TV viewing with walking as a protective strategy against COVID-19 mortality.
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|
---
title: 'Nomogram prediction model of postoperative pneumonia in patients with lung
cancer: A retrospective cohort study'
authors:
- Fan Jin
- Wei Liu
- Xi Qiao
- Jingpu Shi
- Rui Xin
- Hui-Qun Jia
journal: Frontiers in Oncology
year: 2023
pmcid: PMC9996165
doi: 10.3389/fonc.2023.1114302
license: CC BY 4.0
---
# Nomogram prediction model of postoperative pneumonia in patients with lung cancer: A retrospective cohort study
## Abstract
### Background
The prediction model of postoperative pneumonia (POP) after lung cancer surgery is still scarce.
### Methods
Retrospective analysis of patients with lung cancer who underwent surgery at The Fourth Hospital of Hebei Medical University from September 2019 to March 2020 was performed. All patients were randomly divided into two groups, training cohort and validation cohort at the ratio of 7:3. The nomogram was formulated based on the results of multivariable logistic regression analysis and clinically important factors associated with POP. Concordance index (C-index), receiver operating characteristic (ROC) curve, calibration curve, Hosmer-Lemeshow goodness-of-fit test and decision curve analysis (DCA) were used to evaluate the predictive performance of the nomogram.
### Results
A total of 1252 patients with lung cancer was enrolled, including 877 cases in the training cohort and 375 cases in the validation cohort. POP was found in 201 of 877 patients ($22.9\%$) and 89 of 375 patients ($23.7\%$) in the training and validation cohorts, respectively. The model consisted of six variables, including smoking, diabetes mellitus, history of preoperative chemotherapy, thoracotomy, ASA grade and surgery time. The C-index from AUC was 0.717 ($95\%$CI:0.677-0.758) in the training cohort and 0.726 ($95\%$CI:0.661-0.790) in the validation cohort. The calibration curves showed the model had good agreement. The result of DCA showed that the model had good clinical benefits.
### Conclusion
This proposed nomogram could predict the risk of POP in patients with lung cancer surgery in advance, which can help clinician make reasonable preventive and treatment measures.
## Introduction
As the Global Cancer Statistics reported in 2020, lung cancer has become the second most common cancer and the highest rate of cancer-related death [1]. The treatments of lung cancer mainly include radiotherapy, chemotherapy, surgery, targeted therapy, immunotherapy. However, surgical resection is still an effective and safe intervention for patients with lung cancer.
Unfortunately, the postoperative pulmonary complications (PPCs) of lung cancer surgery are still common problems and major challenges for patient recovery. And postoperative pneumonia (POP) has become the most common PPCs [2, 3]. Several studies have found that the rate of POP in lung cancer patients is about $2\%$-$25\%$ (4–7). And POP could significantly prolong the length of hospitalization, increase hospitalization expense, and even increase perioperative mortality (4, 8–10). Thus, early identification of risk factors associated with POP among patients with lung cancer could be beneficial to forecast the risk of POP in advance.
Potential risk factors for POP existed throughout the whole perioperative period [11]. Preoperative factors mainly include elderly, smoking, pulmonary function, comorbidities and nutritional status [12, 13]. Intraoperative factors include surgery types, duration of surgery, anesthesia types and ventilation mode [6, 14]. Postoperative factors include acute pain and other complications [15, 16]. A retrospective observational cohort ($$n = 7479$$) found that elderly, preoperative pulmonary infection, atrial fibrillation, obesity, and alcohol might be associated with POP among lung cancer patients [10]. Deguchi et al. [ 5] found preoperative asthma might also be independently associated with POP in lung cancer patients. And Yendamuri et al. [ 17] found age >75 years, male, thoracotomy, COPD and American Society of Anesthesiologists (ASA) ≥III might be potential risk factors after analyzing a range of patients ($$n = 12562$$) who underwent pulmonary lobectomy. However, most of the present studies on POP in lung cancer patients had only identified risk factors for POP, studies about prediction models for POP were very limited.
Although there had a model to predict POP for elderly patients who underwent video-assisted thoracoscopic surgery for lung cancer, the data was collected from 2012 to 2019 and other variables associating with POP such as anesthesia types, anesthetics and surgery types were scarce [4]. Thus, the development of a model after fully considering the preoperative and intraoperative variables to forecast the risk of POP in advance would be beneficial to patients.
Therefore, the purpose of this study was to develop and validate a model to predict POP in patients undergoing lung cancer surgery and investigate risk factors for POP so that reasonable preventive and treatment measures could be made earlier.
## Study design
This is a retrospective cohort study, which has been approved by Clinical Research Ethics Committee of the Fourth Hospital of Hebei Medical University (No:2022KS024), Shijiazhuang, Hebei Province, China (Chairperson Prof Hongtao He) on 28 July 2022. The informed consent was exempted with the approval of the local ethics committee. All patients were randomly separated into training and validation cohorts at the ratio of 7:3 which was similar to other studies (18–20). The training cohort was conducted to develop prediction model, while both training and validation cohorts were used to verify the predictive ability of the model.
## Participants
We retrospectively analyzed patients who underwent thoracic surgery from September 2019 to March 2020 at the Fourth Hospital of Hebei Medical University. The inclusion criteria were patients aged ≥ 18 years with pathology diagnosis of lung cancer and underwent surgery. Patients were excluded if they met one or more of following criteria: 1) preoperative pneumonia diagnosed by computed tomography (CT), 2) bilateral pulmonary resection, 3) reoperation within 30 days, 4) admitted to ICU after surgery, 5) missing data.
We initially screened 1651 patients who underwent thoracic surgery from September 2019 to March 2020 (Figure 1). Of these, 233 patients were benign mass and the rest of 1418 patients were included in the study. After data collection, 166 patients were removed from the final analysis: 98 patients had preoperative pneumonia confirmed by CT; 31 patients required bilateral pulmonary resection; 13 patients required reoperation within 30 days; 14 patients admitted to ICU after surgery; 10 patients had missing data. Finally, a total of 1252 patients were admitted into the present study. The training cohort had 877 patients who were aged 60.0 ± 9.4 years. The validation cohort consisted of 375 patients who were aged 59.5 ± 9.7 years. The incidence of POP was $22.9\%$ in the training cohort, $23.7\%$ in the validation cohort and $23.2\%$ among all patients. All variables expect for Hemoglobin ($P \leq 0.05$) were no statistically significant differences between two groups (other $P \leq 0.05$) (Table 1).
**Figure 1:** *Flow chart of patients screening and recruitment.* TABLE_PLACEHOLDER:Table 1
## Perioperative management
All patients received general anesthesia, either alone or in combined with regional nerve block (including paravertebral nerve block, epidural anesthesia, and intercostal nerve block.) according to the type of surgery. Patients underwent lobectomy or sublobectomy according to surgeon’s comprehensive evaluation based on patient’s condition.
Anesthesia induction used propofol and/or etomidate, sufentanil, and rocuronium or cisatracurium. Anesthesia maintenance used sevoflurane or propofol combined with remifentanil or sufentanil. Rocuronium or cisatracurium was used to maintain muscle relaxation. Supplemental drugs such as flurbiprofen axetil were administered when necessary. The aim was to maintain BIS 40-60, blood pressure within $20\%$ of baseline, and temperature 36-37°C.
Double-lumen endotracheal tube of sizes Ch33-39 was used for lung isolation according to patient height. The ventilation mode was volume control mode with 6-8 ml/kg of tidal volume (TV) during two-lung ventilation and 5-6 ml/kg during one-lung ventilation (OLA), and 0-5 cmH2O of positive end-expiratory pressure (PEEP), and 12-20 breaths/min of respiratory rates. The aim was to maintain PETCO2 35-45 mmHg and SpO2 ≥$92\%$. At the end of anesthesia, neostigmine was used to antagonize muscular relaxant before extubation.
Fluid infusion was administrated with crystalloid at a rate of 4–6 mL/kg-1h-1. Colloids or blood product was used according to anesthesiologist’s comprehensive evaluation based on patient’s condition. Patient-controlled intravenous analgesia was used after surgery for postoperative analgesia to maintain numeric rating scales (NRS) ≤ 3 scores.
## Data collection
We collected following variables, including: 1) basic demographics such as age, sex, history of smoking, body mass index (BMI), preoperative chemotherapy, history of lung surgery; 2) preoperative comorbidities containing hypertension, chronic obstructive pulmonary disease (COPD), asthma, diabetes, coronary heart disease, arrhythmia; 3) preoperative laboratory testing including Hemoglobin, serum albumin, Serum glucose; 4) preoperative pulmonary function including forced vital capacity rate of one second(FEV1/FVC), diffusion capacity for carbon monoxide of the lung(DLCO); 5) surgery related characteristics including surgery types, surgery extent, surgery sides, duration of surgery; 6) anesthesia related characteristics including ASA grade, anesthesia types, use of flurbiprofen axetil, use of colloid, allogenic blood transfusion, Input per unit of time (ml·kg-1·h-1). Smoking was defined as smoking index ≥ 400. Duration of surgery was defined as the time interval between skin incision and suture. Input per unit of time was equal to total input divided by duration of surgery and actual weight.
## Diagnosis of pneumonia
POP was occurred during hospitalization, which defined as follows [21]: patient has received antibiotics for a suspected respiratory infection and met one or more of the following criteria: 1) new or changed sputum, 2) new or changed lung opacities, 3) fewer (>38.3°C), 4) white blood cell count >12×109/L.
## Statistical analysis
The normal distribution data were present as mean ± standard deviation (SD) and compared by the independent sample t-test, while the non-normal distribution data were present as median(Q 1, Q 3) and compared by the Wilcoxon test. And the categorical data were present as number and percentages, and compared by the Chi-square test.
The Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression with 5-fold cross-validation was used to adjust the parameter lambda to screen the variables. And the lambda corresponding to the minimum mean square error was used for selecting variables. The multivariate logistic regression analysis was used to analyze characteristic variables selected by LASSO regression to explore the independent risk factors associated with POP. A nomogram was built according to the independent risk factors and clinically important factors associated with POP.
The AUROC and C index were used to measure the discrimination ability according to the data from training and validation cohorts. And the calibration ability was measured by the calibration curve and Hosmer-Lemeshow goodness-of-fit test. The clinical benefit was measured by the decision curve analysis (DCA). Statistical analysis was performed with R software (version 3.5.3; https://www.R-project.org). A $p \leq 0.05$ with two sides was considered statistical significance.
## Development of prediction model
The protential risk factors of POP were selected by LASSO regression (Figure 2). The coefficients of relatively irrelevant variables were minimized to 0 and subsequently were excluded according to the value of lambda. The LASSO regression showed the optimal value of lambda was 0.015 (Table 2). And 12 non-zero representative variables were remained, including age, history of smoking, diabetes, preoperative chemotherapy, FEV1/FVC, DLCO, surgery type, ASA grade, use of flurbiprofen axetil, use of colloid, input per unit of time and duration of surgery (Table 2).
**Figure 2:** *Perioperative variables selection using the Least Absolute Shrinkage and Selection Operator (LASSO) regression.* TABLE_PLACEHOLDER:Table 2 On the multivariate logistic regression analysis, there were five variables independently associated with POP, including diabetes (OR=1.838; $95\%$CI:1.110-3.001); preoperative chemotherapy (OR=3.997; $95\%$CI:2.014-8.093); Thoracotomy (OR=1.891; $95\%$CI:1.126-3.138); ASA grade (OR=1.760; $95\%$CI:1.105-2.780); and duration of surgery (OR=1.486; $95\%$CI:1.268-1.750) (Table 3).
**Table 3**
| Variables | β Coefficient | OR (95%CI) | P-Value |
| --- | --- | --- | --- |
| Diabetes (Y/N) | 0.608 | 1.838 (1.110-3.001) | 0.016 |
| Preoperative chemotherapy(Y/N) | 1.386 | 3.997 (2.014-8.093) | <0.001 |
| Surgery type(Thoracotomy/VATS) | 0.637 | 1.891 (1.126-3.138) | 0.015 |
| ASA(III/II) | 0.565 | 1.760 (1.105-2.780) | 0.016 |
| Duration of surgery (h) | 0.396 | 1.486 (1.268-1.750) | <0.001 |
Although multivariate logistic regression analysis showed smoking was not an independent factor, the coefficient of smoking was larger according to LASSO regression. We thought smoking might influence the incidence of POP. So we used five independent risk factors and smoking to draw a nomogram to develop a POP prediction model. The dynamic nomogram of POP is available online (https://lungcancersurgery.shinyapps.io/DynNomapp/). The code of dynamic nomogram is presented in Supplement Material.
## Validation of prediction model
In the present, the uncorrected C index was 0.717 ($95\%$CI:0.677-0.758) and bootstrap-corrected C index was 0.710 in the training cohort, while the uncorrected C index was 0.726 ($95\%$CI: 0.661-0.790) and bootstrap-corrected C index was 0.709 in the validation cohort (Figure 3). These results showed the nomogram had good accuracy in distinguishing patients with and without POP. Besides, the calibration curve showed good consistency on the presence of POP between prediction by the nomogram and results of actual clinical data (Figure 4), which demonstrated by Hosmer-Lemeshow goodness-of-fit test both in the training and validation cohorts (both $P \leq 0.05$). At the same time, the decision curve analysis showed a positive net benefit when the predicted probability threshold is $0\%$-$80\%$, indicating this nomogram had good clinical benefit (Figure 5).
**Figure 3:** *The receiver operating characteristic (ROC) curve of POP risk nomogram.* **Figure 4:** *The calibration curve of POP risk nomogram. (A) Calibration curve in the training cohort (n = 877). (B) Calibration curve in the validation cohort (n = 375).* **Figure 5:** *The decision curve analysis (DCA) of POP risk nomogram.*
## Discussion
This study developed and validated a nomogram to accurately forecast the risk of POP in patients who underwent lung cancer surgery. This model included six preoperative and intraoperative variables, including smoking, diabetes, preoperative chemotherapy, surgery types, ASA grade and duration of surgery, which predicted well as demonstrated by the uncorrected C index values of 0.717 and 0.726 in the training and validation cohorts. At the same time, the calibration curves showed good consistency between prediction and actual observation, respectively, and the decision curve analysis indicated this nomogram had good clinical benefit.
LASSO regression is a common method for variable selection in fitting high-dimensional generalized linear and has been widely used in clinical research [22, 23]. The LASSO method selects variables via minimizing the coefficients of relatively irrelevant variables to 0 and subsequently removing these variables by constructing a penalty function, which effectively avoids the overfitting and makes the model more refined [24, 25]. So we used the LASSO regression for variable selection in this study.
The nomogram we used is a superior visual tool and has been widely used in the clinical practice, which has several advantages. Firstly, the nomogram could transform predictive model into a single estimate of probability according to patient’s characteristics, making model simple to understand [26]. Secondly, the scoring system has high precision and good stability characteristics in predicting results [27, 28]. Therefore, we used nomogram to build the visual model to help clinicians to stratify patients and develop individual clinical treatment strategies according to patient’s conditions.
Evaluating the characteristics of the predictive model from multiple perspectives and selecting the optimal model could help promotion and application of the model [29]. The calibration ability is model’s capability to demonstrate the consistency between the actual observed and the prediction by the model, which is one of the best indicators to reflect predictive performance of the model [29]. Therefore, we used calibration curve and Hosmer-Lemeshow goodness-of-fit test to evaluate calibration ability of this model. And good agreements between prediction and actual observation were supported by Hosmer-Lemeshow goodness-of-fit test. However, good calibration couldn’t perfectly distinguish patients with or without POP. The ROC curve and C index had certain advantages in measuring discrimination ability of the model [30]. And the results showed that the model could distinguish patients with and without POP. Furthermore, we used the decision curve analysis and net benefit to evaluate the clinical benefit of the model [31]. The results suggested that using this model to assist clinical treatment strategies might help improve patient prognosis.
In the POP risk estimation nomogram, preoperative chemotherapy, thoracotomy, ASA and duration of surgery have been confirmed to increase the risk of POP [17, 32, 33]. This study showed that above factors were also independent risk factors associated with POP in lung cancer patients. In addition, we illustrated that diabetes was associated with POP in patients with lung cancer surgery.
In previous reports, diabetes was associated with POP after surgery [34]. The incidence of POP in type 2 diabetes mellitus patients was $21\%$ higher than non-diabetic patients [35]. In the present study, we found the risk of POP was higher in diabetes patients (OR=1.838; $95\%$CI:1.110-3.001) after lung cancer surgery. The potential mechanisms were diabetes could destroy innate immunity in pulmonary, impair pulmonary function and reduce cardiorespiratory fitness making patients more susceptible to infections [36].
However, this study still has some limitations. At first, the bias of patient selection could not be entirely avoided because it was a single-center retrospective study. However, we screened patients through strict inclusion and exclusion criteria, which could reduce population homogeneity to some extent. Secondly, the definition of POP in our study might be generic and sensitive to diagnose pneumonia, which made the incidence of POP a little higher than other studies. However, we used the same criteria in diagnosing pneumonia in our study which could make result reliable to some extent. Thirdly, intraoperative respiratory parameters, such as tidal volume, minute ventilation, PEEP were not included in our study because these variables could not be collected. However, some studies had found that intraoperative ventilation strategy might not be associated with postoperative pulmonary complications [37, 38]. Finally, some postoperative variables, such as postoperative pain, postoperative aerosolized inhalation might influence the rate of POP among patients after lung cancer surgery, but these variables were not analyzed in the study because we aimed at predicting POP through preoperative and intraoperative variables rather than postoperative variables.
## Conclusion
This study developed and validated a predictive model representing by the nomogram to quantify the risk of POP in patients with lung cancer surgery. This model showed good discrimination ability, calibration ability and clinical benefit which could help make better prevention and individual treatment strategies in advance.
## Data availability statement
The code of dynamic nomogram in the study is included in the article/ Supplementary Material. Further inquiries can be directed to the corresponding author.
## Ethics statement
The study involving human participants was reviewed and approved by Ethics Committee of The Fourth hospital of Hebei Medical University. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
JF: This author helped in data acquisition, data analysis, and manuscript drafting. LW: This author helped in data acquisition, and data analysis. XQ: This author helped in data acquisition, and data analysis. JS: This author helped in data acquisition. RX: This author helped in data acquisition. H-QJ: This author helped in concept and design, data analysis, data interpretation, revision of the manuscript, and final approval of submission. 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/fonc.2023.1114302/full#supplementary-material
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|
---
title: 'Exploring the provision and support of care for long-term conditions in dementia:
A qualitative study combining interviews and document analysis'
authors:
- Jessica Rees
journal: Dementia (London, England)
year: 2023
pmcid: PMC9996169
doi: 10.1177/14713012231161854
license: CC BY 4.0
---
# Exploring the provision and support of care for long-term conditions in dementia: A qualitative study combining interviews and document analysis
## Abstract
### Background
The challenge of managing multiple long-term conditions is a prevalent issue for people with dementia and those who support their care. The presence of dementia complicates healthcare delivery and the development of personalised care plans, as health systems and clinical guidelines are often designed around single condition services.
### Objective
This study aimed to explore how care for long-term conditions is provided and supported for people with dementia in the community.
### Methods
In a qualitative, case study design, consecutive telephone or video-call interviews were conducted with people with dementia, their family carers and healthcare providers over a four-month period. Participant accounts were triangulated with documentary analysis of primary care medical records and event-based diaries kept by participants with dementia. Thematic analysis was used to develop across-group themes.
### Findings
Six main themes were identified from eight case studies: 1) Balancing support and independence, 2) Implementing and adapting advice for dementia contexts, 3) Prioritising physical, cognitive and mental health needs, 4) Competing and entwined needs and priorities, 5) Curating supportive professional networks, 6) Family carer support and coping.
### Discussion
These findings reflect the dynamic nature of dementia care which requires the adaptation of support in response to changing need. We witnessed the daily realities for families of implementing care recommendations in the community, which were often adapted for the contexts of family carers’ priorities for care of the person living with dementia and what they were able to provide. Realistic self-management plans which are deliverable in practice must consider the intersection of physical, cognitive and mental health needs and priorities, and family carers needs and resources.
## Background and objectives
As people age, the risk of developing long-term conditions increases (Barnett et al., 2012). So too, does the risk of developing dementia (Corrada et al., 2010). The number of people living with dementia worldwide is predicted to double every twenty years (Dementia Forcasting Collaborators, 2022; Prince et al., 2015). Long-term conditions are common in people with dementia (Poblador-Plou et al., 2014), with an estimated 8 in every 10 people with dementia living with another long-term condition, such as diabetes, hypertension and stroke (Public Health England, 2019). With a healthcare system that is often designed around single condition services, improving the treatment and management of multiple long-term conditions is an important challenge for health services (Coulter et al., 2013).
When living with a chronic disease, self-management is a daily task (Lorig & Holman, 2003). This task is severely impacted by the symptoms of dementia (Ibrahim et al., 2017). Self-management support from family carers, social care and primary care can facilitate the optimal management of long-term conditions to prevent hospitalisation, slow cognitive decline, and enable people with dementia to live independently at home for longer (Bordier et al., 2014; Doraiswamy et al., 2002; Fox et al., 2014). A recent systematic review highlighted the importance of collaboration between family members, healthcare professionals and homecare workers to support long-term condition management for people with dementia (Rees, Tuijt, et al., 2020).
The presence of dementia complicates healthcare delivery and the development of person-centred physical health care plans (Bunn, Burn, et al., 2017). The ideal of person-centred care lies at the centre of many models of care (Coulter et al., 2013; Wagner et al., 1996) and health and social care policies (Department of Health, 2016; NHS, 2014; NHS England, 2019) which have been developed to manage complex care needs. Such care is proposed to be holistic, integrated and organised by need and not disease (World Health Organisation, 2015). A greater understanding of the needs of people with dementia and multiple long-term conditions is required to ensure condition-specific guidelines are relevant (Scrutton & Brancati, 2016). In a recent secondary analysis of qualitative interviews, we highlighted the importance of identifying changes in self-management ability as dementia progresses to adapt care accordingly (Rees, Burton, et al., 2020). This study provided a breadth of information regarding what stakeholders consider priorities for care delivery, however few studies have explored in-depth the experiences of people with dementia and long-term conditions. The triangulation of multiple data sources can also enable a greater understanding of health and social issues (Farmer et al., 2006).
In the present study, we used multiple data sources to qualitatively explore how care for long-term conditions in dementia was provided and supported in the community. As our study took place in $\frac{2020}{1}$, we also explored how care provision for long-term conditions in dementia were affected during the pandemic.
## Setting and sample
Participants were eligible to take part if they had a documented diagnosis of dementia of any severity, and an additional diagnosed long-term condition. This was defined as a health condition requiring ongoing support from primary care or significant elements of self-management. We used prevalence studies to identify common long-term conditions in people with dementia (Browne et al., 2017; Public Health England, 2019). List of eligible conditions included, diabetes, asthma, Chronic Obstructive Pulmonary Disease (COPD), arthritis, stroke, and heart failure/disease. We included participants with dementia living in the community, including those who lacked capacity to consent to research. Abiding by the Mental Health Capacity Act of England and Wales [2005], the lead author assessed capacity of people with dementia. If the person with dementia lacked capacity to decide whether to take part, family carers were invited to act as personal consultee. People with dementia who had capacity were not required to have a family carer who provided regular support to participate in the study.
We included family carers providing regular support (at least weekly contact) for the person with dementia to manage their health-related activities. This included medication and appointment management and broader aspects of health such as exercise and nutrition. Health and social care professionals were deemed eligible if they were identified by participants with dementia and/or their family carers as supporting management of or providing care for long-term conditions. We used purposively sampling to ensure a diverse range of experience including type of long-term condition, stage of dementia, age, gender, ethnicity of the person living with dementia and extent of involvement of family members and health or social care professionals. We recruited participants via social media, previous dementia research studies, Join Dementia Research and six general practices supported by the National Institute for Health and Care Research, Clinical Research Network (North Thames) using letters of invitation or by direct approach by healthcare professionals. Health and social care professionals identified by people with dementia (and family carers) taking part in the study were invited to participate via email. Written informed consent was obtained from all participants.
## Data collection
This study involved two complementary remote data collection methods. Firstly, JR undertook qualitative semi-structured interviews based on a topic guide (see Supplemental Material), followed by a series of participant led interviews over 4 months with people living with dementia and family carers via telephone or video call. Topic guides focused on the person with dementia’s health history, daily long-term condition management and support, understanding of current care plans, and impact of COVID-19 on condition management. Subsequent interviews explored how issues raised at the initial interview evolved, and any new issues which arose or where managed. Interviews were audio-recorded and transcribed verbatim. JR took observational field notes of dyadic interactions between participants with dementia and those involved in their care during video interviews.
Secondly, we undertook document analysis of consultation notes and care plans provided by primary care. We requested, with participant consent, the last ten consultation notes and care plans from general practitioner (GP) practices of participants. All identifiable information was redacted prior to being stored electronically on university systems. JR also invited participants with dementia if they were able, to complete event-based diaries. Over a two-week period, participants recorded specific events related to their management of long-term conditions, what they did to look after their health and who was involved.
## Data analysis
Using a reflexive thematic analysis approach (Braun & Clarke, 2019) from a post-positivist, or critical realist perspective (Bhaskar, 1979) we analysed data using NVivo 12 (QSR International Pty, 2018). Based on approaches which focus on the uniqueness of individual experience (Smith et al., 2009) we used a dual approach to analysis by developing themes within case studies before considering themes across cases. Following analysis of each case, co-authors (JR, AB, KW, CC, CC, KW, AB) met to discuss key concepts and reflected on interpretation of meaning and emerging themes. To triangulate data across sources, stakeholders and time, we used a ‘back-and-forth’ approach (Bowen, 2009). For example applying codes from the analysis of interview transcripts to the content of documents and vice versa. We then grouped themes within each case based on shared patterns of meaning and used discussion to develop and refine across group themes. To present themes, we used a thematic structure drawing on examples of individual experiences from case studies to further evidence central organising concepts (Clarke & Waring, 2018).
## Reflexivity
Reflexivity is core component for qualitative research (Rankl et al., 2021). During data collection and analysis, the lead author acknowledged their positionality as an ‘outside researcher’(Hellawell, 2007) with no experience of dementia or chronic illness. JR kept a reflective journal of the influence of her personal and academic biography on the research process, in addition to professional biographies of co-authors including an old age psychiatrist, GP and qualitative health researcher.
## Sample characteristics
Data were collected from 18 participants (that formed eight case studies, one case included two people living with dementia) between September 2020 and May 2021. Participants comprised of nine people with dementia, seven family carers and two healthcare professionals (GP and neurologist). Of the seven family carer participants, four were female and three were male. Four were spousal-carers and three child-carers. Table 1 provides the demographic characteristics of participants with dementia. Table 1.Characteristics of participants with dementia. Number of participants Age 70–793 80–893 90+3 Gender Female5 Male4 Ethnicity White7 South Asian2 *Marital status* Married6 Widowed2 Divorced1 Living arrangements With family8 Alone1 Type of dementia Alzheimer’s disease5 Vascular1 Mixed1 Frontotemporal1 Posterior cortical atrophy1 Stage of dementia Early2 Moderate4 Late3 Long-term conditions Diabetes3 Cardiovascular conditions*6 Visual impairment2 Hearing impairment2 Respiratory conditions3 Renal failure2 Arthritis3 Depression/anxiety3*included hypertension, stroke and heart failure.
JR conducted 26 interviews (between one and five per case study). Primary care records were collected for all nine participants with dementia. Two people with dementia kept an event-based diary. Observational field notes were recorded from four video interviews. The majority of interviews were conducted on a one-to-one basis with a family carer. For three case studies, dyadic interviews were conducted with the person with dementia and their family carer. Three people with dementia had capacity to consent to research, of whom two participated in interviews independently. For the remaining six, a family member acted as a personal consultee.
## Qualitative analysis findings
Across eight in-depth, diverse case studies (see Table 2) we identified six over-arching themes regarding how care for long-term conditions is provided and supported in dementia. 1) Balancing support and independence focused on the tension between the value of independence in health management for people with dementia, and the need for increased support by family carers as cognition declined. 2) Implementing and adapting advice for dementia contexts related to how family carers adapted care to overcome symptoms of dementia and in the absence of a holistic approach to care from services, family carers attempted to integrate advice for dementia and other long-term conditions. 3) Prioritising physical, cognitive and mental health needs considered the inevitable trade-offs between caring for physical, cognitive and mental health, often creating a hierarchy, which shifted according to changing needs. 4) Competing and entwined needs and priorities described the interconnectedness of the needs of people with dementia and their family carers and the influence of this on proxy-decision making. 5) Curating supportive professional networks focused on the role of the family carer in curating supportive professional relationships, from primary, secondary and social care, to facilitate healthcare. 6) Family carer support and coping considered decisions about when to access support and how this was influenced by perceptions of capacity to cope with the demands of caring. This included decisions to involve homecare workers to support the management of long-term condition in dementia. In the theme descriptions below, each name is a pseudonym, matched to gender and ethnicity of participants with dementia. Table 2.Overview of participant case studies. Case studiesCase descriptionData collection1. Edith and ScottEdith is in her 90s and has mixed Alzheimer’s disease and vascular dementia. She lives alone, supported by her son Scott and homecare workers who visit four times a day. Main issues are reduced mobility due to knee osteoarthritis and low mood. She uses a walking frame and is restricted by risk of falls to the first floor as she cannot use stairs. She accesses services (vaccination, opticians) via home visits. Dyadic video interview (x1)Family carer video interview (x4)Consultation notes (x1)2. Doris and BertDoris is in her mid-70s and has severe Alzheimer’s disease. Main concerns are nutrition, frailty and toileting. Husband Bert manages her diet, medication and appointments. Doris had chronic Myeloid Leukaemia for 16 years and retains a close relationship with the oncologist and pharmacist from these times. Family carer telephone interview (x1)Consultation notes (x1)3. Harold and DoraHarold is in his mid-80s and has early Alzheimer’s disease. He has insight into his declining cognition and increasing dependence on his wife, Dora. His main issues are visual impairment (glaucoma, cataracts) for which he takes eye drops. He has atrial fibrillation and is under investigation for heart failure due to progressing swollen legs. He self-monitors his heart rhythm via an app and jogs daily to manage low mood. Person with dementia telephone interview (x3)Family carer telephone interview (x2)Event-based diary (x1)Consultation notes (x1)4. Fiona and DeclanFiona is in her early 70s and has posterior cortical atrophy, causing severe dementia. Issues include swallowing, continence, agitation and poor sleep. Husband Declan manages her medication including a daily inhaler for COPD. Declan is recovering from open heart surgery during data collection, so daughter, Maeve, who has been furloughed due to the COVID-19 pandemic, supports with housework, food shopping and medication collection. Family carer telephone interview (x4)Consultation notes (x1)5. Margaret, Jonathan and SophieMargaret and Jonathan are husband and wife who both have dementia. Margaret, in her 80s has Alzheimer’s disease, anxiety and depression, COPD and renal failure. Jonathan, in his 90s, has vascular dementia since a stroke and diet-controlled diabetes. They are supported by their daughter Sophie who is their live in carer paid through personal budgets. Dyadic video interview (x1)Family carer video interview (x1)Family carer telephone interview (x1)Healthcare professional interview (x1)Consultation notes (x1)6. HassanHassan is in his late 80s and has mild Alzheimer’s disease. His main issues are insulin-controlled diabetes, arthritis and he has a colostomy. He lives in a multi-generational household with two sons and his daughter in law. Person with dementia telephone interview (x2)Consultation notes (x1)7. Samira and SarahSamira is in her 70s and has primary progressive aphasia (fluent variant). Her main issue is arthritic knee pain and a trapped nerve in her back, for which she has decided to have an elective operation. She manages her own medication for asthma and diabetes, supported by her daughter Sarah, who lives with her in a multi-generational household. Dyadic video interview (x1)Dyadic telephone interview (x3)Event-based diary (x1)Consultation notes (x1)Healthcare professional interview (x1)8. Albert and JeanAlbert is in his 90s and has moderate Alzheimer’s disease. He uses a walking frame to reduce his risk of falls as his balance worsened following a stroke. He has hearing loss. Albert lives with his wife Jean and is supported by a homecare worker who visits daily to facilitate personal care. Dyadic video interview (x2)Family carer telephone interview (x3)Consultation notes (x1)NB. Each name is a pseudonym, matched to gender and ethnicity of participants with dementia.
## Theme 1. Balancing support and independence
Balancing the need for increased support with declining cognition was an important process due to the value of independence for people living with dementia. For Hassan, a participant living with mild to moderate dementia, it was important to feel he did not depend on others. He stated ‘I do it myself’ eleven times across both interviews relating to his self-administration of insulin twice daily. GP consultation notes record that he was ‘taking all his meds.’ However, it is unclear whether this was the GP’s own assessment or Hassan’s self-report.
This strong sense of independence may have been related to insight into increasing dependence on others. Harold, another participant in the early stages of dementia, only had partial insight into his self-management abilities and his need for an increasing level of support from his wife, Dora, to manage his medication, eye drops and to advocate on his behalf in appointments. In his event-based diary, Harold stated he self-managed his medication: “I have my medication (many pills) well organised.” Dora was conscious of a need to balance Harold’s care needs and his need to perceive himself as autonomous, as she did not ‘want him to lose his independence.’ In the context of dementia, the desire for independence could at times lead to compromised quality of disease management. In the quote below, Hassan recounts support from his family when experiencing hypoglycaemic faints, suggesting he may have had difficulties self-managing his insulin injections. “ Once in the bathroom I passed out and couldn’t get up. Couldn’t get up at all. So I had to call my son and he had to call the ambulance and all that. And I was lying down in the bathroom for some time. And then slowly they pulled me up. This happened two, three times.” ( Hassan, Interview 1) A commonality across these accounts was the difficult balancing act for family carers, to ensure appropriate management of long-term conditions. Dora describes reaching a tipping point in this balance, where she intervened to ensure Harold received help for progressive cardiac failure. “ Now he has got a very swollen feet, extremely swollen and he just dismissed it as if nothing was wrong with it. I want him to go to the doctor. I have to insist for him to make an appointment, and at this stage I don’t want him to lose his independence. I just try to help as much as I can but I want to go to the doctor with him for that reason otherwise he does everything for himself.” ( Dora, Interview 1) In other scenarios, family carers felt able to accept decisions they did not agree with. Samira was successfully able to take an active role in managing her healthcare and care decisions with daily support from her daughter, Sarah. This included making many decisions independently, for example about having surgery. “ I didn’t really want her to have it. I thought obviously age, diabetes and the health and stuff, it’s not really conducive to do it. But I mean she did it. It was okay and the pain came back. And there was always the possibility the pain would not go away…But it’s part of, the complications were she probably end up with more back pain and she was happy to go through with the surgery.” ( Sarah, Interview 2)
A facilitator of this appeared to be the slow rate of Samira’s cognitive decline enabling her to maintain relative independence. In her event-based diary, Samira showed an awareness of her medical issues: “I have to see the doctor to discuss about my leg pains to discuss about root injection or surgery or an epidural.” For diabetes management, Sarah provided support to monitor Samira’s blood glucose levels.
## Theme 2. Implementing and adapting advice for dementia contexts
Implementing the advice given by healthcare professionals in the home to manage long-term conditions was a challenge for family carers, who adapted care to overcome symptoms of dementia. Bert and Declan, both husband carers for a person in the later stages of dementia, described needing to adapt condition-specific advice to dementia contexts. Both described the challenge of interpreting symptoms when their wives were unable to verbally communicatee with them due to dementia. Bert described how his ability to interpret symptoms with limited feedback was facilitated by his knowledge of his wife, Doris. “ You rely on the patient you say what works, how are you feeling, did that hurt, if I push you here what does… it is notoriously difficult because you get no, all medication requires patient feedback. And with Alzheimer’s that gradually diminishes to zero. You have to interpolate [sic], and if you know someone very well like [Doris] and I do, that makes life…very easy. It must be incredible difficult for people who handle this with strangers I would guess.” ( Bert, Interview 1) One common difficulty was around managing swallowing difficulties associated with severe dementia. Bert considered carefully whether Doris’s reluctance to eat related to swallowing difficulties or apathy. Declan described needing to request orodispersable tablets due to Fiona’s swallowing difficulties. Declan perceived a lack of integrated services for dementia and physical health that made caring harder. He felt primary care focused on physical health management and reported how little he perceived dementia to be mentioned. There was a sense in his accounts that integration of medical advice happened at home and was his responsibility. “ Err…well they are dealing with the attending problems of dementia, but they never really speak about the dementia itself or how I’m coping with it or what’s happening in her life. How she and I are coping with it. They completely ignore that. Just medical problems they’re concerned with. They don’t really want to know about anything else you know. I think that’s probably in the domain of [area] people as well. The memory service and that sort of thing.” ( Declan, Interview 4)
## Theme 3. Prioritising physical, cognitive and mental health needs
The co-occurrence of physical, cognitive and mental health needs in people with dementia led to inevitable trade-offs during the prioritisation of care. For Edith, who lived with moderate dementia, fall risk management strategies meant she was housebound, despite a deleterious impact that her son perceived on her quality of life through reduced social interactions. Edith was prepared to accept such compromises as her priority was remaining at home, but her son felt this contributed to her low mood. “ I would like to live in my own home for as long as possible even if it means not being able to go down the stairs.” ( Edith, Memory service care plan) Managing the risk of falls was also a key issue for Albert, who lived with late onset dementia, and his wife Jean. When I first met Albert and Jean, initiation of anti-dementia medication was postponed as side-effects included dizziness, which could ultimately have increased the risk of falls. In later interviews Jean described how Albert’s dementia had worsened. His memory service notes record how he can become ‘another self’ being ‘rude’ and ‘bad tempered’ when forgetting things. At this point, supported by primary care, Jean re-considered the need for antipsychotic medication ‘although not been keen on medication in the past’. She described the difficult trade-off between the need to reduce behaviours that challenged and to avoid medication that increased falls risk.
There were a number of instances across accounts where physical health care appeared to be prioritised, either over cognitive or mental health needs. In terms of improving cognition, there was a sense of futility, while physical conditions were perceived as addressable. Harold described not discussing his dementia with his GP as he ‘assume[d] it’s just going to progress.’ *In his* diary, Harold described his mental health as ‘not stable’ as he conveyed the impact of his deteriorating cognition on his low mood. This was not reflected in his consultation notes, which focused on his physical health, specifically vision impairment and cardiac problems. Harold’s proactive approach to physical health management appeared not to extend to his management of cognitive and mental health. “ *In a* way it’s just there, it’s like having arthritis. You can’t suddenly get rid of it. It goes on for maybe a couple of days and then I’m alright. I’m alright today and it’s very hard to explain it. It’s not something I had before I got the diagnosis. And I think it was a reaction to the diagnosis as much as anything. You know we all, all human beings rely hugely on memory and I thought I wasn’t going to have any memory. Pretty awful. And of course now I forget things which I ought to remember and that reminds me that things aren’t good.” ( Harold, Interview 3)
## Theme 4. Competing and entwined needs and priorities
At times the needs of family carers and the people living with dementia felt competing and entwined. Decisions were often made based on dyadic needs, that family carers usually needed to judge alone. Scott, for example, limited Edith’s contact with services, as he considered some services to be logistically difficult to attend and feared that visits might be ‘too disruptive’ resulting in a negative impact on her health. For example, the ability to attend appointments due to mobility issues were discussed as a factor in Scott’s decision to remove Edith from the waiting list for her cataract operation. “ As I said, I felt rotten that I had to, well she’s been taken off the list. Removed. Because other people need the slots. I would have kept it on, I would have kept on but eventually it was not right for me, morally wrong to keep saying yes, yes she’ll be there, she’ll be back there again, she'll be in there again for another. When I know that she couldn't possibly get there.” ( Scott, Interview 1) In her memory service care plan, Edith felt she could ‘make it down the stairs if she’s careful’ suggesting how her perception on risk management may differ from Scott’s. Similarly, for Margaret and Jonathan, a married couple with dementia, their GP considered that while health management was enabled by their daughter Sophie who loved and cared for them, it was also complicated by her anxieties. Sophie described fearing that her caring responsibilities might overwhelm her, but also that her parents would not be able to cope with knowing they had dementia. “ And the anxieties that are projected from the carers are very valid, because they are with them the whole time, but you wonder how much you are treating the carer rather than actually treating your patient.” ( GP, Interview 1) “When I go to the hospital I say please don’t mention my parents condition [dementia]. I know personally it would worry them, they wouldn’t be able to handle it. I mean obviously they were told in the beginning, they’ve forgot. But doctors have said no it’s important they should know. But I know my parents would…and if someone told me and I couldn’t remember that I had it I would panic. It would make me worse.” ( Sophie, Interview 1) Albert and Jean both needed support from homecare workers, and it was often practical to consider their needs collectively. For example, a physiotherapist attending the home to provide neurotherapy treatment for Jean’s vertigo was able to assess Albert following a fall reported in our second interview. “ It just so happened that the physiotherapist had come here to see me… later that morning. So she very kindly, because they know [Albert] anyway from previous sessions of physio. So she did all the observations, you know, take his temperature and pulse, blood pressure. And everything was normal so when I reported that to the doctor.” ( Jean, Interview 2) “Review by community therapy team following a fall at home...the nature of the fall seemed to be mechanically-related due to losing his footing when transferring to the sofa.” ( Letter from Physiotherapist to GP)
## Theme 5. Curating supportive networks
To navigate the health system and access care when needed, family carers actively curated and managed relationships with professionals. In some cases they developed personal closeness, which at times proved useful to circumvent challenges of access to care during the pandemic. Sarah’s close relationship with the neurologist was perceived to facilitate care for Samira through improved communication. “ *Communication is* very easy. In some ways, it should be, and it is mostly for all patients I see with these long-term conditions, they have either my email or some form of point of contact, my secretaries’ email. My phone number. One of the nurse specialists. I mean in [Sarah’s] case it’s very easy isn’t it because you know I’ve got to know her quite well. So yeah she can contact me any time.” ( Neurologist, Interview 1) Similarly, Bert recounted how the oncology department kept in touch reporting ‘we don’t want you to fall through the cracks.’ Curating such supportive networks meant professionals were aware of need and adapted care accordingly. For example, consultants coordinated care to accommodate Bert’s preference of not wishing to attend hospital during COVID-19 due to concerns about Doris’s frailty. “ Doris needed a blood test check. And I was reluctant to go into hospital for that. So they, the senior nurse there who I’ve got on with for years, very kindly said she’d sort out with the GP to do it…But when I was talking to [name] the neurologist he spotted on the computer notes that the consultant, [name], had been in touch with the GP, and that he’d arranged, they are in the same hospital, so he arranged for one of the girls, well the nurses there, to take Doris’s blood and send it off. So they arranged it that way and I haven’t had to go to doctors at all.” ( Bert, Interview 1)
## Theme 6. Carer support and coping
Family carer attitudes towards acceptance of support influenced their ability to cope, with COVID-19 also impacting the availability of support. Furlough enabled Fiona’s daughter Sophie to provide care while Declan recovered from open-heart surgery. On the other hand, Sophie experienced decreased support from other family members who visited less due to concerns of COVID-19 transmission. This sense of additional responsibility contributed to high levels of carer burden. However, some decision making support from family was retained remotely, for example relating to COVID-19 vaccination. “ Yeah it is left up to me but I’m really really indecisive. Really indecisive. Like with the vaccination for example I wasn’t sure I was a bit worried about letting them have it, and I asked all my other members of family. I’m not very assertive. And I’ve got good support with my family.” ( Sophie, Interview 1) Family carer acceptance of support was an important theme across these narratives. Declan organised homecare worker support yet discontinued after one session as it felt difficult to organise and was experienced as unsupportive. “ No paid carers at all. I’ve been reluctant to do that…If I left her with someone I wouldn’t go out or do anything, I’d only sit out there fretting. So I’m really managing myself.” ( Bert, interview 1) This reflected a wider sense in his narratives that ‘nothing can be done’ to manage Fiona’s challenging behaviour. “ Oh yes, from several of the people that neurology said they would contact through doctor referrals they have been in touch. But really there’s very little any of them can do… Because they can’t really, [Fiona] just…there’s not much they can do with her you know.” ( Declan, Interview 3) A similar view was expressed by Sophie who, based on negative past experience, felt reluctant to involve homecare workers, and again by Bert who expressed difficulties in sharing caring responsibilities for his wife. In both accounts family carers also expressed concerns that although the caring burden was overwhelming, sharing it with homecare workers risked increasing their burden, due to the additional coordination of care involved, or through fear that they would be let down if the service was not provided.
## Main findings
We identified six interrelated themes using multiple data sources to explore how care for long-term conditions in dementia is provided and supported. Balancing the need for increased support with declining cognition was an important process due to the value of independence for people with dementia. Family carers were responsible for adapting care to overcome the symptoms of dementia, and actively curated and managed relationships with professionals to access support when required. The co-occurrence of physical, cognitive and mental health needs led to inevitable trade-offs during the prioritisation of care needs. Interconnection between family carers and the people with dementia they supported led to a sense of competing needs and priorities. Finally, the availability of support for family carers was impacted by COVID-19, acceptance of which was influenced by perceptions of their own coping ability.
Loss of independence has been identified as a key stage in the dementia journey (Forbes et al., 2012). From the perspective of participants in the early stages of dementia in this study, the value of independence in health management was an important priority. The transition from self-management to dependence in the context of long-term conditions in dementia has previously been described (Bunn, Burn, et al., 2017). As cognition declined, participants in this study became increasingly dependent on family carers to ensure quality of disease management. Research to date has focused on the transition experiences of family carers providing support for multiple long-term conditions in dementia (Lam et al., 2020; Ploeg, Northwood, et al., 2020). By considering perspectives across case studies, this study highlights the interacting nature of transitions in this context. For example, the lack of insight of a person with dementia into the severity of health issues (heart failure) led to the increased involvement of his wife (primary carer) and a transition to greater dependence on the family carer for care of long-term conditions.
The concept of interdependencies is well recognised in person-centred dementia care (Manthorpe & Samsi, 2016). Family carers in this study were responsible for adapting care for long-term conditions to overcome symptoms associated with dementia. In addition to personal and medical tasks, husband carers of people with dementia and multiple long-term conditions have been found to adopt the role of ‘protector’ of personhood (Sanders & Power, 2009). A particular challenge noted by participants related to the integration of physical and cognitive advice where this was not done by health professionals. Findings from this study emphasise how people with dementia and family carers were adaptive in prioritising needs in a holistic way, which may be physical, mental or cognitive depending on the stage of cognitive or physical decline. Salient issues identified in previous literature include those requiring immediate or ongoing attention such as safety and pain (Ploeg, Garnet, et al., 2020). This reflects our findings, where functional needs such as mobility and reducing the risk of falls took higher priority than mental health in the hierarchy of needs.
Recent theories recognise illness management as a dyadic phenomenon. Lyons and Lee [2018] in their Theory of Dyadic Health Management suggest that addressing incongruence in illness appraisal between patients and their family carers is key to achieving better health through collaborative management. Findings from this study suggest how interconnecting needs and priorities can lead to incongruence between the wishes of the person with dementia, and the decision making of the family carer. Difficulties in separating the perspectives of people with dementia and their family carers have been reported in the pain literature (Amspoker et al., 2021). Another goal of the Theory of Dyadic Health Management relates to optimising the health of both dyad members (Lyons & Lee, 2018). Spousal carers of people with dementia are often also managing their own health conditions (Lam et al., 2020). Participants in this study described relinquishing caregiving responsibility whilst recovering from surgery.
Previous research into the management of diabetes in dementia suggests how regular contact with a supportive professional improves management by providing flexible individualised care (Bunn, Goodman, et al., 2017). In this study, to facilitate the management of long-term conditions, family carers of people with dementia curated support from wider family, healthcare professionals and homecare workers. These findings are consistent with the House of Care Model, where engaged and informed patients interact with healthcare professionals to work in partnership to achieve personalised care (Coulter et al., 2013). For some participants in this study, relationships with professionals lead to improved communication which facilitated access to remote consultations during the pandemic. Changes in service provision during COVID-19 have been found to impact the quality of consultations with primary care particularly when addressing physical health needs (Tuijt et al., 2021). Findings from this study suggest how pandemic-related factors also impacted availability of support, contributing to family carer burden. Participants accounts reflected how such factors impact decision making processes, specifically around vaccination and the involvement of homecare workers. Factors associated with discontinuing homecare during the pandemic include risk of COVID-19 transmission and concerns around adequate use of Personal Protective Equipment (Giebel et al., 2020).
## Strengths and limitations
To our knowledge, this paper presents the first study to use multiple qualitative data sources, such as interviews and document analysis, to explore how care for long-term conditions is provided and supported for people living with dementia at home. Collection of data from multiple sources enables triangulation, or the comparison of different data sources for comprehensive insight, and to improve validity of findings (Mays & Pope, 2000; Roper & Shapira, 2000). Strengths of this study include the diverse range of participant experiences, including type of dementia, long-term condition, level of cognitive impairment, degree of support and interaction with services. We recognise the variability in available data sources per case. To mitigate this, we treated each case as individual during analysis prior to developing themes across groups. In the final write up, we ensured all case studies were used to evidence themes. Recruitment of healthcare professional and homecare workers was limited due to service pressures during COVID-19. Consultation notes were particularly useful during analysis to compare the accounts of people with dementia and/or family carers with those who provided care. The majority of interviews were conducted via telephone which may have been preferred in this population due to familiarity and access (Unnithan, 2021). However, conducting research remotely may have changed the degree of inclusivity for participants with dementia and excluded some participants with low digital literacy or sensory impairment. As data collection for this study spanned from September 2019 to May 2020, the pandemic context meant that findings relate to experiences at an unprecedented time of healthcare delivery.
## Implications
Living with multiple long-term conditions in dementia requires the management and prioritisation of a variety of physical, cognitive and mental health needs. Our findings highlight the dynamic nature of dementia care, which has implications for people with dementia and their family carers accessing services annually for long-term conditions. Previous research suggests that older people do not necessarily differentiate between co-existing conditions, thus find it challenging when services focus on a single disease (Ploeg et al., 2017). To consider needs holistically, care and support should be adapted to the context of dementia at a service level. Our findings illustrate how intertwined psychosocial and physical needs are, yet how psychological needs can be overlooked if they are conceptualised as an inevitable sequelae of dementia. The bi-directional impact of physical, cognitive and mental health needs suggest they should be considered together in clinical practice. These findings accord with the integrated logic model of care which posits that psychosocial, mental, cognitive and physical needs should be addressed simultaneously due to their influence on each other (Hansen et al., 2017).
Due to the integral role of family carers, these findings underline the importance of conceptualising people with dementia and those who support their care as an interdependent team (Lyons & Lee, 2018). Research recommendations by the National Institute for Health and Care Excellence include exploring effective care planning methods for people who do not have regular contact with a carer (NICE, 2018). People who dementia who live alone are more likely to use homecare services, and experience unmet social and medical needs (Miranda-Castillo et al., 2010). Thus, exploring how long-term conditions in people with dementia without support are managed would be an important direction for future research.
## Conclusions
Due to the dynamic nature of dementia care, support for long-term conditions in dementia is required to be adapted in response to changing need. As dementia progresses, it is important that care is organised using a family-centred approach that acknowledges the daily realities of implementing care recommendations in the community. Realistic self-management plans which are deliverable in practice must consider the interacting nature of physical, cognitive and mental health needs, and acknowledge that these needs are often conflated with family carers in the context of dementia.
## ORCID iD
Jessica Rees https://orcid.org/0000-0002-9471-2134
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|
---
title: 'Unsung heroes in health education and promotion: How Community Health Workers
contribute to hypertension management'
authors:
- Kim Bush
- Carlea Patrick
- Kimberly Elliott
- Michael Morris
- Yordanos Tiruneh
- Paul McGaha
journal: Frontiers in Public Health
year: 2023
pmcid: PMC9996176
doi: 10.3389/fpubh.2023.1088236
license: CC BY 4.0
---
# Unsung heroes in health education and promotion: How Community Health Workers contribute to hypertension management
## Abstract
Rural communities are noted as having poor health outcomes. Rural areas experience barriers to care primarily due to a lack of resources, including education, health insurance, transportation, and social support. Additionally, poor health outcomes are a consequence of poor health literacy skills. Community Health Workers (CHWs) are utilized as a resource to combat these issues. This study focused on a CHW led Self-Management Blood Pressure (SMBP) program offered through the University of Texas at Tyler Health Science Center. The goal of the program was to improve management of hypertension through awareness, education, navigation, advocacy, and resource assistance. The SMBP program included structured workshops and regular follow-up with participants including connections to community resources and social support. CHWs worked closely with physicians providing bi-directional feedback on referrals and engagement of communities through outreach events. Furthermore, CHWs aided to bridge cultural or linguistic gaps between service providers and community members. Data is provided indicating this CHW-led intervention played a significant role in improving hypertension through education of how to make lifestyle changes that impact overall health and quality of life. Participants gained knowledge encouraging them to create lifelong healthy habits, coping skills, stress management, self-care, and accountability. Through this innovative approach, participants thrived in the supportive and encouraging environment led by CHWs as well as improved their blood pressure management.
## Introduction
Cardiovascular disease and stroke are respectively the number one and five leading causes of death in the United States [1] and in Northeast Texas [2]. Northeast *Texas is* documented as one of the least healthy regions of the state, with a prevalence of chronic disease significantly above both state and national averages [2]. While these statistics have remained stable over time [1, 2], it is widely recognized that hypertension is a modifiable risk factor for both deadly conditions [3]. Improving the control of hypertension is a complex task, which is compounded in rural regions such as Northeast Texas. It is well documented that in rural communities there is less access to care, the quality of care is often lower, and community resources are less readily available to support health within the community [4, 5]. A class of strategies that has been utilized to attempt to overcome the complexity of hypertension management among rural [6, 7] and other underserved populations [8] are Community Health Workers (CHW) led interventions.
CHWs are known to build community capacity in efforts to improve health outcomes. Additionally, CHWs play a significant role in shifting the trajectory of chronic disease by encouraging lifestyle changes that impact overall health and quality of life [9]. CHWs also serve to bridge cultural and/or linguistic gaps between service providers and community members. By providing education and resources to improve health literacy about symptoms, negative consequences, and treatment outcomes associated with chronic illness, CHWs empower individuals to play an active role in reducing the severity of their chronic illness.
CHWs play an important role in their communities, providing services at the individual and group level [10, 11]. According to the American Public Health Association [12], CHWs are trusted in communities they serve which results in improved relationships between community members and health and social service organizations. Additionally, given proper training, CHWs provide health knowledge through individual and community capacity building through various screening and educational activities [13]. Furthermore, Hartzler et al. [ 14] observed that CHWs provide self-management support to patients through counseling involving collaborative goal setting, problem-solving, and action-planning. Their strength derives in part from their building relationships on trust, emotional attendance, and authenticity [15]. Although CHWs are widely known across the nation, the profession may not be utilized to its potential or fully recognized as essential in the broader landscape of chronic disease management.
CHW led interventions have been shown to be effective in the management of non-communicable health conditions [16]. For example, the Education to Promote Improved Cancer Outcomes (ÉPICO) project, utilized Spanish-speaking CHWs or promotors, to enhance cancer knowledge among residents of the Rio Grande Valley of Texas [17]. Similarly, CHW led interventions have been effective in changing health behaviors among minority populations with diabetes [18]. The evidence on CHW's influence on the prevention and management of hypertension extends at least back to the 1970s, with benefits demonstrated in improved health related knowledge and behaviors, and blood pressure reduction [19]. More recent systematic reviews by Kim et al. [ 20] Scott et al. [ 21], and Cabellero et al. [ 22] further illustrate that CHW led interventions can be effective in improving the management of chronic disease, including hypertension, among rural and other vulnerable populations.
This study examines the outcomes of a hypertension focused CHW-led Self-Management Blood Pressure (SMBP) program operated by the University of Texas at Tyler Health Science Center. The population of interest is people living with hypertension in Northeast Texas. This study: [1] describes a CHW-led SMBP workshop series designed to improve health outcomes among patients with hypertension receiving care at a rural academic health center; [2] evaluates the program's impact on participant knowledge and behavior regarding blood pressure management before and after completion of the program; [3] assesses the effect of the intervention on hypertension control from blood pressure measures taken at baseline and at the end of the program; and [4] reports participants perceptions and experiences of the program.
## Materials and methods
The University of Texas at Tyler Health Science Center's Self-Management Blood Pressure (SMBP) program is a multi-component lifestyle change intervention that combines and adapts evidence-based components from lifestyle change and hypertension management curricula disseminated by the National Heart, Lung, and Blood Institute [23] and the American Heart Association [24] and TARGET:BP collaboration between AHA and American Medical Association [25]. Using these materials, a hybrid curriculum was developed into a 12-week hypertension-management workshop series. Additional materials were provided by the Texas Department of State Health Services (DSHS).
The process began with an orientation session during which participants learned more about the program and completed all required paperwork including an informed consent form before participating in the program. Participants who agreed to join the program were provided blood pressure monitors to use throughout the program and upon graduation they were allowed to keep the apparatus. Participants were taught how to use the blood pressure monitor according to TARGET:BP [26] guidelines and tasked with checking their blood pressure twice daily (once in the morning and once in the evening). Participants were also required to record their daily BP (blood pressure) readings in a blood pressure booklet (My Blood Pressure Passport) Texas Department of State Health Services and Texas Health and Human Services [27] developed by the Texas Heart Disease and Stroke Program and Health Promotion and Chronic Disease Prevention section through the Texas Department of State Health Services (DSHS) and Texas Health and Human Services (HHSC). Additionally, participants were encouraged to share this document with their primary care providers. The workshop participants met bi-weekly over 12 weeks for ~1 h. During these sessions, CHWs provided heart health education to the participants and collected blood pressure readings from the preceding 2-week period. These data were de-identified and stored electronically in a secured file. De-identified data was also shared with DSHS upon cohort graduation. Participants received a $5 gas/gift card upon attendance and participation in the bi-weekly sessions. Participants who completed the entire workshop series were awarded the blood pressure monitor that they used during the program as well as a certificate of successful completion upon graduation. Furthermore, participants were invited to attend a 3-, 6-, 9-, and 12-month follow-up reunion where they acquired additional educational information as well as continued support for maintaining their blood pressure at healthy levels. As an additional incentive, participants were also presented with a $5 gas/gift card for attending post-program follow-up sessions.
The SMBP program is a community-based health program therefore participants self-selected into the program. No statistically based sampling method was employed in the selection of participants, it was a convenient sample. Participants in the SMBP program were recruited from the East Texas community at large through several methods: (i) The primary modes of recruitment were through referrals from University of Texas Health East Texas (UTHET) physician clinics and by word-of-mouth referrals from persons who had previously participated in the program. ( ii) UTHET Emergency Medical Services Mobile Integrated Health unit also provided referrals for some of their patients to the SMBP program. ( iii) Community outreach fairs and presentations at assisted living centers and the Tyler Library contributed the remaining SMBP participants.
The SMBP sessions included in this study were conducted from August 2019 to June 2022. Three separate data sets, capturing different dimensions of program performance, were collected from persons who enrolled in and participated in the SMBP program. These were [1] the assessment of changes in participant knowledge and behaviors, [2] change in systolic and diastolic blood pressure and, [3] the participants' self-reported experiences of completing the SMBP program.
## Assessment of changes in participant knowledge and behaviors
Participants completed knowledge and skills assessments related to blood pressure management at the start of the program and then upon program completion. The pre-test was comprised of a 5-question knowledge exam followed by a three-question self-assessment of the participant's skills for controlling hypertension. This pre-test was self-administered at the time the individual enrolled in the program. The post-test repeats the questions from the pre-test and is administered at the graduation ceremony from the program. Because results from the pre- and post-test were collected anonymously and tabulated in the aggregate for record retention, we were unable to present measures of significance for the variation in the data.
## Assessment of changes in systolic and diastolic blood pressure
CHWs taught participants how to accurately take their own blood pressure using the automated arm-cuff monitor so that they were able to record daily blood pressure readings in their booklet, My Blood Pressure Passport.
A one group pre-test/post-test design was utilized to assess changes in the participants' mean blood pressure, with separate analyses conducted for changes in systolic and diastolic blood pressure. The pre-test time point was defined as the time of the blood pressure reading taken during the program's first-week session. The post-test time was defined as the time of the measurement conducted during the program's last session week 12. Participants measured their blood pressures in millimeters of mercury using the Omron 7 Series Blood Pressure Monitor and following American Heart Association/American Medical Association TARGET:BP: SMBP guidelines [26]. Separate paired sample t-tests were utilized to assess the hypotheses that there were no differences between pre- and posttest mean systolic and diastolic blood pressures for the study population. Within group changes in mean blood pressure were assessed for each participant's characteristics available in the data, including age, gender, race, ethnicity, education level, body mass index (BMI), whether the individual was currently on antihypertensive medications, whether they had ever smoked, and whether they were subject to additional comorbidities, diabetes and/or cardiovascular disease. Based on the demographic composition race was defined in this study as being White or Black. Ethnicity was defined as either Hispanic or non-Hispanic. Racial and Ethnic categories are not considered to be mutually exclusive, for example, an individual can be Black and Hispanic or Black and non-Hispanic. Race and *Ethnicity data* were self-reported by participants. Underlying assumptions for the use of paired t-tests were assessed, with no influential outliers noted, and the results of a Shapiro-Wilk test of normality met the requirement for approximate normality. All analyses were conducted using STATA version 16. Statistical significance was set at the 0.05 level.
## Assessment of participant experience
The final data source utilized in this study was a participant experience survey completed at the end of the week 12 intervention. This was a self-administered survey. Responses were anonymous and tabulated at the aggregate level for record retention. The instrument was comprised of five-questions that used a Likert format gauging agreement with the base question on a scale of strongly agree to strongly disagree. All individuals who finished the SMBP program completed at least part of the survey. Because response data were retained at the aggregate level, we were not able to calculate measures of significance for variation in responses. In addition to the formatted questions, participants were invited to provide any additional comments in free form text. For the purposes of reporting here, the responses were divided into four categories, (i) including constructive comments about the CHWs who ran the workshop, (ii) the workshop content, (iii) delivery mode, and (iv) the overall experience with the program.
## Results
Total enrollment for these SMBP cohorts was 242. Enrollment in the program consists of a participant's agreement confirmed by a signed consent to participate in the program. The consent was delivered to potential participants by email and then electronically signed and returned prior to attending the orientation session. The actual number of participants who started the program was 212, which includes a signed consent form, attendance at the orientation or session one, and providing a minimum of one blood pressure reading. Of these enrollees, 197 completed the program by regularly providing blood pressure readings. However, five of the 197 participants failed to complete all 12 weeks of the program and thus did not provide blood pressure readings at the final week of the program leaving us with 192 subjects included in the blood pressure analytical file. The decline from participant enrollment [242] to participants who completed the program [197] could be attributed to multiple factors including, lost to follow-up, family emergencies, COVID-19 pandemic related stress and/or anxiety, over commitment, and other causes. Comparison of the individuals who were dropped from the study sample with those included in the analysis showed no statistically significant differences in gender ($$p \leq 0.32$$), race ($$p \leq 0.43$$), age ($$p \leq 0.65$$), or education level ($$p \leq 0.28$$).
Table 1 shows the demographic and health-related characteristics of participants in the program. Our sample skewed older ($69.4\%$) and over three fourth ($76.6\%$) were female. Whites ($72.9\%$) were the predominant racial group (Blacks $27.1\%$) represented in the study population and non-Hispanics ($82.3\%$) were the more prevalent ethnic group (Hispanics $34.0\%$). The educational level of participants was evenly distributed, with a slight majority having earned at least some college credit. BMI skewed significantly toward obesity, with $65.1\%$ of participants falling into this category. Similarly, participants were highly likely to be currently on blood pressure medication ($73.9\%$). Regarding comorbidities, $31.4\%$ reported being diabetic, while $24.7\%$ suffered from diagnosed cardiovascular disease. Fewer than a quarter ($22.4\%$) of the participants reported a history of smoking.
**Table 1**
| Characteristic | Study population |
| --- | --- |
| | N (%) |
| Overall | 192 |
| Age in years | Age in years |
| 20–39 | 17 (8.85) |
| 40–59 | 61 (31.77) |
| 60–79 | 106 (55.21) |
| >79 | 8 (4.17) |
| Sex | Sex |
| Female | 147 (76.56) |
| Male | 45 (23.44) |
| Race # | Race # |
| Black | 52 (27.08) |
| White | 140 (72.92) |
| Ethnicity # | Ethnicity # |
| Hispanic | 34 (17.71) |
| Non-Hispanic | 158 (82.29) |
| Education | Education |
| < High school | 34 (17.71) |
| High school | 43 (22.40) |
| Some college | 57 (29.69) |
| College or more | 58 (30.21) |
| Body mass index | Body mass index |
| Normal | 21 (10.94) |
| Overweight | 46 (23.96) |
| Obese | 125 (65.10) |
| Hypertension medicine | Hypertension medicine |
| Yes | 142 (73.96) |
| No | 50 (26.04) |
| Smoker | Smoker |
| Yes | 43 (22.40) |
| No | 149 (77.60) |
| Cardiovascular disease | Cardiovascular disease |
| Yes | 44 (24.72) |
| No | 134 (75.28) |
| Diabetes | Diabetes |
| Yes | 58 (31.35) |
| No | 127 (68.65) |
## Results assessment of changes in participant knowledge and behaviors
As depicted in Tables 2, 3, survey results indicate that among participants in the SMBP program there was a high baseline level of knowledge and skills regarding blood pressure management. Despite the high proportion of participants at baseline who answered knowledge questions correctly and indicated that they agreed or strongly agreed with the statements about their hypertension management abilities, post-test results indicated improvement across board on the measures captured in the surveys. Of note, are the increases in self-reported knowledge on how to take blood pressure (pre-test $83\%$ to post-test $99\%$), understanding how to read blood pressure measurements (pre-test $85\%$ to post-test $100\%$), and taking their blood pressure medication (pre-test $76\%$ to post-test $86\%$).
## Results assessment of changes in systolic and diastolic blood pressure
Results of our paired t-test analyses of change in blood pressure, presented in Table 4, indicate that, for the overall study population mean systolic blood pressure dropped by 4.48 mm/Hg from the beginning through the end of the study ($p \leq 0.05$). The association between the intervention and a statistically significant drop in mean systolic blood pressure was found for both women and men as well as for all racial and ethnic groups in the study population. Similarly, significant reductions in mean systolic blood pressure were observed for both those currently on blood pressure medications and those not taking medications. Individuals who had not earned college degrees experienced a significant reduction in mean systolic pressure, while those with college degrees experienced a non-significant decline. Individuals who were overweight or obese experienced significantly lower mean systolic blood pressure, but reductions for participants of normal weight did not reach statistical significance. Program participants who smoked or had diabetes experienced significant decreases in systolic pressure, but those with cardiovascular disease did not.
**Table 4**
| Characteristic | Mean time 1 | Mean time 12 | Mean time 1.1 | Mean time 12.1 |
| --- | --- | --- | --- | --- |
| | Systolic blood pressure | Systolic blood pressure | Diastolic blood pressure | Diastolic blood pressure |
| | mm/Hg | mm/Hg | mm/Hg | mm/Hg |
| | (Std. err.) | (Std. err.) | (Std. err.) | (Std. err.) |
| Overall (n = 192) | 135.36 (1.23) | 130.88 (1.19)* | 82.05 (0.82) | 79.32 (0.76)* |
| Age in years | Age in years | Age in years | Age in years | Age in years |
| 20–39 | 125.90 (3.14) | 119.92 (2.89) | 85.23 (3.12) | 80.53 (2.00) |
| 40–59 | 134.22 (2.34) | 131.54 (2.37) | 84.93 (1.39) | 82.41 (1.45)* |
| 60–79 | 136.91 (1.55) | 131.79 (1.49)* | 80.16 (1.04) | 77.60 (0.97)* |
| >79 | 143.68 (7.76) | 137.23 (5.61) | 78.34 (5.32) | 76.05 (3.68) |
| Sex | Sex | Sex | Sex | Sex |
| Female | 134.08 (2.25) | 129.53 (1.32)* | 80.73 (0.92) | 78.49 (0.83)* |
| Male | 139.56 (1.44) | 135.30 (2.58)* | 86.37 (1.64) | 82.05 (1.71)* |
| Race # | Race # | Race # | Race # | Race # |
| Black | 136.05 (2.28) | 136.05 (2.28)* | 82.17 (1.85) | 79.62 (1.59) |
| White | 135.11 (1.46) | 135.11 (1.46)* | 82.01 (0.89) | 79.21 (0.85)* |
| Ethnicity # | Ethnicity # | Ethnicity # | Ethnicity # | Ethnicity # |
| Hispanic | 131.94 (3.92) | 126.23 (3.19)* | 79.90 (2.20) | 77.44 (2.11) |
| Non-Hispanic | 136.10 (1.23) | 131.89 (1.26)* | 82.52 (0.87) | 79.73 (0.80)* |
| Education | Education | Education | Education | Education |
| < High school | 130.91 (3.03) | 127.16 (3.09)* | 78.30 (2.09) | 76.05 (2.15) |
| High school | 142.28 (3.09) | 136.40 (3.09)* | 84.91 (1.88) | 82.44 (1.80) |
| Some college | 136.53 (2.07) | 130.29 (1.82)* | 83.92 (1.18) | 79.85 (1.07)* |
| College or more | 131.70 (1.80) | 129.57 (1.85) | 80.30 (1.51) | 78.41 (1.28) |
| Body mass index | Body mass index | Body mass index | Body mass index | Body mass index |
| Normal | 132.35 (3.99) | 129.41 (3.23) | 81.24 (2.34) | 80.44 (1.90) |
| Overweight | 133.31 (2.67) | 129.39 (2.58)* | 81.43 (1.84) | 78.97 (1.65)* |
| Obese | 136.63 (1.47) | 131.68 (1.47)* | 82.42 (0.99) | 79.26 (0.94)* |
| Hypertension medicine | Hypertension medicine | Hypertension medicine | Hypertension medicine | Hypertension medicine |
| Yes | 136.94 (1.44) | 132.37 (1.40)* | 81.81 (0.95) | 79.17 (0.91)* |
| No | 130.88 (2.28) | 126.66 (2.14)* | 82.74 (1.61) | 79.76 (1.35) |
| Smoker | Smoker | Smoker | Smoker | Smoker |
| Yes | 142.21 (2.48) | 135.76 (2.81)* | 84.96 (1.92) | 80.15 (1.66)* |
| No | 133.39 (1.38) | 129.48 (1.28) | 81.22 (0.89) | 79.09 (0.85)* |
| Cardiovascular disease | Cardiovascular disease | Cardiovascular disease | Cardiovascular disease | Cardiovascular disease |
| Yes | 140.40 (2.59) | 137.10 (2.93) | 80.55 (1.63) | 78.32 (1.75) |
| No | 132.14 (1.29) | 127.48 (1.16) | 81.69 (0.93) | 78.87 (0.83) |
| Diabetes | Diabetes | Diabetes | Diabetes | Diabetes |
| Yes | 138.51 (2.26) | 133.71 (2.15)* | 80.88 (1.39) | 78.86 (1.47)* |
| No | 133.68 (1.52) | 129.60 (1.46) | 82.82 (1.03) | 79.87 (0.90)* |
The results for diastolic blood pressure, presented in Table 4, showed significant reductions, on average 2.73 mm/Hg for the study population over the study period. Significantly lower readings were observed at time point 12 for both males and females and for Non-Hispanics and Whites. Blacks and Hispanics experienced lower mean diastolic blood pressure but did not meet the threshold for statistical significance. Participants between the ages of 40 and 79 experienced statistically significant lower diastolic blood pressure, while the lower readings in blood pressure obtained among the older and younger participants did not reach significance. While lower mean diastolic blood pressures were achieved across education groups, these readings reached significance only for the individuals with some college education. As was seen with systolic blood pressure, diastolic blood pressure was significantly lower in week 12 among overweight and obese individuals but failed to reach significance for participants of normal weight. Both smokers and non-smokers experienced statistically significant drops in diastolic blood pressure as did those taking antihypertensive medications. Statistically significant improvements in diastolic blood pressure were observed for both diabetics and non-diabetics. No significant reductions in diastolic blood pressure were observed for individuals with cardiovascular disease.
## Results assessment of participant experience
Findings from the participant experience surveys, presented in Table 5, illustrate that the SMBP program was both easy to understand and useful in helping participants to increase their knowledge of hypertension and hypertension management. Almost all ($99\%$) either agreed or strongly agreed that the SMBP program was easy to understand. An overwhelming majority indicated that the program helped them to understand facts about hypertension ($99\%$ agreed or strongly agreed) and understand the complications of hypertension ($99\%$ agreed or strongly agreed). Similarly, most respondents felt that their questions about hypertension were answered and that they know how to use their blood pressure cuff.
**Table 5**
| Questions | Strongly agree (=4) | Agree (=3) | Disagree (=2) | Strongly disagree (=1) | Median (interquartile range) |
| --- | --- | --- | --- | --- | --- |
| | n (%) | n (%) | n (%) | n (%) | |
| Question 1. The workshop helped me to understand facts about hypertension | 153 (80.95) | 35 (18.52) | 0 (0) | 1 (0.53) | 4 (4, 4) |
| Question 2. The workshop helped me understand complications of hypertension | 153 (80.95) | 35 (18.52) | 0 (0) | 1 (0.53) | 4 (4, 4) |
| Question 3. The workshop did not answer all my questions about hypertension | 16 (8.47) | 13 (6.88) | 61 (32.28) | 95 (50.26) | 1 (2, 1) |
| Question 4. The information provided in the workshop was easy to understand | 156 (82.54) | 32 (16.93) | 0 (0) | 1 (0.53) | 4 (4, 4) |
| Question 5. I am not able to adequately use my blood pressure cuff at home | 9 (4.76) | 6 (3.17) | 43 (22.75) | 131 (69.31) | 1 (2, 1) |
In addition to completing the participant experience survey, forty-eight percent ($48\%$) of participants completing the SMBP program provided written feedback at the end of the 12-week program. These free form comments provide a qualitative perspective on the program. Three percent ($3\%$) of participants who provided feedback felt the program should include additional visual aids, experienced issues with virtual connectivity, or commented on the time of day a specific workshop occurred. Twenty-two percent ($22\%$) of participants felt the content presented by CHWs was helpful in creating healthier lifestyle habits.
## Discussion
This study evaluates a CHW-led hypertension self-management program implemented among 197 participants in rural northeast Texas. We used three different data sources to evaluate the program. These were: a pre-test knowledge and skills assessment survey, an assessment of change in systolic and diastolic blood pressure before and after the 12-week program, and an assessment of participants' experience with the program after the completion of the intervention. The study found that knowledge about hypertension and hypertension management has improved. Participants showed behavioral change in hypertension management as measured by monitoring their blood pressure at home regularly and taking their medication as prescribed. Changes in both systolic and diastolic blood pressure were observed between mean baseline and last (at the end of the 12-week program) BP measures across subpopulation groups. Finally, participants expressed positive experiences of the program in terms of the information that they were able to obtain as well as the ease of understanding the content.
Knowledge about hypertension and hypertension management improved from baseline measurements. From a conceptual perspective, the increase in participant awareness of the risks of hypertension and understanding of the management of their condition are the first steps toward improving health outcomes. In our study, participants demonstrated increased awareness (from $88\%$ to $91\%$) that high blood pressure was a risk factor for heart disease and stroke. Similarly, knowledge of proper technique for taking blood pressure ($83\%$ to $99\%$) and interpreting its measurement ($85\%$ to $100\%$) improved from baseline to the week 12 assessment. These findings are conceptually consistent with those found by Boulware et al. [ 8] where CHW led training was linked to improved hypertension problem solving capabilities including self-management knowledge.
Our findings showed that participants reported an improved behavior to monitor their hypertension and take their medications. The majority ($92\%$) of the participants reported that they were able to adequately use their BP cuffs at home to monitor their blood pressure regularly. Adherence to hypertension medication also improved as $86\%$ of the participants self-reported taking their medications properly after the program compared to ($76\%$) of those who did so before the intervention. These findings are similar to other CHW-led interventions designed to improve disease control and medication adherence. These includes interventions for diabetes and/or hypertension program in Mexico [28], Diabetes Self-Management Education Program in the US [29], and CHW-led intervention in Tanzania in HIV (Human Immunodeficiency Virus) infected pregnant mothers to improve adherence and retention to care [30]. The reported behavioral change in most of these programs could be due to CHW's ability to spend more time to educate patients than providers would be able to. Thus, patients are more likely to ask all their questions and be willing to apply the suggested behavioral changes. It is, however, important to examine if these changes are long-lasting past the intervention's completion as most of these studies, including ours, assessed change shortly after the completion of the program. It will be important to continue to monitor graduates from the SMBP program to assess long-term patient adherence as longer monitoring, even at a distance has been reported to increase the likelihood of patient adherence in maintaining a healthy BP range [31]. Thus, there is a need for high-quality, rigorous studies to determine long term effectiveness of CHW-led interventions on medication adherence and control of chronic conditions.
Our study participants experienced changes in blood pressure measures (both systolic and diastolic) after the 12-week intervention. Analyses of changes in blood pressure indicated an association between the Self-Management Blood Pressure (SMBP) hypertension program and reductions in both mean systolic and diastolic blood pressure among a sample of 192 individuals from a region of Texas that faces complex, well-documented health challenges [2]. The mean difference in baseline and final (at the end of the 12-week program) systolic measure was 4.8 mm/Hg, and the number of diastolic BP was 2.73 mm/Hg. This change was experienced by all patients across all participants. Significant reductions were associated with the program among both men and women and across racial and ethnic groups. While these findings must be viewed cautiously because of design elements, including a relatively small sample size, the lack of a control group, and the reliance on only one pre- and one post-test time points, they do support growing consensus in the literature that CHW-led interventions lighten the community burden caused by chronic diseases [9]. Among the most encouraging findings regarding this CHW-led SMBP intervention is the broad-based impact that it is associated with on systolic blood pressure. This is important, as many see systolic blood pressure as the more pivotal marker of hypertension control among older adults such as comprise the overwhelming majority of our study population (32–34). While we did not find significant drops in diastolic blood pressure among people who are over 71 years, the program still has good reach, with all the population strata captured in these data sets showing at least somewhat lower diastolic blood pressure.
These results show that the program had a positive effect on improving certain dimensions of access for rural East Texas community members to information and support to improve their health outcomes. Through the dissemination of information and instruction on a schedule that was flexible and accessible through a distance learning management system, participants were able to self-pace how the information was consumed within the limitations of week-long modules of content. The dissemination of information via CHWs who delivered instruction using language, practical examples and vocabulary that were regionally and culturally sensitive to this specific audience. This observation aligns with similar studies that used CHWs to address health outcomes (diabetes and hypertension management) for specific minority populations [35]. Regional and cultural sensitivity matters as instructional methods can be tailored to meet the participants on their intellectual, cultural, and social levels.
This study's findings regarding the effectiveness of CHW-led SMBP interventions in combating hypertension in largely rural East Texas are encouraging. They also indicate the need for further research in this field. Introducing a matched control group would enhance the design, as would the incorporation of multiple pre- and post-test measurements of participant blood pressure. Having additional post-test measures would also make it possible to examine the sustainability of the program's influences on blood pressure levels. A final area of promise for future research is in implementation science. A robust examination of the delivery process from the participants and program staff perspectives could reveal areas of strength in the program and areas that need modification.
This study evaluated the efficacy of a SMBP program among rural community participants. Rural communities exhibit disproportionately poorer health outcomes as compared to their urban counterparts [36]. Unfortunately, due to the sampling method, racial and ethnic diversity among participants were not under the control of the researchers. This study only evaluated outcomes based on participant self-reported surveys. It may be helpful to use clinical data to confirm changes in blood pressure as measured in a controlled clinical environment. Additionally, participants were identified and recruited via convenience sampling methods. Therefore, participants who agreed to join the programs were particularly motivated to self-enroll into the program in order to address their BP conditions. Hence, if we were to align participants' motivations to change their BP-related health behaviors using the Health Behavior Theoretical Model [37]—the participants in our program would be more likely to be in the “cue to action” phase than potential community members who may have been randomly selected to enroll in the program. Participants' readiness to change their behaviors might disproportionately affect the outcomes for the SMPB program. Finally, since this study was based on one group pre-test and post-test analysis, it is not possible to certain that all the changes that were observed were due to the SMBP program. Further studies using rigorous methods are warranted to expand the effects of CHW-led interventions to control cardiovascular diseases and improve outcomes.
## 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 the University of Texas Health Science Center at Tyler. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
KB developed, supervised, and directed the project. CP was involved with planning, coordinating, and delivery of the project. KB and CP validated the data. PM served as principal investigator of the project. MM completed the statistical analysis of the data. KE and YT provided an analysis of the results and discussion sections. All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it 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.
## Author disclaimer
The contents are those of the author(s) and do not necessarily represent the official views of, nor an endorsement, by Texas DSHS, CDC/HHS, or the US Government.
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|
---
title: Can healthcare apps and smart speakers improve the health behavior and depression
of older adults? A quasi-experimental study
authors:
- Dasom Kim
journal: Frontiers in Digital Health
year: 2023
pmcid: PMC9996178
doi: 10.3389/fdgth.2023.1117280
license: CC BY 4.0
---
# Can healthcare apps and smart speakers improve the health behavior and depression of older adults? A quasi-experimental study
## Abstract
### Purpose
This study identified the effects of applying information and communication technologies (ICT) to the health management of older adults aged 65 or older.
### Methods
Older adults registered at public health centers were provided with the health management app “Health Today” and a smart speaker for 6 months to perform assigned healthcare missions. The program was conducted for 6 months by dividing participants into two groups: one that received both the health management app and the smart speaker, and another that used only the health management app. Depression, self-efficacy, number of days of moderate-intensity exercise, relative grip strength, balance tests, and five-times-sit-to-stand tests were measured during the pre- and post-evaluation.
### Results
Both groups showed a positive health status and behavioral changes at post-evaluation. However, no reduced depression was observed due to communication and music listening functions in the group that was additionally provided smart speakers.
### Conclusion
ICT use in healthcare can be beneficial for older adults. However, whether these devices meet the purpose of the national health project must be determined, and an effect evaluation must be undertaken prior to providing these ICT devices for the health management of older adults in the public domain.
## Introduction
Information and communication technology (ICT) has recently come to refer to all fields of collecting, processing, and consuming information, rather than merely communication-related technology that transmits information. Mobile health (mHealth) and Internet of Things (IoT) are emerging keywords in various industries, including healthcare, and are being applied to multiple fields.
Aging is an unavoidable demographic trend worldwide, with the main health problem being frailty. The increase in weakness and chronic diseases of older adults consumes socioeconomic resources in the community, and the increase in medical expenses is a serious problem. Care for vulnerable older adults has often been undertaken in medical and nursing facilities, but many recent studies have shown that providing healthcare in a familiar home environment has the same or more effective clinical outcomes compared to care in medical facilities (1–3). In particular, the perceived quality of life increases when older adults live in their own homes. The fact that they can continue to be in an environment they are familiar with gives them psychological stability, which can have a positive effect on their mental and social health.
From the service receiver's perspective, there is no need for older adults to wait until formal care services become available. This reduces waiting time and increases the participant's ability to self-manage, which can have great long-term health effects. Ultimately, the goal of the healthcare service using ICTs is to serve as a self-management mechanism that can be intuitively used by participants without requiring special effort by health professionals [1, 2]. In the past, to evaluate the physical activity of older adults, one had to use a pedometer and write evaluation notes in a notebook. However, when ICT is used, the number of steps taken and calories consumed are automatically measured by the smartphones that are linked to the database, so that health experts can check and manage it in real time. The purpose of using ICT for health management is to incorporate technology into the life of the user, thus helping them efficiently manage their health with minimal effort.
ICT provides an advantage for service providers in that one health professional can manage more people simultaneously, thereby increasing work efficiency and reducing costs. The time for providing indirect services such as data collection and preparation for patient visits is greatly reduced, which allows health professionals to focus more on direct healthcare services. In terms of efficiency and effectiveness, ICT healthcare services targeted towards older adults are an approach with great potential [1].
Representative projects that aimed to prevent the frailty of older adults using ICT are PERSSILAA (Personalized ICT Supported Service for Independent Living and Active Aging), SPRINTT, and My-AHA [4, 5]. They were implemented to prevent weakness in older adults in the community and to achieve independent and successful aging. Cognitive, nutrition, and exercise programs were conducted after primary screening and secondary detailed evaluation in groups and individually. Technologies such as video calls, messages, remote monitoring, and health measurement through smartphone apps were grafted. These projects yielded positive results in improving activities of daily living, quality of life, and frailty. A previous study confirmed that healthcare services using ICT had a positive effect on exercise ability, cognitive function, and depression [4, 6]. Smart speakers have different effects depending on the participants’ attitudes toward smart devices and gender. They are also easy to use because they operate as a voice interface and can have a positive effect on emotion through a conversation function [6, 7].
Home care services are implemented at 254 public health centers across South Korea as part of a community-wide health promotion project, in which a nurse visits the homes of those aged 65 or older, periodically checking health status and counselling. However, from 2020 to 2022, it was difficult to manage health through direct home visits due to the spread of COVID-19 in the community. To solve this problem, a pilot project, known as the “AI-IoT Healthcare Service for Older Adults,” has been implemented since 2020 by the Ministry of Health and Welfare, the Korea Health Promotion and Development Institute, and Korea Social Security Information Services to convert healthcare visits for the vulnerable from face-to-face to virtual visits.
This study statistically verifies the effect of the local health center's newly attempted “AI-IoT healthcare service for older adults” in Seoul, South Korea. The pilot project consisted of providing wearable devices to older adults living in the local area, along with a smartphone app that can check healthcare missions and monitor this information to offer non-face-to-face professional counseling with exercise experts, nutritionists, and home care nurses.
According to the theory of planned behavior, attitudes, subjective norms, and perceived behavioral control influence intentions. Further, behavioral intentions are strongly correlated with behaviors. Attitudes toward health behaviors are determined by beliefs about health outcomes and evaluations of the values associated with those outcomes [8]. Health interventions using ICT can increase value and a perceived sense of control over health outcomes. Positive health results were expected in the group that was additionally provided the smart speaker, due to higher adherence to health behaviors than the group that used the App alone. Previous studies found that the communication function of smart speakers, music, and ASMR functions had a positive effect on relieving depression [7, 9]. Previous research has also shown that healthcare services using ICTs are an effective approach for the successful aging of community-dwelling older adults, but it is necessary to accumulate more knowledge about the acceptability and effectiveness of the various types of interventions. Therefore, this study discerns the effect of ICT healthcare services for older adults on depression and health behaviors [10].
The purpose of this study is [1] to identify the effect of health management services using healthcare apps and smart speakers with older adults in the community on health behavior, health status, and depression, and [2] to compare the effects on health status, health behavior, and depression between the group that was provided both the healthcare app and the smart speaker and the group that was only provided the healthcare app.
## Study design
This study consisted of a nonequivalent control group pretest-posttest design. The participants were either assigned to the experimental group [i.e., the smart speaker group (SS)], who were provided with a smartphone app (“Health Today”), wearable devices, and smart speakers, or to the control group, that only received a smartphone app and wearable devices [i.e., the healthcare app group (HA)].
Older adults registered with the health center home care service who agreed to participate in the study but did not agree to the provision of smart speakers were assigned to the control (HA) group. Those who agreed to use the smart speaker were assigned to the SS group, in accordance with the Korean national project guidelines for those who live alone, have low social contact, or experience depression. The recruitment of study participants started in July 2021 after the IRB approval date, and the preliminary evaluation was completed by August 2021. The post evaluation was conducted between December 2021 and January 2022.
## Participants
Our participant sample consisted of older adults aged 65 or older registered for the home care services provided by a public health center in Seoul. The criteria for the selection of study participants were the ability to maintain cognitive function to use IoT devices and to understand the survey or follow the instructions of the visiting nurse. Chronic diseases such as hypertension, diabetes, cancer, hyperlipidemia, cerebrovascular disease, and cardiovascular disease may be present in the participants. The exclusion criteria for participation in the study were those diagnosed with dementia or significantly reduced cognitive function who were unable to complete questionnaires and follow the visiting nurse's instructions. People who had taken drugs or been diagnosed by a doctor for alcoholism, depression, schizophrenia, or any other type of psychosis were also excluded. If it was determined that it was impossible for a participant to continue participating in the study due to a deterioration of health during the study or if they passed away, they were excluded. Further, if voluntary participation was difficult to guarantee, or if participants wanted to withdraw their participation, these individuals were removed from the study.
## Ethical consideration
The entire process of this study was planned after deliberation by the Public Institutional Review Board of the Korea National Institute for Bioethics Policy, and the study termination report was completed in compliance with the ethical guidelines (Public IRB No. 2021-0808-004). Recruitment and pre- and post-evaluation of the control and intervention groups were conducted at public health centers. Participants provided consent after the lead researcher explained the research at the time of registration and pre-evaluation. The participants also received a separate explanation through written consent forms.
## Interventions
Following a booking for a visit to the public health center, the participants underwent a multicomponent intervention which included a consent form, pre-evaluation, 6 months of non-face-to-face health counseling, and health management information for using ICT devices. At the end of the 6-month service, the same items were subject to a post-evaluation. All the participants received non-face-to-face health counseling at least once a month. The healthcare missions consisted of the following: eating 3 meals per day, walking 5,000 steps or 30 min per day, taking prescribed medication on time, going outside at least once a day, measuring blood pressure once a day if participants had hypertension, measuring glucose level regularly if participants had hyperglycemia and drinking 8 cups of water per day (see Supplementary Figure S1). The participants connected their health data (step count, blood pressure, blood glucose, healthcare mission) to the smartphone app through wearable devices in real-time. This information was remotely monitored by visiting nurses, exercise experts, nutritionists, and other experts from the health center. Non-face-to-face consultations were conducted more than once based on this information. Health education materials were also provided in a non-face-to-face manner, and pictures or video links related to healthcare were sent to the participants’ mobile phones at least once a month. Using the app's push notifications, we sent a text message encouraging the participants to perform a healthcare mission at least once a week. The home care nurses monitored blood pressure, blood glucose levels, and step count levels at least once a week and provided consultations if there were any abnormalities. Table 1 presents the functions of smart speakers, smartphone apps, and wearable devices provided for each group.
**Table 1**
| Provided devices | Provided devices.1 | Healthcare app + smart speaker user group (SS group) |
| --- | --- | --- |
| Smart speaker | Emotional support | Tactile or voice recognition type conversation |
| Smart speaker | Disease management | Chronic disease related medication notification |
| Smart speaker | Safety management | Sending voice rescue messages: 24/7 monitoring in conjunction with security companies |
| Smart speaker | Cognitive function | Cognitive Enhancement Quiz Program |
| Smart speaker | Life information | –Various information necessary for senior life (living information, health information provided)–Wakeup call |
| Smart speaker | Exercise | Provide gymnastics program according to voice guidance |
| Smart speaker | Sound contents | News, music, radio, religious content, ASMR (sound of waves, wind, etc.) |
| Smartphone app (“Health Today”a) | –Monthly healthcare mission assignment (e.g., “Eat three meals a day,” “Walk more than 5,000 steps every day,” etc.)–Remote consultations with exercise therapists, nutritionists, and visiting nurses–Send health-related text messages and push notifications on the smartphone | |
| Wearable devices | Wrist-worn activity monitor, Bluetooth blood pressure monitor, Bluetooth blood glucose monitor, Bluetooth scale (Health condition monitored in conjunction with the smartphone app) | |
## General characteristics
The factors reported to potentially influence depression and health behavior, such as sex, age, family type (living alone, couple of older adults, multicultural families, etc.) were investigated.
## Exercise self-efficacy
The exercise self-efficacy tool, developed by Marcus et al. [ 15] and translated by Lee and Jang [16], was used with a total of 5 items. This tool uses a 5-point scale that assesses confidence in one's ability to consistently perform exercise in any situation. The higher the value, the higher the self-efficacy, with 1 point for “not at all confident” and 5 points for “very confident,” and a higher score indicating a higher sense of self-efficacy. Total scores range from 5 to 25.
## Depression
The Geriatric Depression Scale (GDS) was developed with 30 items. Sheikh and Yesavage [17] developed a short form version consisting of 15 items based on the diagnostic validity study on GDS. In Cho et al. 's study [18], the validity of the Korean version of the GDS in short form was verified and the reliability was 0.88. It consists of a total of 15 items and measures “yes” and “no” on a binary scale for each symptom. A higher score indicates a higher level of depression. If the cut-off point is 8 or higher, it reveals a risk of depression.
## Data analysis
Of the total of 356 participants, those who lived alone, had low social contact, or were depressed were assigned to the SS group, according to the health center project guidelines. 74 out of 356 people refused to participate in the study. 85 people were assigned to the SS group and 197 people to the HA group. Two people in the SS group and four people in the HA group dropped out due to an accident or because they withdrew from the study. A propensity score matching (PSM) method using depression scores was used to accurately analyze the effects of the SS group and the HA group. The propensity score was calculated by performing logistic regression analysis, with age and depression as the independent variables, and the provision of a smart speaker as the dependent variable. Participants with similar scores were matched 1:1 between the two groups; 83 people who used the healthcare app, and 83 people who used both the app and the smart speaker were matched and included in the final analysis (Figure 1).
**Figure 1:** *CONSORT flow diagram of the study.*
The Shapiro-Wilk normality test was performed on the data; a t-test and the Wilcoxon rank sum test were performed on the continuous variables; the Chi-squared test and Fisher's exact test were performed on the categorical variables as appropriate methods, according to the normality results. The homogeneity of the pre-screening by group was verified for the analysis of the intervention effect. For continuous variables, the Wilcoxon rank sum test or t-test for differences in pre-post values was used, and for qualitative variables, Fisher's exact test or χ2 test was used. Statistical analysis was performed using Stata 17.0 [19]. Statistical significance was based on an alpha value of 0.05.
## Participants’ general characteristics
Table 2 presents the general characteristics of the study participants: $24.1\%$ were male and $75.9\%$ were female, and both groups showed no statistical difference. The mean age was 71.05 ± 4.65 years, and there was no statistical difference between the two groups. Among those living alone, 55 were from the SS group ($66.27\%$), and 36 ($43.37\%$) were from the HA group. This study was conducted as part of a public health center project; these results were produced according to the standard for distributing smart speakers to people living alone, and it is therefore necessary to pay attention to the interpretation of the results.
**Table 2**
| Category | Category.1 | Total | HA | SS | t or χ2 | p |
| --- | --- | --- | --- | --- | --- | --- |
| Category | Category | (N = 166) | (N = 83) | (N = 83) | t or χ2 | p |
| Category | Category | n (%) or mean ± SD | n (%) or mean ± SD | n (%) or mean ± SD | t or χ2 | p |
| Sex | Male | 40 (24.1) | 20 (24.1) | 20 (24.1) | 0.00 | >0.99 |
| Sex | Female | 126 (75.9) | 63 (75.9) | 63 (75.9) | 0.00 | >0.99 |
| Age | 71.05 ± 4.65 | 70.37 ± 4.83 | 71.73 ± 4.4 | 1.90 | 0.059 | |
| Characteristics of family | Multicultural family | 1 (0.6) | 0 (0) | 1 (1.2) | 12.686 | 0.027* |
| Characteristics of family | Living with grandchildren | 1 (0.6) | 1 (1.2) | 0 (0) | 12.686 | 0.027* |
| Characteristics of family | Living alone | 91 (54.82) | 36 (43.37) | 55 (66.27) | 12.686 | 0.027* |
| Characteristics of family | Living with spouse | 46 (27.71) | 31 (37.35) | 15 (18.07) | 12.686 | 0.027* |
| Characteristics of family | Living with children | 27 (15.72) | 15 (18.07) | 12 (14.46) | 12.686 | 0.027* |
| Hypertension | No | 72 (43.37) | 39 (46.99) | 33 (39.76) | 0.88 | 0.347 |
| Hypertension | Yes | 94 (56.63) | 44 (53.01) | 50 (60.24) | 0.88 | 0.347 |
| Diabetes | No | 123 (74.1) | 65 (78.31) | 58 (69.88) | 1.53 | 0.215 |
| Diabetes | Yes | 43 (25.9) | 18 (21.69) | 25 (30.12) | 1.53 | 0.215 |
| Stroke | No | 157 (94.58) | 79 (95.18) | 78 (93.98) | 0.12 | 0.732 |
| Stroke | Yes | 9 (5.42) | 4 (4.82) | 5 (6.02) | 0.12 | 0.732 |
| Cancer | No | 152 (91.57) | 80 (96.39) | 72 (86.75) | 4.99 | 0.026* |
| Cancer | Yes | 14 (8.43) | 3 (3.61) | 11 (13.25) | 4.99 | 0.026* |
| Arthritis | No | 120 (72.29) | 64 (77.11) | 56 (67.47) | 1.91 | 0.167 |
| Arthritis | Yes | 46 (27.71) | 19 (22.89) | 27 (32.53) | 1.91 | 0.167 |
| Hyperlipidemia | No | 113 (68.07) | 62 (74.7) | 51 (61.45) | 3.353 | 0.067 |
| Hyperlipidemia | Yes | 53 (31.93) | 21 (25.3) | 32 (38.55) | 3.353 | 0.067 |
| Number of chronic diseases | 0 | 31 (18.67) | 21 (25.3) | 10 (12.05) | 8.575 | 0.0726 |
| Number of chronic diseases | 1 | 57 (34.34) | 31 (37.35) | 26 (31.33) | 8.575 | 0.0726 |
| Number of chronic diseases | 2 | 40 (24.1) | 18 (21.69) | 22 (26.51) | 8.575 | 0.0726 |
| Number of chronic diseases | 3 | 30 (18.07) | 10 (12.05) | 20 (24.1) | 8.575 | 0.0726 |
| Number of chronic diseases | 4 | 8 (4.82) | 3 (3.61) | 5 (6.02) | 8.575 | 0.0726 |
A total of 94 ($56.63\%$) out of the 166 participants had hypertension, 43 ($25.9\%$) had diabetes, 9 ($5.42\%$) had a stroke, 14 ($8.43\%$) had cancer, 46 ($27.71\%$) had arthritis, and 53 ($31.93\%$) had dyslipidemia. Those without chronic disease accounted for $18.67\%$ of the total participants, compared to those with one or more disease at $81.33\%$.
A BMI of 25 kg/m2 or more was considered as obese and less than 18.5 kg/m2 as underweight; hence, 63 ($37.95\%$) of participants were obese and 8 ($4.82\%$) were underweight. For the relative grip strength, 32 ($19.28\%$) of the participants were at the risk level and 53 ($31.93\%$) were rated as weak, with no significant difference between the two groups. Ten ($10.84\%$) participants were evaluated as weak in the one leg balance test, and 45 ($25.45\%$) were assessed as weak in the five-times-sit-to-stand test, with no significant difference between the two groups.
## Effects of smart speaker and healthcare app on health behavior and depression
Table 3 presents the results of comparison between pre- and post-values of the participants. The level of depression increased in both groups in the post-test compared to the pre-test, and there was no difference in the degree of increase in the post-test between the two groups (HA: 0.83 ± 3.77 and SS: 1.73 ± 3.38, $p \leq 0.05$).
**Table 3**
| Category | Category.1 | Pre-test | Pre-test.1 | Pre-test.2 | t or χ2 | p | Post-test n (%) or d (Post-pre) | Post-test n (%) or d (Post-pre).1 | Post-test n (%) or d (Post-pre).2 | t or χ2.1 | p.1 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Category | Category | Total | HA | SS | t or χ2 | p | Total | HA | SS | t or χ2 | p |
| Category | Category | (N = 166) | (N = 83) | (N = 83) | t or χ2 | p | (N = 166) | (N = 83) | (N = 83) | t or χ2 | p |
| GDS | 3.5 ± 3.19 | 3.18 ± 3.05 | 3.82 ± 3.32 | 1.276 | 0.204 | 1.27 ± 3.6 | 0.83 ± 3.77 | 1.73 ± 3.38 | 1.589 | 0.114 | |
| Exercise self-efficacy | 18.47 ± 4.37 | 18.55 ± 4.56 | 18.39 ± 4.2 | −0.248 | 0.805 | 0.58 ± 3.68 | 0.92 ± 3.61 | 0.25 ± 3.73 | −1.162 | 0.247 | |
| Moderate exercise frequency/week | 0 | 76 (45.78%) | 34 (40.96%) | 42 (50.6%) | 3.893 | 0.143 | 57 (34.34%) | 29 (34.94%) | 28 (33.73%) | 0.361 | 0.835 |
| Moderate exercise frequency/week | 1–2 | 12 (7.23%) | 9 (10.84%) | 3 (3.61%) | 3.893 | 0.143 | 12 (7.23%) | 5 (6.02%) | 7 (8.43%) | 0.361 | 0.835 |
| Moderate exercise frequency/week | Over 3 times | 78 (46.99%) | 40 (48.19%) | 38 (45.78%) | 3.893 | 0.143 | 97 (58.43%) | 49 (59.04%) | 48 (57.83%) | 0.361 | 0.835 |
| DDS | 0–2 | 26 (15.66%) | 11 (13.25%) | 15 (18.07%) | 0.949 | 0.622 | 23 (13.86%) | 12 (14.46%) | 11 (13.25%) | 6.130 | 0.047* |
| DDS | 3–4 | 92 (55.42%) | 46 (55.42%) | 46 (55.42%) | 0.949 | 0.622 | 87 (52.41%) | 36 (43.37%) | 51 (61.45%) | 6.130 | 0.047* |
| DDS | 5 | 48 (28.92%) | 26 (31.33%) | 22 (26.51%) | 0.949 | 0.622 | 56 (33.73%) | 35 (42.17%) | 21 (25.3%) | 6.130 | 0.047* |
| BMI | Normal | 95 (57.23%) | 51 (61.45%) | 44 (53.01%) | 1.294 | 0.524 | 99 (59.64%) | 53 (63.86%) | 46 (55.42%) | 1.227 | 0.542 |
| BMI | Underweight | 8 (4.82%) | 4 (4.82%) | 4 (4.82%) | 1.294 | 0.524 | 9 (5.42%) | 4 (4.82%) | 5 (6.02%) | 1.227 | 0.542 |
| BMI | Obesity | 63 (37.95%) | 28 (33.73%) | 35 (42.17%) | 1.294 | 0.524 | 58 (34.94%) | 26 (31.33%) | 32 (38.55%) | 1.227 | 0.542 |
| Relative hand grip strength | Normal | 80 (48.19%) | 40 (48.19%) | 40 (48.19%) | 1.138 | 0.767 | 94 (56.63%) | 48 (57.83%) | 46 (55.42%) | 0.568 | 0.753 |
| Relative hand grip strength | Risk | 32 (19.28%) | 15 (18.07%) | 17 (20.48%) | 1.138 | 0.767 | 32 (19.28%) | 17 (20.48%) | 15 (18.07%) | 0.568 | 0.753 |
| Relative hand grip strength | Weak | 53 (31.93%) | 27 (32.53%) | 26 (31.33%) | 1.138 | 0.767 | 40 (24.1%) | 18 (21.69%) | 22 (26.51%) | 0.568 | 0.753 |
| Relative hand grip strength | Missing | 1 (0.6%) | 1 (1.2%) | 0 (0%) | 1.138 | 0.767 | 0 (0) | 0 (0) | 0 (0) | 0.568 | 0.753 |
| One leg balance test | Normal | 148 (89.16%) | 76 (91.57%) | 72 (86.75%) | 0.997 | 0.318 | 150 (90.36%) | 77 (92.77%) | 73 (87.95%) | 1.107 | 0.293 |
| One leg balance test | Weak | 18 (10.84%) | 7 (8.43%) | 11 (13.25%) | 0.997 | 0.318 | 16 (9.64%) | 6 (7.23%) | 10 (12.05%) | 1.107 | 0.293 |
| FTSTS | Normal | 123 (74.55%) | 63 (76.83%) | 60 (72.29%) | 0.448 | 0.503 | 137 (82.53%) | 70 (84.34%) | 67 (80.72%) | 1.510 | 0.219 |
| FTSTS | Weak | 42 (25.45%) | 19 (23.17%) | 23 (27.71%) | 0.448 | 0.503 | 24 (14.46%) | 9 (10.84%) | 15 (18.07%) | 1.510 | 0.219 |
| FTSTS | Missing | 0 (0) | 0 (0) | 0 (0) | 0.448 | 0.503 | 5 (3.01%) | 4 (4.82%) | 1 (1.2%) | 1.510 | 0.219 |
The average value of exercise on self-efficacy in the pre-test was 18.55 ± 4.56 in the HA group and 18.39 ± 4.2 in the SS group. There was no difference between the posttest and pretest (d) between the two groups (HA: 0.92 ± 3.61 and SS: 0.25 ± 3.73, $p \leq 0.05$). The proportion of those who did not exercise at all was $45.78\%$ (76 out of 166 participants) and decreased to 57 ($34.34\%$) after 6 months of intervention. However, there was no difference between the HA and SS groups (χ2 = 3.893, $p \leq 0.05$).
26 people ($15.66\%$) had a dietary diversity score between 0 and 2. After 6 months, the number decreased to 23 ($13.86\%$). In addition, the proportion of those who ate from all five food groups evenly increased from 48 ($28.92\%$) to 56 ($33.73\%$). Although the pre-test values were the same, there was a statistically significant difference between the two groups in the post-test. In the HA group, the number increased from $31.33\%$ to $42.17\%$, but in the SS group, it decreased from $26.51\%$ to $25.3\%$. However, in the SS group, those who consumed 0–2 food groups decreased from $18.07\%$ to $13.25\%$, and those who consumed 3 or 4 food groups increased from $55.42\%$ to $61.45\%$, thus indicating a positive change.
In the case of BMI, 95 people ($57.23\%$) were evaluated as normal at the pre-evaluation, and 99 ($59.64\%$) at the post-evaluation, a similar level. The obese group also remained at a similar level: 63 ($37.95\%$) at the pre-evaluation compared to 58 ($34.94\%$) at the post-evaluation. There was no statistically significant difference between the HA group and the SS group for both pre and post values.
In terms of relative handgrip strength, the number of those evaluated as normal increased from 80 ($48.19\%$) to 94 ($56.63\%$), and the pre- and post-evaluation were the same for those evaluated as at-risk at 32 ($19.28\%$). The number of those evaluated as weak decreased from 53 ($31.93\%$) in the pre-evaluation to 40 ($24.1\%$) in the post-evaluation. There was no statistically significant difference between the HA and SS groups (χ2 = 0.568, $p \leq 0.05$).
As a result of the one leg balance test, the normal group increased slightly from 148 ($89.16\%$) to 150 ($90.36\%$), but no significant difference was found between the HA and SS groups (χ2 = 1.107, $p \leq 0.05$).
Those who were evaluated as normal during the FTSTS test increased from 123 ($74.55\%$) in the pre-evaluation to 137 ($82.53\%$) in the post-evaluation. However, there was no significant difference between the two groups (χ2 = 1.510, $p \leq 0.05$).
## Discussion
Owing to the recent COVID-19 pandemic, the introduction of non-face-to-face healthcare has accelerated. In South Korea, the introduction of ICT healthcare services is progressing rapidly with the full support of the government, not only in private medicine but also in the public health field. To integrate digital technology as one of the health management methods, significant economic, time, and human resources are being mobilized. However, rather than the indiscriminate introduction of the digital method, it is time to determine what specific function of digital devices to provide to the target population and to accurately verify its effectiveness.
The first important finding of this study is that both the HA and SS group showed positive health status and behavior changes at the time of post-evaluation. Though older adults with low digital literacy should first be educated on these technologies, our study demonstrated that there were significant improvements in health behaviors after adaptation to digital devices. As Table 3 reveals, perceived self-efficacy in exercise, moderate exercise frequency per week, the diet diversity scale, relative grip strength, and FTSTS were positively changed during post-evaluation compared to the pre-evaluation. On the contrary, depression increased, and BMI and balance test scores were maintained at similar levels in both groups after 6 months.
According to a systematic review, studies have found that IoT-enabled health care services can lead to improved outcomes in health and wellbeing for older adults, such as improved medication adherence, better management of chronic conditions, and improved quality of life [20]. Similar to this study, previous studies also showed a statistically significant increase in exercise frequency and improved eating habits (21–24). Recording eating habits and exercise frequency with mobile apps and wearable devices can raise the level of awareness of health behaviors, making it easier for older people to change their behavior, rather than just counseling them to change. In the study of Barnason andZimmerman [21], 36 telephone counseling sessions were conducted intensively over 3 months, during which self-efficacy improved as in the results of this study. However, Fukuoka and Gay [23] reported no improvement in self-efficacy after providing a multicomponent intervention using a mobile app and wearable device for 5 months. Unlike many previous studies that showed significant improvements in BMI and weight loss, this study showed similar levels after 6 months [21, 23, 25]. In addition, among objective indicators such as BMI, relative grip strength, balance test, and FTSTS test levels were classified according to risk level, so there may not have been a significant difference across categories during the 6-month study period. There were no previous studies using FTSTS, relative grip strength, balance test, etc., which are important indicators for predicting frailty.
Second, unlike previous studies, our hypothesis that listening to songs, Autonomous Sensory Meridian Response (ASMR), and conversation functions (which are the main functions of smart speakers) will have a positive effect on reducing depression has not been supported [26]. Rather, the feeling of depression increased further in the post-test, which may be due to the influence of the Corona-blue due to COVID-19, an external environmental factor, or the test-retest bias. In addition, as there was no statistically significant difference in the degree of increase in depression between the two groups, it was not possible to reveal any additional benefits of the smart speakers on depression in older adults living alone or those who were socially frail. According to the results of a recent study, depression and loneliness were significantly reduced in older adults after 2 months of using the same smart speaker used in our study. However, no statistically significant difference was found between those who frequently used smart speakers and those who used them intermittently. Therefore, it is difficult to infer that depression decreased due to the direct influence of smart speakers [9].
There are studies that show a statistically significant reduction in depression when a smart speaker in the form of a child doll is provided to an elderly person with type 2 diabetes and cognitive decline who lives alone [7, 27, 28]. In addition, in a path analysis of the effects of smart speakers on health behavior and depression, it was found that health behavior mediates attitudes toward smart speakers, leading to an alleviation of depression [29]. Only older adults who had a positive attitude toward a smart speaker showed a significant effect.
A limitation of this study is that it was not possible to control attitudes and usage patterns toward smart speakers. Follow-up studies should aim to identify and control usage patterns for digital devices. Since most previous studies applying eHealth or mHealth were for middle-aged people, more research on mHealth-related effects in older adults seems necessary [20]. Variables such as relative grip strength, balance test, and FTSTS, which are mentioned as reliable tools to predict frailty, should be considered.
The importance of chronic disease management for older adults is further emphasized when considering the threat of infectious diseases and mortality statistics [30, 31]. Another problem is that vulnerable older adults, who are the main target group of public health, may have difficulty managing their diseases due to fear of visiting public health centers, hospitals, and clinics, and due to concerns regarding social distancing. For public health centers to achieve chronic disease management and health promotion in addition to their role in quarantine, it is necessary to expand non-face-to-face healthcare and attempt to effectively integrate it with existing services.
Despite the advantages of international trends and previous research results, the reason that ICT has not been widely used as a health promotion intervention for older adults is that there are doubts about its acceptability and effectiveness from health professionals and older adults [32]. Since qualitative research still dominates the research field of ICT healthcare services, many experimental studies need to be conducted [33].
Health management results may differ depending on the ability to use smartphones and the IoT, and the ability to acquire, understand, and utilize health information. Therefore, if the device or application used by older adults is not developed so that the user can intuitively use it, it can become a barrier to health management. Although the acceptance of ICT by older adults is still lower than that of adults, it is gradually gaining acceptance among the former [34]. Hence, active intervention by health experts is important, which is why accessible technology for older adults needs to be further developed [35].
## Conclusion and recommendations
ICT can be sufficiently attempted even for older adults as it is an efficient healthcare approach that can be used more actively as the older adult population becomes increasingly accustomed to digital devices. In addition, as older adults in the community cannot be managed by medical staff near them, such as in hospitals, ICT is a useful method to monitor symptoms of chronic diseases, detect abnormalities, and manage health behaviors remotely, and it will be an important leap forward for the healthcare industry. However, to provide an ICT device for the health management of socially and economically vulnerable older adults in the public domain, such devices should be introduced after verifying the older adult-friendly interface and functions of the healthcare device and ascertaining whether it meets the purpose and goal set by the national health project.
## Data availability statement
The datasets presented in this article are not readily available because this study was conducted at a public health center in South Korea, and disclosure of data other than for research purposes is not permitted. Requests to access the datasets should be directed to Dasom Kim, [email protected].
## Ethics statement
The studies involving human participants were reviewed and approved by the Korea National Institute for Bioethics Policy. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
DK contributed to conception and design of the study; DK organized the database; DK performed the statistical analysis; DK wrote the first draft of the manuscript; DK wrote sections of the manuscript; DK contributed to manuscript revision, read, and approved the submitted version.
## Conflict of interest
The author declares 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/fdgth.2023.1117280/full#supplementary-material.
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|
---
title: 'Dynamic evolution of left ventricular strain and microvascular perfusion assessed
by speckle tracking echocardiography and myocardial contrast echocardiography in
diabetic rats: Effect of dapagliflozin'
authors:
- Juan Liu
- Yixuan Wang
- Jun Zhang
- Xin Li
- Lin Tan
- Haiyun Huang
- Yang Dai
- Yongning Shang
- Ying Shen
journal: Frontiers in Cardiovascular Medicine
year: 2023
pmcid: PMC9996187
doi: 10.3389/fcvm.2023.1109946
license: CC BY 4.0
---
# Dynamic evolution of left ventricular strain and microvascular perfusion assessed by speckle tracking echocardiography and myocardial contrast echocardiography in diabetic rats: Effect of dapagliflozin
## Abstract
### Background
This experimental study aimed to determine the dynamic changes in myocardial strain and microvascular perfusion in diabetic rats by comprehensive echocardiography while evaluating the effect of dapagliflozin (DAPA).
### Materials and methods
Male Sprague–Dawley rats ($$n = 128$$) were randomly divided into four groups based on the presence or absence of a high-fat diet and streptozotocin-induced diabetes with or without DAPA treatment ($$n = 32$$/group). Serial conventional ultrasound, two-dimensional speckle tracking echocardiography (2D-STE) and myocardial contrast echocardiography (MCE) were performed at 2, 4, 6, and 8 weeks, and left ventricular global longitudinal strain (GLS), myocardial blood flow velocity (MBFV), myocardial blood flow (MBF), and myocardial blood volume (MBV) were determined. All animals were sacrificed immediately after the last echo measurement for histopathological assessment.
### Results
Despite similar conventional Doppler-echo indexes among the groups at 2, 4, 6, and 8 weeks ($p \leq 0.05$), left ventricular GLS, MBFV, MBF, and MBV were decreased at 8 weeks in diabetic rats ($p \leq 0.05$) as detected by both 2D-STE and MCE. These indexes were significantly improved at 6 and 8 weeks after treatment with DAPA for diabetic rats ($p \leq 0.05$), reaching similar values observed in non-diabetic controls. DAPA treatment was associated with increased myocardial vacuolization and microvessel density and reduced interstitial fibrosis in diabetic rats.
### Conclusions
Combined 2D-STE and MCE is sensitive for detecting left ventricular deformity and impaired microvascular perfusion in prediabetes and the early stage of diabetes mellitus. DAPA exerts a beneficial effect on protecting myocardial perfusion in diabetic rats.
## 1. Introduction
It is well recognized that chronic hyperglycemia reduces the self-protective ability of vascular endothelial cells, impairs microvascular perfusion, and decreases cardiac systolic and diastolic function, ultimately leading to diabetic cardiomyopathy. Cardiac dysfunction in the absence of coronary artery disease, hypertension, and valvular disease is called diabetic cardiomyopathy, which is one of the most common complications of diabetes mellitus (DM). The main pathological changes of diabetic cardiomyopathy includes myocardial interstitial fibrosis, myocardial stiffness and microvascular rarefaction, eventually leading to diastolic and systolic dysfunction. Timely intervention of diabetic cardiomyopathy is very challenging [1], therefore, the detection of alterations in myocardial and microvascular function in the process from prediabetes to the early stage of diabetes mellitus (earlyDM) plays a vital role in the prevention of serious complications [2]. Although conventional ultrasound techniques, such as M-mode, two-dimensional echocardiography, coupled with color Doppler flow and tissue Doppler imaging, are routinely used for evaluating cardiac structure and function, two-dimensional speckle tracking echocardiography (2D-STE) provides quantitative assessment of local and global left ventricular mechanical changes [3], and myocardial contrast echocardiography (MCE) serves as a tool to determine the total amount and speed of myocardial microvascular blood perfusion [4]. However, data are lacking concerning the role of these techniques used alone or in combination for evaluating dynamic changes in myocardial function and microvascular perfusion, especially for patients with diabetes mellitus.
Several randomized trials and observational studies have shown that sodium-glucose cotransporter 2 (SGLT2) inhibitors significantly reduce the risk of cardiovascular adverse events in diabetic patients (5–7). Further studies indicated that these agents could also provide beneficial cardiovascular effects even for non-diabetic patients, suggesting that myocardial protection with SGLT2 inhibitors may occur through a glucose-independent pathway (8–10). Nevertheless, further in-depth studies are warranted to clarify the mechanism of the protective effect of SGLT2 inhibitors on the myocardium.
Since changes in myocardial function and microvascular perfusion in prediabetes and earlyDM have not yet been well studied and whether early intervention with SGLT2 inhibitors can effectively prevent myocardial damage is not clear, in this experimental study, we used conventional ultrasound techniques, 2D-STE and MCE, to evaluate the dynamic evolution of myocardial function and microvascular perfusion and to explore the potential beneficial effect of the SGLT2 inhibitor dapagliflozin (DAPA) in a diabetic rat model.
## 2.1. Animal model and groups
Male Sprague–Dawley rats (120–150 g) aged 5 weeks were obtained from the Laboratory Animal Center of the Third Military Medical University and kept in a constant temperature and humidity environment under a 12-h light/dark cycle with free access to food and water. The experimental protocol was approved by the Laboratory Animal Welfare and Ethics Committee of the Third Military Medical University (No. AMUWEC20224071).
A total of 128 animals were randomly divided into four groups ($$n = 32$$ in each group): the normal control group included rats with 8 weeks of standard diet plus saline followed by citrate buffer; the DAPA-control group consisted of rats with 8 weeks of standard diet plus oral administration of DAPA followed by citrate buffer; the diabetic group comprised rats with 8 weeks of high-fat diet (HFD) plus saline followed by intraperitoneal injection of streptozotocin (STZ), and the DAPA-diabetic group comprised rats with 8 weeks of HFD plus oral administration of DAPA followed by intraperitoneal injection of STZ. In brief, a HFD consists of $79.85\%$ general animal feed, $15\%$ fat (oil), $5\%$ custard powder, and $0.15\%$ cholesterol (Laboratory Animal Center of the Third Military Medical University). DAPA (AstraZeneca) was given orally at a dose of 1 mg/kg/d for 8 weeks. STZ (Sigma–Aldrich, St. Louis, MO) was injected at a dose of 30 mg/kg under fasting conditions, and diabetes was identified when the fasting blood glucose level was >11.1 mmol/L at day 5 (Figure 1).
**Figure 1:** *Study design. For each group, echo-Doppler, 2D-STE, and MCE were performed every 2 weeks. The myocardium was collected at sacrifice for histopathological analysis.*
## 2.2. Conventional Doppler echocardiography
Ultrasound scanning of rats was performed under anesthesia with $3\%$ isoflurane and oxygen with a heart rate maintained at 300~400 beats/min. Animals were placed on a prewarmed rodent platform, and body temperature was maintained at approximately 37°C. The anterior chest hair was then cleared.
An ultrahigh resolution animal imaging system (VEVO 2100, VisualSonics, Toronto, Canada) with a linear array probe (MS400, a frequency of 24 MHz) was used to carry out conventional transthoracic echocardiography. M-mode images were recorded at the level of the papillary muscles in the best parasternal long axis view. Left ventricular end-diastolic (EDD) and end-systolic diameters (ESD) and end-diastolic posterior wall thickness (PWd) and interventricular septum thickness in diastole (IVSd) were measured, and fractional shortening (FS) and left ventricular mass index (LVMI) were calculated. Left ventricular end-diastolic (EDV) and end-systolic volumes (ESV) and ejection fraction (EF) were determined by two-dimensional echocardiography. An average of three consecutive cycles was used.
Spectral Doppler in the best 4-chamber view with the sampling site positioned at the level of the mitral valve was used to measure peak flow velocity (E), isovolumic systolic time (ICT), isovolumic diastolic time (IRT), and LV ejection time (ET). Tissue Doppler imaging (TDI) was applied to determine myocardial motion velocity (e') at the junction between the septum and mitral annulus. The ratio of E to e' (E/e') and the Tei index, (ICT + IRT)/ET, were calculated [11].
## 2.3. Two-dimensional speckle tracking echocardiography
Parasternal long- and short-axis images were acquired for at least 3 cardiac cycles with optimal visualization of the LV myocardium. 2D-STE analysis was performed offline using TOMTEC software (TOMTEC Image System GmbH, Germany). Both endocardial and epicardial boundaries of the left ventricular myocardium were manually traced. This software can automatically track the movement of the entire myocardium. All tracked contours were checked, and, when poorly tracked, the data were excluded. Left ventricular global longitudinal strain (GLS), global circumferential strain (GCS), and global radial strain (GRS) were then derived. All values were averaged from 3 cardiac cycles.
## 2.4. Myocardial contrast echocardiography
Real-time MCE was performed using a Philips ultrasound diagnostic system (EPIC 7, Philips Medical Systems, Andover, MA, USA) with a linear array probe (eL18-4, 20 MHz). In brief, a commercially available contrast agent (SonoVue, Bracco, Italy) was diluted twice by adding 5 ml of normal saline, and a 24-gauge cannula was placed into the tail vein with continuous infusion (0.3–0.4 ml/min) [12]. All images were optimized for each animal, including penetration depth 2 cm, near field focused on the middle of the left ventricle, gains adjusted without signal intensity of the myocardium, maximal dynamic range (60 dB), and mechanical index 0.06. When contrast agent was filled in the myocardium, left ventricular parasternal long axis images were stored for at least 20 cardiac cycles. The “flash (high energy pulse, mechanical index >1.0)” function was immediately triggered to destroy all myocardial microbubbles and then automatically switched to a low-energy real-time contrast state.
All video recordings were analyzed offline using a QLAB (version 6.0, Philips Healthcare) workstation. Regions of interest (ROIs) were manually placed on the middle segment of the anterior myocardium with an area of approximately 1.5 mm2, and each frame was manually checked to avoid partial volume effects from the right and left ventricular cavities. The first frame image after “flash” was set as the background frame, and then the time-signal intensity curve was fitted to an exponential function [13]: where A is the peak intensity in the plateau phase, reflecting myocardial blood volume (MBV), β is the rising slope of signal intensity, reflecting myocardial blood flow velocity (MBFV), and A*β equals myocardial blood flow (MBF) [14].
## 2.5. Histopathological examination
After MCE, all animals were anesthetized in an unconscious state, then euthanized with thoracotomy and heart removal. The heart was fixed in $4\%$ neutral buffered formalin and embedded in paraffin, then serially sectioned along the long axis of the left ventricular papillary muscle (4 μm thick), which was similar to the MCE plane. Hematoxylin and eosin (H&E) staining was performed to assess cardiac histomorphological changes. Masson's trichrome staining was performed to visualize the status of cardiac tissue fibrosis, and CD31 (Abcam ab182981) was used to evaluate myocardial vessels, which were subsequently examined by a pathologist who was blinded to the ultrasonic results.
## 2.6. Statistical analysis
The Shapiro–Wilk test was performed to evaluate the distribution of the data. Continuous and normally distributed data were presented as mean ± SD. Differences among groups were determined by one-way ANOVA followed by Bonferroni post hoc tests. A value of $p \leq 0.05$ was considered significant. All statistical analyses were performed by SPSS (version 21.0, SPSS Inc., Chicago, IL, USA), and GraphPad Prism (version 7.04, GraphPad Software, Inc., La Jolla, CA, USA) was used to generate statistical figures.
## 3.1. Comparison of conventional echo-Doppler results
Left ventricular geometric measurements, E/e' and Tei index were unchanged among the four groups from 2 to 8 weeks (all $p \leq 0.05$). The left ventricular EF decreased gradually in the diabetic group but remained stable in the DAPA-diabetic group. However, the difference did not reach statistical significance (Table 1, Figure 2).
## 3.2. Comparison of 2D-STE data
In the diabetic group, GLS decreased stepwise from 2 to 8 weeks, with a statistically significant difference between 2 and 8 weeks ($$p \leq 0.02$$). At 8 weeks, left ventricular strain parameters were significantly reduced in the diabetic group compared to the normal control ($$p \leq 0.011$$) and DAPA-control groups ($$p \leq 0.032$$). However, no differences were observed among the normal control, DAPA-control, and DAPA-diabetic groups (Table 2, Figure 3).
## 3.3. Comparison of MCE quantitative data
In the diabetic group, the values of myocardial perfusion as expressed by MBFV and MBF decreased stepwise from 2 to 8 weeks, with statistically significant differences between 2 and 6 weeks ($$p \leq 0.019$$, $$p \leq 0.023$$) and 2 and 8 weeks ($$p \leq 0.001$$, $$p \leq 0.001$$). Compared with the normal control and DAPA-control groups, MBFV, MBF, and MBV were decreased gradually in the diabetic group across 2, 4, 6, and 8 weeks, reaching levels of statistically significant differences at 6 weeks for MBFV and MBF ($$p \leq 0.047$$, $$p \leq 0.026$$) and at 8 weeks for MBV ($$p \leq 0.033$$). There were no significant differences in these values among the normal control, DAPA-control, and DAPA-diabetic groups (Table 3, Figure 4).
## 3.4. Histopathological findings
Histopathological changes were found in the diabetic group. Figure 5 shows typical tissue morphology following H&E, Masson's trichrome, and CD31 staining in each group at 8 weeks. In the normal control group, myocardial cell morphology was normal, with closely packed cells. In contrast, in the diabetic group, myocardial cells were vacuolated and disorganized; increased collagen fibers deposition and decreased myocardial microvascular density were also seen. However, in the DAPA-diabetic group, the morphology of myocardial cells was essentially normal, with closely packed cells. In addition, myocardial fibrosis and myocardial microvascular density decreased were not as obvious.
**Figure 5:** *Typical pathological images of the four groups at 8 weeks. (A) Representative H&E staining images of the four groups at 8 weeks. Magnification: × 200. (B) Representative Masson's trichrome staining images of the four groups at 8 weeks. Magnification: × 200. (C) Representative CD31 staining images of the four groups at 8 weeks. Magnification: × 400. NC, normal control group; DAPA-C, DAPA-control group; DM, diabetic group; DAPA-DM, DAPA-diabetic group.*
## 4. Discussion
The results of this study showed that the combined use of 2D-STE and MCE was a sensitive way to detect left ventricular deformity and impaired microvascular perfusion in prediabetes and earlyDM. DAPA exerts a cardio-protective effect in this setting.
Myocardial dysfunction of varying degrees often occurs in earlyDM [15]. With the prolongation of the disease process, disturbance of energy metabolism causes myocardial and endothelial cell remodeling, apoptosis, fibrosis and myocardial microvascular abnormalities (16–18). In addition, increased wall thickness and mass reduce left ventricular diastolic compliance [19]. Eventually, left ventricular systolic function declines, leading to diabetic cardiomyopathy. Therefore, timely detection of early changes in cardiac structure and function in diabetic patients is of great clinical relevance [20]. In this study, we built a diabetic model that was first fed a HFD for 8 weeks to induce insulin resistance and then injected with STZ to induce high glucose (21–23). Next, we performed comprehensive echo assessments at different time points in the process from prediabetes to earlyDM to evaluate the dynamic evolution of myocardial function and microvascular perfusion.
One major finding of this study is the advantage of 2D-STE and MCE, in comparison with conventional echo-Doppler techniques, for early detection of left ventricular deformity and abnormal microvascular and myocardial perfusion in prediabetes and earlyDM. In fact, no significant changes in left ventricular geometry or systolic and diastolic function were observed using conventional echo-Doppler parameters in diabetic rats, which is consistent with previous studies [24]. Although tissue Doppler imaging has been utilized to evaluate myocardial motion, especially when its direction is parallel to sound velocity, the accuracy of such measurements is angle dependent, limiting its value in assessing cardiac function in patients with diabetes [25]. 2D-STE regards the myocardium as uniformly distributed acoustic spots and then tracks each spot and calculates its motion trajectory to accurately determine myocardial strain and strain rate [3]. Previous studies have shown that 2D-STE can effectively reveal minor myocardial damage and provide quantitative assessment of left ventricular function in coronary heart disease [26], diabetic cardiomyopathy [27], and structural heart disease [28]. Importantly, GLS is helpful in the early detection of subclinical myocardial dysfunction with preserved left ventricular EF for diabetic patients [27, 29]. Likewise, MCE is adopted to dynamically evaluate the functional changes in myocardial microcirculation. Signal intensity in MCE imaging (microbubble volume) is thought to be equivalent to corresponding myocardial blood flow and has been used to estimate the local status of myocardial perfusion. Additionally, MCE has excellent temporal and spatial resolution and is low cost and simple to operate. Numerous experiments have shown that MCE is an effective method for detecting abnormal myocardial blood flow perfusion [4] and that it is superior to positron emission tomography (PET) and single photon emission computed tomography (SPECT) [30].
Another important finding is that DAPA exerts a beneficial effect on left ventricular function and myocardial perfusion in diabetic rats. SGLT2 inhibitors are a new class of oral hypoglycemic drugs that can lower blood glucose levels by inhibiting sodium-glucose reabsorption in proximal renal tubules and by promoting urine glucose excretion. These agents not only effectively reduce glycosylated hemoglobin but also have a low risk of hypoglycemia. Recent studies have confirmed that DAPA slightly decreases systolic and diastolic blood pressure, improves myocardial energy utilization, and inhibits myocardial fibrosis. Recently, DAPA has been embraced as a drug of choice for the treatment of heart failure with reduced or preserved EF in diabetic and non-diabetic patients [5, 10]. In view of the significant advantages of DAPA for cardiovascular benefits, we intended to apply DAPA in prediabetes and earlyDM to explore its role in reversing myocardial dysfunction by improving myocardial strain and microcirculation in a rat model. Our results of 2D-STE and MCE studies showed that indexes of left ventricular deformity (GLS, GCS, and GRS) and parameters of myocardial perfusion (MBFV, MBF, and MBV) gradually recovered to baseline levels after treatment with DAPA, suggesting that the SGLT2 inhibitor DAPA may have cardioprotective effects in this diabetic setting.
In addition, our histopathological study demonstrated that myocardial cells were vacuolated and disorganized in prediabetes and earlyDM. Increased collagen fibers deposition and decreased myocardial microvascular density were also seen, which is in line with previous reports [31]. Interestingly, after treatment with DAPA, myocardial cells became essentially normal with closely packed cells. Furthermore, myocardial fibrosis and myocardial microvascular density decreased were not as obvious. These results further proved the effect of cardiovascular protection caused by DAPA.
In conclusion, the findings of this experimental study support the view that the combined use of 2D-STE and MCE is more sensitive than the conventional Doppler-echo technique for detecting impaired myocardial function and microvascular perfusion in prediabetes and earlyDM. DAPA exerts a beneficial effect on protecting left ventricular function and myocardial perfusion in diabetic rats. This information may provide experimental basis for the pathophysiology of diabetic cardiomyopathy and should be useful to physicians who are involved in decision-making for diabetic patients.
This study is subject to several limitations. First, the current study focused on the changes in left ventricular strain and microvascular perfusion in prediabetes and the early stage of diabetes mellitus and the effect of DAPA but did not focus on the changes in the late stage and the corresponding role of DAPA. Future studies on the changes in the late stage of diabetes and the role of DAPA will be carried out. Second, in the present study, resting MCE was performed; however, in the future, stress MCE may be used to further evaluate myocardial microvascular perfusion.
## 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.
## Ethics statement
The animal study was reviewed and approved by Laboratory Animal Welfare and Ethics Committee of the Third Military Medical University.
## Author contributions
JL, YW, YSha, and YShe participated in study design, data analysis and interpretation, and drafting the manuscript. JL, JZ, XL, and LT performed experiments. JL, HH, YD, YSha, and YShe revised the manuscript before final approval. All authors read and approved the final manuscript.
## 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.2023.1109946/full#supplementary-material
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|
---
title: Development and validation of a Systemic Sclerosis Health Literacy Scale
authors:
- Meng Zhuang
- Cheng-Cheng Li
- Shan-Yu Chen
- Xin-Hua Tu
- Lian Liu
- Xi-Lai Chen
- Cheng-Wei Xu
- Jing Wang
journal: Frontiers in Public Health
year: 2023
pmcid: PMC9996225
doi: 10.3389/fpubh.2023.1038019
license: CC BY 4.0
---
# Development and validation of a Systemic Sclerosis Health Literacy Scale
## Abstract
### Background and aim
Health literacy levels are strongly associated with clinical outcomes and quality of life in patients with chronic diseases, and patients with limited health literacy often require more medical care and achieve poorer clinical outcomes. Among the large number of studies on health literacy, few studies have focused on the health literacy of people with systemic sclerosis (SSc), and there is no specific tool to measure health literacy in this group. Therefore, this study plans to develop a health literacy scale for patients with SSc.
### Methods
This study included 428 SSc patients from the outpatient and inpatient departments of the Department of Rheumatology and Immunology, the first affiliated Hospital of Anhui Medical University and the first affiliated Hospital of University of Science and Technology of China. The formulation of the scale was completed by forming the concept of health literacy of SSc patients, establishing the item pool, screening items, and evaluating reliability and validity. Classical measurement theory was used to screen items, factor analysis was used to explore the construct validity of the scale, and Cronbach's alpha coefficient was used to assess the internal consistency.
### Results
Our study population was predominantly middle-aged women, with a male to female ratio of 1:5.7 and a mean age of 51.57 ± 10.99. A SSc Health Literacy scale with 6 dimensions and 30 items was developed. The six dimensions are clinic ability, judgment/evaluation information ability, access to information ability, social support, treatment compliance and application information ability. The Cronbach's alpha coefficient of the scale is 0.960, retest reliability is 0.898, split-half reliability is 0.953, content validity is 0.983, which has good reliability and validity.
### Conclusion
The Systemic Sclerosis Health Literacy Scale may become a valid tool to evaluate the health literacy level of patients with SSc.
## 1. Introduction
SSc is an autoimmune disease that often presents with abnormal expression of the immune system, microvascular involvement, and fibrosis of skin and visceral cells [1, 2]. Patients with SSc mainly show symptoms such as hard and tight skin, swollen and painful joints, and joint dysfunction [3]. Interstitial lung disease and pulmonary arterial hypertension are common complications of systemic sclerosis, and are the leading cause of death in patients [4, 5]. SSc is a chronic non-communicable disease with complex etiology, insidious onset, long course and persistent disease [6]. In recent years, many scholars have devoted themselves to the study of the causes and pathogenesis of SSc [7, 8]. However, the causes and processes of SSc development are not fully understood, and pharmacotherapy is the main treatment, although some drugs have been shown to improve the fibrosis or complications of SSc, but not to achieve a cure [9, 10].
The increasing focus on patient-centered treatment options and patient self-care skills in the treatment of chronic diseases [11], and the requirement for patients to be able to make clear medical decisions, has made health literacy highly relevant in healthcare settings [12]. Patients with limited health literacy are often accompanied by poor health outcomes, poor adherence to treatment, and underutilization of health care resources [13, 14]. Many studies on health literacy and the health outcomes of chronic diseases have emerged (15–18). Multiple studies have indicated that health literacy is strongly associated with health outcomes, and that low health literacy affects an individual's ability to read and access health information [19], communicate with doctors [20], adopt a healthy lifestyle, and respond to disease warnings [21]. It is observed that health literacy is a potential factor affecting the quality of life and disease management of chronic patients. In a study on chronic disease prevention in China, health literacy was linked to a reduction in the likelihood of comorbidity [22].
What is health literacy? Different scholars have developed inconsistent definitions of health literacy, and the most widely used is the definition developed by the US. National Library [23], “the ability of an individual to access, understand, and process basic health information or services to make appropriate health decisions.” In addition, WHO defines health literacy as “the ability to obtain, understand, evaluate and apply health information to make judgments and decisions in health care, disease prevention and health promotion, thereby improving the quality of life” [24]. In recent years, Healthy People 2030 defines health literacy in terms of individuals and organizations, retaining the connotation of individuals finding, understanding, and using health information and services, and emphasizing the roles and responsibilities of organizations in health information and services [25].
Current research on health literacy levels in patients with rheumatic diseases has focused on systemic lupus erythematosus and rheumatoid arthritis [26, 27]. The findings show an association between patient health literacy and disease activity, medication adherence, functional status, and additional health outcomes (28–30). We found only one study related to SSc patient health literacy, which, unlike traditional health literacy studies, was an assessment of e-health literacy, focusing on assessing patients' use of e-health resources and need for web-based support [31]. In this study, we focused on the ability that SSc patients have to be able to make health decisions, rather than the ability to access the Web, for which no relevant literature has been found.
As we know, the awareness rate of SSc is low, and people often report being unfamiliar with the disease and need to be aware of it if they are diagnosed. SSc occurs mostly in middle-aged and old women, who generally have a low level of health awareness, lack of understanding of the disease, difficulty in correctly recognizing disease characteristics, insufficient self-management ability, low treatment compliance, and poor clinical outcomes. In addition, patients with limited health literacy use more outpatient services and are hospitalized more frequently, increasing the socioeconomic burden and additional financial burden of care [32, 33]. Leonardo Martin Calderon et al. reviewed published articles on the economic impact and healthcare resource utilization associated with SSc, noting that the total annual cost of SSc ranges from $14,959 to $23,268 in the United States, which is a significant economic burden on patients and health resources [34]. Therefore, we believe it is necessary to pay attention to the health literacy level of this group of SSc, raise patients' awareness, and maximize the use of limited resources as much as possible, thus improving patients' awareness of the disease, reducing the waste of medical resources, and alleviating patients' economic burden.
Health literacy scales are currently the primary measurement tool for measuring the health literacy level of study participants. Commonly used health literacy scales include “Test of Functional Health Literacy in Adults, TOFHLA” [35], “Rapid Estimate of Adult Literacy in Medicine, REALM” [36], “Brief Health Literacy Screen, SILS” [37], and “Health Literacy Scale-Europe, HLS-EU-Q [38].” Of these, TOFHLA and REALM were the first to be developed, but they mainly measured test takers' reading comprehension or numerical ability, and the tests were poorly practical and were gradually being replaced. With only three short questions, SILS takes very little time and is often used for rapid screening of clinical patients. However, the three questions included in the scale only assess the patient's understanding of medical information and do not broadly assess the patient's ability to understand and evaluate information about the disease and communicate with physicians in all areas. The HLS-EU-Q scale is aimed at the general healthy population and focuses on health education, disease prevention and health promotion for the test subjects from a public health perspective. Therefore, in order to accurately measure the wide range of competencies that SSc patients should have in the process of disease treatment, this study attempted to develop a specific health literacy scale for SSc patients from the perspective of clinical treatment, assessing patients' awareness of disease, ability to communicate with physicians, ability to obtain, understand, judge and apply medical information, and including treatment adherence and available social support.
The purpose of this study was to develop a health literacy scale for SSc patients and assess its reliability and validity, which can more widely and comprehensively evaluate the ability of SSc patients to manage their health, especially the various skills required in the course of disease treatment, and can more objectively reflect the health literacy level of SSc patients. It is hoped that this scale can provide reference for more relevant studies on health literacy of patients with SSc.
## 2.1. Ethics
This study is in accordance with the Declaration of Helsinki, and the work design was approved by the Biomedical Ethics Committee of Anhui Medical University (number 20210649). All subjects agreed to participate in this study and signed the informed consent form.
## 2.2. Scale development procedure
The study was divided into three stages: the first stage summarized published definitions of health literacy and identified the concept of health literacy in the SSc population. In the second stage, the item pool of the scale was formed based on a review of the literature; the Delphi method was used to determine the first draft items of the scale after two rounds of expert consultation; face-to-face interviews with patients were conducted to adjust the language of the scale items to form the first draft of the scale. In the third stage, main surveys were conducted to adjust the content and structure of the scale by combining classical measurement theory and factor analysis, and to evaluate the validity and reliability of the scale to finalize the development of the scale (Figure 1).
**Figure 1:** *Flow chart of systematic sclerosis Health Literacy Scale development.*
## 2.2.1. Stage 1: Define the concept of health literacy
Previous studies have counted more than 250 definitions of health literacy, and summed up six definitions commonly used in the literature [39]. The impact of individual capacity on health literacy, especially the ability to acquire and understand information, is consistently highlighted in these definitions. We summarized the common features of the different meanings of health literacy, while considering the influence of social support on health literacy, and defined the health literacy of SSc patients as: The ability of people with systemic sclerosis to access, understand, communicate, evaluate, and apply medical information or health information, including the social support available to make judgments and decisions about health care, disease management, to maintain or slow disease progression and improve quality of life.
## 2.2.2. Stage 2: Create a pool of items and form a first draft of the scale
Literature review: From its establishment to December 2020, relevant articles about the health literacy scale were searched in Pubmed, web of sciences, China knowledge Network and Health Literacy Tool Shed. With the combination of “health literacy” and “scale,” “measure,” “assessment,” “screening” or “instrument” as the key words, 1,341 articles of Pubmed, 2,873 articles of web of sciences, 895 articles of China knowledge Network and 216 articles of Health Literacy Tool Shed were searched, mainly including the original research. Focus on the definition, dimensions and fields of the scale, remove the repetitive literature, and finally include 57 articles.
Establish item pool: Review the included health literacy scale and measurement items, divide the items according to the dimensions of access, understanding, communication, evaluation, application and social support, and delete duplicate items. Conduct expert interviews and focus groups to brainstorm, evaluate the included items, and add new items according to the previous status survey, and finally form the item pool of the scale. The item pool contains six dimensions with 42 items, namely [1] access to health information (7 items); [2] understanding health information (8 items); [3] communicating health information (8 items); [4] assessing health information (8 items); [5] applying health information (6 items); and [6] social support (5 items).
Delphi method: The Delphi method is a “back-to-back” survey in which experts evaluate the importance and applicability of items and dimensions. The authority of an expert can be calculated based on the experts' familiarity with each item and the basis of their judgment. The higher the authority of the expert, the higher the accuracy of the prediction. The degree of coordination of experts' opinions refers to whether there is a large disagreement between experts' evaluations of each item, and is commonly judged by the p-value of the Kendall W coordination coefficient test, with $p \leq 0.05$ indicating a good degree of coordination among the indicators [40]. Expert selection criteria: ➀ intermediate or above professional title; ➁ bachelor degree or above; ➂ 10 years or more working experience in related professional field; ➃ willing to participate in this study and give some expert advice and guidance. In order to ensure the authority of expert opinions, 15–20 experts are planned to be invited.
Expert consultation: We eventually invited 16 experts, all with master's degree or above, 2 intermediate titles and 14 senior titles. The average working years of experts is 17.81 ± 5.12, and they are familiar with systemic sclerosis and health literacy. Based on the results of experts' familiarity and judgment basis for each item in the first round of expert consultation, the index judgment coefficient, familiarity coefficient and authority coefficient of experts are 0.89, 0.73, and 0.81, respectively. An authority coefficient >0.70 is an acceptable value, representing a high degree of authority of the chosen consulting expert. In this round, we have deleted four items according to expert opinions: “you can fill in the written information during diagnosis and treatment”; “you can exchange credible health information with others”; “you can judge whether the health information obtained can solve related problems”; “you can judge whether the health information said by relatives and friends is correct.” The contents of the first three items are cross-duplicated with other items, and the last one is not relevant.
After the second round of expert consultation, the expert authority coefficient is 0.85 and the Kendall W coordination coefficient of expert opinion was 0.127 ($p \leq 0.001$). The average importance score of each item by experts is 3.44–4.31, and the coefficient of variation is 0.105–0.280. Only the item “you can judge which daily behaviors are related to your health” had an average importance score of < 3.5 and a coefficient of variation >0.25, so it was deleted. The final scale content contains six dimensions with 37 items.
## 2.2.3. Stage 3: Main survey, completion of the scale
Participants and sample size: The study population was obtained from the outpatient and inpatient departments of the Department of Rheumatology and Immunology, the First Affiliated Hospital of Anhui Medical University and The First Affiliated Hospital of University of Science and Technology of China, and met the diagnostic criteria for SSc established by the American College of Rheumatology (ACR) and the European League for Rheumatology (EULAR) in 2013 [41]. Based on the factorial analysis requiring a sample size of 5–10 times the number of items [42], we planned to include at least 200 study subjects in each of the exploratory factor analysis and confirmatory factor analysis.
Data collection: *In this* study, data were collected using a convenience sampling method by face-to-face interaction with patients in the outpatient and inpatient departments of the two hospitals mentioned above from March 2021 to June 2022, using verbal questioning. In addition, a small number of patients were unable to come to the hospitals due to the epidemic, and data collection from patients was conducted using telephone questioning. During this process, the researcher used uniform language expressions whenever possible to minimize information bias.
## 2.3. Statistical analysis
SPSS 23.0 was used for correlation analysis, exploratory factor analysis and reliability evaluation, and Amos Graphics 26.0 for confirmatory factor analysis. If the continuous variable accords with the normal distribution, it is expressed by mean and standard deviation, otherwise it is expressed by median and quartile. In correlation analysis, if the variables conform to normal distribution, Pearson correlation analysis is used, and vice versa with Spearman correlation analysis.
## 2.3.1. Item selection
Items were screened according to classical measurement theory. Classical measurement theory includes eight methods, and items that meet five or more of these retention criteria will be retained.
[1]. Frequency analysis: If the responses are focused on a specific selection (more than $80\%$) or if a selection is not answered at all, delete.
[2]. Coefficient of variation: *In* general, deletion can be considered when the coefficient of variation is < 0.25.
[3]. High-low group comparison: The total scale scores were sorted from smallest to largest, and the score values corresponding to the 27th percentile and 73rd percentile were used as the upper limit for dividing the low group and the lower limit for the high group, respectively, to compare whether there was a difference between the scores of the low group and the high group on eachitem, and if there was no difference, they were deleted.
[4]. Correlation coefficient method: [5]. Factor analysis: Item deletion criteria: (a) the factor loading on the belonging factor is < 0.5; (b) the difference in factor loading on two or more factors is small (in this study, the difference in factor loading is not >0.05); (c) the belonging factor contains only one item [43].
[6]. Cronbach's alpha coefficient method: If the Cronbach's alpha coefficient increases significantly after the removal of an item, it indicates that the item has the effect of reducing the internal consistency of this dimension, and can be deleted.
## 2.3.2. Factor analysis
Exploratory factor analysis and confirmatory factor analysis assessed the construct validity of the scales. Exploratory factor analysis is typically used to distill a set of correlated data into a comprehensive factor structure, and confirmatory factor analysis is used to assess the fit of that factor structure. We randomly divided the collected data into two parts, one for exploratory factor analysis and one for confirmatory factor analysis. The items in the scale were grouped into several factors using principal component analysis and maximum variance rotation. It is generally accepted that factor analysis is meaningful only when the Kaiser-Meyer-Olkin (KMO) value is >0.7 and the Bartlett test is < 0.05 [44]. The fit validity of the model was judged using the fit index, and the COMSIN manual proposed a strict criterion: χ2/df < 3, χ2 test results with $P \leq 0.05$, goodness of fit index >0.95 and root mean square error of approximation < 0.06 has good measurement properties [45, 46]. The average variance extraction (AVE) and combination reliability were calculated on the basis of confirmatory factor analysis, and the square root of AVE of the dimension in question was generally considered to be greater than the correlation between the dimension and other dimensions, indicating a good discriminant validity.
## 2.3.3. Reliability and validity
The performance evaluation of scales includes validity and reliability. Content validity index (CVI) is often used to measure content validity, including item level content validity index and scale level content validity index. In the process of expert inquiry by Delphi method, experts are asked to make judgments about the relevance of each item to the corresponding content dimension. Their judgments are divided into two parts, one that is considered relevant and one that is not, and the composition ratio of experts who consider the items relevant is calculated, namely, item-level CVI. It is generally considered that item-level CVI ≥ 0.78 represents better content validity at the itemlevel. In addition, the mean value of item-level CVI is often used to indicate scale-level CVI, and it is commonly thought that scale-level CVI ≥ 0.90 represents better content validity at the scale level [47, 48]. Test-retest reliability and split-half reliability are commonly used to assess the reliability of the scale, it is generally expressed as the intraclass correlation coefficient (ICC) and the simple correlation coefficient (r), ICC or r >0.7 is generally considered a good confidence level. Cronbach's alpha coefficient was used to assess internal consistency and to test the degree of agreement between the scale and the internal items of each dimension.
## 2.3.4. The assignment of scale scores
We eventually developed a “Systemic Sclerosis Health Literacy Scale” containing 6 dimensions and 30 items with a score of 30–150. The health literacy levels of SSc patients were classified into four levels according to the total scale scores of < $40\%$, 40–$60\%$, 60–$80\%$ and more than $80\%$, namely low (30–60 score), limited (61–90 score), intermediate (91–120 score), adequate (121–150 score). A higher score on the scale means a higher level of health literacy.
## 3.1. Characteristics of the SSc population
The study ultimately included 428 eligible study subjects. Among them, 364 were female, with a male to female ratio of 1:5.7, and the mean age of the patients was 51.57 ± 10.99, mainly middle-aged women. The majority of patients were from rural areas, predominantly farmers or otherwise working, and nearly a quarter of patients reported not working or being unable to work due to their disease. The overall education level of the patients was low, mainly the primary school education level, and only $17.9\%$ of the patients had high school education or above. Most patients had a normal body mass index (BMI), some patients had symptoms of weight loss, and patients who were wasted or overweight accounted for about $33.7\%$. Nearly $90\%$ of patients had limited systemic sclerosis, with a mean disease duration of 7.22 ± 6.89. More than $70\%$ of patients had Raynaud's phenomenon, with common complications of ILD ($34.6\%$) and PAH ($28.0\%$) (Table 1).
**Table 1**
| Variables | Unnamed: 1 | Number (N = 428) | Percent (%) |
| --- | --- | --- | --- |
| Age (years) | | 51.57 ± 10.99 | – |
| Sex (female %) | | 364 | 85.0 |
| Career | Farmer | 99 | 23.1 |
| | Public institutions and government officials | 37 | 8.6 |
| | Professionals | 57 | 13.3 |
| | Other | 133 | 31.1 |
| | | 102 | 23.9 |
| Education level | Illiteracy | 70 | 16.4 |
| | Primary school | 178 | 41.6 |
| | Junior high school | 103 | 24.1 |
| | Senior high school/technical secondary school | 38 | 8.9 |
| | College/bachelor degree or above | 39 | 9.0 |
| BMI | < 18.4 | 61 | 14.3 |
| | 18.5–23.9 | 275 | 64.3 |
| | 24–27.9 | 83 | 19.4 |
| | ≥28 | 9 | 2.0 |
| Course of disease | – | 7.22 ± 6.89 | – |
| SSc type, limited | – | 384 | 89.7 |
| Raynaud's phenomenon | – | 301 | 70.3 |
| Interstitial lung disease | – | 148 | 34.6 |
| Pulmonary artery hypertension | – | 120 | 28.0 |
## 3.2.1. Frequency analysis method
The response rate of each item was $100\%$, and no item had a response rate of more than $80\%$ on a certain option, and all items were retained.
## 3.2.2. Coefficient of variation method
The range of score means for the 37 items was 2.06–4.14, the range of standard deviations was 0.611–1.265, and the range of coefficient of variation was 0.165–0.529. The coefficient of variation of 11 of the items was < 0.25, namely Q2.1, Q2.7, Q3.1, Q3.2, Q5.1, Q5.5, Q5.6, Q6.1, Q6.2, Q6.3, and Q6.4.
## 3.2.3. High-low grouping comparison method
There were significant differences in the scores of all items between high and low groups ($P \leq 0.001$).
## 3.2.4. Correlation coefficient method
1) Internal item correlation coefficient method: The correlation coefficient between item Q5.1 and the three items in the dimension is < 0.20, so consider deleting.
2) Item-dimension consistency method: The correlation coefficient between item Q6.1 and the scores of other items in the dimension is $r = 0.143$, so it is considered to be deleted.
3) Item-dimension correlation coefficient method: There are 6 items that meet the deletion criteria, that is, Q3.1, Q3.2, Q4.1, Q5.4, Q5.5, Q5.6.
## 3.2.5. Factor analysis method
Items Q1.1, Q1.5, Q1.6, Q4.1 all have factor loadings in both dimensions and the difference is < 0.05.
## 3.2.6. Cronbach's alpha coefficient method
The Cronbach's alpha coefficient for the social support dimension is 0.694, and after deleting Q6.1, the Cronbach's alpha coefficient rises to 0.763, and similarly, after deleting Q6.5, the Cronbach's alpha coefficient rises to 0.747. Therefore, deleting Q6.1 and Q6.5 is considered.
## 3.2.7. Summary and analysis of item screening results
In the above 37 item screening analysis, item Q6.1 only satisfied four screening methods, so it was deleted. All other items meet the retention criteria (Table 2).
**Table 2**
| items | I | II | III | IV | IV.1 | IV.2 | V | VI | Number of standards achieved | Screening results |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | | | i | ii | iii | | | | |
| Q1.1. You can find information about systemic sclerosis on the web | √ | 0.529 | P < 0.001 | 0 | 0.813 | 0.746 | 0.599* | ↓ | 7.0 | Retain |
| Q1.2. You know the common signs and symptoms of systemic sclerosis | √ | 0.341 | P < 0.001 | 0 | 0.757 | 0.660 | 0.739 | ↓ | 8.0 | Retain |
| Q1.3. You can find information about complications of systemic sclerosis | √ | 0.370 | P < 0.001 | 0 | 0.796 | 0.664 | 0.762 | ↓ | 8.0 | Retain |
| Q1.4. You have actively searched for ways to improve your disease symptoms | √ | 0.264 | P < 0.001 | 0 | 0.761 | 0.652 | 0.659 | ↓ | 8.0 | Retain |
| Q1.5. You usually pay attention to information about health, such as food and nutrition, physical exercise, etc. | √ | 0.404 | P < 0.001 | 0 | 0.771 | 0.701 | 0.527* | ↓ | 7.0 | Retain |
| Q1.6. You can find information on some common chronic diseases | √ | 0.433 | P < 0.001 | 0 | 0.845 | 0.768 | 0.599* | ↓ | 7.0 | Retain |
| Q1.7. You can find information on mental health | √ | 0.484 | P < 0.001 | 0 | 0.842 | 0.768 | 0.639 | ↓ | 8.0 | Retain |
| Q2.1. You can understand the doctor's description of your condition | √ | 0.240* | P < 0.001 | 0 | 0.698 | 0.690 | 0.505 | ↓ | 7.0 | Retain |
| Q2.2. You can understand the meaning of the medical written instructions | √ | 0.390 | P < 0.001 | 0 | 0.880 | 0.789 | 0.661 | ↓ | 8.0 | Retain |
| Q2.3. You can judge whether the lab index is normal or not according to the reference range on the lab report | √ | 0.419 | P < 0.001 | 0 | 0.869 | 0.768 | 0.635 | ↓ | 8.0 | Retain |
| Q2.4. You can read and understand drug instructions | √ | 0.441 | P < 0.001 | 0 | 0.874 | 0.771 | 0.590 | ↓ | 8.0 | Retain |
| Q2.5. You can understand the benefits and drawbacks of the medication prescribed by your doctor | √ | 0.363 | P < 0.001 | 0 | 0.826 | 0.775 | 0.621 | ↓ | 8.0 | Retain |
| Q2.6. You can read the signs in the hospital | √ | 0.293 | P < 0.001 | 0 | 0.879 | 0.758 | 0.647 | ↓ | 8.0 | Retain |
| Q2.7. You can understand your doctor's advice on your daily life | √ | 0.195* | P < 0.001 | 0 | 0.798 | 0.711 | 0.593 | ↓ | 7.0 | Retain |
| Q3.1. You are able to clearly describe your symptoms and discomfort when talking to your doctor | √ | 0.227* | P < 0.001 | 0 | 0.703 | 0.750* | 0.630 | ↓ | 6.0 | Retain |
| Q3.2. You can understand most of what is said when you talk to the doctor | √ | 0.249* | P < 0.001 | 0 | 0.755 | 0.808* | 0.664 | ↓ | 6.0 | Retain |
| Q3.3. When in doubt about medical advice, you proactively ask your doctor | √ | 0.281 | P < 0.001 | 0 | 0.842 | 0.755 | 0.758 | ↓ | 8.0 | Retain |
| Q3.4. You will check with your doctor to make sure that you understand the medical advice correctly | √ | 0.321 | P < 0.001 | 0 | 0.800 | 0.675 | 0.762 | ↓ | 8.0 | Retain |
| Q3.5. You will discuss treatment options with your doctor | √ | 0.415 | P < 0.001 | 0 | 0.756 | 0.669 | 0.697 | ↓ | 8.0 | Retain |
| Q3.6. You will ask your doctor for the tests or treatments you want | √ | 0.498 | P < 0.001 | 0 | 0.758 | 0.747 | 0.685 | ↓ | 8.0 | Retain |
| Q3.7. You will discuss health issues with people other than your doctor | √ | 0.391 | P < 0.001 | 0 | 0.723 | 0.67 | 0.527 | ↓ | 8.0 | Retain |
| Q4.1. You can judge whether what the doctor says fits your condition | √ | 0.378 | P < 0.001 | 0 | 0.788 | 0.805* | 0.589* | ↓ | 6.0 | Retain |
| Q4.2. You can determine if the information you receive about systemic sclerosis is correct | √ | 0.464 | P < 0.001 | 0 | 0.894 | 0.791 | 0.768 | ↓ | 8.0 | Retain |
| Q4.3. You can judge the usefulness of the information you receive about systemic sclerosis | √ | 0.449 | P < 0.001 | 0 | 0.887 | 0.786 | 0.747 | ↓ | 8.0 | Retain |
| Q4.4. You can make medical decisions based on the information collected about your disease | √ | 0.357 | P < 0.001 | 0 | 0.840 | 0.736 | 0.686 | ↓ | 8.0 | Retain |
| Q4.5. You will change doctors to expect a different opinion | √ | 0.292 | P < 0.001 | 0 | 0.629 | 0.557 | 0.600 | ↓ | 8.0 | Retain |
| Q5.1. You will take your medication in strict accordance with your doctor's instructions or the drug instructions | √ | 0.211* | P < 0.001 | 3* | 0.473 | 0.24 | 0.866 | ↓ | 6.0 | Retain |
| Q5.2. You will not reduce or stop your medication without consulting your doctor | √ | 0.269 | P < 0.001 | 2 | 0.576 | 0.255 | 0.862 | ↓ | 8.0 | Retain |
| Q5.3. You will come to the hospital for regular review | √ | 0.303 | P < 0.001 | 0 | 0.483 | 0.218 | 0.605 | ↓ | 8.0 | Retain |
| Q5.4. You can know exactly how your disease is developing | √ | 0.263 | P < 0.001 | 1 | 0.468 | 0.665* | 0.576 | ↓ | 7.0 | Retain |
| Q5.5. You will engage in behaviors that will improve your health | √ | 0.200* | P < 0.001 | 2 | 0.454 | 0.466* | 0.608 | ↓ | 6.0 | Retain |
| Q5.6. You can take a positive approach to coping with the stress of the disease | √ | 0.208* | P < 0.001 | 0 | 0.367 | 0.471* | 0.609 | ↓ | 6.0 | Retain |
| Q6.1. Have a family member or friend with you at your doctor's appointment | √ | 0.220* | P < 0.001 | 2* | 0.143* | −0.110 | 0.522 | ↑* | 4.0 | Delete |
| Q6.2. For those who do not understand the information, you will have a family member or friend or medical staff to help you understand | √ | 0.165* | P < 0.001 | 0 | 0.626 | 0.254 | 0.812 | ↓ | 7.0 | Retain |
| Q6.3. When you feel uncomfortable, you are surrounded by people who understand what you are going through | √ | 0.190* | P < 0.001 | 0 | 0.639 | 0.339 | 0.799 | ↓ | 7.0 | Retain |
| Q6.4. If you need help, you have reliable people around you | √ | 0.215* | P < 0.001 | 0 | 0.630 | 0.302 | 0.865 | ↓ | 7.0 | Retain |
| Q6.5. You understand and apply for medical coverage | √ | 0.268 | P < 0.001 | 0 | 0.242 | 0.384* | 0.532 | ↑* | 6.0 | Retain |
## 3.3. Factor analysis
The 214 collected data were subjected to exploratory factor analysis with a KMO value of 0.949 and Bartlett's spherical test $P \leq 0.001$, and the data were suitable for factor analysis. After removing item Q6.1, six common factors are extracted based on the eigenvalues >1, and the cumulative contribution rate of variance is $72.680\%$. Among them, items Q1.6, Q3.7, and Q4.1 are distributed in two dimensions and the difference of factor loadings is < 0.05, so they are deleted. The factor loading of item Q6.5 is < 0.5, so it is deleted (Table 3).
**Table 3**
| Unnamed: 0 | Factor 1 | Factor 2 | Factor 3 | Factor 4 | Factor 5 | Factor 6 | Extraction |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Q1.1 | | | 0.621 | | | | 0.758 |
| Q1.2 | | | 0.739 | | | | 0.754 |
| Q1.3 | | | 0.765 | | | | 0.79 |
| Q1.4 | | | 0.658 | | | | 0.689 |
| Q1.5 | | | 0.548 | | | | 0.687 |
| Q1.7 | | 0.597 | | | | | 0.798 |
| Q2.1 | 0.515 | | | | | | 0.659 |
| Q2.2 | 0.677 | | | | | | 0.788 |
| Q2.3 | 0.653 | | | | | | 0.759 |
| Q2.4 | 0.613 | | | | | | 0.763 |
| Q2.5 | 0.642 | | | | | | 0.741 |
| Q2.6 | 0.665 | | | | | | 0.765 |
| Q2.7 | 0.608 | | | | | | 0.74 |
| Q3.1 | 0.639 | | | | | | 0.754 |
| Q3.2 | 0.673 | | | | | | 0.78 |
| Q3.3 | 0.76 | | | | | | 0.776 |
| Q3.4 | 0.757 | | | | | | 0.713 |
| Q3.5 | 0.696 | | | | | | 0.657 |
| Q3.6 | | 0.669 | | | | | 0.732 |
| Q4.2 | | 0.749 | | | | | 0.839 |
| Q4.3 | | 0.725 | | | | | 0.842 |
| Q4.4 | | 0.68 | | | | | 0.769 |
| Q4.5 | | 0.614 | | | | | 0.535 |
| Q5.1 | | | | | | 0.879 | 0.826 |
| Q5.2 | | | | | | 0.87 | 0.836 |
| Q5.3 | | | | | | 0.564 | 0.596 |
| Q5.4 | | | | | 0.584 | | 0.702 |
| Q5.5 | | | | | 0.65 | | 0.611 |
| Q5.6 | | | | | 0.599 | | 0.56 |
| Q6.2 | | | | 0.812 | | | 0.715 |
| Q6.3 | | | | 0.84 | | | 0.781 |
| Q6.4 | | | | 0.885 | | | 0.829 |
Finally, 32 items with 6 dimensions were retained. Dimension 1 includes 12 items to evaluate patients' ability to understand and communicate information, that is, patients' ability to attend the clinic; dimension 2 includes 6 items to evaluate patients' ability to judge / assess information; dimension 3 includes 5 items to evaluate patients' ability to obtain information; dimension 4 includes 3 items to evaluate social support; dimension 5 includes 3 items to evaluate patients' ability to apply information; Dimension 6 includes 3 items to evaluate the regularity of patients' medication and review, that is, patients' treatment compliance.
The confirmatory factor analysis was performed on the remaining 214 samples to explore the construct validity of the scale (Figure 2). The results of the dimensions that Q1.7 and Q3.6 belonged to in the exploratory factor analysis were different from those initially classified, and we fitted the model to each of the four cases, including retaining Q1.7 and Q3.6, deleting one of them, and deleting both. The results showed that deleting Q1.7 and Q3.6 had the best fit validity. After consulting with experts, we decided to delete these two items and keep the remaining ones. The results of the final scale fit showed that X2/df = 1.798 < 3, RMSEA = 0.061, GFI = 0.945, IFI = 0.945, TLI = 0.937, which are close to COSMIN criteria, implying that the overall fit validity of the scale did not meet the criteria of goodness of fit. However, according to the COMSIN manual's comprehensive consideration of measurement standards, this study result is completely acceptable.
**Figure 2:** *Structural validity of confirmatory factor analysis.*
On the basis of construct validity, the aggregate validity of the scale was evaluated according to average variance extraction (AVE) and combination reliability (Table 4). The results showed that the square root of AVE for dimension 1 was smaller than the maximum value of the absolute value of its inter-factor correlation coefficient of 0.879, implying slightly poorer discriminant validity, but all other dimensions showed better discriminant validity, and we considered the overall convergent validity and discriminant validity of the scale to be up to standard.
**Table 4**
| Unnamed: 0 | Factor 1 | Factor 2 | Factor 3 | Factor 4 | Factor 5 | Factor 6 |
| --- | --- | --- | --- | --- | --- | --- |
| Factor 1 | 0.675 | 0.723 | 0.637 | 0.671 | 0.496 | 0.582 |
| Factor 2 | 0.834 | | | | | |
| Factor 3 | 0.879 | 0.828 | | | | |
| Factor 4 | 0.329 | 0.287 | 0.334 | | | |
| Factor 5 | 0.8 | 0.683 | 0.778 | 0.383 | | |
| Factor 6 | 0.208 | 0.184 | 0.27 | 0.323 | 0.316 | |
| AVE square root | 0.821 | 0.85 | 0.798 | 0.82 | 0.704 | 0.762 |
## 3.4.1. Validity evaluation
Content validity: The item-level CVI ranges from 0.875 to 1.000, and the scale-level CVI is 0.983. The results all meet the criteria, indicating that the overall content validity of the scale is good.
## 3.4.2. Reliability evaluation
Test-retest reliability: A sample of 50 people was taken for a second survey within 2 weeks after the first survey, and 49 valid questionnaires were returned. The intraclass correlation coefficient was calculated for each item of the two measurements and the total scale, and the results showed that the intraclass correlation coefficient ranged from 0.712 to 0.851, and the total scale intraclass correlation coefficient was 0.898 ($p \leq 0.05$), which indicates that the stability of the scale is good.
Split-half reliability: The items were divided into two equal parts, namely even-numbered items and odd-numbered items, and the correlation coefficient between the two parts was calculated ($r = 0.953$, $P \leq 0.001$).
Internal consistency: The internal consistency of the scale is often assessed by the Cronbach's alpha coefficient of each dimension, and a Cronbach's alpha coefficient of more than 0.7 for each dimension indicates good internal consistency of the scale (Table 5).
**Table 5**
| Dimensions | Cronbach's alpha coefficient | Eigenvalues | Cumulative contribution rate (%) |
| --- | --- | --- | --- |
| Clinic ability | 0.961 | 18.154 | 21.371 |
| Judgment/evaluation information ability | 0.909 | 2.616 | 37.378 |
| Access to information ability | 0.903 | 1.704 | 51.635 |
| Social support | 0.715 | 1.378 | 59.311 |
| Treatment compliance | 0.855 | 1.223 | 66.749 |
| Application information ability | 0.729 | 1.09 | 72.68 |
## 3.5. Health literacy level of SSc patients
In this study, the percentage of patients with adequate health literacy level was $14.49\%$, which is close to the health literacy level of the general population ($14.18\%$) reported in 2017 in China [49]. In our collection, SSc patients had extremely low levels of health literacy in terms of finding health information ($10.3\%$) and assessing health information dimensions ($8.0\%$); more than half ($52.8\%$) showed good treatment compliance, but this was not enough; and nearly half ($48.1\%$) reported being able to use the information they already had to help them slow the progression of their disease. Only $26.2\%$ of the patients indicated that they had sufficient medical treatment ability and could make use of medical service resources very effectively, and $29.4\%$ of the patients had adequate social support.
Additionally, we assessed the health literacy levels of patients of different ages and education levels (Table 6). It can be seen that with the increase of age, the scale score is gradually declining, except for dimension 4 “social support,” the scores of other dimensions show a downward trend. Spearman correlation coefficient showed that age was negatively correlated with health literacy level (rs = −0.321, $p \leq 0.05$). But, as we suspected, health literacy scores increased with education, and this phenomenon also showed up in all dimensions except for dimension 6, “treatment compliance.” Spearman correlation coefficient showed that education level was positively correlated with health literacy level (rs = 0.654, $p \leq 0.05$).
**Table 6**
| Variables | Variables.1 | Number (N = 428) | Score on “Systemic Sclerosis Health Literacy Scale” | Score on “Systemic Sclerosis Health Literacy Scale”.1 | Score on “Systemic Sclerosis Health Literacy Scale”.2 | Score on “Systemic Sclerosis Health Literacy Scale”.3 | Score on “Systemic Sclerosis Health Literacy Scale”.4 | Score on “Systemic Sclerosis Health Literacy Scale”.5 | Score on “Systemic Sclerosis Health Literacy Scale”.6 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | | | Dimension 1 (total: 60) | Dimension 2 (total: 20) | Dimension 3 (total: 25) | Dimension 4 (total: 15) | Dimension 5 (total: 15) | Dimension 6 (total: 15) | Total score: (150) |
| | < 40 | 64.0 | 45.22 ± 8.38 | 12.88 ± 3.30 | 16.63 ± 3.88 | 10.72 ± 1.71 | 11.50 ± 2.00 | 11.87 ± 1.83 | 108.81 ± 16.72 |
| | 40–49 | 77.0 | 40.95 ± 10.77 | 11.08 ± 3.44 | 14.43 ± 4.49 | 10.11 ± 2.17 | 11.27 ± 1.98 | 11.68 ± 2.95 | 99.51 ± 22.05 |
| Age (years) | 50–59 | 202.0 | 38.13 ± 9.26 | 10.05 ± 3.36 | 13.33 ± 3.98 | 10.30 ± 1.60 | 11.10 ± 1.90 | 11.48 ± 2.37 | 94.38 ± 17.83 |
| | 60–69 | 71.0 | 38.03 ± 8.85 | 9.83 ± 3.37 | 12.71 ± 4.28 | 10.40 ± 1.70 | 11.09 ± 2.01 | 11.31 ± 2.69 | 93.37 ± 18.27 |
| | >70 | 14.0 | 26.22 ± 13.05 | 7.89 ± 3.66 | 9.56 ± 4.90 | 10.67 ± 1.41 | 9.44 ± 2.56 | 10.89 ± 2.03 | 74.67 ± 22.88 |
| | Illiteracy | 70.0 | 27.88 ± 8.06 | 7.13 ± 2.28 | 9.90 ± 3.67 | 9.97 ± 1.85 | 9.63 ± 2.48 | 10.88 ± 2.49 | 75.38 ± 16.06 |
| | Primary school | 178.0 | 37.58 ± 8.63 | 9.80 ± 3.04 | 12.91 ± 3.70 | 10.20 ± 1.59 | 11.01 ± 1.67 | 11.48 ± 2.56 | 92.98 ± 16.57 |
| Education level | Junior high school | 103.0 | 43.56 ± 6.75 | 12.04 ± 2.76 | 15.10 ± 3.71 | 10.22 ± 1.74 | 11.56 ± 1.55 | 12.02 ± 2.45 | 104.50 ± 13.85 |
| | Senior high school/technical secondary school | 38.0 | 47.74 ± 6.13 | 12.79 ± 3.01 | 17.05 ± 3.19 | 11.11 ± 1.37 | 12.26 ± 1.52 | 11.79 ± 1.78 | 112.74 ± 12.60 |
| | College/bachelor degree or above | 39.0 | 49.89 ± 3.90 | 14.68 ± 2.34 | 18.84 ± 2.32 | 11.53 ± 1.84 | 12.42 ± 1.71 | 11.47 ± 2.17 | 118.84 ± 9.05 |
## 4. Discussion
This study was the first to develop a health literacy scale based on factor analysis and reliability evaluation to assess the health literacy level of SSc patients. The SSc health literacy scale contains six dimensions with 30 items. The results showed that the scale has good validity and reliability and may become a valid assessment tool.
In the original design of the scale, understanding and communicating health information are two separate dimensions, but in our results, the comprehension and communication ability of SSc patients influence each other greatly. This result may be related to the sample size, as a larger sample size can be used to assess more information and compensate for smaller differences. The larger the sample size, the more it reflects the patient's true ability to understand and communicate information [50]. We define the ability to understand and communicate information together as clinic ability.
Before the scale was developed, we referred to existing scales and classified patients' medication-taking and regular review behaviors as applied competencies [38, 51]. In the results of our study, although some patients do not have a clear understanding of their condition and do not take additional measures to improve their health, they have high drug compliance and are subject to regular reexaminations. This is one factor for which some researchers have postulated that no association was shown between health literacy and medication adherence [52]. Therefore, we define these items as treatment compliance and define patients' application ability to make some behaviors conducive to improving the disease according to the progress of the disease.
Most importantly, we have focused here on the social support of the patient. The help of medical staff and the support of family members all contribute to the improvement of the patient's disease and influence the impact of the patient's health literacy level on clinical outcomes [53]. At the same time, some studies suggest that people with systemic sclerosis may benefit from the social support of intimate relationships [54].
Currently, the “China Health Literacy Monitoring Questionnaire” is widely used to assess the health literacy level of the Chinese population, including those with chronic diseases [55, 56]. The scale mainly assesses the level of health knowledge, disease prevention awareness, and emergency skills of the study population and is not specific to diseases [49]. Moreover, the “health literacy scale for chronic patients” has been frequently used in research studies and includes four dimensions: access to information, communication of interactive information, willingness to improve health, and financial support [57]. The scale developed in this study also assesses patients' ability to understand and evaluate information based on these scales. The majority of patients in our study results reported having applied for medical services to reduce the financial burden, so we did not factor in economics. Our emphasis on social support was more focused on the whole range of social concerns about the patient's consultation process, outcomes, and psychological aspects.
We also assessed the health literacy level of SSc patients, which was basically close to that of the general Chinese population. Among them, patients were the least able to assess information and had difficulty discriminating between the health information obtained, which is coherent with the results of other population studies on health literacy [58]. Our results show that few patients are proactive in accessing health information and have less health information, but can use their limited health knowledge to manage their disease. SSc patients show the same characteristics in terms of access to electronic information [31]. As a result, we should focus on the level of health knowledge of patients and increase health promotion and education, so that patients have more understanding of health, so as to make use of more health information.
In this study, few patients were able to make adequate use of medical information, which may result in patients repeatedly using medical resources or even appearing to be unable to use them correctly. Treatment adherence is crucial in the long-term treatment of SSc, but our findings show that only half of the patients have good adherence, while others experience poor medication adherence and irregular reviews. This result is in line with the results of a study on the knowledge of medication use in patients with chronic diseases [59].
The study also found a correlation between age, education level and health literacy among SSc patients. This finding is consistent with the results of other studies [60, 61]. However, when analyzing each dimension specifically, it was found that the dimension “social support” did not decrease with age, where the younger group indicated that they had access to more policy information, more medical content, and could actively obtain more social support. However, with the increase of age, patients over 40 years old will receive different attention and social support, instead, elderly people will get more care and help. In the analysis of education levels and health literacy, it was found that patients with higher education levels had poorer treatment compliance, which may be due to the fact that patients with higher education levels undertake more social work, leading to delayed medical treatment. It is also possible that this group does not have enough health awareness and will make wrong decisions based on their own ideas.
The current study developed the SSc health literacy scale and assessed the health literacy level of this group. Although the final evaluation of the validity and reliability of the scale is good, the study also has some limitations. Firstly, the cross-cultural applicability of the “Systemic Sclerosis Health Literacy Scale” is unclear because this scale was developed and validated based on Chinese populations and Chinese medical settings, and most of the current papers are written in English, the ease of finding accurate medical information differs for those who cannot read English and those who can read English. Several studies have shown that English literacy is independently associated with seeking health information, that people with lower English proficiency also have lower utilization of health information, and that respondents who use Chinese have higher rates of limited health literacy than those who speak English [62, 63]. Next, the subjects of this study were mainly from the First Affiliated Hospital of Anhui Medical University and the First Affiliated Hospital of China Medical University, and most of the patients had a low literacy level and an average economic level, which may have created a selection bias.
## 5. Conclusion
In short, this study focused for the first time on the health literacy level of SSc patients and developed the SSc Health Literacy Scale with 6 dimensions and 30 items. The scale has high reliability and validity, and the items are relatively simple and the time is short. The scale can be developed as a health literacy assessment tool for SSc patients and identify key issues such as patients' ability to see a doctor.
## 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 Biomedical Ethics Committee of Anhui Medical University (number 20210649). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
MZ, C-CL, S-YC, X-HT, and LL completed the data collection and organization. MZ, X-LC, C-WX, and JW analyzed the data, and the first draft of the manuscript was completed jointly by MZ, C-CL, S-YC, and JW. All authors contributed to the study design and process, approved, and ratified the final manuscript.
## 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.
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|
---
title: Comprehensive analysis of miRNAs, lncRNAs and mRNAs profiles in backfat tissue
between Daweizi and Yorkshire pigs
authors:
- Chen Chen
- Yitong Chang
- Yuan Deng
- Qingming Cui
- Yingying Liu
- Huali Li
- Huibo Ren
- Ji Zhu
- Qi Liu
- Yinglin Peng
journal: Animal Bioscience
year: 2022
pmcid: PMC9996253
doi: 10.5713/ab.22.0165
license: CC BY 4.0
---
# Comprehensive analysis of miRNAs, lncRNAs and mRNAs profiles in backfat tissue between Daweizi and Yorkshire pigs
## Abstract
### Objective
Daweizi (DWZ) is a famous indigenous pig breed in China and characterized by tender meat and high fat percentage. However, the expression profiles and functions of transcripts in DWZ pigs is still in infancy. The object of this study was to depict the transcript profiles in DWZ pigs and screen the potential pathway influence adipogenesis and fat deposition,
### Methods
Histological analysis of backfat tissue was firstly performed between DWZ and lean-type Yorkshire pigs, and then RNA sequencing technology was utilized to explore miRNAs, lncRNAs and mRNAs profiles in backfat tissue. 18 differentially expressed (DE) transcripts were randomly selected for quantitative real-time polymerase chain reaction (QPCR) to validate the reliability of the sequencing results. Finally, gene ontology (GO) and *Kyoto encyclopedia* of genes and genomes (KEGG) enrichment analysis were conducted to investigate the potential pathways influence adipocyte differentiation, adipogenesis and lipid metabolism, and a schematic model was further proposed.
### Results
A total of 1,625 differentially expressed transcripts were identified in DWZ pigs, including 27 upregulated and 45 downregulated miRNAs, 64 upregulated and 119 downregulated lncRNA, 814 upregulated and 556 downregulated mRNAs. QPCR analysis exhibited strong consistency with the sequencing data. GO and KEGG analysis elucidated that the differentially expressed transcripts were mainly associated with cell growth and death, signal transduction, peroxisome proliferator-activated receptors (PPAR), AMP-activated protein kinase (AMPK), PI3K-Akt, adipocytokine and foxo signaling pathways, all of which are strongly involved in cell development, lipid metabolism and adipogenesis. Further analysis indicated that the BGIR9823_87926/miR-194a-5p/AQP7 network may be effective in the process of adipocyte differentiation or adipogenesis.
### Conclusion
Our study provides comprehensive insights into the regulatory network of backfat deposition and lipid metabolism in pigs from the point of view of miRNAs, lncRNAs and mRNAs.
## INTRODUCTION
Adipose tissue in pigs is associated with important traits of carcass and meat quality [1], and backfat deposition greatly influences porcine growth performance, meat production and final farming profit [2]. Importantly, the pig is emerging as an attractive biomedical model for studying obesity and related diseases in human because of the similar physiology, anatomy, and metabolic features [3,4]. Therefore, understanding the molecular mechanism of backfat deposition in pigs is not only conducive to the progress of genetic breeding, but also expands the comprehension of obesity related metabolic diseases in human.
microRNAs (miRNAs) are a kind of small non-coding RNAs of about 19 to 23 nucleotides in length that negatively regulates gene expression [5]. lncRNAs, a class of non-protein coding RNAs of >200 nucleotides in length, are poorly conserved and expressed in a cell-, tissue-, and stage-specific manner, and lncRNAs can be classified into sense, antisense, intergenic, and bidirectional lncRNAs according to their genomic position [6]. Accumulating evidence indicates that miRNAs play various crucial roles in the processes of adipocyte differentiation, adipogenesis and lipid metabolism [7]. In recent years, studies on identification and functional analysis of porcine lncRNAs were progressively performed, and several differentially expressed lncRNAs and potential regulatory lncRNAs were obtained either in adipose tissue or in the process of adipogenesis from two different pig breeds [8–13].
Daweizi (DWZ), a famous indigenous pig breed in China, is characterized by tender meat, slow growth rate and high fat percentage [14]. In contrast, Yorkshire, a worldwide well-known pig breed, exhibits rapid growth rate with low fat percentage under the intensive selection. In view of the distinctive difference in term of fat content, the two pig breeds can be regarded as the appropriate objects to study the molecular mechanism of fat deposition.
However, the molecular mechanism of the obvious difference in backfat deposition between DWZ and Yorkshire pigs has not yet been studied. In the present investigation, the expression profiles of miRNAs, lncRNAs, and mRNAs were compared in backfat tissue between DWZ and Yorkshire pigs by RNA sequencing (RNA-seq) technology. Then, the differentially expressed miRNAs (DE miRNAs), differentially expressed lncRNAs (DE lncRNAs) and differentially expressed mRNAs (DE mRNAs) associated with porcine adipogenesis were identified, and the functional enrichments and lncRNA-miRNA-mRNA interaction network were further analyzed. The present investigation provides more insights into the mechanism of backfat deposition from the point of view of miRNAs, lncRNAs, and mRNAs.
## Experimental animal and tissue collection
Three healthy castrated male DWZ pigs (180-day-old, average slaughter weight 73.83±1.88 kg) and three healthy castrated male Yorkshire pigs (180-day-old, average slaughter weight 121.30±1.33 kg) were raised under the same conditions at Tianfu Ecological Agricultural Limited Company in Hunan province, China. After slaughtered in a commercial slaughter plant, the left side carcass was hung upside down, and the midline backfat thickness at the position of the thickest point in shoulder, last rib and lumbosacral junction was separately measured, and then the average value was calculated. Subsequently, backfat tissues collected at the position of the thickest point in shoulder were divided into two parts. One part was fixed in $4\%$ paraformaldehyde for histological analysis, and the other part was immediately frozen in liquid nitrogen and stored in a −80°C refrigerator. All the experiments in this study were reviewed and approved by the Institutional Animal Care and Use Committee of Hunan Institute of Animal and Veterinary Science (Approval number: 20200110).
## Histological analysis of backfat tissue
The paraformaldehyde-fixed backfat samples were embedded in paraffin. The serial tissue sections were cut using cryostat (Leica RM2235; Leica company, Wetzlar, Germany) and were then stained with hematoxylin/eosin. The sections were viewed at 200× magnification using microscope (Leica DM3000, Germany), and five areas were randomly selected in each sample for measuring the diameter of adipocyte.
As shown in Figure 1A, the average backfat thickness in DWZ pigs was notably higher (1.64-fold) than that in Yorkshire pigs. There was an obvious difference in adipocyte phenotype of backfat tissue between DWZ and Yorkshires pigs (Figure 1B), and DWZ pigs had bigger adipocyte diameter that Yorkshire pigs (Figure 1C).
## RNA extraction and library construction
Total RNA from backfat tissue was isolated using Trizol (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instruction. The quantity and integrity of total RNA were assessed by Agilent 2100 bioanalyzer (Santa Clara, CA, USA), which exhibited that the RNA integrity number was more than 8.0 for each sample. The main processes of small RNA library construction include: i) Total RNA was purified by electrophoretic separation on a $15\%$ urea denaturing polyacrylamide gel electrophoresis (PAGE) gel, and small RNA regions corresponding to the 18 to 30 nt bands were excised and recovered. ii) Small RNAs were ligated to adenylated 3′-adapters annealed to unique molecular identifiers (UMI), followed by ligation of 5′-adapters. iii) The adapter-ligated small RNAs were transcribed into cDNA by SuperScript II Reverse Transcriptase (Invitrogen, USA) and then several rounds of polymerase chain reaction (PCR) amplification were performed to enrich cDNA fragments. iv) PCR products with 110 to 130 bp in length were acquired on PAGE gel and were then purified by QIAquick Gel Extraction Kit (QIAGEN, Düsseldorf, Germany). Finally, these PCR products were circularized and then sequenced using the DNBseq platform (BGI-Shenzhen, China). The brief procedures for construction of mRNAs and lncRNAs libraries were as follows: i) Ribosomal RNA (rRNA) was removed using MGIEasy rRNA kit (BGI, China), and the remained RNA was fragmented into small pieces using divalent cations under elevated temperature. ii) The cleaved RNA fragments were transcribed into first strand cDNA using reverse transcriptase and random primers, followed by second strand cDNA synthesis using DNA polymerase I and RNase H with dUTP instead of dTTP. iii) These cDNA fragments were ligated with an ‘A’ base and sequencing adapter. iv) The products were enriched with PCR and then heat denatured and circularized by the splint oligo sequence. v) The single strand circle DNA was formatted as the final library which was sequenced on DNBseq platform (BGI-Shenzhen, China).
## Sequencing data analysis
For analysis of small RNA sequencing data, the raw reads containing polyA, shorter than 18 nt in length, with low-quality reads (the number of bases with quality score less than 10 is ≤4 and with quality score less than 13 is ≤6), with 5′-adapter contamination, without 3′-adapter or inserted fragments were filtered to obtain clean reads by SOAPnuke (v1.5.0). Subsequently, the clean reads were mapped to porcine reference genome (GCF_000003025.6_Sscrofa11.1) and other small RNA databases including miRNA from miRbase (v22.1), and siRNA, piRNA, and snoRNA from NCBI GenBank by Bowtie2. Particularly, cmsearch was performed for mapping Rfam database (http://rfam.xfam.org/). To make each unique sRNA have a unique annotation, the priority order of miRbase>pirnabank>snoRNA>Rfam>other sRNA was followed to traverse the annotation. The unannotated sequences were used to predict novel miRNA candidates by miRDeep2 program (v0.1.3) based on the secondary structure. The novel miRNAs were aligned to mature miRNAs from other mammals in the miRbase by BLAST.
For analysis of mRNA and lncRNA sequencing data, clean reads were obtained from raw reads by removing adapter pollution, low-quality reads (more than $20\%$ bases with quality score less than 15) and reads whose unknown base ratio was greater than $5\%$ using SOAPnuke. These clean reads were aligned to the porcine reference genome using HISAT2 (v2.0.4), and Bowtie2 (v2.2.5) was applied to align the clean reads to the reference sequence. Then the mapped reads were assembled using StringTie. The transcripts were then screened by Pfam database and three softwares including coding potential calculator (score <0), txCdsPredict (score <500), and coding-non-coding index (score <0). The transcripts that unmatched Pfam database and passed through at least two of the three softwares were considered to be lncRNAs.
## Identification of differentially expressed transcripts
After mapping clean reads to the porcine reference sequence by Bowtie2, the expressions of small RNAs were calculated by counting absolute numbers of molecules using UMI, and then DE miRNAs analysis was performed using DEGseq. The fragments per kilobase of transcript per million (FPKM) reads mapped value was used to estimate the expressions of lncRNAs and mRNAs by RSEM software (v1.2.12), and analysis of DE mRNAs and DE lncRNAs were examined by DEseq2. Q value (adjusted p value) ≤0.05 and |log2(DWZ/Yorkshire)| ≥1 were set as thresholds for significant differential expression. The heatmap of DE transcripts was drawn by pheatmap (v1.0.8) with default parameter.
## Target genes prediction and functional analysis of DE miRNAs and DE lncRNAs
The predicted target genes of miRNAs were implemented by using RNAhybrid, miRanda and TargetScan softwares. lncRNAs can regulate target genes by acting in cis and in trans. If lncRNAs and genes exhibit similar expression patterns, their biological functions may be highly correlated. Accordingly, the targets genes of DE lncRNAs were predicted as follows. Based on the Spearman and Pearson correlation coefficients of lncRNA-mRNA pair being ≥0.6, mRNAs located within 50 kb upstream and 50 kb downstream of DE lncRNAs were selected for cis-acting regulation, and RNAplex was utilized to predict the combination of lncRNA and mRNA for trans-acting regulation with binding energy <–20. To explore the potential biological functions of DE transcripts, GO and KEGG analysis were carried out for the DE mRNAs and targets of DE miRNAs and DE lncRNAs based on GO (http://www.geneontology.org/) and KEGG (https://www.kegg.jp/) databases, and then the functional enrichment analysis was performed by Phyper based on Hypergeometric test. The significant levels of terms and pathways were calculated with a rigorous threshold (Q value ≤0.05) by Bonferroni.
## Construction of PPI and lncRNA-miRNA-mRNA network
The STRING was applied to construct protein-protein interaction (PPI) network for DE mRNAs, which was visualized by Cytoscape software. As mentioned above, the target genes of DE miRNAs and DE lncRNAs were predicted, and the interactions of lncRNA-miRNA, lncRNA-mRNA, and miRNA-mRNA were obtained. Consequently, the potential regulatory network of lncRNA-miRNA-mRNA was also visualized using Cytoscape.
## Quantitative real-time polymerase chain reaction analysis
The validation of miRNA expression levels was detected by stem-loop QPCR method [15]. cDNA was synthesized using RevertAid first strand cDNA synthesis kit (K1622, Fermentas) according to the manufacturer’s instructions. QPCR analysis was performed using SYBR Green Supermix (Biomed, Beijing, China) on CFX96 machine (Bio-Rad, Hercules, CA, USA). Porcine glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as endogenous control for lncRNAs and mRNAs, and U6 snRNA was used for miRNAs. Each QPCR reaction was performed in triplicate, and the relative expression level of transcripts was calculated using the 2−ΔΔCt method. The sequences of QPCR primers for mRNAs, miRNAs and lncRNAs are listed in Supplementary Table S1.
## Statistical analysis
Statistical analysis of the data from adipocyte diameter and QPCR assay was performed by one-way analysis of variance program with SPSS 20.0 software. Mean values and standard error were presented, and p value of less than 0.05 was deemed statistically significant (* p≤0.05 and ** p≤0.01).
## Overview of sequencing data
To identify small RNA distribution of DWZ and Yorkshire pigs, six small RNA libraries were constructed. After quality filtering and trimming of contaminant and adapter sequences, a total of 23,504,459, 23,631,041, 23,682,253 and 23,528,342, 23,339,095, 23,800,508 clean reads were obtained in DWZ1, DWZ2, DWZ3 and Yorkshire1, Yorkshire2, Yorkshire3, respectively (Supplementary Table S2). About $93.66\%$, $94.71\%$, $94.57\%$ and $93.67\%$, $93.57\%$, $92.96\%$ of clean reads, respectively, were mapped to the porcine reference genome. For lncRNAs analysis, a total of 122.32Mb, 134.68Mb, 134.34Mb and 134.87Mb, 134.50Mb, 119.25Mb clean reads with greater than $91.55\%$ of Q30 were obtained in DWZ1, DWZ2, DWZ3 and Yorkshire1, Yorkshire2, Yorkshire3, respectively. Among them, $95.53\%$, $94.70\%$, $95.40\%$, $96.43\%$, $96.31\%$, $96.38\%$ clean reads were mapped to the porcine reference genome, and $91.16\%$, $91.66\%$, $91.77\%$, $92.47\%$, $91.14\%$, $92.15\%$ were uniquely mapped in DWZ1, DWZ2, DWZ3 and Yorkshire1, Yorkshire2, Yorkshire3, respectively (Supplementary Table S3).
The length distribution of reads in small RNA library was analyzed, and the results demonstrated that the majority ($74.04\%$ to $82.81\%$) of reads were between 21 nt and 23 nt in length, which is the typical length of mature miRNA. The 22 nt reads accounted for approximately $50\%$ of the reads in libraries of DWZ and Yorkshire pigs (Supplementary Table S4). In addition, the abundance of mRNA was expectedly higher than that of lncRNA, and both mRNA and lncRNA showed similar distribution in both pig breeds (Supplementary Figure S1).
## Identification and characterization of miRNAs and lncRNAs
A total of 1,740 miRNAs (356 known miRNAs and 1,384 novel miRNAs), 16,618 lncRNAs (5,448 known lncRNAs and 11,170 novel lncRNAs), and 51,123 mRNAs were identified in DWZ and Yorkshire libraries (data not shown). Furthermore, the length and exon number distribution of lncRNAs and mRNAs were analyzed. The most common length of lncRNAs ($60.16\%$) was less than 1,500 bp (Figure 2A). Interestingly, this research also found that the majority ($65.55\%$) of novel lncRNAs were less than 1,000 bp in length, while almost half of the known lncRNAs were between 501 bp and 2,500 bp (Figure 2B). Further analysis demonstrated that the majority of known lncRNAs had two to four exons, while novel lncRNAs mainly had one to three exons, which were significantly less than the exon number of mRNAs (Figure 2C). The present results are highly in line with that in subcutaneous adipocytes from Large White and Chinese Jiaxing Black pigs [16]. Taken together, the biological replicates and matching efficiency demonstrated the desirable sequencing quality for subsequent analysis.
## Analysis of highly abundant miRNAs and lncRNAs
As exhibited in Table 1, the top 20 most abundant miRNAs in DWZ and Yorkshire libraries were the following: ssc-miR-22-3p, ssc-miR-26a, ssc-miR-206, ssc-miR-133a-3p, ssc-miR-126-3p, ssc-miR-378, novel-ssc-miR-857-5p, ssc-miR-499-5p, ssc-miR-451, ssc-miR-191, ssc-miR-99a-5p, ssc-miR-151-5p, novel-ssc-miR-324-3p, ssc-miR-143-3p, ssc-miR-16, ssc-miR-7f-5p, ssc-miR-423-3p, ssc-miR-1, ssc-miR-24-3p, ssc-miR-92a. The top 20 most abundant lncRNAs were the following: BGIR9823_101660, BGIR9823_79653, BGIR9823_ 101661, BGIR9823_101542, BGIR9823_81016, BGIR9823_ 101532, BGIR9823_ 101403, BGIR9823_101535, BGIR9823_ 88517, BGIR9823_101401, BGIR9823_101546, BGIR9823_ 81019, BGIR9823_81018, BGIR9823_91558, BGIR9823_ 81014, BGIR9823_101756, XR_115737.4, BGIR9823_101544, BGIR9823_101672, BGIR9823_101821 (Table 2). Furthermore, the top 20 most abundant miRNAs contributed to $83.30\%$, $80.60\%$, and the top 20 most abundant lncRNAs contributed to $77.02\%$, $74.21\%$, of the total counts in DWZ and Yorkshire library, respectively. The profiles of the highly abundant miRNAs and lncRNAs were similar in the two libraries, indicating that these miRNAs and lncRNAs may play important roles in maintenance of the physiological state by acting as housekeeping factors.
## Differentially expressed and specifically expressed miRNAs, lncRNAs, and mRNAs
To further understand the difference of regulatory mechanism in backfat deposition between the two pig breeds, comparative transcriptome analysis was performed. Compared with Yorkshire library, 72 miRNAs (27 upregulated and 45 downregulated), 183 lncRNAs (64 upregulated and 119 downregulated) and 1,370 mRNAs (814 upregulated and 556 downregulated) were differentially expressed in DWZ library (Figure 3; Supplementary Table S5). In addition, a total of 225 miRNAs (8 known and 217 novel miRNAs), 1,434 lncRNAs (851 known and 583 novel lncRNAs) and 5,612 mRNAs were specifically expressed (named specifically expressed [SE] miRNAs, SE lncRNAs, and SE mRNAs, respectively) in DWZ library, and 224 SE miRNAs (11 known and 213 novel miRNAs), 1,602 SE lncRNAs (959 known and 643 novel lncRNAs) and 4,756 SE mRNAs were detected in Yorkshire library (Supplementary Table S6).
## Functional analysis and PPI network of DE mRNAs
A total of 1,370 DE mRNAs were subjected to GO annotation and KEGG enrichment, and the potential functions and signaling pathways were analyzed. GO terms were catalyzed into three main processes (biological process, cellular component, and molecular function) (Supplementary Table S7). In the category of biological process, GO terms were mainly involved in cellular process, biological regulation, and metabolic process. In the category of cellular component, GO terms were closely connected with cell and cell part. In the category of molecular function, DE mRNAs related to binding and catalytic activity were considerably enriched. In addition, the results of KEGG pathway enrichment exhibited that several signaling pathways associated with lipid metabolism were enriched, such as peroxisome proliferator-activated receptor (PPAR), p53, AMP-activated protein kinase (AMPK), and PI3K-Akt signaling pathways. Moreover, arachidonic acid metabolism, glycolysis/gluconeogenesis, retinol metabolism and cellular senescence were significantly enriched (Supplementary Table S7), all of which are strongly associated with lipid metabolism, adipogenesis and cell development. On this basis of the above-mentioned studies, a total of 141 potential DE mRNAs related to fat deposition and lipid metabolism were selected (Figure 4A; Supplementary Table S8), and then a PPI network was constructed. The proteins including acyl-CoA oxidase 1 (ACOX1), aldolase, fructose-bisphosphate B (ALDOB), forkhead box O3 (FOXO3), AKT serine/threonine kinase 2 (AKT2), and Janus kinase 3 (JAK3) were at the hub positions (Figure 4B), and the expression profile of DE mRNAs presented in the PPI network was displayed as heatmap (Figure 4C). Thus, they might be important for regulating fat metabolism and adipogenic differentiation in pigs.
## GO and KEGG analysis of DE miRNAs and DE lncRNAs based on target genes
The potential functions of DE miRNAs and DE lncRNAs were explored by further analysis of their target genes, and the predicted targets of DE miRNAs and DE lncRNAs are listed in Supplementary Table S9 and Supplementary Table S10, respectively. And then, the targets were subjected to GO annotation and KEGG enrichment, and the GO annotation results were similar to that of DE mRNAs (Figure 5A; Supplementary Table S11). In KEGG enrichment analysis, several enriched pathways associated with adipocyte differentiation and proliferation, adipogenesis, and lipid metabolism were obtained, including AMPK, adipocytokine, foxo, apelin, insulin and PPAR signaling pathways, glycolysis/gluconeogenesis, and fatty acid degradation (Figure 5B; Supplementary Table S11). And the signaling pathways, such as AMPK, adipocytokine, insulin and PPAR, were also detected in enrichment analysis of DE mRNAs.
## Construction of lncRNA-miRNA-mRNA network
To identify the key lncRNAs and mRNAs related to regulation of adipogenesis and lipid metabolism, the target genes of DE miRNAs and DE lncRNAs were predicted, and the lncRNA-miRNA-mRNA network was constructed. There were 11 nodes and 8 connections between 3 lncRNAs, 4 miRNAs, and 4 mRNAs (Figure 6).
## Validation of identified transcripts
To validate the reliability of the sequencing results, a total of 18 transcripts, including 6 DE miRNAs (novel-ssc-miR537-5p, ssc-miR-10383, novel-ssc-miR1063-5p, novel-ssc-miR1379-5p, novel-ssc-miR882-3p, ssc-miR-375), 6 DE lncRNAs (LOC110259691, CMTM6, TMEM170B, LOC100521600, POMK, LOC106504881), and 6 DE mRNAs (zinc finger DHHC-type palmitoyltransferase 5 [ZDHHC5], actin binding LIM protein 1 [ABLIM1], homeodomain interacting protein kinase 1 [HIPK1], nuclear receptor coactivator 6 [NCOA6], spectrin beta, non-erythrocytic 1 [SPTBN1], splicing factor 3b subunit 3 [SF3B3]), were randomly selected for QPCR verification (Figure 7). These results manifested strong consistency with the sequencing data.
## DISCUSSION
Fat deposition is closely linked with the genetic background and is influenced by various transcription factors, crucial genes and signaling pathways. To better understand the regulatory network controlling backfat deposition, expression profiles of miRNAs, lncRNAs, and mRNAs in backfat tissue between *Chinese indigenous* (obese-type) DWZ and foreign lean-type Yorkshire pig breeds were analyzed for the first time by RNA-seq technology. A total of 1,625 DE transcripts were identified, including 72 DE miRNAs, 183 DE lncRNAs and 1,370 DE mRNAs. And the expression levels of several DE transcripts were validated by QPCR analysis, indicating that data of RNA-seq was reliable with strong consistency.
Several of highly abundant miRNAs identified in this study are closely related to adipogenesis. miR-26a, miR-206, miR-378, miR-499, and miR-191 have been reported to participate in adipocyte differentiation and adipogenesis [17–21]. Meanwhile, some of the DE miRNAs are indispensable for adipogenesis, lipid metabolism and adipose development. For instances, miR-375 exerts a positive regulatory effect on adipocyte differentiation. miR 204-5p favors the adipogenic differentiation and lipid synthesis, of human adipose-derived mesenchymal stem cells by suppressing Wnt/β-catenin signaling, of 3T3–L1 cells by targeting KLF transcription factor 3 (KLF3). Recently, transcriptome and miRNAome analysis have been used to identify miRNA expression profiles and characterize the possible regulatory relationships for backfat deposition in pigs [1,22–26]. In these researches and our study, miR-26a, miR-206, miR-99a-5p, miR-16, let-7f-5p, miR-1, and miR-24-3p were the top 20 most abundant miRNAs, additionally, miR-375, miR-204–5p, miR-205, miR-192, miR-194a-5p, miR-29b, miR-29c, miR-708–5p, miR-127, miR-369, miR-493–5p, miR-323, miR-432–5p, and miR-493–5p were the identified DE miRNAs. However, some of the DE miRNAs in our study were different from that in previous research, which may be caused by the differences in pig breeds, days of age, or threshold of the fold change.
To identify the differential regulatory networks in backfat tissue between the two pig breeds, GO and KEGG enrichment analysis were fulfilled. For DE mRNAs, several signaling pathways associated with lipid metabolism and adipogenesis were discovered, such as PPAR, p53, AMPK, and PI3K-Akt signaling pathways [8,27–29]. Several hub proteins were identified in PPI network of DE mRNAs. ACOX1, ALDOB, protein kinase AMP-activated catalytic subunit alpha 2 (PRKAA2), JAK3, and protein tyrosine phosphatase non-receptor type 11 (PTPN11) have been confirmed to be closely related to β-oxidation of fatty acid, lipogenesis, cholesterol metabolism, and lipodystrophy [30–35]. Moreover, several genes, including PPARδ, PPARα, fatty acid binding protein 3 (FABP3), acyl-CoA synthetase long chain family member 4 (ACSL4), B cell lymphoma 2 (BCL2), FOXO3 and TBC1 domain family member 1 (TBC1D1), are closely associated with fatty acid metabolism, adipogenic differentiation and cell cycle progression. PPARδ and PPARα are vital regulators in cell differentiation, lipid accumulation, fatty acid oxidation and lipid catabolism. FABP3 is positively correlated with the backfat thickness in beef cattle, and it regulates transport of fatty acids and lipid deposition. Furthermore, FABP3 is notably downregulated in subcutaneous adipose tissue from Yorkshire pigs than that in *Chinese indigenous* Laiwu pigs [8], which is similar to our study. ACSL4 converts free long-chain fatty acids into fatty acyl-CoA esters which are the key intermediates in the synthesis of complex lipids. Previous study demonstrated that the expression of ACSL4 gradually increases during adipogenesis of porcine primary intramuscular preadipocytes and ACSL4 knockdown decreases lipid accumulation. However, another research found that the expression of ACSL4 was gradually decreased during different differentiation stages of subcutaneous preadipocytes in Erhualian pigs [10], and our result also exhibited that the expression of ACSL4 in backfat tissue is significantly lower in DWZ pigs than that in Yorkshire pigs. In addition, AKT2 knockdown remarkably reduces preadipocyte proliferation, adipogenic differentiation and fat mass in human, but the present result showed that AKT2 expression was drastically lower in DWZ pigs than that in Yorkshire, which is contrary to the result reported in a former article about Bamei and Large White pigs. The discrepancy may be because of the different experimental methods and cell sources. BCL2 mediates obedience to proliferation and resistance to apoptosis in adipocytes. In addition, FOXO3 and TBC1D1 have been shown to be involved in lipid accumulation, fatty acid oxidation and obesity development. In our study, lower expressions of ACOX1, AKT2, PRKAA2, ACSL4, FOXO3 and higher expressions of ALDOB, JAK3, PTPN11, PPARδ, PPARα, FABP3, BCL2, TBC1D1 were observed in DWZ pigs. Taken together, these regulatory relationships may partly illuminate the mechanism of porcine backfat deposition.
The biological function of lncRNAs is generally mediated by their targets. Therefore, the targets of DE lncRNAs were predicted and then underwent enrichment analysis. Several pathways were enriched, among which AMPK, apelin and insulin signaling pathways are closely related to adipocyte differentiation and lipid metabolism. AMPK signaling pathway is believed to act as a key master switch that modulates cholesterol synthesis and lipid metabolism [36,37]. The apelin signaling pathway inhibits adipogenesis and lipolysis through distinct molecular pathways. Insulin is a key regulator and activates the transcription and proteolytic maturation of sterol regulatory element binding transcription factor 1c (SREBP1c), and then SREBP1c induces the expression of a family of genes involves in fatty acid synthesis, such as acetyl-CoA carboxylase (ACC) and fatty acid synthase (FAS).
The present study indicated that BGIR9823_87741, BGIR 9823_78329, BGIR9823_79919, and BGIR9823_85300 targets FABP3, BCL2, FOXO3, and PPARα, respectively. Meanwhile, it is noteworthy that XR_002341917.1, BGIR9823_80765, BGIR9823_79960, BGIR9823_93430, BGIR9823_84443, BGIR9823_80428, BGIR9823_85841 and XR_002340248.1 potentially regulates H2A histone family member Y (H2AFY), neuronal regeneration related protein (NREP), carboxypeptidase A4 (CPA4), optic atrophy 1 (OPA1), FKBP prolyl isomerase 5 (FKBP5), monoacylglycerol O-acyltransferase 1 (MGAT1), angiopoietin like 2 (ANGPTL2) and protein kinase AMP-activated non-catalytic subunit gamma 2 (PRKAG2), respectively. In our study, H2AFY, NREP, and CPA4 are highly expressed in Yorkshire pig. Former research showed that deletion of H2AFY results in lipid accumulation in murine liver [38]. Treatment of HepG2 cells with NREP knockdown exhibits greater lipid droplet accumulation and increases triglyceride and cholesterol content through TGFβR/PI3K/AKT signaling pathway. CPA4 knockdown enhances differentiation of human preadipocytes and CPA4 expression in subcutaneous adipose tissue negatively correlates with indices of insulin sensitivity. These results demonstrated that H2AFY, NREP, and CPA4 might inhibit fat deposition in Yorkshire pigs. Moreover, FABP5 and MGAT1 were markedly upregulated in backfat tissue in DWZ pigs. FKBP5 expression in human subcutaneous adipose tissue tends to be increased in type II diabetes subjects and is associated with genes involved in lipid metabolism and adipogenesis. Another research suggested that mice lacking FKBP5 gene had reduce body weight and were resistant to diet-induced obesity, and knockdown of FKBP5 in 3T3-L1 cells had a strong anti-adipogenic impact. It has been found that MGAT1 encodes the enzyme that catalyzes monoacylglycerol and fatty acyl-CoA to form diacylglycerol, which is indispensable for triacylglycerol synthesis, and MGAT1 knockdown exhibits a significant reduction in lipid accumulation. ANGPTL2, predominantly secreted by adipose tissue, enhances fatty acid synthesis and lipid accumulation in mice, and ANGPTL2 knockdown inhibits adipogenic differentiation of 3T3-L1 cells. On above basis, FABP5, MGAT1, and ANGPTL2 might enhance adipogenic differentiation and lipid formation in DWZ pigs. Accordingly, the DE lncRNAs target these genes might play crucial roles in adipogenesis and lipid metabolism in porcine backfat tissue, and further studies are needed to be fulfilled to validate this speculation.
The target relationships between miRNAs and mRNAs were further analyzed. ANXA3, the predicted target of miR-122-3p which plays important roles in cholesterol synthesis and lipogenesis [39,40], is downregulated at an early phase of adipocyte differentiation in 3T3–L1 cells, and suppression of ANXA3 causes elevation of the PPARγ2 mRNA level and lipid droplet accumulation. In addition, miR-583-3p, miR-161-3p, miR-671-3p, and miR-817-3p targets activating transcription factor 3 (ATF3), triacylglycerol synthase 1 (TGS1), protein kinase C and casein kinase substrate in neurons 2 (PACSIN2), and ataxin 2 (ATXN2), respectively, and these genes have been verified to be associated with fat weight, triglyceride synthase and lipid droplet formation. Furthermore, AQP7 is the predicted target of miR-194a-5p, and previous report showed that the body weight and fat mass increases significantly in AQP7 knockout mice, and adipocytes are large and exhibits accumulation of triglyceride by elevating adipose glycerol kinase activity [41]. These results indicated that the BGIR9823_87926/miR-194a-5p/AQP7 network may affect the process of adipocyte differentiation or adipogenesis (Figure 8), but their functions and interactions needed further validation.
## CONCLUSION
The comparative transcriptome analysis of miRNAs, lncRNAs, and mRNAs in backfat tissue between DWZ and Yorkshire pigs was conducted, and numerous DE miRNAs, DE lncRNAs, and DE mRNAs, which may influence fat development, were further identified. The BGIR9823_87926/miR-194a-5p/AQP7 network may affect the process of adipocyte differentiation or adipogenesis. The present study provides comprehensive insights into the regulatory network of backfat deposition and lipid metabolism in pigs.
## SUPPLEMENTARY MATERIAL
Supplementary file is available from: https://doi.org/10.5713/ab.22.0165;
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|
---
title: Role of antioxidants in fertility preservation of sperm — A narrative review
authors:
- Ahmad Yar Qamar
- Muhammad Ilyas Naveed
- Sanan Raza
- Xun Fang
- Pantu Kumar Roy
- Seonggyu Bang
- Bereket Molla Tanga
- Islam M. Saadeldin
- Sanghoon Lee
- Jongki Cho
journal: Animal Bioscience
year: 2022
pmcid: PMC9996255
doi: 10.5713/ab.22.0325
license: CC BY 4.0
---
# Role of antioxidants in fertility preservation of sperm — A narrative review
## Abstract
Male fertility is affected by multiple endogenous stressors, including reactive oxygen species (ROS), which greatly deteriorate the fertility. However, physiological levels of ROS are required by sperm for the proper accomplishment of different cellular functions including proliferation, maturation, capacitation, acrosomal reaction, and fertilization. Excessive ROS production creates an imbalance between ROS production and neutralization resulting in oxidative stress (OS). OS causes male infertility by impairing sperm functions including reduced motility, deoxyribonucleic acid damage, morphological defects, and enhanced apoptosis. Several in-vivo and in-vitro studies have reported improvement in quality-related parameters of sperm following the use of different natural and synthetic antioxidants. In this review, we focus on the causes of OS, ROS production sources, mechanisms responsible for sperm damage, and the role of antioxidants in preserving sperm fertility.
## INTRODUCTION
Male fertility can be negatively impacted by multiple exogenous and endogenous stressors including reactive oxygen species (ROS). ROS are produced during oxygen metabolism either owning to the electron transport chain system or different conditions associated with enhanced energy demands. The highly reactive nature of ROS enables them to react with and modify any molecule through oxidation resulting in structural and functional alterations [1]. The most common type of produced ROS includes superoxide anion radicals (O2–•), hydrogen peroxide (H2O2), and hydroxyl radicals (OH•).
Recent reports have shown that ROS plays an important role in both reproductive physiology and pathology. This dual nature of ROS depends on the source, concentration, production site, and exposure time [2]. The physiological level of ROS is considered important for the proper accomplishment of different functions associated with gamete fertility including proliferation, maturation, the release of oocytes [3], capacitation, hyperactivation, acrosomal reaction, and fertilization [4]. However, ROS overproduction can trigger pathological responses damaging cells and tissues.
Living organisms are equipped with natural defense systems (antioxidants) to scavenge and neutralize the effects of ROS. Reports have verified the presence of a wide range of antioxidants in the seminal plasma that can protect sperm against the detrimental effects of ROS [5]. The enzymatic antioxidants present in the seminal plasma include superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx), whereas, non-enzymatic antioxidants include vitamins A and C, carnitine, glutathione (GSH), and pyruvate [5].
Under physiological conditions, an equilibrium is maintained in the male reproductive tract between ROS production and neutralization. However, excessive ROS production can overcome the antioxidant defense systems resulting in oxidative stress (OS). OS has been reported as a major cause of sperm damage affecting fertility [2]. The plasma membrane of the mammalian sperm is rich in polyunsaturated fatty acids (PUFAs) that increase vulnerability to oxidative damage [1]. Moreover, sperm with limited cell repair machinery lacks significant antioxidant protection owning to the limited volume and restricted cytoplasm distribution in the sperm cells [5].
Recent findings have shown that OS is associated with an increased percentage of damaged sperm due to the oxidation of deoxyribonucleic acid (DNA), lipids, proteins, and nucleotides [6]. Ultimately, the structural integrity of the plasma membrane is lost along with reduced sperm motility, an increase in morphological abnormalities, and cellular apoptosis [7]. Therefore, OS leads to infertility through impaired sperm function. The seminal plasma of fertile individuals shows greater antioxidant capacity than that of infertile ones [8]. One approach to control OS-induced damage is through the use of antioxidants that act by scavenging and neutralizing ROS. Over the years, researchers have used different antioxidants (natural and synthetic) either alone or combined and at different dosages for varying durations. Several in-vivo and in-vitro studies have reported beneficial outcomes achieved following antioxidant use including enhanced sperm concentration and motility, reduced morphological abnormalities and DNA fragmentation, better antioxidant capacity of seminal plasma, and improved outcomes of assisted reproductive biotechnologies [9]. However, in some cases, in-vitro improved sperm quality failed to improve fertility in clinical trials. In this review, we focus on the sources of ROS in semen, physiological and pathological effects of ROS on sperm, and the role of endogenous and exogenous antioxidants in preserving sperm fertility.
## Sperm
Sperm are the primary source of ROS produced in the semen. However, the amount of ROS produced depends on the maturation stage of sperm. In mature sperm, ROS production occurs either in the plasma membrane by nicotinamide adenine dinucleotide phosphate (NADPH) oxidase or in the presence of a nicotinamide adenine dinucleotide (NADH)-dependent oxidoreductase in the inner mitochondrial membrane, which ensures ROS production through electron leakage from the electron transport chain [10].
## Leukocytes
The leukocytes found in the semen are the second major source of ROS that acts as an integral part of the cellular defense system against infection, varicocele, spinal cord injury, and prolonged sexual absentia, and inflammation [10]. Leukocytic infiltration is enhanced during infection to counter infectious agents. Increased production of cytokines associated with inflammatory processes, such as interleukin-8, along with decreased production of SODs results in a respiratory burst and excessive ROS production leading to OS [11].
## Extrinsic sources
Excessive ROS production in the semen may be associated with certain extrinsic factors including the use of alcoholic drinks, air pollution, smoking, obesity, heat stress, toxicants or radiation exposure, aging, environmental factors, and nutritional deficiencies [12]. The ROS concentration in semen also depends on in-vitro techniques used for sperm washing. Pelleting sperm by cycles of centrifugation and resuspension induces ROS production [13].
## Effects of physiological levels of ROS on sperm function
For proper functioning and fertility, mammalian sperm need to acquire certain properties including normal morphology, motility, and capability to undergo different events such as capacitation and acrosomal reaction. Research evidence has shown that physiological levels of ROS act as intracellular signaling molecules necessary for different physiological processes including maturation, hyperactivation, capacitation, acrosomal reaction, and oocyte-sperm fusion [4]. A recent study reported that ROS levels in the semen of fertile individuals using chemiluminescence assay in terms of the mean±standard deviation and median 25th to 75th percentiles were 0.01±0.02×106 photons per minute (cpm)/20×106 sperm and 0.009 (0.004–0.014)×106 cpm/20×106 sperm [14]. During sperm maturation, a low magnitude of OS is required for other physiological events including mitochondrial activity, enhanced zona binding of sperm ROS are required for the proper packing of chromatin material essential for stability [15].
Sperm maturation occurs inside the ductus epididymis involving different steps such as alterations in the plasma membrane, rearrangement of membrane proteins, enzymatic modulations, and nuclear remodeling [16]. All of the above-mentioned steps are regulated through the appropriate signaling pathways modulated by the ROS present in the seminal plasma [17,18]. ROS are also involved in the formation of disulfide bonds to ensure chromatic stability and prevent damage to the chromosomal DNA.
Sperm capacitation and hyperactivation are considered prerequisite events to ensure successful fertilization. Reports have indicated two main changes occurring at the cellular level responsible for sperm capacitation including the generation of physiological levels of ROS and phosphorylation of protein tyrosine. It was found that ROS induces phosphorylation as in-vitro inhibition of ROS by the introduction of 2-deoxyglucose resulted in a reduced concentration of tyrosine-phosphorylated proteins [19]. The process is triggered by an influx of calcium (Ca2+) and bicarbonate ions. According to Du Plessis and his coworkers [17], both Ca2+ ions and ROS are involved in the initiation of the capacitation to cause the activation of adenylate cyclase which results in the production of cyclic adenosine monophosphate (cAMP). cAMP further causes the activation of downstream protein kinase A (PKA). PKA stimulates a membrane-bounded NADPH oxidase resulting in enhanced ROS production and phosphorylates serine and tyrosine, which leads to the activation of protein tyrosine kinase. ROS not only promotes the protein tyrosine kinase but also inhibits phosphotyrosine activity that normally dephosphorylates tyrosine. Protein tyrosine kinase phosphorylates tyrosine present in the fibrous sheath surrounding the axoneme of sperm flagellum. The enhanced phosphorylation of tyrosine is observed in the capacitation. A-kinase anchoring proteins are phosphorylated proteins that play a role in binding PKA with the fibrous sheath of the sperm [20] suggesting their possible involvement in the hyperactivity of sperm.
Following capacitation, the acrosome reaction is considered the last step of sperm maturation to acquire fertility [17]. The acrosome reaction is initiated by zona pellucida (ZP), progesterone hormone, and ROS. Acrosome reaction results in the release of acrosomal enzymes mainly acrosine from the sperm head that helps sperm in penetrating the ZP of the oocyte. Release of Ca2+ ions from the acrosome results in the breakdown of phosphatidylinositol-4-5-biphosphate yielding diacylglycerol (DAG) and inositol triphosphate. Inositol triphosphate causes the activation of actin-serving proteins that facilitates the fusion of the acrosome and plasma membrane of sperm to trigger exocytosis. Whereas, DAG triggers protein kinase C (PKC) activation leading to a greater influx of Ca2+ ions and activation of phospholipase A2 to release large amounts of fatty acids from the plasma membrane required for the fusion of sperm with the oocyte [21,22].
## Hydrogen peroxide
A proper level of H2O2 plays an important role in sperm function including sperm maturation, chromatin stability, capacitation, hyperactivation of sperm, and acrosome reaction, and increases the rate of sperm-oocyte fusion [23]. Furthermore, peroxides have been reported to be involved in the formation of a “mitochondrial capsule” that protects the mitochondria against proteolytic degradation [24]. Mitochondrial protection is essential for cellular metabolism, mediation of apoptosis, and ROS production. Recent reports have demonstrated that enhanced concentration of H2O2 is involved in sperm capacitation through the activation of adenylyl cyclase to produce cAMP which results in PKA-dependent phosphorylation of tyrosine residue [25]. According to Griveau and his co-workers, a 25 μM concentration of H2O2 enhanced sperm capacitation and hyperactivation following 3 h of incubation in B2 medium [26]. Contrary to the previous study, another study reported that a 50 μM concentration of H2O2 causes a twofold increase in cAMP production through adenylyl cyclase activation that leads to PKA-dependent protein tyrosine phosphorylation essential for capacitation [25].
## Superoxide anion
O2–• is involved in sperm maturation, capacitation, acrosomal reaction, and sperm-oocyte fusion by enhancing the membrane fluidity of sperm [18]. Moreover, O2–• is the major species responsible for ROS-induced sperm hyperactivation possibly resulting from increased intracellular adenosine triphosphate (ATP) levels [27].
The role of O2–• in sperm capacitation was evident by the fact that O2–• produced during the incubation of sperm under capacitation conditions [28]. A study reported that during cryopreservation, O2–• improved the percentage of capacitated bovine sperms demonstrated by the induction of acrosome reaction using lysophosphatidylcholine [29]. Similar findings were reported by Zhang and Zheng [30] that exogenous O2–• significantly improved the percentage of human sperm that underwent capacitation (from 14.0±1.3 to 23.2±2.5) and acrosome reaction (from $4.5\%$±$1.1\%$ to $16\%$±$2.0\%$, respectively). However, another study reported that no increase in the spontaneous acrosome reaction was observed following a direct addition of O2–• to the medium [21]. The presence of O2–• resulted in the production of unesterified fatty acids from the membranal phospholipids. Based on these findings it was suggested that O2–• secreted by sperm could be responsible for the ionophore-induced acrosome reaction via the de-esterification of membranel phospholipids [31].
## Nitric oxide
Nitric oxide (NO•) is a free radical with a relatively long half-life. NO• production is catalyzed by nitric oxide synthase. NO• has been identified in the endothelium of testicular blood vessels. Physiological levels of NO• actively participate in signal transduction pathways responsible for sperm motility, capacitation, and acrosomal reaction and could stimulate the hyperactivation of mouse sperm [32].
NO• reported controlling sperm motility, at a low concentration of NO•, an increase in sperm motility has been observed. Whereas, moderate to high concentrations inversely affect sperm motility [33]. Hellstrom and his coworkers reported that a low level of sodium nitroprusside, a NO• producing compound improved sperm motility and viability due to reduced lipid peroxidation (LPO) [34]. Similar findings were observed in another study that low concentrations of sodium nitroprusside (10–7 and 10–8 M) significantly improved the percentage of capacitated sperm following 3 h of incubation [35]. Regulation of enzymatic activity might be the reason for the improved capacitation rate of sperm.
Furthermore, NO•-releasing compounds trigger the capacitation of human sperm but the effect on hyperactivation was not constant [36]. The effect of NO• on sperm capacitation was might be due to the oxidation of cellular components including membrane lipids or thiol groups either due to its direct reaction with H2O2 that leads to the formation of singlet oxygen or due to the oxidation of NO• to form nitrosonium cation that can react with H2O2 to yield peroxynitrite anion [37].
Research evidence has shown that NO• improves the binding of sperm with ZP. A study demonstrated that sperm treated with low concentrations of sodium nitroprusside (10–7 and 10–8 M) resulted in a significantly improved percentage of sperm binding with ZP following 3 h of incubation [35]. The effect of NO• on sperm-oocyte binding may be due to the interaction with H2O2 and O2–•.
## Effects of pathological levels of ROS on sperm function
Male infertility may be associated with excessive ROS production in semen. Excessive ROS production overcomes the antioxidant’s defense systems, disrupting the natural balance between ROS production and neutralization by antioxidants resulting in OS. In infertile individuals, ROS levels examined in the sperm samples in terms of the mean±standard deviation and median 25th to 75th percentiles were 0.35±0.67×106 cpm and 0.06 (0.02–0.33)×106 cpm/20×106 sperm [14]. OS produces pathological defects in major biomolecules including lipids, nucleic acids, proteins, and sugars [16]. The magnitude of oxidative damage depends on different factors including the nature and amount of ROS, duration of ROS exposure, temperature, oxygen tension, and composition of the surrounding environment (ions, proteins, and antioxidants) [38].
## Lipid peroxidation and motility reduction
The plasma membrane of mammalian sperm is rich in PUFAs; fatty acids with more than two carbon-carbon double bonds [38]. These unconjugated double bonds are present between the methylene groups of PUFAs. The double bond near the methylene group reduces the strength of the methylene carbon-hydrogen bond, increasing hydrogen’s susceptibility to oxidative damage [39]. OS leads to a cascade of chemical reactions known as LPO. LPO is regarded as an autocatalytic self-propagating reaction that results in abnormal fertilization; it results in the loss of $60\%$ of the fatty acids present in the plasma membrane, inversely affecting membrane fluidity, enhancing the permeability of ions, and inhibiting the actions of enzymes and receptors, as well as compromising sperm membrane integrity, defective motility, and reduced sperm-oocyte interaction [39].
## DNA damage and apoptosis
OS causes severe damage to the nuclear material of sperm resulting in enhanced DNA fragmentation, modifications of base–pairs, chromatin cross-linking, and chromosomal microdeletions [11]. DNA damage results in cellular apoptosis, reduced fertilization rate, a higher percentage of miscarriage, and offspring mortality [40]. ROS-induced oxidative damage is also responsible for mutations in mitochondrial DNA that inversely affect sperm motility by inhibiting energy production. Agarwal et al [11] reported the involvement of at least one mitochondrial gene (of the 13) that codes for the electron transport chain system for reducing ATP and inducing intracellular ROS production. In infertile individuals, mature sperm are associated with higher ROS levels resulting in a significantly higher percentage of sperm undergoing apoptosis compared to the mature sperm of healthy individuals [41]. Reports have indicated a higher level of cytochrome C in the seminal fluids of infertile individuals, reflecting severe mitochondrial damage [6,11].
## DEFENSE AGAINST ROS IN SEMEN
Antioxidants are compounds or enzymes that can dispose of, scavenge/neutralize, and inhibit ROS production or their actions. Antioxidants help to maintain cell function and structure by protecting the plasma membrane against ROS. Furthermore, antioxidant protects acrosome integrity preventing premature acrosome reaction. Antioxidants work by breaking the oxidative chain reaction resulting in reduced OS. Antioxidants can protect sperm from ROS produced by abnormal sperm or leukocytes, prevent DNA fragmentation and premature sperm maturation, reduce cryodamage, and improve sperm quality. We provide a summarized figure to illustrate the balance in ROS production on the physiological and pathological levels (Figure 1).
During spermatogenesis, sperm lose most of the cytoplasmic contents rendering a very low intracellular antioxidant capacity. Therefore, sperm protection against ROS mainly depends on the antioxidant capacity of the seminal plasma. Seminal plasma serves as the main barrier against extracellular ROS, containing different enzymatic and non-enzymatic antioxidant molecules including CAT, carotenoids (vitamin A), coenzyme Q 10 (CoQ10), GSH, GPx, GSH reductase, pyruvate, SOD, taurine, hypotaurine, uric acid, vitamin C, and vitamin E [5]. The antioxidant system of the body is affected by the dietary intake of antioxidants, minerals, and vitamins [42]. The use of antioxidants to neutralize the overproduction of ROS either directly into the semen extenders or inclusion in the diet has been well-researched and reported in the literature. *In* general, dietary antioxidants demand long-term and more persistent treatment protocols to benefit male fertility. The effect of each antioxidant depends on the dosage used and species of animal involved. Similarly, to preserve the integrity of sperm during freeze-thaw procedures, multiple relationships and mechanisms have been established. However, insights into how antioxidants serve protection and energy to sperm are still paradoxical. In this section, we focus on the role of endogenous antioxidants in preserving sperm fertility (Tables 1, 2).
## Superoxide dismutases
SODs are metalloenzymes present in all life forms. SODs are considered an integral part of the antioxidant defense system that plays an active role in protecting sperm against OS. There are two main SOD isoforms including SOD-1 ($75\%$ of antioxidants) and SOD-3 ($25\%$ of antioxidants) that are derived from the prostate [43]. SOD protect cells against excessive O2–• levels by catalyzing the conversion of two O2–• into molecular oxygen and H2O2 [44]. Based on the presence of transition metal ions at the active site, SODs are classified into four main types: copper/zinc SOD, iron SOD, manganese SOD, and nickel SOD [45]. Peeker et al [43] reported that copper/zinc SOD is predominantly found in both sperm and seminal plasma.
In the male reproductive tract, SODs are secreted into the seminal plasma by the accessory sex glands, epididymis, sperm, and testicles (Sertoli and Leydig cells) and help maintain sperm motility for a long period [46]. Several reports have indicated that a SOD-supplemented semen extender could improve the freeze-thaw quality of bull [47] and stallion sperm [48]. In another study, supplementation of the canine freezing extender with SOD, CAT, and GPx preserved the quality of sperm obtained from fertile and sub-fertile dogs for 10 days at 4°C [49].
## Glutathione peroxidase
GPx is an important enzyme responsible for the detoxification of any lipid peroxide. GPx utilizes GSH as an electron donor to catalyze the reduction of H2O2 and O2–• [50]. GPx is regarded as superior to CAT in maintaining low cellular H2O2 [44]. The active site of GPx contains selenium in the form of selenocysteine [10]. GPx is found in both sperm and seminal plasma. In sperm, GPx is primarily located in the mitochondrial matrix whereas seminal GPx is suspected to originate from the prostate [51]. Moreover, GPx is expressed and secreted from the epididymal head into the semen [46]. The primary function of GPx is to protect the sperm plasma membrane against LPO, and sperm DNA from oxidative damage and chromatin condensation [50].
## Catalase
CAT is an enzyme found in the peroxisomes that decompose H2O2 into water and an oxygen molecule to prevent LPO of the plasma membrane. In semen, CAT was reported to be present in both sperm and seminal plasma. Seminal plasma is considered the main source of CAT; however, developing sperm also show a minimal level of CAT [52]. It was believed that CAT in the seminal plasma originated from the prostate gland [53]. The importance of CAT in seminal plasma is evident based on the observation that the semen of asthenozoospermic individuals may contain lower levels of CAT than that of normospermic individuals [46].
CAT supplementation reduced ROS levels and cryodamage in freeze-thaw sperm samples [54]. Moubasher et al [55] reported that supplementing fresh and processed semen with CAT results in improved freeze-thaw sperm motility, viability, and DNA integrity. Similarly, a CAT-supplemented semen extender prolonged sperm survival in camels [56]. Similar results were reported in another study when the freezing extender was supplemented with both CAT and SODs [57]. It was suspected that improvement in the freeze-thaw sperm quality was attributable to the combined and simultaneous action of both antioxidants against O2–• and H2O2 [57].
## Carotenoids (Vitamin A)
Carotenoids are fat-soluble organic compounds. Being precursors of vitamin A, carotenoids are mainly found in different vegetable dyes including orange, pink, red, and yellow. Carotenoids such as beta-carotenoids and lycopene are important components of the antioxidant defense system. Carotenoids help maintain the integrity of plasma membranes, regulate the proliferation of epithelial cells, and actively participate in spermatogenesis [58]. A carotenoid-deficient diet can lead to reduced sperm motility [10].
## Reduced glutathione
GSH is a natural antioxidant found in reproductive tract fluids and epididymal sperm semen of most of animal species and acts as a substrate in the peroxidase/reductase pathway to maintain the equilibrium and protect sperm from oxidative damage [59].
Reports have indicated that supplementation with GSH and its precursors (cysteine and glutamine) resulted in improved semen quality. In 1996, Irvine reported that the use of GSH for the treatment of infertile individuals with a varicocele or inflamed urogenital system resulted in significantly improved sperm quality [60]. In another study, a GSH-supplemented freezing extender improved the motility of donkey sperm by reducing the intracellular ROS levels [59]. However, GSH supplementation did not significantly affect other parameters of donkey sperm including plasma and acrosomal membrane integrity, mitochondrial membrane potential (MMP), and intracellular O2–• levels. Olfati Karaji and his co-workers reported improved freeze-thaw sperm quality by using a combination of GSH and SOD in the freezing extender of bull [61]. It was suspected that improved sperm quality was associated with reduced LPO and enhanced antioxidant levels.
## Cysteine
Cysteine is a GSH precursor that can restore GSH depletion because of OS and inflammation [62]. In a clinical trial, oral intake of cysteine (600 mg/d) for 3 months improved the sperm quality and antioxidant status of infertile men [63]. Another report indicated that incubation of human sperm with cysteine for 2 h at room temperature significantly improved sperm motility [64]. Moreover, a cysteine-supplemented freezing extender protected sperm during the freeze-thaw procedure and resulted in improved sperm quality in bull [65], chicken [66], and ram [67] sperm.
## Vitamins C and E
Vitamin C is a naturally occurring water-soluble substance having outstanding antioxidant properties. Vitamin C protects sperm against oxidative damage by neutralizing O2–•, H2O2, and OH• [68]. Moreover, Vitamin C could effectively protect sperm DNA from ROS because of its high antioxidant competency [68]. The concentration of vitamin C is 10 times higher in seminal plasma than in the blood (364 vs 40 μmol/L) [10].
Several in-vivo studies have been performed to investigate the therapeutic potential of vitamin C for the restoration of fertility. Dawson et al [69] observed a positive correlation between vitamin C levels and sperm quality-related parameters including sperm concentration, motility, and viability. In that study, oral administration of vitamin C in smokers significantly improved the vitamin C levels in the seminal plasma and serum, resulting in improved semen quality. Similar findings were reported by Akmal et al [70] in infertile men with idiopathic oligozoospermia treated with vitamin C (2 gm/d). In another study, vitamin C supplementation (250 mg twice a day) resulted in improved sperm motility and morphology in patients following the surgical removal of varicocele [71]. However, vitamin C failed to improve the sperm count in such individuals. Vitamin C supplementation could effectively restore the fertility of rats with cyclophosphamide-induced testicular OS and androgenic disorders [72].
Furthermore, several in-vitro studies have reported improved sperm quality following the use of mediums supplemented with vitamin C. Inclusion of vitamin C (800 μmol/L) in the Ringer-*Tyrode medium* protected sperm from ROS-induced damage and improved sperm motility and viability [73]. However, higher concentrations of vitamin C (e.g. 1,000 μM) instead of protecting sperm against H2O2 increased the magnitude of oxidative damage. In another study, the supplementation of *Percoll medium* with vitamin C (600 μM) protected sperm DNA from damage [74]. Similar findings were obtained by another study where vitamin C-supplemented TEST yolk buffer failed to preserve sperm motility [31].
Vitamin E is a naturally occurring fat-soluble compound. Vitamin E is mainly present in the plasma membrane and possesses powerful chain-breaking antioxidant properties with dose-dependent effects. Vitamin E neutralizes free OH• and O2–• anions in the plasma membrane and reduces ROS-induced LPO. Therefore, vitamin E mainly protects the components of the sperm plasma membrane against LPO and improves the function of other antioxidants.
Different in-vivo studies have shown that vitamin E could be effectively used for the treatment of infertile individuals with oligoasthenozoospermia induced by OS [75,76]. Suleiman et al. observed that oral intake of vitamin E significantly increased the motile sperm count by decreasing malonic dialdehyde (end product of LPO) production from sperm [77]. In another study, Eid et al [78]. observed that vitamin E supplementation resulted in improved sperm concentration, motility, viability, and enhanced oxidative function in the seminal plasma of chickens [78].
Similarly, in-vitro studies have reported that vitamin E preserves sperm motility and also enhances their ability to penetrate hamster eggs [79]. During freeze-thaw procedures, the use of vitamin E-supplemented semen extenders at an inclusion rate of 10 mmol/L preserved sperm motility more efficiently compared to an untreated control group [31]. In 2003, Park and his coworkers reported that vitamin E supplementation resulted in reduced sperm damage and improved sperm motility during freeze-thaw procedures [80]. In another study, vitamin E-supplemented *Percoll medium* protected sperm DNA against oxidative damage [74].
Recent studies have shown that combined use of vitamins C and E together or alongwith other antioxidants can effectively improve semen quality. The oral intake of vitamins C and E can greatly reduce ROS-induced DNA damage in the sperm of normozoospermic and asthenozoospermic men [81]. Similarly, Greco et al. also observed reduced sperm DNA damage in infertile individuals following combined supplementation with vitamin C and vitamin E for 2 months [82]. It is believed that the use of hydrophilic vitamin C along with the lipophilic vitamin E results in a synergistic effect that reduces the magnitude of sperm damage induced by OS [83]. Similar findings were observed when vitamin C was used together with vitamin E and GSH [84]. Moreover, vitamin E combined with other antioxidants including β-carotene [85], vitamin C, GSH [86], and selenium [76,87] led to an improved semen profile in infertile individuals.
## Taurine and hypotaurine
Taurine is a sulfur-containing amino acid that protects sperm against ROS when exposed to aerobic conditions or freeze-thaw procedures [88]. The antioxidant nature of taurine is related to its ability to elevate the CAT level in close association with the SOD concentration in bull, ram, and rabbit sperm [88].
An in-vivo study indicated that taurine use can significantly reverse the toxic effects of endosulfan in rats. Taurine treatment improved testicular weight, sperm count, motility, viability, and daily sperm production in endosulfan-treated rats [89].
An in-vitro study indicated that taurine and hypotaurine stimulate sperm capacitation and acrosomal reaction [90]. Furthermore, hypotaurine and taurine can inhibit spontaneous LPO in epididymal sperm [91]. Boatman et al [92] reported that hypotaurine restored the motility of hamster sperm affected by the washing procedure. Several studies have utilized taurine-supplemented semen extenders during different storage procedures. Storage of ram semen at room temperature using a taurine-supplemented extender significantly improved motility, membrane integrity, antioxidant status, and total antioxidant capacity [93]. Moreover, a taurine-supplemented semen extender showed the same protective effect during the chilling of tom [94], stallion [95], and donkey [96] sperm. Taurine supplementation resulted in improved freeze-thaw motility, viability, and plasma membrane integrity of buffalo [88], bull [97], and ram [98] sperm.
## Coenzyme Q10
CoQ10 is a vitamin-like substance synthesized from tyrosine and serves as an important component of the inner mitochondrial membrane, an energy-promoting agent by supporting the mitochondrial electron transport chain, present in the mid-piece of the sperm tail. CoQ10 neutralizes O2–• and peroxides to protect lipids from oxidative damage. Gvozdjáková et al [99] reported that CoQ10 works by regenerating other antioxidants including vitamin E and vitamin C.
Several clinical trials have shown that CoQ10 supplementation resulted in improved semen quality in infertile individuals [100]. A meta-analysis of clinical trials investigating the effects of CoQ10 supplementation showed a significant improvement in sperm motility (total and progressive), sperm concentration, and seminal concentration of CoQ10 [101].
Some clinical trials have reported the beneficial effects of combined use of CoQ10 with other antioxidants. In male rats, oral intake of CoQ10 and L-carnitine attenuated the effects of high and oxidized low density lipoprotein (LDL) resulting in a significantly improved hormonal profile and sperm quality [102]. These improved outcomes may be attributable to efficient energy production from sperm mitochondria, which requires sufficient concentrations of CoQ10 and carnitine [99]. Gvozdjáková et al [99] reported that daily intake of CoQ10 (30 mg), L-carnitine (440 mg), vitamin C (12 mg), and vitamin E (75 IU) improved sperm concentration and pregnancy rates in infertile individuals.
In-vitro studies have shown the protective role of CoQ10 during freeze-thaw sperm procedures [103–105]. A CoQ10-supplemented freezing extender reduced the magnitude of cryodamage and resulted in the improved freeze-thaw quality of buck [106], fish [103], and ram [107] sperm. Similar results were observed when boar semen was stored at 17°C [108] and rooster semen was stored at 5°C [104] after being diluted with a CoQ10-supplemented semen extender. In contrast, a recent study showed that the addition of CoQ10 in the freezing extender of stallions did not affect the freeze-thaw sperm quality, but oral supplementation of stallions resulted in improved motility and membranal integrity of sperm after 24 h of cooling [109]. Similar findings were observed in another study where stallions were orally fed a diet supplemented with CoQ10 (1 gm/d) and improved semen quality was observed in the semen of five out of seven stallions following the cooling and freezing of semen [110].
## EXOGENOUS ANTIOXIDANTS AND SPERM FERTILITY
Under normal conditions, endogenous antioxidant systems are primarily involved in the regulation of redox control. However, certain pathological conditions are associated with excessive ROS production overcoming redox control. In such circumstances, antioxidants from exogenous sources can play an important role in ameliorating the detrimental effects of OS. In this section, we focus on the role of exogenous antioxidants in preserving fertility (Table 3).
## Astaxanthin
Astaxanthin (AXN) is a red keto-carotenoid pigment that has shown antioxidant activity against different oxidants and can inhibit LPO by penetrating biological membranes as well as suppresses ROS-induced damage to DNA, lipids, and proteins [111].
Several studies have reported that AXN has positive effects on fertility. An AXN-supplemented diet improved the osmolality, motility, concentration, and fertilization rate of sperm in goldfish [112]. In a clinical trial, Comhaire et al [113] reported that oral intake of AXN had a positive impact on semen quality and fertility of infertile individuals. In another study, oral intake of AXN combined with vitamins C and E ameliorated infertility in male rats [114]. AXN supplementation was reported to ameliorate the detrimental effects of diabetes on sperm parameters in rats [115].
Recent reports have confirmed the protective role of AXN during sperm preservation. AXN supplementation showed improved and protected sperm motility, viability, membrane integrity, and DNA during liquid preservation of boar [115] and ram [116] semen. Similar findings were observed during freeze-thaw procedures using an AXN-supplemented freezing extender in boar [117], dog [118], and ram [119] sperm.
## Kinetin
Kinetin, a member of the cytokinin family has positive effects on cellular growth and division by reducing cycle length. Previous reports have indicated that kinetin can regulate the antioxidant activities of enzymes including CAT and SOD [120] resulting in reduced oxidative damage and is reported to reduce oxidative damage during in-vitro cell culture [121]. Recently, kinetin use was shown to be effective in alleviating cisplatin-induced testicular toxicity and organ damage by reducing OS, inflammation, and apoptosis [122]. During freeze-thaw procedures, the use of a kinetin-supplemented freezing extender resulted in improved sperm motility, viability, and structural integrity of dog [123] and ram [124] sperm.
## Myo-inositol
Myo-inositol (MYO) is the most important naturally existing inositol and belongs to vitamin B complex group 1. MYO regulates the intracellular level of calcium ions and it has been suggested that MYO has a role in spermatogenesis and sperm function. Sertoli cells secrete MYO in response to the follicle-stimulating hormone that regulates different physiological events associated with sperm including maturation, motility, capacitation, and acrosomal reaction [125].
MYO has the potential to restore the fertility of male gametes and improve the fertilization rate [126]. In a clinical trial, oral intake of MYO resulted in improved sperm quality and balanced hormonal profiles in patients with idiopathic infertility [127]. Condorelli et al. suggested the use of MYO in infertile individuals based on the findings of their study that incubation of sperm in a medium supplemented with MYO (2 mg/mL) reduces the percentage of sperm with low MMP [128]. In another study, Condorelli et al. reported that MYO enhanced the motility of sperm retrieved following the swim-up procedure in both fertile and infertile individuals [129]. Furthermore, an MYO-supplemented freezing extender showed reduced OS and improved freeze-thaw sperm quality in different species including dogs [130], fish [131], bucks [132], and humans [133]. Similar results were observed when MYO supplementation was used during thawing procedures [134].
## Quercetin
Quercetin (QR) is a flavonoid derived from plants and vegetables with strong antioxidant properties owing to the presence of three OH• groups. QR has been used to treat male infertility issues by scavenging ROS, as QR-supplemented sperm showed low levels of H2O2 [135]. Johinke et al [135] reported that sperm medium supplemented with QR protected against OS in 15°C-stored rabbit sperm over 96 h period. Moreover, recent studies have confirmed the protective role of QR against oxidative damage during the freeze-thaw procedure, as QR-supplemented freezing extender induced significant quality improvement in buck [136], bull [137], dog [138], human [139], and stallion [140] sperm.
## Selenium
Selenium is an essential component of a specific group of proteins known as selenoproteins. It is believed that the antioxidant nature of selenium is related to its ability to enhance GSH function. Selenium plays a major role in spermatogenesis and sperm maturation [141] and can protect sperm from ROS-induced DNA damage. The deficiency of selenium leads to certain defects such as mid-piece abnormalities and abnormal sperm motility [142]. Incubation of sperm from asthenoteratozoospermic individuals in a selenium-supplemented medium enhanced the percentage of motile sperm, sperm viability, and MMP [143]. Furthermore, selenium supplementation decreased LPO and DNA fragmentation.
## Zinc
Zinc (Zn2+) is an essential trace element that stimulates total antioxidant status, it helps reduce the production of H2O2 and OH• radicals through the neutralization of redox-active transition metals, such as iron and copper [144]. A recent study showed that Zn2+ can decrease DNA damage caused by the addition of H2O2 [145]. Fertile men have a significantly higher level of Zn2+ in the seminal plasma than subfertile men [146].
Zn2+ has a protective effect on sperm structure, as its deficiency leads to different tail defects including hypertrophy and hyperplasia of the fibrous sheath, axonemal disruption, defects of the inner microtubular dynein arms, and abnormal or absent mid-piece [147]. An in-vitro study reported that 10 μg/mL is the optimum concentration of Zn2+ that positively affects total and progressive sperm motility along with a reduction in DNA fragmentation and LPO [148]. Berkovitz et al [149] reported that Zn2+ supplementation before sperm freezing had beneficial effects on sperm motility and viability. They also observed improved sperm motility in freeze-thaw and in semen samples refrozen after thawing [149].
## Sericin
Sericin is a glue-like structure with strong antioxidant properties. Silkworm covers the silk filament with sericin to connect filaments and provide protection against a harmful environment. Recently, sericin has been used for liquid storage and freezing of sperm in different species including buck [150], bull [151], rabbit [152], and stallion [153]. Sericin supplementation resulted in an improved freeze-thaw sperm quality through an improved antioxidant status and reduced ROS level.
## CONCLUSION
In the male reproductive system, ROS production is associated with different physiological and pathological conditions. ROS overproduction negatively influences fertility by disturbing the natural balance between ROS production and neutralization. OS-induced infertility appears to be a major challenge. Over the years, different antioxidant therapies have been utilized to address the infertility issues associated with OS either in the form of oral consumption or as in-vitro supplementation of different mediums. However, the outcomes of such studies appear to be controversial making it difficult to draw a conclusion. The reasons may include the low sample size used, differences in the concentrations used, and issues with the experimental designs. Moreover, the failure of antioxidants therapy can be attributed to the lack of real-time assessment methods and the inability to accurately quantify seminal OS.
To achieve better results, studies should be performed using larger sample sizes, classical pharmacological concentrations, and better-designed experiments. Furthermore, fertility can be recuperated using a combination of remedies (antioxidants, vitamins, trace minerals) along with knowledge of the underlying cause and severity of infertility. A new combination of antioxidants especially with polyphenols has shown a massive potential to treat infertility. Moreover, ROS levels in infertile individuals should always be correlated with the microenvironment of semen and reproduction outcomes (conception rate, quality of sperm functions, and embryo). This database will help in the development of reliable assays for the assessment of OS in reproductive cells and fluids.
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|
---
title: Conceptus-derived cytokines interleukin-1β and interferon-γ induce the expression
of acute phase protein serum amyloid A3 in endometrial epithelia at the time of
conceptus implantation in pigs
authors:
- Soohyung Lee
- Inkyun Yoo
- Yugyeong Cheon
- Hakhyun Ka
journal: Animal Bioscience
year: 2022
pmcid: PMC9996260
doi: 10.5713/ab.22.0334
license: CC BY 4.0
---
# Conceptus-derived cytokines interleukin-1β and interferon-γ induce the expression of acute phase protein serum amyloid A3 in endometrial epithelia at the time of conceptus implantation in pigs
## Abstract
### Objective
Serum amyloid A3 (SAA3), an acute phase response protein, plays important roles in opsonization, antimicrobial activity, chemotactic activity, and immunomodulation, but its expression, regulation, and function at the maternal–conceptus interface in pigs are not fully understood. Therefore, we determined the expression of SAA3 in the endometrium throughout the estrous cycle and at the maternal–conceptus interface during pregnancy.
### Methods
Endometrial tissues from pigs at various stages of the estrous cycle and pregnancy and with conceptuses derived from somatic cell nuclear transfer (SCNT), conceptus tissues during early pregnancy, and chorioallantoic tissues during mid- to late pregnancy were obtained and the expression of SAA3 was analyzed. The effects of the steroid hormones, interleukin-1β (IL1B), and interferon-γ (IFNG) on the expression of SAA3 were determined in endometrial explant cultures.
### Results
SAA3 was expressed in the endometrium during the estrous cycle and pregnancy, with the highest level on day 12 of pregnancy. The expression of SAA3 in the endometrium was significantly higher on day 12 of pregnancy than during the estrous cycle. Early-stage conceptuses and chorioallantoic tissues during mid to late pregnancy also expressed SAA3. The expression of SAA3 was primarily localized to luminal epithelial cells in the endometrium. In endometrial explant cultures, the expression of SAA3 was induced by increasing doses of IL1B and IFNG. Furthermore, the expression of SAA3 decreased significantly in the endometria of pigs carrying conceptuses derived from SCNT on day 12 of pregnancy.
### Conclusion
These results suggest that the expression of SAA3 in the endometrium during the implantation period increases in response to conceptus-derived IL1B and IFNG. The failure of those appropriate interactions between the implanting conceptus and the endometrium leads to dysregulation of endometrial SAA3 expression, which could result in pregnancy failure. In addition, SAA3 could be a specific endometrial epithelial marker for conceptus implantation in pigs.
## INTRODUCTION
Appropriate interactions between the maternal endometrium and the implanting conceptus are essential for the establishment and maintenance of pregnancy in mammals [1]. In pigs, conceptuses implanting in the uterine lumen between days 10 and 12 of pregnancy dramatically change their morphological shape, elongating from spherical to filamentous form to increase the surface area for attachment and to initiate noninvasive implantation into the endometrium [2]. During this period, conceptuses secrete a variety of signaling molecules, including estrogen, interleukin-1β2 (IL1B2), and prostaglandins (PGs), into the uterine lumen. Then, between days 13 and 18 of pregnancy, the conceptuses produce interferons (IFNs), particularly IFN-γ (IFNG) and IFN-δ [3]. In response to those conceptus signals, the endometrium undergoes morphological and functional changes to induce adhesion of the conceptus trophectoderm and support growth and differentiation of the implanting conceptus [2]. Specifically, the endometrium increases the expression of a variety of genes involved in conceptus attachment, immunity, and PG synthesis and transport [1–3]. Although many endometrial factors induced by conceptus signals at the time of conceptus implantation have been studied, the cellular and molecular functions of those factors at the maternal–conceptus interface in regulating the establishment and maintenance of pregnancy are not completely understood.
Previously, we identified differentially expressed genes (DEGs) in the endometrium between day 12 of the estrous cycle and pregnancy in search of factors involved in the maternal–conceptus interaction during conceptus implantation in pigs. Those DEGs include inhibitor of DNA binding 2, S100 calcium binding protein A7A (S100A7A), salivary lipocalin 1, serum amyloid A3 (SAA3), and transient receptor potential vanilloid type 6 [4]. Among them, the SAA family proteins, including SAA3, are well-known biomarkers for a variety of inflammatory diseases [5–7]. The functions of SAAs include opsonization and elimination of invading pathogens, recruitment of immune cells to inflammatory sites, metabolism and transport of cholesterol, degradation of extracellular matrix, development of many acute and chronic inflammatory diseases, and cancer metastasis [5–7]. In pigs, the SAA family is composed of four genes, SAA1, SAA2, SAA3, and SAA4, of which SAA1 is considered a pseudogene. SAA2, SAA3, and SAA4 are differentially induced during an acute phase response [8]. The expression of SAA2 and SAA4 is induced mainly in the liver, whereas the expression of SAA3, the major circulating isoform in pigs, is induced in various tissues, including the liver, lungs, and spleen [8,9]. The expression of the SAA family is induced by inflammatory cytokines, including tumor necrosis factor-α (TNF-α), IL1, IL6, and IFNG, along with vitamin A in humans and mice [10]. The expression of SAAs increases in the endometrium with endometritis in cattle and mares [11] and in endometrial carcinoma in humans [12]. SAA proteins affect trophoblast invasion by inducing the expression of matrix metalloproteinase 2 in humans [13]. Although the expression and function of the SAA family in the endometrium have been studied in humans and some domestic animals, the expression, function, and regulation of the SAA family in the endometrium during pregnancy in pigs have not been fully studied.
The somatic cell nuclear transfer (SCNT) technique has a broad range of potential applications, including rescue of endangered species, production of transgenic animals, cell transplantation, disease modeling, and regenerative medicine [14]. However, the SCNT technique has extremely low cloning efficiency and is associated with abnormalities of cloned animals, mainly due to incomplete reprogramming of the donor cell nucleus in SCNT-cloned embryos and inappropriate maternal–conceptus interactions in the uterus during pregnancy [15]. Some studies of SCNT-cloned embryos in pigs have shown altered expression of many endometrial and placental genes at the maternal–conceptus interface [16–19]. However, no one has yet determined whether SAAs are appropriately expressed in an endometrium with SCNT-derived embryos and fully explored the mechanisms of those maternal–conceptus interactions in the endometrium with SCNT-cloned embryos.
Therefore, to examine the role of the SAA family during pregnancy in pigs, we determined i) expression of SAA3, the major isoform induced in various tissues in pigs, in the endometrium throughout the estrous cycle and pregnancy; ii) localization of SAA3 mRNA at the maternal–conceptus interface; iii) expression of SAA3 in conceptus tissues during early pregnancy and in chorioallantoic tissues during mid to term pregnancy; iv) regulation of SAA3 expression by steroid hormones and IL1B and IFNG in endometrial tissues; and v) expression of SAA3 in the endometria of gilts with SCNT-derived conceptuses on day 12 of pregnancy.
## Animals and tissue preparation
All experimental procedures involving animals were conducted in accordance with the Guide for the Care and Use of Research Animals in Teaching and Research and approved by the Institutional Animal Care and Use Committee of Yonsei University (No. YWC-P120) and the National Institute of Animal Science (No. 2015-137). Sexually mature, crossbred female gilts of similar age (6 to 8 months) and weight (100 to 120 kg) were assigned randomly to either cyclic or pregnant status. Gilts assigned to the pregnant uterus status group were artificially inseminated with fresh boar semen at the onset of estrus (day 0) and 12 h later. The reproductive tracts of the gilts were obtained immediately after slaughter on day 0, 3, 6, 9, 12, 15, or 18 of the estrous cycle (21 days of cycle; days 0–3, estrus; days 3–6, metestrus; days 6–15, diestrus; days 15–0, proestrus) or on day 10, 12, 15, 30, 60, 90, or 114 of pregnancy ($$n = 3$$–6 gilts/d/status). Pregnancy was confirmed by the presence of apparently normal spherical to filamentous conceptuses in uterine flushings on days 12 and 15 and the presence of embryos and placenta on subsequent days. Uterine flushings were obtained by introducing and recovering 50 mL of phosphate buffered saline (PBS, pH 7.4) at tissue collection (25 mL/uterine horn). Chorioallantoic tissues were obtained from days 30, 60, 90, and 114 of pregnancy ($$n = 3$$–4 gilts/d). Endometrial tissues were also obtained from four gilts carrying embryos generated by SCNT on day 12 of pregnancy, as described previously [16,17]. Some ovoidal, tubular, and elongating conceptuses were recovered in uterine flushings from the uteri of pigs with SCNT-derived embryos. Endometrial tissues dissected free of myometrium and collected from the middle portion of each uterine horn, conceptus tissues obtained in uterine flushings and washed with PBS (pH 7.4), and chorioallantoic tissues were snap-frozen in liquid nitrogen and stored at −80°C prior to RNA extraction. For in situ hybridization, cross-sections of endometrium were fixed in $4\%$ paraformaldehyde in PBS (pH 7.4) for 24 h and then embedded in paraffin, as previously described [20].
## Endometrial explant cultures
To determine the effects of estradiol-17β (E2), progesterone (P4), and IL1B on the expression of SAA3 mRNA in the endometrium, endometrial explant tissues obtained from gilts on day 12 of the estrous cycle were cultured as previously described [20]. Endometrium dissected from the myometrium was placed into warm phenol red–free Dulbecco’s modified Eagle’s medium/F-12 culture medium (DMEM/F-12; Sigma, St. Louis, MO, USA) containing penicillin G (100 IU/mL) and streptomycin (0.1 mg/mL). The endometrium was minced with scalpel blades into small pieces (2 to 3 mm3), and aliquots of 500 mg were placed into T25 flasks with serum-free modified DMEM/F-12 containing 10 μg/mL insulin (Sigma, USA), 10 ng/mL transferrin (Sigma, USA), and 10 ng/mL hydrocortisone (Sigma, USA). Immediately after mincing, the endometrial explants were cultured in the presence of ethanol (control), E2 (10 ng/mL; Sigma, USA), P4 (30 ng/mL; Sigma, USA), P4+E2, P4+E2+ICI182,780 (ICI; an estrogen receptor antagonist; 200 ng/mL; Tocris Bioscience, Ellisville, MO, USA), or P4+E2+RU486 (RU; a progesterone receptor antagonist; 30 ng/mL; Sigma, USA) for 24 h with rocking in an atmosphere of $5\%$ CO2 in air at 37°C. To determine the effect of IL1B on the expression of endometrial SAA3, explant tissues were treated with 0, 1, 10, or 100 ng/mL IL1B (Sigma, USA) in the presence of both E2 (10 ng/mL) and P4 (30 ng/mL) for 24 h at 37°C. To determine the effect of IFNG on the expression of endometrial SAA3, endometrial explants were cultured with rocking in the presence of E2 (10 ng/mL), P4 (30 ng/mL), and IL1B (10 ng/mL) for 24 h in an atmosphere of $5\%$ CO2 in air at 37°C and then for an additional 24 h with 0, 1, 10, or 100 ng/mL of IFNG (R&D Systems, Minneapolis, MN, USA) in the presence of E2 (10 ng/mL), P4 (30 ng/mL), and IL1B (10 ng/mL), as previously described [21]. Explant tissues were then harvested, and total RNA was extracted for real-time reverse transcription–polymerase chain reaction (RT-PCR) to determine the expression level of SAA3 mRNA. These experiments were conducted using endometria from three gilts on day 12 of the estrous cycle, and treatments were performed in triplicate using tissues obtained from each of the three gilts.
## Total RNA extraction, RT-PCR, and cloning of porcine SAA3 cDNA
Total RNA was extracted from endometrial, conceptus, and chorioallantoic tissues using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s recommendations. The quantity of RNA was assessed spectrophotometrically, and the integrity of RNA was validated following electrophoresis in $1\%$ agarose gel. Four micrograms of total RNA were treated with DNase I (Promega, Madison, WI, USA) and reverse transcribed using SuperScript II Reverse Transcriptase (Invitrogen, USA) to obtain cDNA. The cDNA templates were then diluted 1:4 with sterile water and amplified by PCR using Taq polymerase (Takara Bio, Shiga, Japan). The final PCR reaction volume of 50 μL included 3 μL of cDNA, 5 μL of 10× PCR buffer, 4 μL of dNTP mix (2.5 mM), 1 μL of each primer (20 μM), 0.3 μL of Taq polymerase (Takara Bio, Japan), and 36.7 μL of H2O. Initial denaturation was performed at 94°C for 5 min; 40 cycles of amplification were carried out at 94°C for 30 s, 60°C for 30 s, and 72°C for 30 s; and final extension was conducted at 72°C for 10 min. The PCR products were separated on $2\%$ agarose gels and visualized by ethidium bromide staining. The identity of each amplified PCR product was verified by sequence analysis after being cloned into the pCRII vector (Invitrogen, USA).
## Quantitative real-time reverse transcription–polymerase chain reaction
The level of expression of SAA3 cDNA in endometrial and chorioallantoic tissues was analyzed by real-time RT-PCR using an Applied Biosystems StepOnePlus system (Applied Biosystems, Foster City, CA, USA) and the SYBR Green method. Power SYBR Green PCR Master Mix (Applied Biosystems, USA) was used for the PCR reactions. The final reaction volume of 20 μL included 2 μL of cDNA, 10 μL of 2X Master mix, 2 μL of each primer (2 μM), and 4 μL of ddH2O. PCR was performed with an initial incubation at 95°C for 10 min, followed by 40 cycles of 15 s at 95°C and 30 s at 60°C. The sequences of the primer pairs are listed in Table 1. The results are reported as expression relative to that detected on day 12 of the estrous cycle or that detected in control explant tissues after normalization of the transcript amount to the geometric mean of the endogenous ribosomal protein L7 (RPL7), ubiquitin B (UBB), and TATA binding protein (TBP) controls by the 2–ΔΔCT method, as previously described [22].
## Nonradioactive in situ hybridization
Nonradioactive in situ hybridization was performed to determine the localization of SAA3 expression in the uterine endometrium, as previously described with some modifications [23]. Sections (5 μm thick) were rehydrated through successive baths of xylene, $100\%$ ethanol, $95\%$ ethanol, diethylpyrocarbonate (DEPC)-treated water, and DEPC-treated PBS. Tissue sections were boiled in citrate buffer (pH 6.0) for 10 min. After being washed in DEPC-treated PBS, they were digested using 5 μg/mL Proteinase K (Sigma, USA) in TE (100 mM Tris-HCl, 50 mM ethylenediaminetetraacetic acid, pH 7.5) at 37°C. After post-fixation in $4\%$ paraformaldehyde, tissue sections were incubated twice for 15 min each in PBS containing $0.1\%$ active DEPC and then equilibrated for 15 min in 5× saline sodium citrate (SSC) buffer. The sections were prehybridized for 2 h at 68°C in a hybridization mix ($50\%$ formamide, 5× SSC, 500 μg/mL herring sperm DNA, 250 μg/mL yeast tRNA). Sense and antisense riboprobes for each gene were generated using partial cDNAs cloned into pCRII vectors by linearization with appropriate restriction enzymes, and they were labeled with digoxigenin (DIG)-UTP using a DIG RNA labeling kit (Roche, Indianapolis, IN, USA). The probes were denatured for 5 min at 80°C and added to the hybridization mix. The hybridization reaction was carried out overnight at 68°C. Prehybridization and hybridization reactions were performed in a box saturated with a 5× SSC $50\%$ formamide solution to prevent evaporation, and no coverslips were used. After hybridization, sections were washed for 30 min in 2× SSC at room temperature, 1 h in 2× SSC at 65°C, and 1 h in 0.1× SSC at 65°C. Probes bound to the section were detected immunologically using sheep anti-DIG Fab fragments covalently coupled to alkaline phosphatase and nitro blue tetrazolium chloride/5-bromo-4-chloro-3-indolyl phosphate (toluidine salt) as a chromogenic substrate, according to the manufacturer’s protocol (Roche, USA).
## Statistical analysis
Data from real-time RT-PCR for SAA3 expression were subjected to analysis of variance (ANOVA) using the general linear model procedures of SAS (Cary, NC, USA). As sources of variation, the model included day, pregnancy status (cyclic or pregnant, days 12 and 15 post-estrus), and their interactions to evaluate the steady-state level of SAA3 mRNA. Data from real-time RT-PCR were analyzed by least squares regression analysis to assess the effects of days of the estrous cycle (days 0, 3, 6, 9, 12, 15, and 18) and of pregnancy (days 10, 12, 15, 30, 60, 90, and 114) in endometrial tissues, the effects of day of pregnancy (days 30, 60, 90, and 114) in chorioallantoic tissues, and the effects of IL1B and IFNG doses in explant tissues on the expression of SAA3. Preplanned orthogonal contrasts (control vs E2; control vs P4; P4 vs P4+E2; P4+E2 vs P4+E2+ICI; and P4+E2 vs P4+E2+RU) were used to test the effects of hormone treatments, and one-way ANOVA followed by the Tukey post-test was used to evaluate the effects of IL1B and IFNG on endometrial SAA3 expression in the explant cultures. Data from real-time RT-PCR analysis for comparison of SAA3 mRNA levels in the endometrium with SCNT and non-SCNT conceptuses were subjected to the Student’s T-test. Prior to the analyses, all data were tested for normality and homogeneity of variance, and log transformation was performed when necessary. Data are presented as mean with standard error of the mean. p-values less than 0.05 were considered significant, and p-values of 0.05 to 0.10 were considered to indicate a trend toward significance.
## Expression of SAA3 in the endometrium during the estrous cycle and pregnancy
To determine whether the expression of SAA3 mRNA in the endometrium changed during the estrous cycle and pregnancy in pigs, we examined the relative abundance of SAA3 mRNA in the endometrium using real-time RT-PCR (Figure 1). The expression of SAA3 in the endometrium changed stage-specifically during the estrous cycle (quadratic effect of day, $p \leq 0.01$) and tended to change during pregnancy, with the highest level on day 12 of pregnancy (linear effect of day, $$p \leq 0.095$$). On days 12 and 15 post-estrus, SAA3 expression was affected by pregnancy status ($p \leq 0.001$), and the expression of SAA3 was greater on day 12 and day 15 of pregnancy than on day 12 ($p \leq 0.001$) and day 15 ($p \leq 0.001$) of the estrous cycle, respectively.
## Expression of SAA3 in conceptuses on days 12 and 15 of pregnancy and chorioallantoic tissues during the later stages of pregnancy
To determine whether conceptuses expressed SAA3 mRNA during early pregnancy, we performed RT-PCR using cDNA from conceptuses on days 12 and 15 of pregnancy. We found that SAA3 mRNA was expressed in conceptuses on day 15 of pregnancy, as well as in the liver that was used as a positive control tissue (Figure 2A). Real-time RT-PCR analysis showed that the expression of SAA3 in chorioallantoic tissues during mid to term pregnancy (days 30 to 114) increased (quadratic effect of day, $p \leq 0.01$) (Figure 2B).
## Localization of SAA3 mRNA in the endometrium on days 12 and 15 of the estrous cycle and pregnancy
Localization of SAA3 mRNA was determined by in situ hybridization in the endometrium on days 12 and 15 of the estrous cycle and pregnancy and in conceptuses on days 12 and 15 of pregnancy in pigs (Figure 3). The expression of SAA3 mRNA was localized predominantly to luminal epithelial (LE) cells in the endometrium, with strong signal intensity on day 12 of pregnancy. SAA3 mRNA was also detected in the ovarian tissue that was used as a positive control [24] (Figure 3A). SAA3 mRNA was barely detectable in conceptus tissues on day 12 of pregnancy and was readily detectable in trophectoderm and endoderm cells on day 15 of pregnancy (Figure 3B).
## Effects of steroid hormones, E2 and P4, and cytokines IL1B and IFNG on SAA3 mRNA expression in endometrial tissues
Implanting conceptuses secrete E2 and IL1B into the uterine lumen, with the greatest abundance on day 12 of pregnancy, along with IFNG on day 15 of pregnancy. Furthermore, P4 from the corpus luteum regulates the expression of many endometrial genes in pigs [1]. Therefore, we determined the effects of E2, P4, IL1B, and IFNG on the expression of SAA3 mRNA in endometrial explant tissues. Endometrial SAA3 mRNA levels were not changed by E2 or P4 (Figure 4A). However, treatment with IL1B and IFNG increased the expression of SAA3 in a dose-dependent manner in endometrial tissues (linear effect of dose for IL1B, $p \leq 0.01$; linear effect of dose for IFNG, $p \leq 0.05$) (Figure 4B and 4C).
## Expression and localization of SAA3 mRNA in endometria carrying conceptuses derived from SCNT or natural mating
Our real-time RT-PCR analysis showed that the expression of SAA3 mRNA in the endometrial tissues of gilts carrying conceptuses derived from SCNT was lower than that in gilts carrying conceptuses from natural mating on day 12 of pregnancy ($p \leq 0.05$) (Figure 5A). In situ hybridization analysis showed that SAA3 expression was readily detectable in the LE cells of endometria with conceptuses from natural mating, whereas SAA3 expression was rarely detected in the LE cells of endometria with conceptuses derived from SCNT (Figure 5B).
## DISCUSSION
The novel findings of this study in pigs are: i) SAA3 is expressed in the endometrium during the estrous cycle and pregnancy in a status- and stage-specific manner; ii) conceptus tissues during early pregnancy and chorioallantoic tissues from day 30 to term pregnancy express SAA3; iii) SAA3 expression is primarily in the endometrial LE on day 12 of pregnancy and in conceptuses on day 15 of pregnancy; iv) IL1B and IFNG induce the expression of SAA3 in endometrial explant tissues; and v) the expression of SAA3 in the endometria of gilts with SCNT-derived conceptuses was lower than that in gilts with conceptuses from natural mating on day 12 of pregnancy.
SAAs are acute phase proteins, which means that the levels in plasma increase rapidly in response to acute phase responses such as inflammation, infection, and trauma [10]. Because excessive SAA levels lead to SAA deposition, which causes amyloidosis under recurrent or chronic inflammatory conditions, SAA protein is used as a blood biomarker to evaluate and monitor disease severity in patients with amyloidosis or inflammatory rheumatic disease [10]. Several studies also suggest that SAA is associated with pregnancy complications such as infections, endometrial cancer, and preeclampsia. In humans, blood level of SAA does not change during normal pregnancy, but does increase in pregnancies affected by infection or preeclampsia and in patients with endometrial endometrioid carcinoma [25]. In goats, SAA3 mRNA is expressed in the uterus, blood SAA protein level increases during the periparturient period of pregnancy, and exclusive SAA3 deposition in uterine caruncular stroma causes amyloidosis, leading to fetal death [26,27]. Our results in this study clearly show that SAA3 is expressed in the endometrium at increased level during the implantation period in pigs. Although SAAs are valuable biomarkers for pregnancy complications such as infections, preeclampsia, and endometrioid carcinoma in some species, SAA3 is expressed in the uterus during normal pregnancy in pigs. It is thought that the maternal–conceptus interface is mainly regulated by a pro-inflammatory condition during the implantation period [28]. Thus, the increased SAA3 expression we observed reflects the pro-inflammatory condition between the endometrium and the conceptus during this period in pigs. In addition, SAA3 could be a good marker for conceptus implantation in pigs because SAA3 expression is uniquely increased at that time.
SAA production is induced by inflammatory cytokines such as IL1B, IL6, IFNG, and TNF-α and microbial components such as lipopolysaccharide in various cell types [10]. Because the expression of SAA3 in the endometrium was greatest on day 12 during pregnancy and implanting porcine conceptuses secrete estrogen and cytokines, IL1B and IFNG, we postulated that the conceptus-derived estrogen and cytokines would increase endometrial SAA3 expression. Estrogen of conceptus origin is responsible for inducing the expression of many endometrial genes, such as aldo-keto reductase 1B1, fibroblast growth factor 7, IFN-alpha and beta receptor subunit 2, IFNG receptor beta subunit, lysophosphatidic acid receptor 3, S100A7A, S100A8, and secreted phosphoprotein 1 [1,3,29]. IL1B and IFNG also increase the expression of many endometrial genes involved in PG synthesis and transport and immunity, respectively [1]. Indeed, treatment of endometrial explant tissues with IL1B and IFNG upregulated the expression of SAA3. However, estrogen did not have any effect on the expression of SAA3 in endometrial explants, suggesting that conceptus estrogen is not involved in the expression of SAA3 in the endometrium. Overall, these results suggest that conceptus-derived IL1B and IFNG signals could be critical factors for the induction of endometrial SAA3 expression at the time of conceptus implantation.
The results of this study show that the expression of SAA3 is epithelial-specific in the endometrium, especially in LE cells on day 12 of pregnancy. This coincides with previous reports that SAA expression is primarily localized to epithelial cells in various human tissues, including breast, intestine, lung, kidney, and placenta [30], and to endometrial epithelial cells in cows [31]. In addition, we detected SAA3 expression in conceptus trophectoderm and endoderm cells during early pregnancy and chorioallantoic tissues during mid to late pregnancy, with increasing level at term. SAAs are an archetypal component of the acute phase response to infection, where they are involved in opsonizing and eliminating invading pathogens and recruiting immune cells such as macrophages and neutrophils to the site of infection by triggering the release of pro-inflammatory cytokines [10]. In mice and humans, SAAs synthesized from intestinal epithelial cells act as opsonins that activate phagocytes by binding to outer membrane protein A of bacteria, and they also show antimicrobial activity [7]. Our previous studies have shown that the maternal endometrium and conceptus tissues during pregnancy in pigs produce a variety of antimicrobial peptides (AMPs), including cathelicidins [32], S100A7A [29], S100A8, S100A9, S100A12 [33], β-defensin family, peptidase inhibitor 3, and secretory leukocyte protease inhibitor (Lee and Ka, unpublished data). Therefore, our results in this study suggest that SAA3 expressed at the maternal–conceptus interface acts as a component of endometrial and choriallantoic AMPs that protect implantation sites from possible contamination by microorganisms and maintain fertility during pregnancy.
The SCNT technique is a valuable tool for producing genetically valuable or transgenic animals for basic and biomedical use in animal biotechnology, but the high rates of embryonic mortality and pregnancy failure lead to low efficiency in generating viable cloned animals [14,15]. Although the exact cause of low efficiency in SCNT cloning is being investigated, it is thought to be associated with abnormal embryonic reprogramming and inappropriate maternal–conceptus interactions at the maternal endometrium [14,15]. Our previous studies have shown that in endometria with SCNT-derived conceptuses, many genes are abnormally expressed during early pregnancy [16,17,33]. Our results in this study show that SAA3 expression was significantly lower in endometria with SCNT-derived conceptuses than in those with conceptuses from natural mating on day 12 of pregnancy. Because SAA3 expression was upregulated by IL1B, which is secreted by the implanting porcine conceptus on day 12 of pregnancy, the decreased endometrial expression of SAA3 is likely caused by abnormal IL1B secretion by SCNT-derived conceptuses. That dysregulated SAA3 expression could be involved in increased embryonic death in the endometrium among SCNT-derived conceptuses because SAA3 has important antimicrobial functions, such as immune cell recruitment and regulation of the inflammatory response. Overall, the interactions between the conceptus and the maternal endometrium that are critical for the establishment and maintenance of pregnancy do not occur appropriately in pigs with SCNT-derived conceptuses, and the expression of SAA3 could be a good biomarker for appropriate implantation and maternal–conceptus interactions during early pregnancy in pigs.
In addition to opsonization and antimicrobial activity, SAA3 induces chemotactic activity, facilitates cholesterol efflux and inflammasome activation, and induces inflammatory or anti-inflammatory cytokines through a variety of specific receptors, including receptor for advanced glycation end products (AGER), formyl peptide receptor 2, P2X purinergic receptor 7 (P2RX7), scavenger receptor class B type 1, toll-like receptor 2 (TLR2), and TLR4 [5,10]. AGER, P2RX7, TLR2, and TLR4 are expressed at the maternal–conceptus interface in pigs [33] (Cheon and Ka, unpublished data). Therefore, it is likely that SAA3 has pleiotropic functions via specific receptors expressed in the endometrium during the estrous cycle and in the endometrium and chorioallantoic tissues at the maternal–conceptus interface during pregnancy. Especially, the increased expression of SAA3 in chorioallantoic tissues at term may be related to the preparation of parturition by regulating cytokine production.
In conclusion, this study in pigs demonstrated that SAA3 is expressed at the maternal–conceptus interface in a cell type– and pregnancy stage–specific manner and that the expression of endometrial SAA3 is induced by conceptus-derived IL1B and IFNG. In addition, the level of SAA3 expression is diminished in the endometrium with SCNT-derived conceptuses compared to that with natural-mating conceptuses at the time of implantation. Thus, the expression of SAA3 could be a unique endometrial epithelial marker of conceptus implantation in pigs. Although the exact functions of SAA3 during pregnancy need further investigation, these results indicate that the endometrial expression of SAA3, which occurs in response to conceptus signals, could play important roles in regulating endometrial epithelial and placental functions and innate immune responses required for the establishment and maintenance of pregnancy in pigs.
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|
---
title: Improving productivity in rabbits by using some natural feed additives under
hot environmental conditions — A review
authors:
- Magdy Abdelsalam
- Moataz Fathi
journal: Animal Bioscience
year: 2023
pmcid: PMC9996268
doi: 10.5713/ab.22.0354
license: CC BY 4.0
---
# Improving productivity in rabbits by using some natural feed additives under hot environmental conditions — A review
## Abstract
Heat stress is a major challenge to animal production in tropical and subtropical climates. Rabbits suffer from heat stress more than farm animals because they have few sweat glands, and their bodies are covered with thick fur. Intensive farming relies on antibiotics as antimicrobials or growth promoters to increase animals’ productivity and health. However, the European Union and many countries have banned or restricted the use of antibiotics in animal feed for human health concerns. Several studies have found that replacing antibiotics in rabbit feed with natural plants or feed additives increases productivity and improves immune capacity, especially under heat stress conditions. Growth performance, immune response, gut microflora, and carcass yield may be increased in rabbits fed a diet supplemented with some natural plants and/or propolis. In this review article, we discuss and summarize the effects of some herbs and plant extracts as alternative feed additives on rabbit productivity, especially for those raised under hot ambient temperatures.
## INTRODUCTION
Global warming is the main problem facing the farm animal industry. *In* general, high environmental temperatures have a negative impact on animal performance. Rabbits are more sensitive to heat stress than most farm animal species because they have few sweat glands, and their body is covered with a thick fur [1–3]. Growth performance, reproductive aspects, immunity, and health condition dramatically deteriorate in rabbits raised under heat stress [3–6]. Additionally, heat stress has a negative effect on intestinal mucosa and microbiota in rabbits [5]. Inclusion of antibiotics in animal feeds has already been banned in many countries for their harmful effect on human health and food safety. Replacement of antibiotics with natural feed additives in rabbit feeding is a main concern for organic livestock farming. Many plants and their extracts as feed additives have been used in animal nutrition such as Moringa, Yucca and Eucalyptus. Also, propolis, as a natural resinous product of bees is widely used in animal and poultry feeds. There are many important bioactive and antimicrobial compounds found in natural feed additives. They have a positive effect on growth performance [7–10], feed efficiency [11–14] immunological status [15–17], reproduction [18–21] and gut microflora [17,22–24] in rabbits. Additionally, incorporating some natural feed additives into rabbit’s diets can help to mitigate the adverse effects of heat stress [25–29]. In the present review, the advantage of using some natural feed additives in rabbit production under high environmental conditions has been described and reviewed.
## MORINGA
The genus Moringa belongs to the Moringaceae family and includes 13 species. The Moringa tree grows in semiarid, arid, tropical, and subtropical areas of Africa, Central and South America, and Southwest and Southeast Asia. It prefers low water and neutral to slightly acidic, sandy, loamy, or sandy-loam soil [10,30,31]. The moringa name is derived from the Tamil word “murungai”, which means “twisted pod,” i.e., young fruit. Moringa oleifera is one of the moringa species. It is the most widely cultivated species. It has fast growth and medical effects, and the leaves have high protein, vitamin A, C, and E, mineral content, as well as carotenoids, flavonoids, polyphenols, and natural antioxidants [5,8,32,33]. The name is derived from the Latin words oleum “oil” and ferre “to bear”. Moringa oleifera, commonly known as the drumstick-tree, or horse radish tree, or miracle tree, or mother’s best friend [34–36].
## Production performance
The present section deals with the effects of M. oleifera (MO) leaf supplementation on productive performance, carcass traits, biochemical parameters, and immunity of rabbits. Moringa oleifera has a high concentration of quality protein, amino acids, vitamin C and E, mineral content, and flavonoids, which can affect rabbit productivity [37,38]. A significant improvement in final body weight, growth performance, and feed conversion ratio (FCR) in New Zealand White (NZW) growing rabbits fed a ration supplemented with $5.5\%$, $11.0\%$, and $16.5\%$ MO dry leaves instead of $10\%$, $20\%$, and $30\%$ of the protein content of the basal diet [39]. Additionally, El-Desoky et al [19] found significant improvement in body weight, daily gain, and FCR of NZW bunnies fed moringa leaf meal at the end of the fattening period (5 to 13 weeks of age) in favor of the high-level group ($6\%$) followed by the low-level group ($3\%$) and then by the control group. The daily feed intake decreased with using the three moringa leaf meals, with insignificant differences. Similar findings of feed intake and FCR were reported by El-Badawi et al [23], who supplemented NZW rabbits with moringa dry leaves at two levels ($0.15\%$ and $0.30\%$). These results may be due to its important role as a natural growth promoter and having a bacterial probiotic effect resulting from high content of phytochemical compounds. Growing rabbits that received a basal diet supplemented with 30, 60, and 90 mL of aqueous moringa oleifera leaf extract in their drinking water had a significant improvement in final body weight, daily gain, and FCR after an 8-wk-trial [40,41]. A slight increase in the body weight of NZW rabbits fed with moringa oleifera leaf meal (MOLM) at levels of $0\%$, $3\%$, and $6\%$ was noticed from 6 to 16 weeks of age [42]. Khalil et al [43] reported that the NZW growing rabbits supplemented with 200 mg MO leaf/kg diet showed the highest body weight and daily gain. Moreover, FCR improved as the supplementation level increased. El-Adawy et al [13] found that there was improvement in body weight, daily gain, daily feed intake, and FCR of NZW growing rabbits at 12 weeks of age when supplemented with dried moringa oleifera leaf protein ($1.5\%$ of the diet), and the lowest results were found for control rabbits. Selim et al [8] reported that production performance (body weight gain, and FCR) significantly improved for NZW growing rabbits supplemented with moringa oleifera leaves up to 1.5 g/kg diets. These results may be due to the bioactive compounds in moringa leaves. According to Salem et al [10], the productive performance of Alexandria line rabbits supplemented with MO significantly improved, and the highest weights and gains were found in those supplemented with $20\%$ of MO. They attribute their findings to high levels of amino acids, vitamins (A, B, C, D, K), and macro elements in MO. Similar findings were reported by Jiwuba and Ogbuewu [44]. They found a significant increase in body weight and daily weight gain in rabbits given a diet containing $20\%$ or $30\%$ MOLM. In the hot weather of Saudi Arabia, Aljohani and Abduljawad [45] reported increasing body weight, daily gain, and average daily dry matter intake in NZW rabbits given a diet supplemented with dried MO leaves (500 or 1,000 mg/kg diet). In addition, FCR significantly improved with the increasing levels of MO. These improvements may be because of the high content of amino acids, minerals, and antimicrobial agents in the leaves of moringa. In Nigeria, Abubakar et al [46] reported that the daily gain of rabbits increased as the dietary level of MOLM increased. They added that weaned rabbits could utilize MOLM at up to $45\%$ without harmful effects. On the other hand, Olatunji et al [33] found that there was no significant difference in the final body weight of supplemented growing rabbits with moringa leaf meal at levels of $5\%$, $10\%$, $15\%$, and $20\%$. In West Africa, Djakalia et al [47] reported that the heaviest body weight and the best growth rate were recorded in rabbits fed a diet supplemented with moringa. In China, Sun et al [48] found an improvement in weight gain and FCR of NZW rabbits given a diet containing $20\%$ rather than $30\%$ of MO leaves. They attributed this result to the high content of phytochemical compounds in the leaves. Yasoob et al [9] studied the effect of the dietary inclusion of MO leaves powder on the productive performance of NZW rabbits under heat stress (35°C for 7 h daily). They found a significant increase in average daily gain, daily feed intake, and FCR in the rabbit supplemented groups.
On the other hand, Badawi et al [36] and Gomaa et al [49] did not detect an improvement in body gain or final body weight of NZW rabbits fed different levels of MOLM. Likewise, El-Badawi et al [5] reported that NZW growing rabbits fed on moringa oleifera did not show a positive result in daily feed intake, average daily gain, and FCR. Under hot environmental conditions, there was no improvement in the daily gain of mixed rabbit breeds supplemented with MOLM at levels of $25\%$ and $50\%$ as a replacement of protein [50]. Similarly, Abiodun and Olubisi [51] found a significant reduction in the weight gain of crossbred rabbit bucks (NZW×Chinchilla) fed a supplemented diet with MOLM. They reported that this negative result may be due to of tannin and saponin content in MOLM. Additionally, in crosses of New Zealand, California, and English rabbit breeds, Hernández-Fuentes et al [52] indicated that body weight and FCR were similar for all treatments ($0\%$, $10\%$, $20\%$, and $30\%$ MOLM). Moreover, the daily gain decreased throughout the experimental period in treated rabbits compared with the non-supplemented group. Bakr et al [42] reported that there was no effect of dietary supplementation of MOLM on daily feed intake or FCR in NZW growing rabbits kept under Egyptian conditions.
## Carcass traits
Numerous studies found that including moringa olifera in the diet or drinking water had a positive effect on carcass traits [39,41]. Nuhu [38] found that there were numerical increases in the slaughter weight, hot carcass weight, and dressing percentage in mixed rabbit breeds given moringa olifera leaf meal at levels of $5\%$, $10\%$, $15\%$, and $20\%$. In addition, the results show that the meat quality significantly improved in groups supplemented with MOLM owing to increased protein content and lower fat level in the meat. El-Badawi et al [23] reported that the carcass traits and meat yield were higher in rabbits fed $0.15\%$ or $0.30\%$ moringa leaf powder than in the control group. Abubakar et al [46] reported that the carcass weight increased with increasing morigna levels ($15\%$, $30\%$, and $45\%$) in supplemented diet of weaned rabbits. However, dressing percentage, organ weight, and abdominal fat were not affected by moringa supplementation levels. Omara et al [39] noticed that there was an improvement in weights of slaughter, carcass, head, heart, tests, and dressing percentage, while abdominal fat weight decreased with increasing levels of moringa oleifera supplementation. El-Desoky et al [19] showed that the carcass weight, dressing percentage, and back quarters percentage were significantly higher in rabbits fed a supplemented diet with moringa. Conversely, Gomaa et al [49] found that there were no differences in carcass characteristics among all the treated groups of NZW growing rabbits aged 12 weeks. Additionally, Mos et al [53] reported that the MOLM failed to show a difference in carcass traits of rabbits. Jiwuba and Ogbuewu [44] suggested that rabbits fed MOLM had higher slaughter weight, carcass weight, dressing percentage, hind limb %, liver %, and kidney %. In addition, rabbits fed $20\%$ MOLM showed the highest percentages of loin, fore limb, and thoracic cage. They attributed the increase in cuts to the heavier weight of rabbits as a result of the biological value of MOLM and added that the higher values of liver and kidney are due to the increase in physiological and metabolic activity that removes the toxicity. Selim [8] found that the NZW supplemented with MO leaves showed an increase in dressing percentage, spleen percentage, and intestinal length. In addition, the abdominal fat content decreased as MO leaves increased in diets. They attributed the increment in dressing percentage to the heavier weights of rabbits at slaughter, while the improvement in spleen percentage was attributed to the improving immunity of treated rabbits. Sun et al [48] reported that MO addition had no significant effect on the yield of the carcass. In the hot season of Saudi Arabia, Aljohani and Abduljawad [45] reported that there was a significant improvement in the percentage of carcass, liver, and total edible parts of NZW rabbits supplemented with dried moringa olefira leaves. They attributed these results to the improvement in the metabolism and immunity of treated rabbits. Baker et al [42] reported a significant increase in the empty carcass and total edible part weights of NZW growing rabbits supplemented with $6\%$ MOLM. There were no differences in dressing percentage or weight of the head and edible giblets among treatment groups [42].
Body weight at slaughter and carcass traits of NZW rabbits did not differ among groups fed 0, low, and high levels of eucalyptus [69]. The same trend was found for the percentage of heart, kidney, and liver, while inedible parts and full stomach showed a significant increase in groups fed with eucalyptus leaves. On the other hand, Fathi et al [17] reported that the highest dressing percentages, fore part (%) and mid part (%), were found in growing rabbits supplemented with $0.1\%$ eucalyptus, while those supplemented with $0.2\%$ eucalyptus had the lowest ones. They attributed their results to the high tannin level of $0.2\%$. The opposite result was found for the percent of the hind part, where rabbits fed 0.2 percent eucalyptus had the highest percent, followed by those supplemented with 0.1 percent and then by the control group.
Unfortunately, only a few references that deal with the carcass characteristics of rabbits supplemented with Yucca can be found. Abaza and El-Said [75] results of slaughtering rabbits at the end of the experiment indicate that the dressing percentage and abdominal fat significantly decreased as the level of yucca increased in the diet of rabbits. Ashour et al [11] reported that yucca supplementation had a significant effect on carcass yield, dressing percentage and relative weights of kidney, skin, and legs, where the highest results were found in the carcass of rabbits supplemented with the highest level of yucca (600 mg/kg diet). In addition, the treatments failed to show an effect on the relative weights of the heart, spleen, liver, and lung.
Many studies found that propolis supplementation had no effect on the carcass traits of fattening rabbits [116,120,122]. Attia et al [109] found that the NZW rabbits supplemented with propolis showed a significantly higher dressing percentage than the control group under summer conditions. The carcass of growing rabbits supplemented by propolis failed to show a significant effect on carcass traits, but there was a higher relative weight of testes and lower body fat weight [96]. Hashem et al [116] reported that the propolis administration did not affect most of the carcass traits except the relative weights of lung and abdominal fat, where the relative weights of lung increased while the relative weight of abdominal fat decreased. *In* general, they reported that the propolis administration (150 and 300 mg/kg diet) did not affect the relative weight of internal organs in growing rabbits. In Spain, Oliveira et al [123] did not find any effect for adding a green propolis at 0, 50, 100, 150, and 200 mg/kg body weight on the entire carcass traits. Waly et al [14] found that supplementing NZW rabbits with *Egyptian propolis* increased the percentage of edible parts, the dressing percentage, and decreased internal fat percentages. They added that the organ percentages, such as liver, kidney, and heart, failed to show any effect in supplemented rabbits.
## Hematological parameters and blood biochemistry constituents
Many studies determined the change in the blood profile of rabbits due to natural plant supplementation. Aljohani and Abduljawad [45] reported that the level of glucose, urea, and total cholesterol significantly decreased as the dried moringa oleifera leaves level increased in weaning rabbits of NZW. While, aspartate transaminase (AST), alanine transaminase (ALT), alkaline phosphatase (ALP), red blood cell (RBC), white blood cell (WBC), and platelet (PLT) were significantly higher in the treated groups. They added that supplementation with dried moringa olefira leaves up to 1,000 mg/kg diet may improve biochemical parameters and blood constituents, which reflect on the health of rabbits. Sun et al [48] found that albumin, low-density lipoprotein (LDL) cholesterol, T3 and T4 values, and the activity of superoxide dismutase (SOD) and catalase (CAT) were significantly affected by supplemented moringa oleifera leaves (MOL) in a rabbit’s diet. Khalil et al [43] pointed out that moringa oleifera leaf powder (MOLP) has no harmful effects on protein metabolites (total protein, albumin and globulin), kidney (creatinine and urea), and liver (AST and ALT) functions. They reported that MOLP supplementation at level 200 mg/kg diet had positive effect on hematological parameters and lipid profile.
On the other hand, Debola et al [54] found that packed cell volume (PCV), RBC, haemoglobin, and WBC increased as the moringa-based diet increased, with insignificant differences in supplemented cross-bred rabbits with moringamat $25\%$ and $50\%$ levels. The rabbits fed on a $50\%$ moringa-based diet had higher neutrophils. Lower lympocyte values were found in rabbits fed a $25\%$ moringa-based diet. The lowest values of monocytes, eosinophils, and basophils were found in rabbits fed $50\%$. Rabbits fed a $50\%$ moringa based diet showed the lowest values of total protein, albumin, and total cholesterol. They concluded that moringa leaf can be added up to a $50\%$ level without any harmful effect on hematological or serum biochemistry. The physiological status improved because of the low total cholesterol. Salem et al [10] pointed out that rabbits supplemented with $20\%$ moringa leaves had the highest hemoglobin, Hb, and PCV because MOL contains vitamins, iron, and protein. They found low values of cholesterol, LDL and malondialdehyde (MDA) and high values of WBCs, globulin, high-density lipoprotein (HDL), and total antioxidants in the blood of rabbits fed MOL.
The results of Fathi et al [17] on two breeds of rabbit (V-Line and Jabali) pointed out that rabbit supplemented with eucalyptus leave with a level of $0.1\%$ had the highest hemoglobin (HGB), RBCs, and haematocrit without significant difference. The PLTs had the highest value for the rabbits fed $0.20\%$ eucalyptus. On the other hand, Bello [72] in quails and Liu et al [71] in rabbits found that supplementing the diet with eucalyptus significantly increased HGB and RBC. They attributed their results to the iron, beta-carotene, and vitamin C content in eucalyptus. There were different results concerning total protein, serum globulin, and serum albumin. Ahmed et al [69] reported that supplementing diets with eucalyptus showed a significant increase in albumin values while globulin had significantly lower values. Fathi et al [17] found that total protein and serum globulin significantly increased in rabbits fed a diet supplemented with $0.20\%$ eucalyptus. They added that blood cholesterol and triglycerides concentrations were not affected by supplementing with eucalyptus. Ahmed et al [69] found that a diet supplemented with eucalyptus resulted in increasing values of AST, ALT, and alkaline phosphates. They added that adding eucalyptus to the diets of rabbits is considered safe until 13.5 percent. In addition, Waly et al [71] found that there was no effect of supplementation with eucalyptus on the AST and ALT activities in growing rabbits. Fathi et al [17] reported that total antioxidant capacity improved as eucalyptus levels increased in the diets of V-line and Jabali rabbits. This development may be due to the presence of polyphenols, 1,8-cineole, and tannins in eucalyptus, which play a vital role in increasing the antioxidant activity Liu et al [73] on rabbit, and Chen et al [74] on laying hen).
Amber et al [85] showed that yucca extract decreases the level of ammonia and urea in the blood, which has an impact on the improving health of rabbits. Abaza and El-Said [75] reported that the best levels of globulin, total protein, RBCs, WBCs, and PCV were found in the blood of rabbits supplemented with 100 mg/kg diet Yucca Schidigera powder. There was an insignificant effect of the different treatments on the values of total protein, albumin, and Hb concentration. In addition, they found that urea in the blood decreased as the level of yucca supplementation increased, and they attributed this result to saponins in the yucca and ammonia in the intestines. Ashour et al [11] pointed out that yucca supplementation with levels of 200, 400, and 600 mg/kg diet failed to show a significant effect on total protein, LDL-cholesterol, AST, and ALT, while the treatment had a significant effect on ammonia, triglycerides, total cholesterol, and HDL-cholesterol, where these levels decreased as yucca supplementation increased.
## Immunity and gut microflora
A recent review illustrated that supplementing rabbits with moringa leaves improved immunity response because moringa leaves have proteins and various peptides. Isitua and Ibeh [35] on adult male Chinchilla rabbits, reported that moringa leaves stimulate B-cells to produce antibodies to improve immunity. Also, there was an increase in CD4 cells, which play an important role in stimulating cell mediated immunity. Sun et al [48] reported that there were high values of liver and spleen indexes in NZW rabbits fed moringa oleifera leaves at levels of $20\%$ and $30\%$, which means that moringa leaves have a positive effect on immunity in rabbits. Khalil et al [43] reported that the highest immunity status (antibody and titer of lysozyme) was found in growing rabbits fed 200 mg of moringa oleifera leaf powder per kg diet. Selim [8] found that there are high levels of total protein and globulin in the rabbits supplemented with moringa oleifera leaves, which means better immunity. They illustrated that there was a decrease in abdominal fat index in the rabbit supplemented with moringa leaves, which had a positive effect on immunity. Also, in non-tropical area (under winter conditions), Salem et al [10] indicated that increasing the inclusion of moringa levels resulted in a significant increase in lymphocytes, immunoglobulin Y (IgY) and immunoglobulin M (IgM) levels compared to the control rabbit group. Moreover, an increase in CD4 cells and an improvement in cell-mediated immunity were found.
According to the literature, eucalyptus improves immune response due to its tannin content [17,73] and phenolic compounds or essential oils [55,67]. These components play an important role in improving immunity and reducing total bacteria in the gut or cecum. Liu et al [73] reported that there was decreasing MDA in rabbits fed a diet containing tannins. Sebei et al [55] showed that essential oils had antibacterial activity against *Listeria and* Bacillus. Fathi et al [17] illustrated that the immunity of rabbits increased as eucalyptus levels increased. They added that there was a reduction in total bacterial count, E. coli, and Salmonella sp. Rabbits fed $0.1\%$ and $0.2\%$ eucalyptus leaves showed the following results: in addition, Mohebodini et al [67] on poultry found that the cecal E. coli population was reduced as the eucalyptus level increased, while Clostridium spp. and coliforms were not affected.
Results of Ashour et al [11] pointed out that yucca supplementation to the basal diet of rabbits had a significant effect on parameters of immunity. They found that yucca supplementation improved immunity where the glutathione peroxidase and catalase activities were affected with a significant effect.
Because of its flavonoids and esters content, propolis extract, or propolis powder has been shown in the literature to improve rabbit immunity. Oršolić and Bašić [126] and Park et al [127] indicated that propolis supplementation increases macrophage activity and interleukin levels, which allow them to produce immunoglobulins. In addition, the specific and nonspecific immune responses improved when ethanolic extract of propolis was administrated in combination with formalized inactivated *Pasteurella multocida* vaccine [121]. Attia et al [109] found that administration of propolis alone was unable to improve immune responses in rabbits, but an improvement occurred when propolis was administered in combination with bee pollen. Braakhuis [128] explained the positive effect of propolis supplementation on the increased synthesis of antibodies from lymphoid organs. He added that the phytochemical components of propolis are considered the source of immunological properties, i.e. phenolic acids, flavonoids, esters, diterpenes, sesquiterpenes, lignans, aromatic aldehydes, alcohols, amino acids, fatty acids, vitamins and minerals.
## EUCALYPTUS
The *Eucalyptus genus* belongs to the Myrtaceae family. Eucalyptus is a general name for up to 700 different species that are found in many regions worldwide, including Australia, China, India, Portugal, Spain, Egypt, Algeria, the southern United States and South America. Eucalyptus is a tall, evergreen tree that is fast growing. The name *Eucalyptus is* derived from the Greek words, where “eu” means “good or well or true” and “kalypto” means “cover or hide”. That refers to the operculum covering the flower buds. There is a large variation in the chemical composition of the Eucalyptus species. The highest percentages of essential oils in the leaves of Eucalyptus are 1,8-cineol and -pinene, which ranged from $49.07\%$ to $83.59\%$ and $1.27\%$ to $26.35\%$, respectively [55]. Many investigators reported that Eucalyptus oil and leaves had high values of neutral detergent fiber (NDF), acid detergent fiber (ADF), and lignin [56], p-cymene, 1,8-cineole, β-phellandrene, spathulenol, cryptone aldehydes, cuminal, uncommon and phellandral, α-phellandrene, β-phellandrene leading to multi-functional such as antibacterial, antifungal, anti-inflammatory and antioxidative properties [55,57–59]. Dogan et al [60] reported that essential oil in the leaves of Eucalyptus inhibited the growth of either Gram-positive (S. aureus and B. subtilis) or Gram-negative (E. coli and Streptococcus sp.) bacteria strains. These results are related to the increase in feed utilization efficiency and enhanced immunity, which reflect on health and growth [61–63]. Many studies have shown that eucalyptus oil and leaves can be used to improve productive, immunological, and physiological traits in humans [64,65], poultry [59,66,67], and ruminants [68]. The effect of dietary eucalyptus supplementation in rabbits on the productivity, hematological parameters, and biochemicals of blood and immunity traits under high environmental temperatures is scanty.
## Effect on productivity performance
There are inconsistent results on the supplementation of eucalyptus leaves or eucalyptus oil in rabbit performance (body weight, body gain, feed intake). Ahmed et al [69] reported that either body weight or daily gain of rabbits fed diets containing two levels of Eucalyptus leaves failed to show any differences. While rabbits fed a diet with eucalyptus leaves had a higher feed intake value than that of the control group. They attributed the higher feed intake to the improvement of the palatability of diets containing eucalyptus leaves as a result of the content of eucalyptus volatile oil. The results of Kaur et al [70] showed that the crude fiber and crude protein digestibility of laying hens fed a diet supplemented with eucalyptus did not differ from that of the control group. Fathi et al [17] did not detect significant differences between rabbits given a ration supplemented with eucalyptus leaves and the un-supplemented group in body weight or weight gain. The group supplemented with $0.1\%$ eucalyptus showed the highest feed intake, while those supplemented with $0.2\%$ had the lowest feed intake. They found that there was no significant difference due to eucalyptus supplementation on FCR. Different results were found by Waly et al [71] in terms of body gain and feed intake, where they found that body gain of rabbits supplemented with eucalyptus showed greater gain and lower feed intake. They added that FCR was improved as eucalyptus levels increased. These results may be due to the positive effect of eucalyptus on primary antibody responses. In addition, Mohebodini et al [67] on broiler chickens found that there was a linear increase in body weight gain and a reduction in FCR.
## YUCCA
Yucca schidigera (known as yucca, or the Mojave yucca, or Spanish dagger) is one of the flowering plants and it is one of the species of yucca plants belonging to the family Agaves (Agavaceae). It is well grown in the hot regions of the southwestern United States, the Caribbean islands and Mexico. Yucca grows mostly in the desert and semi-desert areas and needs sun, sandy soil, and good ventilation. Recently, yucca plant extracts have been widely used as natural additives for livestock. The Yucca extract is prepared by drying and grinding the plant. The reviewed publications pointed out that Yucca extract has a high level of saponins, enzymes, and phenolic compounds with antioxidant action [11,22,75–79]. In addition, Svoradová et al [21] and Piacente et al [80] reported that resveratrol, a phytochemical component in yucca, is reasonable for antiviral, antiplatelet, and antioxidant purposes. Therefore, *Yucca is* used as a natural feed additive to improve feed efficiency, digestibility of nutrients, productivity, and reproductively of livestock animals, rabbits, and poultry [22,77,81–83]. In addition, yucca reduces either ammonia levels in the livestock environment or methane production [11,75,84]. Most of the research studied the effect of yucca supplementation on growth performance, but few investigators studied its effect on carcass traits, blood parameters, and immunity. Many authors pointed out that yucca supplementation had no effect in reducing either ammonia levels (in blood and in houses) or urea in the blood of rabbits.
## Effect on productive performance
Amber et al [85] used yucca extract or probiotic supplementation in the diets of NZW rabbits from 5 to 13 weeks of age. They found that the rabbits fed on yucca showed the highest daily gain, FCR, and digestibility values of dry matter, crude protein, and ether extract. They attributed these improvements to the improvement in the health of rabbits as a result of decreasing ammonia levels and urea in the blood. Results of Abaza and El-Said [75] illustrated that yucca addition improved body weight, weight gain, and FCR of rabbits supplemented with 100 mg of yucca/kg diet. Chrenková et al [22] attributed the improving body weight, weight gain, and feed efficiency to the high level of steroidal saponins and phenolic in yucca extract. Földešiová et al [82] studied the effects of two concentrations of yucca powder added to the diets of rabbits (low, 5 gm/100 kg diet, and high, 20 gm/100 kg diet) on weight gain. They found that the low supplementation had the heaviest body weight and total body weight gain of New Zealand rabbit does aged three months. They pointed out that the improvement may be due to yucca’s rich source of polyphenols, which encourage body weight gain. Földešiová [7] found that supplementation of yucca to NZW rabbit does improve the growth performance because of the effect of plasma levels of progesterone (P4), oxytocin, and prostaglandin F. Ashour et al [11] found that there was an improvement in the FCR of rabbits with a high level of yucca. This may be due to saponins and phenolic materials in yucca extract, which have antimicrobials, antioxidant, and antiviral properties. Results of studies conducted in non-tropical areas, suggested that the dietary supplementation of *Yucca schidigera* extract can stimulate rabbit growth [7] and fecundity [7,86]. On the other hand, Bergero et al [87] found that yucca supplementation did not show any improvement in body weight, gain, and digestibility of organic matter, energy, and crude protein in a rabbit flock raised in Italy. Ashour et al [11] reported that there was insignificant improvement in body weight and body gain of NZW rabbits supplemented with three levels of yucca extract (200, 400, and 600 mg yucca extract/kg diet), but the rabbits supplemented with 600 mg yucca showed heavier body weight than the control group without any significant differences.
Because propolis contains antioxidants, vitamins, minerals, phenolic compounds, and enzymes, adding propolis to either diets or drinking water for rabbit feeding increased body weight, weight gain, and FCR. In Egypt, Kamel et al [103] added propolis extract orally to a water suspension containing 100, 200, and 300 mg/kg body weight (BW)/d. They showed that NZW rabbit females treated with propolis increased body weight and reduced feed intake, especially for rabbits treated with the medium dose of propolis (200 mg/kg BW) compared to the control group. The improvement in growth performance may be attributed to different nutritive compounds in propolis. The same results were also found for bunnies treated with propolis, where they had heavier weights and gains from birth to 28 days. Also, Hashem et al [116], on V-line rabbits aged 5 weeks and weighing 586.7 g, received a basal diet supplemented with 150 or 300 mg of propolis/kg dry matter of diet for 5 weeks. They found that rabbits supplemented with propolis at two levels showed significantly higher body weights and weight gain than the control group. The authors attributed the increased weight and gain to the effects of 3,3-dimethyl-2-phenyl-2-(1-oxo-1,2,3,4-tetrahydronaphthalen-2-yl) azirane, which is found to make up $21.40\%$ of propolis. In addition, Attia et al [109] and Mona et al [117], pointed out that there was an increase in body weight, weight gain, feed intake, and FCR in rabbits with propolis. They went on to say that the antioxidants, vitamins, minerals, phenolic constituents, and enzymes found in propolis may be responsible for these effects. The offspring of V-Line rabbits fed a diet supplemented with propolis exhibited improved growth performance at 28 days of age under Saudi Arabia conditions [118]. Also, they supplemented does aged 5 months with propolis orally as a water suspension for three days a week for five weeks (1 week before mating and 4 weeks after mating), and they found that does with propolis had significantly heavier body weights, higher gain, and the lowest feed intake. In addition, under Egyptian summer conditions, Gabr et al [96] found that adding propolis significantly improved the live body weight, daily gain, and FCR of NZW growing rabbits. Additionally, a significant increase in body weight, body weight and FCR was found in NZW rabbits fed a diet supplemented with *Egyptian propolis* for eight weeks [14]. They attributed their findings to the effect of propolis on the growth of beneficial bacteria in the intestine as well as the stimulation of saccharase, amylase, and phosphatase activities. In Poland, Kupczyński et al [119] noted that adding ethanolic extract of propolis to drinking water for rabbits with chronic diarrhea resulted in reducing the duration of diarrhea and improving final body weight and feed intake. However, there is a difference in many experiments in the effects of using propolis in animal feeding. This difference may be due to the dosage added or chemical composition of propolis. On the other hand, many scientific authors reported that there were no significant effects on the body weight of rabbits as a result of propolis supplementation [97,120,121]. The same trend was found in the case of feed utilization, where Piza et al [97] reported that adding crude propolis did not affect the feed efficiency or diet digestibility in New Zealand rabbits.
## PROPOLIS
Propolis is one of the phytogenic feed additives, which are considered a product of plant resinous substances collected by honeybees. It has characteristics such as strong antioxidant, anti-inflammatory, and immunomodulation activities [88].
## Characteristics of propolis
The term “propolis” was derived from the Greek language (two Greek words: “pro” and “polis”). The first word, “pro,” means “in defense of”; the other word, “polis,” means the city. Thus, bees used propolis to repair and protect their combs [89–92]. Propolis is sometimes called bee glue [93,94]. Propolis is considered one of the natural feed additives. It is a complex of resinous substances collected by honeybees from different parts of plants, such as buds, flowers, leaf buds, branches, barks, exudates, and wax [95–97]. The main sources of propolis are poplar trees in North America, Europe, Asia, and the northern regions of KSA and Egypt [91,98]; *Baccharis dracunculifolia* leaf in Brazil; *Betula verrucosa* in Russia [99,100]; and *Clusia rosea* in Cuba [98,101,102]. The color of propolis varies depending on its origin; it can be creamy, yellow, green, or light to dark brown [103,104]; and it can have different biological activities [105]. Recently, propolis use has become widespread, especially in temperate zones, for its effects. Propolis has biological activities as antioxidants and antimicrobials. Propolis contains high amounts of phenolic acids and esters, flavonoids, amino acids, vitamins, minerals, and enzymes [106–108]. In addition, propolis has antibacterial effects [109]. Itavo et al [110] reported that propolis affects all gram-positive and some gram-positive bacteria.
The type of plants used by the bees and the season had a strong effect on the chemical composition of propolis [98, 111–113]. Therefore, Araujo et al [114] reported that the propolis in temperate zones differs in its chemical composition. They added that this propolis contains $50\%$ to $60\%$ resins and balsams, $30\%$ to $40\%$ of wax, $5\%$ to $10\%$ of essential and aromatic oils, $5\%$ of pollen, and $5\%$ of other substances. On the other hand, Sforcin [115] noticed that there was no different effect of season on *Brazilian propolis* composition all year because the propolis was found and collected in the summer season only in the Northern Hemisphere, which is considered a temperate zone. The biological properties of propolis have been reported by numerous authors, and we will illustrate these effects below.
## Hematological parameters and blood biochemistry
Due to flavonoids, steroids, and phenolic acids in propolis, many results indicated that there was an improvement in the health of rabbits fed propolis. Attia et al [118] reported that supplemented V-line rabbits fed orally with propolis have higher values of total protein, albumin, globulin, glucose, and total lipids than control ones. They added that values of globulin/albumin ratio, cholesterol, plasma urea, urea/creatinine ratio, and liver enzymes (AST and ALT) were lower in the treated than in the control group. El-Hanoun et al [124] found the same results and attributed their results to the higher biological activity of propolis to prevent peroxidation of lipids. In addition, Kamel et al [103] supplemented rabbit does with propolis and found high values of plasma total proteins, globulin, and glucose. Also, there was no variation in plasma albumin and creatinine concentrations of doe rabbits treated compared to the control group. A significant decrease was found in total lipids, urea, cholesterol serum, and liver enzyme activity (AST and ALT). They reported that there was an improvement in the functions of the kidney and liver because of a reduction in lipids, urea, and cholesterol. The authors attributed the higher hemoglobin, red blood cell, white blood cell, and packed cell volume to the high iron and selenium in propolis. Similar results were found in growing rabbits supplemented with propolis, where there were decreased values of plasma albumin, total lipids, cholesterol, urea, creatinine, AST, and ALT, while values of plasma globulin were high. The authors said that these effects may be due to the high immunity response of supplemented rabbits. Nassar et al [121] found similar results where liver enzymes had lower values in the treated group with propolis. Also, a low level of serum creatinine and urea was reported. They attributed the reduction results to the propolis compounds derived from flavonoids, steroids, phenolic acids, and their esters. In Spain, serum biochemical profiles did not change in growing rabbits fed a diet supplemented with green propolis compared to the control group [123]. Attia et al [20] illustrated that rabbits fed propolis at 300 mg had significantly higher values of T4 than the control group, while values of T3 decreased in the plasma of those fed propolis at 150 mg. In addition, the control group had higher AST and ALT levels than those supplemented with propolis or bee pollen. A high level of creatinine was found in the group supplemented with a high level of propolis. Some hematological parameters of alloxan-induced diabetic rabbits fed a diet given *Iraqi propolis* was studied [125]. The highest values of RBC’s and PCV were recorded in rabbits supplemented with 200 mg of Iraqi propolis/kg body weight. The given propolis may be reduce the negative effect of alloxan [125].
## CONCLUSION
The routine use of antibiotics in intensive farming comes into force due to harmful residual effects on human health. The use of natural feed additives is gaining concern in livestock production. Using some plant extracts or natural products as alternative feed additives has beneficial effects on growth performance and physiological stimulants, as well as for health enhancement. Most of them improve nutrient utilization and absorption by enhancing digestibility. Additionally, the activities of antimicrobial, anti-inflammatory, antioxidant, immune-stimulant, and stabilizing beneficial microflora are also improved, particularly in farm animals suffering from heat stress. The beneficial effects of using moringa, propolis, yucca, and eucalyptus have been intensively reviewed and discussed in rabbits raised either in hot or temperate environmental conditions. Table 1 summarizes the effect of some natural feed additives on the rabbit’s performance and productivity under different environmental conditions.
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|
---
title: Potential use of TG68 - A novel thyromimetic - for the treatment of non-alcoholic
fatty liver (NAFLD)-associated hepatocarcinogenesis
authors:
- Andrea Caddeo
- Marina Serra
- Francesca Sedda
- Andrea Bacci
- Clementina Manera
- Simona Rapposelli
- Amedeo Columbano
- Andrea Perra
- Marta Anna Kowalik
journal: Frontiers in Oncology
year: 2023
pmcid: PMC9996294
doi: 10.3389/fonc.2023.1127517
license: CC BY 4.0
---
# Potential use of TG68 - A novel thyromimetic - for the treatment of non-alcoholic fatty liver (NAFLD)-associated hepatocarcinogenesis
## Abstract
### Introduction
Several lines of evidence suggest that the thyroid hormone signaling pathway is altered in patients with NAFLD and that pharmacological strategies to target the thyroid hormone/thyroid hormone nuclear receptor axis (TH/THR) in the liver may exert beneficial effects. In this study, we investigated the effect of TG68, a novel THRβ agonist, on rat hepatic fat accumulation and NAFLD-associated hepatocarcinogenesis.
### Methods
Male rats given a single dose of diethylnitrosamine (DEN) and fed a high fat diet (HFD) were co-treated with different doses of TG68. Systemic and hepatic metabolic parameters, immunohistochemistry and hepatic gene expression were determined to assess the effect of TG68 on THRβ activation.
### Results
Irrespectively of the dose, treatment with TG68 led to a significant reduction in liver weight, hepatic steatosis, circulating triglycerides, cholesterol and blood glucose. Importantly, a short exposure to TG68 caused regression of DEN-induced preneoplastic lesions associated with a differentiation program, as evidenced by a loss of neoplastic markers and reacquisition of markers of differentiated hepatocytes. Finally, while an equimolar dose of the THRβ agonist Resmetirom reduced hepatic fat accumulation, it did not exert any antitumorigenic effect.
### Discussion
The use of this novel thyromimetic represents a promising therapeutic strategy for the treatment of NAFLD-associated hepatocarcinogenesis.
## Introduction
Non-alcoholic fatty liver disease (NAFLD), the most common cause of chronic liver disease in Western countries [1], comprises a wide spectrum of clinical entities ranging from simple steatosis to non-alcoholic steatohepatitis (NASH), fibrosis, cirrhosis and, ultimately, to hepatocellular carcinoma (HCC) [2, 3]. Recent meta-analyses revealed that the global prevalence of NAFLD is approximately $30\%$ and it is increasingly growing worldwide [4]. This unequivocally implicates that NAFLD is becoming the emerging risk factor for HCC [5]. Unfortunately, patients with NAFLD-related HCC present more advanced tumor stage, lower eligibility for curative treatment, shorter survival time and higher rates of tumor recurrence [6, 7]. Although several molecular mechanisms have been identified and pharmacological candidates are currently in advanced stages of clinical trials, there are still no approved pharmacological therapies for the treatment of NAFLD [8]. This aspect seems even more critical as HCC is a cancer type with limited therapeutic options that confer only a modest improvement in overall survival [9]. For these reasons, new therapies for NAFLD and NAFLD-related HCC are urgently needed. In this context, experimental and clinical studies suggested that alterations of the thyroid hormones (THs) signaling in the liver play a key role in the development and progression of NAFLD and HCC. THs, 3,5,3’-triiodo-L-thyronine (T3) and 3,5,3’,5’-tetraiodo-L-thyronine (thyroxine or T4), are essential regulatory molecules for normal growth, development and for maintaining metabolic homeostasis [10]. Most of THs effects are mediated by nuclear receptors (THRs): thyroid hormone receptor α (THRα) and thyroid hormone receptor β (THRβ), whose distribution is heterogeneous among different tissues and/or during developmental stages [11]. THRβ is the most abundant isoform in the liver where it mediates T3 effects on lipid metabolism and regulation of metabolic rate [12]. As to NAFLD, several clinical investigations showed that overt and subclinical hypothyroidism and reduced THRβ expression correlated with NAFLD stage (13–15). Moreover, subclinical hypothyroidism and low-normal thyroid function were independent predictors of NASH and advanced fibrosis [16]. Even if this correlation has been questioned by other studies that found a positive association of free T3 levels with the severity of hepatic steatosis and fibrosis [17, 18], exogenous T3 administration showed encouraging results in lowering hepatic fat content in various models of NAFLD in mice and rats [19, 20].
With regard to HCC, three independent case-control studies indicated that hypothyroidism represents a risk factor for human HCC (21–23). Moreover, severe local hypothyroidism was reported in rat hepatic preneoplastic lesions and in rat and human HCCs, suggesting that this condition may represent a favorable event for HCC development (24–26). The finding that exogenous T3 administration inhibits HCC progression [27] and induces an almost complete regression of advanced HCCs in rats [28], further strengthens the role of the TH/THR axis in hepatocarcinogenesis.
Nevertheless, these potentially therapeutic effects of T3 required to induce the anti-steatotic and anti-tumoral effects, occur whilst inducing a thyrotoxic state, including life-threatening tachyarrhythmias, muscle wasting, bone mass loss, all hampering the use of thyroid hormone. Since most of the harmful effects of T3, directed towards cardiovascular system, are mediated by THRα, THRβ-selective thyromimetics, such as GC-1 (Sobetirome), KB2115 (Eprotirome), the Hep-Direct prodrug VK2809 (MB07811) and Resmetirom (MGL-3196), which have reproduced T3-related biological effects on lipid metabolism without overt cardiotoxic effects, have been synthesized (20, 29–31). Recently, using GC-1 as a scaffold compound, our group reported the synthesis of a novel halogen free THRβ-selective agonist namely TG68 that showed a very high affinity for the THRβ [32]. We demonstrated that TG68 strongly reduced hepatic fat accumulation in vitro [32] and in vivo in mice fed a high fat diet (HFD), in the absence of overt deleterious effects in extra-hepatic tissues, such as kidney or heart [33]. On these premises, the aim of the present study was to investigate the effect of TG68 on an experimental model of NAFLD-associated hepatocarcinogenesis and to unveil the molecular mechanisms underlying the possible anti-tumorigenic effect of TG68.
## Rats and drug treatments
Four-week-old male Fischer-344 (F-344) rats were purchased from Charles River Italy (Calco, Italy). Rats were housed for two weeks at 22°C with free access to basal rodent diet (Mucedola s.r.l., Settimo Milanese, Italy) and drinking water, and with a 12-hours light/dark daily cycle before starting the experiments. All animal procedures were approved by the Italian Ministry of Health (the authorization codes are $\frac{1247}{15}$-PR and $\frac{560}{2019}$-PR), complied with national ethical guidelines for animal experimentation and were conducted in accordance with the guidelines of the local ethical committee for in vivo experimentation.
Three experimental protocols were adopted: Experimental Protocol 1. Eleven rats given a single intraperitoneal dose of diethylnitrosamine (DEN, 150 mg/kg body weight) were fed ad libitum a high fat diet (HFD, $42\%$ kcal/fat diet containing sucrose and 1,$25\%$ cholesterol, (Mucedola s.r.l.) for 30 weeks. HFD-fed rats were then split into two groups: group 1 ($$n = 5$$) was maintained on HFD for further three weeks; group 2 ($$n = 6$$) was fed a HFD plus TG68 (9.35 mg/kg in drinking water for 3 weeks). The dose 9.35 mg/kg was selected based on the dose-response to MGL-3196 on cholesterol lowering in DIO mice [30] and on our previous experiments [32].
Experimental Protocol 2. Twenty-three rats given a single dose of DEN as in Experimental Protocol 1 were fed ad libitum a HFD for 30 weeks and then split into three groups: group 1 ($$n = 6$$) was maintained on HFD for further two weeks; group 2 ($$n = 6$$) was fed a HFD plus TG68 (2.8 mg/kg, in drinking water) while a third group of rats ($$n = 6$$) was fed a HFD plus TG68 (1.4 mg/kg in drinking water). Animals fed a basal diet were used as control group ($$n = 5$$). Animals given TG68 were sacrificed 2 weeks later.
Experimental Protocol 3. Thirteen rats given DEN as in Experimental Protocol 1 and 2 were fed ad libitum a HFD for 39 weeks and then split into three groups: group 1 ($$n = 4$$) was maintained on HFD for further two weeks; group 2 ($$n = 5$$) was fed a HFD plus TG68 (2.8 mg/kg, in drinking water). A third group ($$n = 4$$) was fed HFD plus Resmetirom (MGL-3196, 3 mg/kg in drinking water, MedChemExpress), for the last two weeks. All animals were sacrificed under isoflurane anaesthesia. Blood and tissues, including liver, heart and kidney, were collected.
## Analysis of serum triglycerides, cholesterol, glucose, aspartate aminotransferase, and alanine aminotransferase
Blood samples were collected from abdominal aorta. Serum was separated by centrifugation and tested for triglycerides (TGs), cholesterol (CH), glucose, aspartate aminotransferase (AST) and alanine aminotransferase (ALT) using a commercially available kit from Boehringer (Mannheim, Germany).
## Determination of hepatic TGs
Lipid extraction and measurement of TGs were performed according to Schroeder-Gloeckler et al. [ 34]. Briefly, liver samples were homogenized in 8 volumes of deionized water and in 1 volume of 5 M NaCl. Subsequently, a 200 µl aliquot of the homogenate was mixed with 500 µl of methanol and 250 µl of chloroform. Following centrifugation, the organic phase was collected. Complete extraction of any residual lipids was achieved by re-extracting with 250 µl chloroform:methanol (9:1). The organic phase was separated by centrifugation and samples were dried at room temperature (RT). The lipids were dissolved in a solution of $90\%$ isopropanol:$10\%$ Triton X-100 ($2\%$) to disperse the TGs for assay. Hepatic TG content was measured colorimetrically using a kit from Sigma-Aldrich (TR0100; Sigma-Aldrich, Milan, Italy).
## Histology and immunohistochemistry
Immediately after sacrifice, liver, heart and kidney were weighted; sections were fixed in $10\%$ buffered formalin and processed for histological analysis (hematoxylin and eosin, H&E) or immunohistochemistry (IHC). The remaining tissues were snap-frozen in prechilled 2-methylbutane in liquid nitrogen and stored at -80°C until use. To determine the hepatic neutral lipid content, frozen liver sections were stained with Oil Red O (ORO, Sigma Aldrich) for 15 min, rinsed with $60\%$ isopropanol, and stained with Mayer hematoxylin. The ORO staining positive area for each sample was quantified by using ImageJ analysis software (National Institute of Mental Health, Bethesda, Maryland, USA). To investigate glucose-6-phosphatase (G6Pase) activity, 15 μm serial frozen sections were cut in a cryostat (Leica LMD6000), and stained for G6Pase and Glutathione S-transferase Placental form (GSTP) as previously described [27].
Paraffin-embedded tissues were cut into 4 μm sections, dewaxed, and hydrated. Slides were microwaved in citrate buffer pH 6.0 and incubated overnight with the primary antibodies: GSTP (#311, MBL International); Glucose-6-Phosphate Dehydrogenase (G6PD, ab87230, Abcam). Sections were incubated with the appropriate polymer DAKO Envision secondary antibody at RT. Signal was detected using the VECTOR® NovaRED™ Peroxidase (HRP) Substrate Kit (Vector Laboratories). Sections were counterstained with Harris hematoxylin solution (Bio-Optica).
## Cytometric analysis
The area of GSTP-positive preneoplastic lesions was measured with ImageJ according to Abramoff et al. [ 35].
## RNA extraction and qRT-PCR
Total RNA was extracted from snap-frozen rat liver tissues by using Qiazol Lysis Reagent (Qiagen, Hilden, Germany) followed by RNeasy extraction kit (Qiagen). Extracted RNA was reverse-transcribed by using High-Capacity cDNA Reverse Transcription kit with RNase inhibitor (Thermo Fisher Scientific, Waltham, MA, USA). RNA was quantified by NanoDrop ND1000 (Thermo Fisher Scientific), while RNA integrity was assessed by Agilent Bioanalyzer 2100. Gene expression analysis was performed using TaqMan Gene expression Master Mix (Thermo Fisher Scientific) and the following specific TaqMan probes: Dio1 Rn00572183_m1, Myh7 Rn00568328_m1, Myh6 Rn00568304_m1, Cpt1a Rn00580702_m1, Acox1 Rn01460628_m1, Pnpla2 Rn01479968_g1, Fasn Rn00569117_m1, Dgat1 Rn00584870_m1, Thrsp Rn01511034_m1, Klf9 Rn00589498_m1, G6pd Rn01529640_g1, Gstp1 Rn00821792_g1, Gapdh 4351317. Each sample was run in triplicate and all measurements were normalized to Gapdh. Relative mRNA expression analysis for each gene was calculated by using the 2-ΔΔCt method.
## Statistical analyses
All data were expressed as the mean ± standard deviation (SD). Differences between groups were compared by student’s t-test or by using one-way ANOVA followed by Tukey post hoc analysis using the GraphPad software (Prism 9) (La Jolla, California). P-values were considered significant at $p \leq 0.05.$
## TG68 caused a reduction of liver weight and steatosis
To investigate the effect of TG68 on hepatic fat accumulation, in the first set of our experiments, we administered 9.35 mg/kg of the drug - dissolved in drinking water - to HFD-fed rats for the last 3 weeks (See experimental protocol in Figure 1A). TG68 caused a reduction of body weight, albeit not significant, compared to HFD alone, despite the fact that the food intake was similar in both the groups (Figure 1B). While an increase of heart and kidney weight was observed following TG68 treatment (Figure 1C), a significant reduction of liver weight and liver weight/body weight ratio compared to HFD-fed untreated rats was detected. ( Figure 1D).
**Figure 1:** *Effect of a three-week treatment with TG68 on kidney, heart and liver weight and hepatic steatosis. (A) Experimental design and timeline of the in vivo experiments; (B) Body weight and Daily food consumption throughout the whole experiment; (C) Heart weight and Kidney weight; (D) Liver weight and Liver weight/body weight ratio; (E) Representative images of liver sections stained with hematoxylin and eosin (H&E, 10x) or Oil Red O (ORO, 10x) at 9.35 mg/kg of TG68; (F) ORO staining positive area quantification by using ImageJ. Data were normalized to HFD alone. Groups were compared by student’s t-test. Values are shown as mean ± standard deviation of 5 to 6 rats/per group. *p<0.05, **p<0.01 HFD, High Fat Diet; DEN, Diethylnitrosamine; H&E, hematoxylin and eosin; ORO, Oil Red Staining.*
To investigate whether the observed reduction in liver weight was due to an amelioration of the hepatic steatosis, liver samples from both groups were subjected to comparative pathological analysis. The microscopic analysis of hematoxylin and eosin (H&E) stained sections confirmed the presence of steatosis in all the HFD livers from untreated mice (Figure 1E). ORO staining for neutral lipid content supported the histological observation highlighting the impressive reduction of hepatic fat content caused by TG68 (Figures 1E, F).
The reduction of liver fat accumulation was accompanied by a significant up-regulation of Phospholipase Domain Containing 2 (Pnpla2), and Carnitine Palmitoyltransferase-1 (Cpt1a), highlighting the effect of TG68 in decreasing the content of TGs on the one hand, and improving mitochondrial fatty acid oxidation in rats subjected to HFD on the other hand (Figure 2A). TG68 did not modify the mRNA levels of Acyl-CoA oxidase1 (Acox1), Fatty acid synthase (Fasn) and Diacylglycerol O-acyltransferase1 (Dgat1) (Figure 2A). Taken together, these results suggested that TG68 increased lipolysis in steatotic liver without a major impact on lipogenesis. In addition to the effect on hepatic fat accumulation, TG68 caused a striking reduction of circulating TGs and CH accompanied by a significant reduction of blood glucose levels, compared with HFD rats (Figure 2B).
**Figure 2:** *Effect of a three-week treatment with TG68 on lipid metabolism, serum triglycerides, cholesterol and glucose levels and mRNA levels of target genes of thyroid hormone receptor. (A) Gene expression analysis of Cpta1, Pnpla2, Acox1, Fasn, Dgat1 in rat livers exposed to Experimental Protocol described in Figure 1A. Gene expression is reported as fold-change relative to livers from rats fed HFD alone; (B) Effect of TG68 on serum triglycerides, cholesterol and glucose levels; (C) Gene expression analysis of Dio1 and Thrsp in rat liver. Gene expression is reported as fold-change relative to livers from rats fed HFD alone; (D) Serum levels of Alanine aminotransferase (ALT). Groups were compared by student’s t-test. Values are shown as mean ± standard deviation of 5 to 6 rats/group. *p<0.05, **p<0.01. HFD, high fat diet; ALT, alanine aminotransferases.*
To verify whether the observed effects were associated with TG68-induced activation of THRs, the expression of deiodinase 1 (Dio1) and thyroid hormone responsive (Thrsp), two well-known THR target genes, were investigated. As shown in Figure 2C, the expression of Dio1 and Thrsp was significantly increased following treatment with TG68.
Notably, unlike what was observed in the liver of HFD fed mice [33], microscopic analysis of rat liver did not exhibit any major sign of cell damage typically associated with NAFLD, such as cell swelling, Mallory-Denk bodies, acidophilic bodies or spotty necrosis. No detectable sign of liver cell injury following TG68 treatment was observed at light microscopic examination, as also shown by serum levels of ALT (Figure 2D). On the other hand, an increased cholangiocyte proliferation was occasionally observed in the liver of rats fed a HFD and co-treated with TG68.
## TG68 caused a reduction of the number and size of DEN-induced preneoplastic hepatic lesions
Experimental evidence has shown that T3 exerts an anti-tumorigenic effect at early and late stages of the process [27, 28]. To investigate whether TG68 could exert a similar effect, we scored the presence of preneoplastic lesions immuno-stained against GSTP, the best marker for the identification of preneoplastic rat liver foci/nodules [36]. As shown in Figure 3A, a single treatment with DEN followed by HFD feeding for 30 weeks resulted in a high number of GSTP+ foci (50.9/cm2), with $0.66\%$ of the liver being positive for GSTP staining. Co-administration of TG68 for the last three weeks caused a significant reduction in the number (4.3/cm2, respectively) and the percentage of liver area occupied by GSTP-positive lesions ($0.09\%$) (Figure 3A). Interestingly, while the vast majority of the GSTP-positive foci present in the liver of rats fed a HFD displayed an intense and homogeneous staining (persistent foci), almost all the ones observed in TG68-treated rats showed only a faint and discontinuous staining (remodeling foci) (Figures 3B, C), suggesting their reversion to a more differentiated phenotype following treatment with the thyromimetic.
**Figure 3:** *Effect of a three-week treatment with TG68 on DEN-induced preneoplastic foci. (A) Number of GSTP+-foci (left panel) and percentage of liver section occupied by GSTP+ lesions (right panel); (B) GSTP immunohistochemical staining of preneoplastic foci of rats fed HFD alone or co-treated with 9.35 mg/kg of TG68 (GSTP, 5X; Inset, 20X); (C) Percentage of remodeling foci in HFD and HFD+TG68 livers; (D) Representative pictures showing GSTP staining in hepatic bile ducts of rats fed Basal Diet (BD), HFD alone or co-treated with 9.35 mg/kg of TG68 (GSTP, 10x); (E) Immunohistochemistry on serial liver sections stained for GSTP and G6PD (GSTP/G6PD: 5x, 20x 10x); (F) Number of G6PD+-foci in rats fed HFD alone or co-treated with 9.35 mg/kg of TG68; (G) qPCR analysis of G6pd mRNA in rats fed HFD alone or co-treated with TG68; (H) qRT-PCR analysis of Klf9 mRNA levels in rats fed HFD alone or co-treated with TG68. Gene expression is reported as fold-change relative to livers from HFD-fed rats. The bar graphs represent mean values + SD of 5 to 6 rats/group. Groups were compared by using student’s t-test. *p<0.05, **p<0.01. HFD, high fat diet.*
The observed loss of GSTP staining caused by TG68 was not the consequence of a general transcriptional repression of GSTP expression by the drug, but a specific effect of TG68 on preneoplastic lesions. Indeed, immunohistochemistry showed no inhibitory effect of the drug on GSTP protein levels of bile ducts. ( Figure 3D), thus indicating that the loss of GSTP immunostaining was due to the reacquisition of a differentiated phenotype of preneoplastic lesions. Further support to this proposition comes from the finding that TG68 caused an almost complete disappearance of preneoplastic lesions also when they were identified by G6PD immunostaining. Indeed, while almost all preneoplastic GSTP+ lesions in animals fed the HFD alone were also G6PD+ (Figures 3E, F), in rats co-treated with TG68 they were losing also G6PD positivity (Figures 3E, F), in spite of the increased hepatic mRNA levels of G6pd (Figure 3G). Taken together, these findings support the concept that TG68 caused the regression of preneoplastic foci by inducing a differentiated biochemical phenotype, and support the notion that activation of THRs by TG68 exerts an antitumoral effect. Support to the pro-differentiating effect of TG68 comes also from the finding of the enhanced expression of Klf9 (Figure 3H), a Kruppel-like factor that contains a thyroid hormone response element and is implicated in the regulation of the balance between pluripotency, self-renewal differentiation, and metabolism.
## Reduction of fat accumulation but not regression of preneoplastic lesions is achieved by further decreasing the dose of TG68
T3 and other thyromimetics have been shown to exert their effects on several organs, including heart and kidney [37]. Indeed, as shown in Figure 1C, treatment with TG68 for three weeks caused a significant increase in both heart and kidney weight.
Searching for experimental conditions that could avoid any possible impact of TG68 on extrahepatic organs, we adopted a protocol wherein rats given DEN and fed HFD were exposed to 1.4 or 2.8 mg/kg of TG68 for only 2 weeks (Experimental Protocol 2, Figure 4A). As shown in Figure 4B, no changes in food and water intake or body weight were detected at both the doses of the drug compared to HFD alone. Notably, heart and kidney weight were not affected compared to animals fed HFD alone (Figure 4C). Although no evidence of tissue damage could be observed by histological analysis (Figure 4D), to further investigate whether TG68 could cause damage to the heart through activation of THRs, we determined the mRNA levels of Myosin heavy chain 6 (Myh6) and Myosin heavy chain 7 (Myh7), two genes under TH control [38]. As shown in Figure 4E, qRT-PCR analysis did not reveal any significant change in the cardiac expression of Myh6 and Myh7 in rats treated with both doses of TG68 compared to control rats. Notably, Dio1 mRNA levels were not modified following TG68 administration (Figure 4E) suggesting a low delivery of the drug to cardiomyocytes, at least at the doses used in this experimental protocol.
**Figure 4:** *TG68 effectively reduces fat accumulation in the absence of significant cardiotoxic effects. (A) Experimental design and timeline of the in vivo experiments; (B) Daily food intake, water consumption and body weight of rats fed basal diet (BD), HFD alone or co-treated with 1.4 or 2.8 mg/kg of TG68; (C) Heart and kidney weight of rats treated as in A; (D) H&E staining of heart sections from rats treated as in A (H&E 20x); (E) qRT-PCR analysis of Myh6, Myh7 and Dio1 mRNA in the heart of rats treated as in A; (F) Liver weight and liver weight/body weight ratio; (G) Serum ALT levels; (H) Representative images of liver sections stained with hematoxylin and eosin (H&E, 10x) or Oil Red O (ORO 10x) at 1.4 or 2.8 mg/kg of TG68, and quantification of ORO staining positive area by using ImageJ; (I) Hepatic content of triglycerides and serum levels of triglycerides, cholesterol and glucose. Gene expression is reported as fold-change relative to livers from rats fed a basal diet. The bar graphs represent mean value + SD of 3 to 6 rats/group. Groups were compared by using one-way ANOVA followed by Tukey post hoc analysis. *p<0.05, **p<0.01, ***p<0.001,****p<0.0001. BD, basal diet; DEN, Diethylnitrosamine; HFD, high fat diet.*
On the other hand, both doses of TG68 caused a significant reduction of liver weight and liver weight/body ratio compared to HFD-fed animals (Figure 4F). These changes occurred in the absence of any significant sign of liver injury, as indicated by ALT serum levels as well as histologic analysis (Figures 4G, H). ORO staining confirmed that a massive reduction of fat accumulation accounted for the decreased liver weight observed with both the doses of TG68 (Figure 4H). Reduction of hepatic fat accumulation was accompanied by a significant decrease of the levels of TGs, CH and glucose in the blood (Figure 4I).
Interestingly, while no clear difference on the regression of steatosis was observed between the two doses of TG68, a remarkably different effect on the regression of preneoplastic lesions was observed only with the dose of 2.8 mg/kg. Indeed, as shown in Figures 5A, B 2.8 mg/kg of TG68 led to a striking reduction of the number of GSTP+ foci and percentage of liver occupied by GSTP positive area (25/cm2 ± 4.8 and $0.4\%$ in rats fed HFD alone vs. 4/cm2 ± 2.3 and $0.1\%$ in rats HFD vs. TG68 co-treated animals). On the other hand, only a small decrease of the number of GSTP+ foci and no difference in the percentage of the liver occupied by GSTP+ lesions were observed in rats treated with the dose of 1.4 mg (17/cm2 ± 7.1 and $0.3\%$). Notably, while the intensely stained GSTP+ foci in the liver of rats fed HFD alone were virtually negative for G6Pase, an enzyme highly expressed by differentiated hepatocytes (Figure 5C), a faint staining of GSTP was observed in the preneoplastic lesions still present following TG68 treatment in concomitance with a re-expression of G6Pase (Figure 5C). This finding further supports the hypothesis that the anti-tumorigenic effect of TG68 is, at least in part, due to its ability to induce a switch of preneoplastic hepatocytes towards a differentiated phenotype.
**Figure 5:** *Reduction of fat accumulation does not always associate with regression of preneoplastic lesions. (A) Representative images of GSTP+ preneoplastic lesions from rats fed HFD alone or co-treated with 1.4 or 2.8 mg/kg of TG68 for the last 2 weeks (GSTP, 10x); (B) Number of GSTP+-foci per cm2 and percentage of liver occupied by GSTP+ lesions; (C) Representative image of serial liver sections. Arrows indicate that foci that are positive for GSTP are negative for G6Pase in rats fed HFD alone, but not in animals co-treated with TG68 (GSTP 10x; G6Pase 10x). The bar graphs represent mean values + SD of 5 to 6 rats/group. Groups were compared by using one-way ANOVA followed by Tukey post hoc analysis. *p<0.05, **p<0.01, ****p<0.0001. HFD, high fat diet.*
## Reduction of fat accumulation but not regression of preneoplastic lesions are achieved by an equimolar dose of Resmetirom
Next, we investigated whether i) TG68 could exert its anti-tumorigenic effect also at later stages of the carcinogenic process, and ii) a similar effect could be exerted also by Resmetirom (MGL-3196), a THRβ agonist that provided significant reductions in liver fat, low-density lipoprotein cholesterol and other atherogenic lipids vs. placebo in a Phase II trial [30]. As illustrated in the Experimental Protocol 3 (Figure 6A), the rats exposed to DEN and fed a HFD for 39 weeks were then given either TG68 (2.8 mg/kg) or MGL-3196 (3 mg/kg) for 2 weeks. As shown in Figure 6B, only TG68 caused a decrease of body weight as well as of liver weight and liver weight/body weight ratio. Notably, a slight decrease of heart and kidney weight was observed with TG68 that was more pronounced with MGL-3196, compared to HFD-fed rats (Figure 6C). Both drugs strongly reduced the lipid content in the liver as detected by histological analysis and ORO staining (Figure 6D). Furthermore, TG68 also led to a decline in the levels of TGs and CH, although only the treatment with TG68 caused a statistically significant reduction of these parameters compared to the HFD group (Figure 6E). On the other hand, while both the drugs did not modify the serum levels of ALT or GGT, they caused a strong reduction of total bilirubin (Figure 6F).
**Figure 6:** *Resmetirom, unlike TG68, does not induce regression of preneoplastic foci. (A) Experimental design and timeline of the in vivo experiments; (B) Body weight, liver weight and liver weight/body weight ratio in rats fed HFD and then co-treated with 2.8 mg/kg of TG68 or 3.0 mg/kg of Resmetirom; (C) Heart and kidney weight of rats HFD alone or co-treated with 2.8 mg/kg of TG68 or 3.0 mg/kg of Resmetirom; (D) Representative images of liver sections stained with hematoxylin and eosin (H&E 10x) oor Oil Red O (ORO 10x) in rats treated as in A. Quantification of ORO staining positive area by using ImageJ; (E) Serum levels of triglycerides and cholesterol; (F) Serum levels of ALT, GGT and total bilirubin; (G) Number of GSTP+ foci and percentage of liver occupied by GSTP+ The bar graphs represent mean values + SD of 4 to 5 rats/group. Groups were compared by using one-way ANOVA followed by Tukey post hoc analysis. *p<0.05, **p<0.01, ***p<0.001,****p<0.0001. ALT, alanine aminotransferase; DEN, Diethylnitrosamine; HFD, high fat diet; MGL-3196, Resmetirom; GGT, gamma-glutamyl transpeptidase.*
Subsequently, we investigated and compared the effect of TG68 and MGL-3196 on DEN-induced preneoplastic foci. As shown in Figure 6G, at 41 weeks after DEN treatment the number of GSTP+ foci and the percentage of area occupied by GSTP+ foci in rats treated with HFD alone were much higher than those observed at 33 weeks (43 vs. 25 and 2.1 vs. 0.6, respectively). Interestingly, TG68 exerted a striking anti-tumorigenic effect even at this later stage of hepatocarcinogenesis. Indeed, it caused a $50\%$ decrease in the number of GSTP+ foci (23/cm2 vs. 44/cm2) and an even stronger reduction in the % area occupied by preneoplastic lesions ($0.8\%$ vs. $2.1\%$). On the opposite, no significant change in the number of GSTP+ foci (42/cm2) or in the % area occupied by these lesions ($1.5\%$) was induced by MGL-3196.
## Discussion
In the present study, we investigated the effect of a novel halogen free THRβ-selective agonist TG68 on hepatic steatosis and regression of preneoplastic lesions in rats exposed to DEN and HFD. In the last years, growing evidence has demonstrated that thyroid hormones and THRs are implicated in human HCC development and progression (21–23), and that severe local hypothyroidism takes place in rat hepatic pre- and neoplastic lesions, as well as in human HCCs, suggesting that this condition may represent a favorable event for HCC development (26–28). As to NAFLD, an emerging risk factor for HCC, the incidence of hypothyroidism resulted higher in patients with NAFLD/NASH compared to age-matched controls [15, 39]. In this context, previous reports in animal models demonstrated that T3 exerts an anti-tumorigenic effect at early and late stages of hepatocarcinogenesis associated with a switch from Warburg to oxidative metabolism and loss of markers of poorly differentiated hepatocytes [27, 28]. Moreover, T3 suppressed HCC onset in DEN-treated mice via activation of autophagy [40] and in HBV-encoded X protein-induced hepatocarcinogenesis [41]. With regard to THRβ agonists, it has been observed that the treatment with GC-1 strongly reduced the number of preneoplastic foci generated in two different experimental models of liver carcinogenesis, the Resistant-Hepatocyte model and a nutritional model consisting in the feeding the choline-methionine deficient diet [42]. In the current study, we applied an experimental protocol consisting in the administration of the initiating agent DEN and feeding a HFD diet. As already reported, chronic exposure of animals to a HFD closely recapitulates the complex pathological events associated with NAFLD in humans [43].
Here, we report that a short-term (two/three weeks) treatment of rats fed a HFD with a liver-selective THR-β agonist TG68 not only led to a reduction of hepatic fat accumulation and of serum triglycerides and cholesterol, but also caused a significant reduction of the number and size of DEN-induced preneoplastic hepatic lesions. We demonstrated that TG68 negatively influenced the carcinogenic process through an induction of a differentiation program of preneoplastic hepatocytes, as indicated by the loss of the preneoplastic marker GSTP, which is absent in differentiated hepatocytes.
The TG68-induced shift towards a differentiated phenotype was further supported by histochemical analysis showing reacquisition of G6Pase activity, an established marker of differentiated hepatocytes. Furthermore, the regression of preneoplastic lesions was associated with a return to a differentiated phenotype and was also sustained by the enhanced expression of Klf9, a transcription factor involved in the regulation of the balance between pluripotency, self-renewal and differentiation [44]. As reported by Cvoro et al. [ 45], THRs cooperate with Klf9 to regulate hepatocyte differentiation and THR activation leads to KLF9 induction in transformed and non-transformed liver cells, and in stem cells. We also report that TG68 caused the regression of preneoplastic lesions by inducing a differentiated biochemical phenotype, as we observed an almost complete disappearance of G6PD, the rate-limiting enzyme of the oxidative branch of the pentose phosphate pathway (PPP). Remarkably, in our previous study we showed that an increase in G6PD expression in HCC patients was significantly associated with high-grade HCCs, and positively correlated with metastasis formation and decreased overall survival [46].
Although previous studies have already demonstrated the effectiveness of several THRβ agonists in reducing hepatic steatosis in animal models (47–49), the effect of THRβ-selective thyromimetics on the regression of preneoplastic lesions remained largely unexplored. The importance of the results obtained in our study regards the fact that at present there are no FDA-approved drugs for the treatment of NAFLD and only limited therapeutic options are currently available for this tumor type.
In our study, we investigated also whether reactivation of the T3/THR axis in the preneoplastic lesions by another THRβ agonist – Resmetirom (MGL-3196) - may exert the same effect on the regression of preneoplastic lesions induced by the DEN+HFD regimen. Resmetirom, a liver-directed THRβ agonist orally administered, entered a Phase 3 clinical trial [8]. In a recent study, Resmetirom-treated patients showed a significant reduction of hepatic fat compared with placebo [30]. Moreover, treatment with MGL-3196 reduced markers of fibrosis in adults with biopsy-confirmed NASH [50]. In our previous study [33], MGL-3196 and TG68 shared the capacity to reduce hepatic steatosis in mice fed a HFD diet. In the current study, while both drugs strongly reduced the content of fat accumulation in the rat liver, only TG68 exerted a striking anti-tumorigenic effect, as no significant change in the number of GSTP+ foci was induced by MGL-3196. Further studies aimed at the elucidation of this different effect on the regression of preneoplastic lesions between MGL-3196 and TG68 are needed.
While considering the potential therapeutic use of THRβ-selective thyromimetics for NAFLD and NAFLD-related HCC, adverse effects on the heart should be considered. In this regard, a relevant observation stemming from this study is the lack of toxicity of TG68 on extra-hepatic organs, such as the heart. Indeed, while one of the most important adverse effects limiting the clinical use of thyroid hormone is its cardiotoxicity, neither macroscopic nor histological analyses of the cardiac tissue showed detectable signs of toxicity after treatment with this THRβ-selective thyromimetic. Based on these observations and on the finding that no change of the expression of Myh6, a target of activated THRs [38] occurred following TG68, we conclude that TG68 is sufficiently safe for use in long-term therapies. Nevertheless, other preclinical studies are required prior to its use in clinical trials.
In the light of the lack of approved pharmacological strategies for NAFLD and limited therapeutic options for NAFLD-related HCC, the results obtained in the present study suggest that the novel liver THRβ agonist TG68 might represent an attractive candidate for the treatment of NAFLD and NAFLD-related HCC (Figure 7).
**Figure 7:** *A schematic representation of the effect of TG68 on hepatic steatosis and DEN-induced hepatocarcinogenesis. Treatment with TG68 led to a significant reduction in hepatic steatosis, circulating triglycerides, cholesterol and caused regression of DEN-induced preneoplastic lesions associated with a differentiation program. DEN, Diethylnitrosamine; HFD, High Fat Diet; GSTP, placental form of glutathione-S-transferase; KLF9, Kruppel-like factor 9; G6Pase, Glucose-6-phosphatase; TGs, Triglycerides; CH, Cholesterol. Figure was created with BioRender.com.*
## Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
## Ethics statement
All animal procedures were approved by the Italian Ministry of Health (the authorization codes are $\frac{1247}{15}$-PR and $\frac{560}{2019}$-PR), complied with national ethical guidelines for animal experimentation and were conducted in accordance with the guidelines of the local ethical committee for in vivo experimentation.
## Author contributions
ACa, AP, MK performed the in vivo experiments and analyzed data. AP performed histopathologic classification. ACa, MS analyzed gene expression profile. ACa, MK, MS, FS performed histochemistry and immunohistochemistry. AB, SR synthesized TG68 for in vivo studies. CM proofread the manuscript. ACo, AP, MK conceived and supervised the study, provided funding, wrote the manuscript. All authors contributed to the article and approved the submitted version.
## Conflict of interest
ACo, SR and AP are inventors of a patent related to TG68 and analogs.
The remaining 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.
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|
---
title: 'Yoga and pain: A mind-body complex system'
authors:
- Deepak Chopra
- Eddie Stern
- William C. Bushell
- Ryan D. Castle
journal: Frontiers in Pain Research
year: 2023
pmcid: PMC9996306
doi: 10.3389/fpain.2023.1075866
license: CC BY 4.0
---
# Yoga and pain: A mind-body complex system
## Abstract
### Introduction
The human body's response to pain is indicative of a complex adaptive system. Therapeutic yoga potentially represents a similar complex adaptive system that could interact with the pain response system with unique benefits.
### Objectives
To determine the viability of yoga as a therapy for pain and whether pain responses and/or yoga practice should be considered complex adaptive systems.
### Methods
Examination through 3 different approaches, including a narrative overview of the evidence on pain responses, yoga, and complex system, followed by a network analysis of associated keywords, followed by a mapping of the functional components of complex systems, pain response, and yoga.
### Results
The narrative overview provided extensive evidence of the unique efficacy of yoga as a pain therapy, as well as articulating the relevance of applying complex systems perspectives to pain and yoga interventions. The network analysis demonstrated patterns connecting pain and yoga, while complex systems topics were the most extensively connected to the studies as a whole.
### Conclusion
All three approaches support considering yoga a complex adaptive system that exhibits unique benefits as a pain management system. These findings have implications for treating chronic, pervasive pain with behavioral medicine as a systemic intervention. Approaching yoga as complex system suggests the need for research of mind-body topics that focuses on long-term systemic changes rather than short-term isolated effects.
## Introduction
The human body's response to pain is indicative of a complex adaptive system. While some pain interventions are simple and mechanical, others are themselves complex systems capable of interfacing and influencing the body's nervous system. Therapeutic yoga, in addition to other forms of mind-body therapy, potentially represents such a complex adaptive system. Its multimodal approach produces results expected from a complex adaptive system, more so than could be expected from reducing therapeutic yoga to simple calisthenics. Making this determination is valuable for determining the efficacy and range of benefits related to therapeutic yoga, as complex systems cannot be thoroughly modeled with reductionist methodologies. Without understanding the systemic effect yoga can have on pain, optimal treatment plans will remain incomplete and susceptible to overuse of temporary analgesics such as opioids.
## Background
Yoga has long been used as a treatment for pain, especially chronic pain, and the interactions between pain responses and yoga practice are well documented. Many of the interactions between yoga practice and pain responses demonstrate behavior common to complex systems, which could necessitate new methodologies to study pain management. As current research has not confirmed the presence of such a system, an examination of the basic mechanisms behind pain responses, yoga as a pain treatment, and complex systems is necessary in order to identify whether it is likely a complex system may be present.
## Pain
Pain is an informative sense perception of the brain, an interpretation of signals sent from the limbs, muscles, and organs via afferent nerves of the peripheral nervous system, as a protective modality. In contrast to other information-based organs such as the eyes, ears, etc., the sense of pain serves as a deterrent in order to prevent more pain, avoid dangerous situations, and provide diagnostic information about which unseen part of the body (under the skin) might be the cause of pain [1]. There are no pain nerves as such, just signals that the brain determines should be tagged as painful [2, 3]. Pain is termed nociceptive, the sensory receptors that detect signals from damaged tissues are called nociceptors, and the central nervous system's process of interpreting pain signals is called nociception. There are different types of nociceptors that convey thermal, mechanical, and chemical messages, and silent nociceptors that become responsive during periods of inflammation.
The sensory feedback system of pain responses is a critical component of pain management and one of the main areas of potential interaction with the practice of yoga.
## Yoga
Though sometimes reduced to an exercise program, yoga is a multi-modality practice dating to at least 2700 BCE and which encompasses a variety of components. Traditional yoga observances include postures, breathing, behavior, meditation, and devotional practices that primarily serve the philosophical undertaking of self-knowledge or spiritual liberation [4]. In ancient India, dating to approximately 1st to 2nd century BCE, yoga was also described as a practice that steadies the sense organs [5].
Yoga's relationship with sensory perception is relevant to its use in pain management. The sense organs are pathways of perception that convey incoming information from the environment to the central nervous system [6]. In the human body there are sensory nerve fibers that convey information to the central nervous system, often divided in external perceptions (exteroception): detecting pressure, pain, change in temperature, and internal perceptions (interoception): the need for food, sleep, and evacuation of the bowels and bladder [3, 7]. Figure 1 outlines the basic function of interoception and exteroception. In traditional yoga systems the two sensory pathways of exteroception and interoception are not mutually exclusive [8].
**Figure 1:** *Interoceptive and exteroceptive perception.*
11th to 17th century texts (Hatha Yoga Pradipika, Gheranda Samhita, Yoga Hatharatnavali, Siva Samhita) describe the practice of yoga for strength-training, flexibility, bolstering the immune system, and improving longevity [9]. Contemporary yoga practices have been adapted into the cultural milieu of almost every country in the world, postures (asanas) having become the primary indicator or representation of yoga practice [10]. In addition to cultural purposes, the modern practice of yoga has a growing body of evidence-based research showing efficacy as an adjunctive mind-body therapy and is increasingly used to improve healthcare outcomes (11–14).
Evidence supports the benefits of yoga for numerous health problems, including: •stress-reduction and improving debilitating pain [15, 16]•orthopedic problems such as back pain, knee pain, or other musculoskeletal illnesses [17, 18]•stomach pain [19]•psychological illnesses (20–22)•sleep disruption [23]•cardiovascular disease (24–26)•diabetes [27]•inflammatory disorders [28, 29]•immunological illnesses [30]Yoga's impact on pain is especially well documented and suitable for further examination given the thorough documentation of pain in patients and the reliability its associated biomarkers across the human system [31]. The combination of interactions between pain responses and yoga practice create the potential need for a complex systems approach.
## Complex systems
Complex systems are holistic phenomena that cannot be easily modeled through standard reductionist methods. The study of complex systems focuses on relationships between networks within a system and the sometimes unpredictable behavior that emerges when those relationships change [32]. Complex systems are not limited to any scholarly discipline and can occur in physics as readily as in neuroscience or economics. Examples include ant nests, climate events, the healthcare industry, or the immune system [33]. Scale is less relevant to a system than the strength of the relationships between its network of parts, or nodes [34]. Complex systems are often composted of other complex systems, like the economy being comprised of companies, being staffed by humans, all of which are complex systems.
The characteristics of a complex system are important to understanding the study of a system: •Emergent: its behavior is an emergent phenomenon that operates holistically. The behavior of the system as a whole cannot be reliably extrapolated from studying its parts in isolation, much like the ocean tides cannot be modeled from a drop of water.•Dynamic networks: interdependent means of communicating reactions between nodes are a critical part of the complexity of a system [35]. This network involves both unpredictable stochastic dynamics and multiple scales of simple interactions. Dropping a rock in a pond will send ripples through the pond’s interdependent parts, while dropping a rock on the sand beside the pond would have no such impact because it is outside the dynamic network.•Feedback loops: both damping and amplifying feedback cycles are found in complex systems, such that changes within the system can cause further, cyclical changes. This requires detectors to recognize input and agents to adjust behavior.•Open system: complex systems are generally energy-rate-dense and far from energetic equilibrium. To sustain cohesion they must take in energy as they expend energy overall, though they may maintain systemic stability [36].•Nonlinear: the effects of changes to a complex system are not necessarily proportional to the size of the change. Removing $10\%$ of a human’s body does not leave the human $90\%$ functional. A deeper component involves the adaptive capability of the system in question. Complex adaptive systems (CAS) are capable of changing in response to stimuli while continuing to self-organize [37]. Many biological or health systems are complex adaptive systems and cannot be fully understood unless approached as such [38].
Another method of determining systemic influence is by performing a network analysis. Network analyses quantify the strength of relationships within a connected system, revealing components that share various behaviors or qualities. Networks that show very weak or inconsistent connections across a network analysis are unlikely to represent a complex system.
The identification and study of complex systems is a valuable scientific process, especially within the fields of health and wellbeing [39]. If researchers expect their subject to demonstrate an isolated effect when it is actually a complex system, they will not be measuring the full range of interdependent ripple effects from their tests. If interventions are expected to produce a simple, linear reaction within one network, they will be unable to predict or explain a disproportionate reaction if the system is complex. Most importantly, attempts to explain a complex adaptive system's behavior by reviewing components in isolation will overlook the emergent properties of the system's behavior, producing outcomes that seem unpredictable or contradictory.
Health outcomes and interventions cannot be optimized without awareness and study of the complex adaptive systems being engaged [40].
## Methodology
This paper has multiple aims culminating in a comprehensive assessment of the existence and/or relevance of yoga’s interactions with pain as a complex adaptive system. 1.Evidence overview: a narrative summary of the evidence behind therapeutic yoga for pain. This will determine the efficacy of yogic interventions and which aspects of yoga have been reliably associated with pain responses.2.Literature review and network analysis: a collection of existing publications on the various aspects of this topic will be collated and relevant keywords isolated. Studies and keywords will be examined through network analysis to discover any patterns that could be used to determine the presence of a connected system.3.Complex adaptive system mapping: a visual map of the basic framework common to complex adaptive systems will be cross-referenced with mappings of pain responses and yoga’s interactions with pain pathways. This will help determine whether pain and yoga’s effects on it resemble a CAS.
## Evidence overview
This section will consist of a contextualized narrative of the evidence, as outlined by experts in yoga, chronic illness, mind-body interventions, and systems theory. As a summary this section will focus on clarity and qualifying explanations rather than isolated metrics.
## Literature review
The search terms below were used to capture studies relevant to this paper. Reasons for exclusion include being irrelevant to topic, hyperspecific (dealing exclusively with singular conditions/demographics), overly technical (genetic profiles, fMRI calibration, etc), or overly broad (hypotheticals, unverified predictions, speculation). Search terms were designed to identify factors relevant to this review, such as modes of perception or cultural/pain management components of yoga, in order to prevent overly generalized results. Search filters required the inclusion of abstracts and only studies related to humans. Databases searched include PubMed and arXiv.org. Figure 2 provides a diagram of the search and filtering process.
**Figure 2:** *Methodology for selection of literature, beginning with individual topics and ending with final total to be reviewed.*
## Network analysis
There were 706 initial results, after reviewing titles and abstracts this was reduced to 458 studies relevant to the purpose of this study, after elimination of duplicates the final total was 433. The relevant studies were then processed through factor mapping software, identifying tags and keywords to calculate commonalities and confluences across studies.
The combined keywords across the 433 studies resulted in 1,639 unique tagged keywords. Connections, intersections, and frequency of use across all studies was calculated for each tagged keyword. The results were modeled spatially then subjected to network analysis, as seen in Figure 3.
**Figure 3:** *Graphic representation of nodes made up of KEYWORDS (purple), STUDIES (blue), and TOPICS (red) captured in the literature review.*
The network analysis was performed to determine the studies most linked by these keywords and the results were sorted by the network metrics of degree, closeness, and eigenvector. Measured types of nodes include KEYWORD (content-specific tags relating to the subject of a study or studies), STUDY (peer-reviewed publications on the related topics), and TOPIC (the overarching subject matter behind the literature review search). The network and analytics are available online (Supplementary Material).
## Functional systems mapping
In order to identify whether yoga's interactions with pain constitute a complex adaptive system, a diagram of the essential functions required of a CAS was designed. Yoga and pain interactions will be overlaid onto relevant parts of the diagram to see if the systems correspond. A further overlay will examine the broader application of mind-body therapies for the same purpose.
## Pain pathways
Nerves carry messages regarding pain from the body through the peripheral nervous system to interneurons that pass that information to the brain. Interneurons can inhibit or speed up the passing of information through ion channels, which are temporary openings in the nerves that respond to different stimuli and make the nerves “fire” [3]. The brain responds in three basic ways to stimuli: •Peripheral sensitization, creating more inflammation for healing•Central sensitization, increasing sensitivity between the nerve from the injured area and the nerves to the brain•Cortical reorganization, the part of the brain that maps to the injured body part becomes bigger, ie. mirror therapyChronic pain causes the central nervous system to become more sensitive and signals increase accordingly, beginning a detrimental feedback loop. The brain maps sensations as information by tagging it according to neurological purposes (neuro-tags). The brain stem receives signals and interprets, associating the tagged information with higher centers related to emotions, memory, and perception. Though each individual's sensation map and pain perception differ, the system that creates those maps and perceptions are alike.
The perceptions tagged onto signals can make a significant difference in health outcomes following prolonged or chronic pain. Catastrophizing belief patterns such as “pain is terrifying,” or, “I’ll never recover from this or regain my earlier capabilities,” and other emotional issues contribute to increased levels of pain perception, which in turn increases the feedback loop of pain severity [41]. There is evidence that educating patients about the relationship between their brain, body, and pathways of pain leads to greater recovery, especially when combined with therapeutic exercises (42–44). Without education on pain, and especially for those that have low thresholds for pain sensitivity, catastrophizing can lead to slower recovery rates and greater levels of pain interpretation. Poor body awareness presents with higher levels of pain sensitivity, indicating that mind-body modalities like yoga that encourage positive body awareness and heightened levels of interoception are helpful adjunctive treatments for chronic pain [45, 46].
## Yoga and pain
Yoga is a multi-modality practice that uses both top-down and bottom-up interactions between the central nervous system and peripheral nervous system. These bidirectional interactions have an impact on physiological and emotional health, in part by controlling peripheral inflammatory responses that are involved in pain signaling [47]. Yoga is known through multiple studies to down-regulate sympathetic nervous system hyper-arousal through the HPA axis, increase parasympathetic activation through the vagal nerve complex, and reduce allostatic load to help stabilize the autonomic nervous system [46]. Improvement of vagal tone is closely linked to yoga and helps control the inflammatory response of the adrenergic reactions to chronic anxiety, providing potential mitigation of the detrimental feedback loop of stress and pain [2, 48].
Practice of yoga also leads to a decrease in pain perception through cognitive disengagement, leading to a decrease in the affective aspect of pain sensitivity and increasing interoception [46]. Pain tolerance is increased and the anxiety associated with pain are decreased through reducing the hyper-arousal of the HPA axis and thus reducing the output of stress hormones. This occurs through interaction with the brain structures that support the perception of stress and support mood and cognitive abilities, including the amygdala, insula, and hippocampus [48].
The movement of yoga provides pain benefits beyond basic calisthenics. Through gentle and safe movements, the relief from fear of movement being “bad” can be alleviated [46]. Yoga enhances positive body awareness through both interoception and proprioceptive integration, reducing the anticipation of pain and thereby reducing the sensitization to it. The catastrophizing that accompanies pain and the hesitancy to move can be alleviated, further reducing pain feedback loops. Gentle, guided movement also improves the over sensitization of the nervous system that can occur due to pain [41].
When the sympathetic-parasympathetic balance is restored due to yoga and other mind-body practices, new neuro-tags are created which allow for a reframing of past experiences [49]. Prolonged HPA activation leads to cognitive deficits due to high levels of cortisol, but yoga helps to downregulate the stress response and prevent this deterioration.
Movement is a critical part of yoga, but the effective yoga practice involves other mind-body components, especially meditation [50]. Mindfulness meditation is known to be helpful for reducing anticipatory thoughts, an important step in the sensitization process [51]. There is evidence that the anticipation of pain create neural response that neuro-tags information for pain perception, influencing entire networks of attention and stimuli response to anticipate pain [52]. A study on 160 chronic pain patients showed that greater acceptance of pain led to lower levels of reported pain perception, less pain anxiety, depression, and associated problems of chronic pain, all independent of pain intensity [53]. Yoga and meditative practices have a firm grounding in acceptance, surrender, and relaxation, which can contribute to cognitive reframing of the catastrophizing most strongly associated with perceived pain intensity [54].
As a multi-modality practice, yoga consists of postures, breathing practices, meditation, and attention to diet and sleep habits. Lifestyle habits can have systemic impacts on the neuro-tags attached to signals from the peripheral nervous system, as well as the neural structure itself [45]. A study on experienced yoga practitioners with matched controls showed more gray matter in multiple brain regions of yoga practitioners. An important region impacted included the insula, which is closely correlated with pain tolerance. Yoga practitioners also exhibited increased intra-insular white matter consistent with nociceptive input and parasympathetic regulation. These structural improvements in combination with cognitive reframing and improved interoceptive awareness suggest a significant influence on the system of pain perception [55].
## Complex systems of mind-body interactions
The pain response system, especially in relation to chronic pain, is considered typical of a complex adaptive system [56]. All of the required components are present in the nociceptive network, the body's behavior in response to pain is based on detectors and signaling agents, and the unpredictable nature of pain's long-term feedback loops are nonlinear emergent phenomena. The factors associated with the mind-body response to pain are widely studied as a complex system on multiple scales [57].
Approaching mind-body therapies for pain as CAS is much rarer and an overdue field of study. The CAS similarity between the mind-body response to pain and mind-body practices like yoga or meditation have been explored and suggested, but never quantified or outlined [58]. The simplest and clearest evidence that mind-body therapies are a CAS lies in the fact that mind-body therapies like yoga are capable of interacting with pain on a systemic level [59]. If pain responses are a complex adaptive system, and yoga has been shown to interface with the body's responses through similar methods and scales, logically yoga should be considered a CAS.
Practical concerns and public health utility require more detailed justification, however. A comparison of the mind-body response to both pain and yoga with the components of a complex adaptive system is warranted. •Emergent: through systemic interactions pain can produce behavior in the nervous system that cannot be easily reduced or isolated [60]. Yoga likewise involves exercises, breathing practices, and mental states that affect a wide number of agents on a systemic level [61]. Evidence suggests that isolated physical components of yoga can benefit specific conditions like hypertension, but combinations of breathing, meditation, and mental practices produce a wide range of physical and mental health benefits [15].•Dynamic networks: pain responses are communicated through the peripheral nervous network, HPA axis, and intricate series of interactions between large and small networks of nociceptive responses [62]. Mind-body therapies engage physiological networks associated with exercise, psychological networks associated with stress, and neurological networks associated with neuro-tagging pain signals [46]. Both of these sets of relationships represent dynamic networks consisting of stochastic interactions and multi-scalar systems.•Feedback loops: both pain and mind-body therapies demonstrate the ability to initiate and modulate feedback loops in humans. An example of a feedback loop influenced by both chronic pain and yoga is the inflammatory cycle [63].•Open system: as with nearly any biological function, both pain responses and mind-body practices are far from energetic equilibrium, requiring significant caloric intake and expending energy as either physical movement or neurological reaction [64].•Nonlinear: the effects of chronic pain are often dramatically disproportionate to the ongoing harm stimuli the body is experiencing, constituting a nonlinear reaction [65]. Mind-body interventions like yoga have likewise demonstrated improvements across physical, cognitive, and psychological health that are disproportionate to the amount of time spent practicing [66].
## Network analysis results
Table 1 outlines the search terms included in the network analysis and the bodies of results used to determine which articles and keywords to track.
**Table 1**
| 1. Topic: Pain responses |
| --- |
| 1.1. Search terms: (interocept*[Title/Abstract]) AND (nocicept*[Title/Abstract]); (exterocept*[Title/Abstract]) AND (nocicept*[Title/Abstract]); (sensitization*[Title/Abstract]) AND (catastrophiz*[Title/Abstract]); |
| 1.2. Number of initial results: 240 |
| 1.3. Number of relevant results after perusal: 124 |
| 2. Topic: Pain response and inflammation |
| 2.1. Search terms: (pain sensitization[Title/Abstract]) AND (inflamm*[Title/Abstract]); (catastrophiz*[Title/Abstract]) AND (inflamm*[Title/Abstract]) |
| 2.2. Number of initial results: 151 |
| 2.3. Number of relevant results after perusal: 77 |
| 3. Topic: Yoga cultural background |
| 3.1. Search terms: (yoga[Title/Abstract]) AND (cultural[Title/Abstract]) |
| 3.2. Number of initial results: 48 |
| 3.3. Number of relevant results after perusal: 33 |
| 4. Topic: Yoga and pain |
| 4.1. Search terms: (yoga[Title/Abstract]) AND (nocicept*[Title/Abstract]); (yoga[Title/Abstract]) AND (interocept*[Title/Abstract]); (yoga[Title/Abstract]) AND (pain catastroph*[Title/Abstract]); (yoga[Title/Abstract]) AND (cytokine*[Title/Abstract]) |
| 4.2. Number of initial results: 68 |
| 4.3. Number of relevant results after perusal: 59 |
| 5. Topic: Changing pain responses |
| 5.1. Search terms: (cortical reorganization[Title/Abstract]) AND (behavior[Title/Abstract]); (pain education[Title/Abstract]) AND (inflamm*[Title/Abstract]); (pain educat*[Title/Abstract]) AND (sensitiv*[Title/Abstract]) |
| 5.2. Number of initial results: 53 |
| 5.3. Number of relevant results after perusal: 32 |
| 6. Topic: Complex systems |
| 6.1. Search terms: ARXIV (complex system[Title/Abstract]) AND (dynamic*[Title/Abstract]) |
| 6.2. Number of initial results: 11 |
| 6.3. Number of relevant results after perusal: 10 |
| 7. Topic: Pain and complex systems |
| 7.1. Search terms: PUBMED; ARXIV (pain[Title/Abstract]) AND (complex adaptive system[Title/Abstract]) |
| 7.2. Number of initial results: 20 |
| 7.3. Number of relevant results after perusal: 8 |
| 8. Topic: Yoga efficacy (Due to a large number of responses the following search term was limited to systematic reviews to improve efficiency and reduce disproportionate weighting of keywords) |
| 8.1. (yoga[Title/Abstract]) AND (efficacy*[Title/Abstract]) |
| 8.2. Number of initial results: 115 |
| 8.3. Number of relevant results after perusal: 115 |
## Betweenness
Betweenness is a measure of centrality in a graph based on shortest paths. For every pair of node in a connected network, there exists at least one shortest path between the vertices such that the number of edges that the path passes through [67]. The betweenness for each node is the number of these shortest paths that pass through the node. Betweenness represents the degree to which nodes stand between each other. While other metrics identify nodes that have the greatest input or output, betweenness helps identify the most heavily trafficked pathways.
As seen in Table 2, the nodes with the highest betweenness tended to be STUDIES, with the highest values occurring in nodes about complex systems, followed closely by nodes related to pain pathways and pain management. There are a small number of nodes related to yoga or mind-body interventions No KEYWORDS were in the top 30.
**Table 2**
| Label | Type | Metric | Value |
| --- | --- | --- | --- |
| Small Open Chemical Systems Theory and Its Implications to Darwinian Evolutionary Dynamics, Complex Self-Organization and Beyond | Study | betweenness | 1.76e-05 |
| Extreme value theory of evolving phenomena in complex dynamical systems: firing cascades in a model of neural network | Study | betweenness | 1.66e-05 |
| Understanding and Modelling the Complexity of the Immune System: Systems Biology for Integration and Dynamical Reconstruction of Lymphocyte Multi-Scale Dynamics | Study | betweenness | 1.54e-05 |
| Complexity of Model Testing for Dynamical Systems with Toric Steady States | Study | betweenness | 1.5e-05 |
| Designer dynamics through chaotic traps: Controlling complex behavior in driven nonlinear systems | Study | betweenness | 7.55e-06 |
| Life as Complex Systems—Viewpoint from Intra-Inter Dynamics | Study | betweenness | 6.14e-06 |
| Angiotensin II Triggers Peripheral Macrophage-to-Sensory Neuron Redox Crosstalk to Elicit Pain | Study | betweenness | 5.35e-06 |
| Common Brain Mechanisms of Chronic Pain and Addiction | Study | betweenness | 4.44e-06 |
| Immediate preoperative outcomes of pain neuroscience education for patients undergoing total knee arthroplasty: A case series | Study | betweenness | 4.32e-06 |
| Applying Complexity Theory to a Dynamical Process Model of the Development of Pathological Belief Systems | Study | betweenness | 4.18e-06 |
| The evolution of self-control | Study | betweenness | 4e-06 |
| Low- Versus High-Intensity Plyometric Exercise During Rehabilitation After Anterior Cruciate Ligament Reconstruction | Study | betweenness | 3.95e-06 |
| Forecasting transitions in systems with high dimensional stochastic complex dynamics: A Linear Stability Analysis of the Tangled Nature Model | Study | betweenness | 3.58e-06 |
| A Mechanism-Based Approach to the Management of Osteoarthritis Pain | Study | betweenness | 3.53e-06 |
| Influence of a periodized circuit training protocol on intermuscular adipose tissue of patients with knee osteoarthritis: protocol for a randomized controlled trial | Study | betweenness | 3.33e-06 |
| Psychological processing in chronic pain: a neural systems approach | Study | betweenness | 3.21e-06 |
| Corticotrophin-releasing factor 1 activation in the central amygdale and visceral hyperalgesia | Study | betweenness | 3.2e-06 |
| Cultural adaptation framework of social interventions in mental health: Evidence-based case studies from low- and middle-income countries | Study | betweenness | 3.17e-06 |
| Elite competitive swimmers exhibit higher motor cortical inhibition and superior sensorimotor skills in a water environment | Study | betweenness | 3.13e-06 |
| Remote ischemic conditioning as a cytoprotective strategy in vasculopathies during hyperhomocysteinemia: An emerging research perspective | Study | betweenness | 3.12e-06 |
| Assessing for unique immunomodulatory and neuroplastic profiles of physical activity subtypes: a focus on psychiatric disorders | Study | betweenness | 3.12e-06 |
| Impact of complex, partially nested clustering in a three-arm individually randomized group treatment trial: A case study with the wHOPE trial | Study | betweenness | 3.06e-06 |
| Changes of meningeal excitability mediated by corticotrigeminal networks: a link for the endogenous modulation of migraine pain | Study | betweenness | 3.04e-06 |
| A Dynamical Similarity Approach to the Foundations of Complexity and Coordination in Multiscale Systems | Study | betweenness | 2.87e-06 |
| Creating Inclusive Physical Activity Spaces: The Case of Body-Positive Yoga | Study | betweenness | 2.86e-06 |
| Using focus group methods to develop multicultural cancer pain education materials | Study | betweenness | 2.77e-06 |
| A Randomized, Single-Blind Study Evaluating the Effect of a Bone Pain Education Video on Reported Bone Pain in Patients with Breast Cancer Receiving Chemotherapy and Pegfilgrastim | Study | betweenness | 2.75e-06 |
| A dynamical model of fast cortical reorganization | Study | betweenness | 2.74e-06 |
| General Pathways of Pain Sensation and the Major Neurotransmitters Involved in Pain Regulation | Study | betweenness | 2.74e-06 |
| Complex System | Topics | betweenness | 0.000119 |
## Closeness
The value of closeness centrality, or closeness, determines the distance each vertex is from every other vertex. Points with high closeness tend to be highly correlated with the trends of the broader network. In this study closeness reflects many of the same patterns as degree centrality with slight variations due to interpreting limited quality from its connections, meaning connection to other highly connected vertices increases the value. This indicates a highly interdependent part of the network.
As seen in Table 3, the nodes with the largest closeness values were TOPICS, especially related to pain pathways, yoga interventions, and inflammation. Complex systems ranked relatively low as a TOPIC. STUDIES were significantly lower in value and were led by nodes related to pain pathways, inflammation, sensitization/catastrophization, and mind-body treatments. No KEYWORDS were present in the top 30.
**Table 3**
| Label | Type | Metric | Value |
| --- | --- | --- | --- |
| Angiotensin II Triggers Peripheral Macrophage-to-Sensory Neuron Redox Crosstalk to Elicit Pain | Study | closeness | 0.01494 |
| Psychological processing in chronic pain: a neural systems approach | Study | closeness | 0.014458 |
| Immediate preoperative outcomes of pain neuroscience education for patients undergoing total knee arthroplasty: A case series | Study | closeness | 0.013976 |
| Influence of a periodized circuit training protocol on intermuscular adipose tissue of patients with knee osteoarthritis: protocol for a randomized controlled trial | Study | closeness | 0.013976 |
| Biopsychosocial Influence on Shoulder Pain: Influence of Genetic and Psychological Combinations on Twelve-Month Postoperative Pain and Disability Outcomes | Study | closeness | 0.013494 |
| Common Brain Mechanisms of Chronic Pain and Addiction | Study | closeness | 0.01253 |
| Assessing for unique immunomodulatory and neuroplastic profiles of physical activity subtypes: a focus on psychiatric disorders | Study | closeness | 0.012048 |
| Low- Versus High-Intensity Plyometric Exercise During Rehabilitation After Anterior Cruciate Ligament Reconstruction | Study | closeness | 0.012048 |
| A Mechanism-Based Approach to the Management of Osteoarthritis Pain | Study | closeness | 0.011566 |
| Disease-Related, Nondisease-Related, and Situational Catastrophizing in Sickle Cell Disease and Its Relationship With Pain | Study | closeness | 0.011566 |
| Mind-body therapies and control of inflammatory biology: A descriptive review | Study | closeness | 0.011566 |
| Pain, psychosocial tests, pain sensitization and laparoscopic pelvic surgery | Study | closeness | 0.011566 |
| Pilot study of inflammatory responses following a negative imaginal focus in persons with chronic pain: analysis by sex/gender | Study | closeness | 0.011566 |
| Biopsychosocial influence on shoulder pain: Rationale and protocol for a pre-clinical trial | Study | closeness | 0.011084 |
| Generalized Pain Sensitization and Endogenous Oxytocin in Individuals With Symptoms of Migraine: A Cross-Sectional Study | Study | closeness | 0.011084 |
| Inflammation-induced pain sensitization in men and women: does sex matter in experimental endotoxemia? | Study | closeness | 0.011084 |
| Mindfulness-based stress reduction in relation to quality of life, mood, symptoms of stress, and immune parameters in breast and prostate cancer outpatients | Study | closeness | 0.011084 |
| Pain Catastrophizing and Quality of Life in Adults With Chronic Rhinosinusitis | Study | closeness | 0.011084 |
| Painful After-Sensations in Fibromyalgia are Linked to Catastrophizing and Differences in Brain Response in the Medial Temporal Lobe | Study | closeness | 0.011084 |
| Benefits of Yoga on IL-6: Findings from a Randomized Controlled Trial of Yoga for Depression | Study | closeness | 0.010602 |
| Intensive virtual reality and robotic based upper limb training compared to usual care, and associated cortical reorganization, in the acute and early sub-acute periods post-stroke: a feasibility study | Study | closeness | 0.010602 |
| Self-help Cognitive Behavioral Therapy Improves Health-Related Quality of Life for Inflammatory Bowel Disease Patients: A Randomized Controlled Effectiveness Trial | Study | closeness | 0.010602 |
| The effect of threat information on acquisition, extinction, and reinstatement of experimentally conditioned fear of movement-related pain | Study | closeness | 0.010602 |
| PAIN RESPONSES | Topics | closeness | 0.183614 |
| Yoga Efficacy | Topics | closeness | 0.167711 |
| Pain and Inflammation | Topics | closeness | 0.141446 |
| Yoga and Pain Response | Topics | closeness | 0.109398 |
| Rewriting Pain | Topics | closeness | 0.076145 |
| Yoga Cultural | Topics | closeness | 0.064578 |
| Complex Systems and Pain | Topics | closeness | 0.014578 |
## Degree centrality and indegree
The measurement of degree centrality, or degree, involves a basic, undirected count of the total connections linked to a vertex. It is solely based in quantity; the quality of connections does not affect the value. Degree centrality can be useful for identifying popular connectors or local hubs, but it does not necessarily reflect the behavior of the broader network. For the purposes of this study, degree centrality generally tracks studies which the largest number of relevant keywords. Indegree is a submetric of degree centrality that exclusively measures a node's incoming connections. Nodes that have disproportionate incoming connections tend to be destinations for information or have an output outside of the network.
As seen in Table 4, KEYWORDS were the largest group in degree centrality and the only group for indegree metrics. For both groups demographic identifiers were the highest ranking, followed by nodes related to yoga, pain, and pain pathways. Pain responses and yoga's efficacy were the highest ranking TOPICS. Only one STUDY was in the top 30, related to neurological measurements of pain.
**Table 4**
| Label | Type | Metric | Value |
| --- | --- | --- | --- |
| HUMANS | Keyword | degree | 408 |
| FEMALE | Keyword | degree | 181 |
| MALE | Keyword | degree | 149 |
| ADULT | Keyword | degree | 143 |
| YOGA | Keyword | degree | 127 |
| MIDDLE AGED | Keyword | degree | 120 |
| PAIN | Keyword | degree | 98 |
| AGED | Keyword | degree | 65 |
| PAIN MEASUREMENT | Keyword | degree | 65 |
| QUALITY OF LIFE | Keyword | degree | 65 |
| CHRONIC PAIN | Keyword | degree | 58 |
| YOUNG ADULT | Keyword | degree | 54 |
| CATASTROPHIZATION | Keyword | degree | 46 |
| MEDITATION | Keyword | degree | 45 |
| RANDOMIZED CONTROLLED TRIALS AS TOPIC | Keyword | degree | 45 |
| TREATMENT OUTCOME | Keyword | degree | 43 |
| DEPRESSION | Keyword | degree | 42 |
| SURVEYS AND QUESTIONNAIRES | Keyword | degree | 41 |
| PAIN THRESHOLD | Keyword | degree | 39 |
| ADOLESCENT | Keyword | degree | 37 |
| INFLAMMATION | Keyword | degree | 37 |
| CROSS-SECTIONAL STUDIES | Keyword | degree | 35 |
| ANXIETY | Keyword | degree | 34 |
| CENTRAL NERVOUS SYSTEM SENSITIZATION | Keyword | degree | 33 |
| EXERCISE | Keyword | degree | 31 |
| Angiotensin II Triggers Peripheral Macrophage-to-Sensory Neuron Redox Crosstalk to Elicit Pain | Study | degree | 32 |
| PAIN RESPONSES | Topics | degree | 113 |
| Yoga Efficacy | Topics | degree | 110 |
| Pain and Inflammation | Topics | degree | 74 |
| Yoga and Pain Response | Topics | degree | 56 |
| HUMANS | Keyword | indegree | 408 |
| FEMALE | Keyword | indegree | 181 |
| MALE | Keyword | indegree | 149 |
| ADULT | Keyword | indegree | 143 |
| YOGA | Keyword | indegree | 127 |
| MIDDLE AGED | Keyword | indegree | 120 |
| PAIN | Keyword | indegree | 98 |
| AGED | Keyword | indegree | 65 |
| PAIN MEASUREMENT | Keyword | indegree | 65 |
| QUALITY OF LIFE | Keyword | indegree | 65 |
| CHRONIC PAIN | Keyword | indegree | 58 |
| YOUNG ADULT | Keyword | indegree | 54 |
| CATASTROPHIZATION | Keyword | indegree | 46 |
| MEDITATION | Keyword | indegree | 45 |
| RANDOMIZED CONTROLLED TRIALS AS TOPIC | Keyword | indegree | 45 |
| TREATMENT OUTCOME | Keyword | indegree | 43 |
| DEPRESSION | Keyword | indegree | 42 |
| SURVEYS AND QUESTIONNAIRES | Keyword | indegree | 41 |
| PAIN THRESHOLD | Keyword | indegree | 39 |
| ADOLESCENT | Keyword | indegree | 37 |
| INFLAMMATION | Keyword | indegree | 37 |
| CROSS-SECTIONAL STUDIES | Keyword | indegree | 35 |
| ANXIETY | Keyword | indegree | 34 |
| CENTRAL NERVOUS SYSTEM SENSITIZATION | Keyword | indegree | 33 |
| COMPLEMENTARY THERAPIES | Keyword | indegree | 31 |
| EXERCISE | Keyword | indegree | 31 |
| ANIMALS | Keyword | indegree | 30 |
| MINDFULNESS | Keyword | indegree | 24 |
| EXERCISE THERAPY | Keyword | indegree | 23 |
| STRESS, PSYCHOLOGICAL | Keyword | indegree | 23 |
## Eigenvector
Where degree centrality strictly measures quantity, and closeness centrality measures quantity with a small influence of quality, eigenvector centrality emphasizes quality of connection over quantity. Eigenvector values measure how well connected any given vertex is to the other most well-connected vertices. *In* general vertices with high eigenvector values reflect the leading edge of a network. Though they may not be as widely connected as other values, they tend to have disproportionate influence on the system. In the context of this study, eigenvector centrality is associated with nonlinear connections between areas of study.
As seen in Table 5, STUDIES had some of the highest value eigenvector nodes, and were nearly all related to biological complex systems. KEYWORDS were the largest block of high ranking eigenvector nodes, with all the top nodes being related to complex systems and modeling. Immune and inflammatory systems were next, followed by nodes related to pain, then several related to biology and physics. There was one TOPIC node, referencing complex systems.
**Table 5**
| Label | Type | Metric | Value |
| --- | --- | --- | --- |
| MODELING, COMPLEX SYSTEM | Keyword | eigenvector | 0.030769 |
| QUANTITATIVE BIOLOGY—QUANTITATIVE METHODS | Keyword | eigenvector | 0.030769 |
| BIOLOGICAL SYSTEM | Keyword | eigenvector | 0.023077 |
| MODELING, BIOLOGICAL | Keyword | eigenvector | 0.023077 |
| NONLINEAR SCIENCES—ADAPTATION AND SELF-ORGANIZING SYSTEMS | Keyword | eigenvector | 0.023077 |
| QUANTITATIVE BIOLOGY—NEURONS AND COGNITION | Keyword | eigenvector | 0.023077 |
| COMPLEX ADAPTIVE SYSTEM | Keyword | eigenvector | 0.015385 |
| NONLINEAR SCIENCES—CHAOTIC DYNAMICS | Keyword | eigenvector | 0.015385 |
| COMPUTER SCIENCE—SYMBOLIC COMPUTATION | Keyword | eigenvector | 0.007692 |
| IMMUNE SYSTEM | Keyword | eigenvector | 0.007692 |
| INFLAMMATION | Keyword | eigenvector | 0.007692 |
| MATHEMATICS—ALGEBRAIC GEOMETRY | Keyword | eigenvector | 0.007692 |
| MATHEMATICS—DYNAMICAL SYSTEMS | Keyword | eigenvector | 0.007692 |
| PAIN | Keyword | eigenvector | 0.007692 |
| PAIN RESPONSE | Keyword | eigenvector | 0.007692 |
| PHYSICS—BIOLOGICAL PHYSICS | Keyword | eigenvector | 0.007692 |
| PHYSICS—CHEMICAL PHYSICS | Keyword | eigenvector | 0.007692 |
| QUANTITATIVE BIOLOGY | Keyword | eigenvector | 0.007692 |
| QUANTITATIVE BIOLOGY—CELL BEHAVIOR | Keyword | eigenvector | 0.007692 |
| QUANTITATIVE BIOLOGY—TISSUES AND ORGANS | Keyword | eigenvector | 0.007692 |
| A Dynamical Similarity Approach to the Foundations of Complexity and Coordination in Multiscale Systems | Study | eigenvector | 0.069231 |
| Applying Complexity Theory to a Dynamical Process Model of the Development of Pathological Belief Systems | Study | eigenvector | 0.069231 |
| Complexity of Model Testing for Dynamical Systems with Toric Steady States | Study | eigenvector | 0.069231 |
| Designer dynamics through chaotic traps: Controlling complex behavior in driven nonlinear systems | Study | eigenvector | 0.069231 |
| Extreme value theory of evolving phenomena in complex dynamical systems: firing cascades in a model of neural network | Study | eigenvector | 0.069231 |
| Forecasting transitions in systems with high dimensional stochastic complex dynamics: A Linear Stability Analysis of the Tangled Nature Model | Study | eigenvector | 0.069231 |
| Life as Complex Systems—Viewpoint from Intra-Inter Dynamics | Study | eigenvector | 0.069231 |
| Small Open Chemical Systems Theory and Its Implications to Darwinian Evolutionary Dynamics, Complex Self-Organization and Beyond | Study | eigenvector | 0.069231 |
| Understanding and Modelling the Complexity of the Immune System: Systems Biology for Integration and Dynamical Reconstruction of Lymphocyte Multi-Scale Dynamics | Study | eigenvector | 0.069231 |
| Complex System | Topics | eigenvector | 0.069231 |
## Reach efficiency
Reach measures the portion of the network within two steps of an element. *In* general, elements with high reach can spread information through the network through close friend-of-a-friend contacts. Reach efficiency normalizes reach by dividing it by size (number of neighbors). *In* general, elements with high reach efficiency are less connected but gain more exposure through each direct relationship. Reach efficiency is useful for determining influence as well as indicating how coherent and consistent that influence is.
As seen in Table 6, reach efficiency was mostly split between KEYWORDS and STUDIES in terms of quantity, but the highest values were among STUDIES. The leading STUDY nodes all involved complex systems and pain. The leading KEYWORDS were highly heterogeneous and patterns were not readily identifiable.
**Table 6**
| Label | Type | Metric | Value |
| --- | --- | --- | --- |
| ACCIDENTS, HOME | Keyword | reach-efficiency | 0.000241 |
| ACQUIRED BRAIN INJURY | Keyword | reach-efficiency | 0.000241 |
| ANALYSIS OF VARIANCE | Keyword | reach-efficiency | 0.000241 |
| ANTERIOR CINGULATE CORTEX | Keyword | reach-efficiency | 0.000241 |
| CANCER RELATED PAIN | Keyword | reach-efficiency | 0.000241 |
| CELLS, CULTURED | Keyword | reach-efficiency | 0.000241 |
| COUNSELING | Keyword | reach-efficiency | 0.000241 |
| GASTROESOPHAGEAL REFLUX | Keyword | reach-efficiency | 0.000241 |
| LASERS | Keyword | reach-efficiency | 0.000241 |
| LEADERSHIP | Keyword | reach-efficiency | 0.000241 |
| PSYCHOLOGICAL TREATMENT | Keyword | reach-efficiency | 0.000241 |
| REPETITIVE SENSORY STIMULATION | Keyword | reach-efficiency | 0.000241 |
| SUPINE POSITION | Keyword | reach-efficiency | 0.000241 |
| A Dynamical Similarity Approach to the Foundations of Complexity and Coordination in Multiscale Systems | Study | reach-efficiency | 0.001252 |
| COMPLEX SYSTEMS | Study | reach-efficiency | 0.001252 |
| Forecasting transitions in systems with high dimensional stochastic complex dynamics: A Linear Stability Analysis of the Tangled Nature Model | Study | reach-efficiency | 0.001252 |
| Life as Complex Systems—Viewpoint from Intra-Inter Dynamics | Study | reach-efficiency | 0.001252 |
| Applying Complexity Theory to a Dynamical Process Model of the Development of Pathological Belief Systems | Study | reach-efficiency | 0.001124 |
| Designer dynamics through chaotic traps: Controlling complex behavior in driven nonlinear systems | Study | reach-efficiency | 0.001124 |
| Complexity of Model Testing for Dynamical Systems with Toric Steady States | Study | reach-efficiency | 0.001032 |
| CRPS: A contingent hypothesis with prostaglandins as crucial conversion factor | Study | reach-efficiency | 0.001032 |
| Small Open Chemical Systems Theory and Its Implications to Darwinian Evolutionary Dynamics, Complex Self-Organization and Beyond | Study | reach-efficiency | 0.001032 |
| Extreme value theory of evolving phenomena in complex dynamical systems: firing cascades in a model of neural network | Study | reach-efficiency | 0.000963 |
| Structure and dynamics of dynorphin peptide and its receptor | Study | reach-efficiency | 0.000963 |
| Painful intelligence: What AI can tell us about human suffering | Study | reach-efficiency | 0.00091 |
| Understanding and Modelling the Complexity of the Immune System: Systems Biology for Integration and Dynamical Reconstruction of Lymphocyte Multi-Scale Dynamics | Study | reach-efficiency | 0.00091 |
| Hormesis, adaptation, and the sandpile model | Study | reach-efficiency | 0.000867 |
| Pain pathogenesis in rheumatoid arthritis—what have we learned from animal models | Study | reach-efficiency | 0.000867 |
| A Deep Learning Approach to Diagnosing Multiple Sclerosis from Smartphone Data | Study | reach-efficiency | 0.000803 |
| Complex adaptive systems allostasis in fibromyalgia | Study | reach-efficiency | 0.000803 |
## Synthesis
All nodes (studies, topics, and keywords) were mapped through the network analysis and the highest 30 values for each network metric isolated. Every node was cross-referenced and any node that had multiple high network metric values were highlighted for examination. Leaders in this synthesis will be helpful in identifying the similarities in keywords and patterns of topics between fields that are not usually linked.
As seen in Table 7, several nodes were high ranking across three metrics, potentially acting as indicators of the broader pattern of the research.
**Table 7**
| Label | Type |
| --- | --- |
| A Dynamical Similarity Approach to the Foundations of Complexity and Coordination in Multiscale Systems | Study |
| Angiotensin II Triggers Peripheral Macrophage-to-Sensory Neuron Redox Crosstalk to Elicit Pain | Study |
| Applying Complexity Theory to a Dynamical Process Model of the Development of Pathological Belief Systems | Study |
| Complexity of Model Testing for Dynamical Systems with Toric Steady States | Study |
| Designer dynamics through chaotic traps: Controlling complex behavior in driven nonlinear systems | Study |
| Extreme value theory of evolving phenomena in complex dynamical systems: firing cascades in a model of neural network | Study |
| Forecasting transitions in systems with high dimensional stochastic complex dynamics: A Linear Stability Analysis of the Tangled Nature Model | Study |
| INFLAMMATION | Keyword |
| Life as Complex Systems—Viewpoint from Intra-Inter Dynamics | Study |
| PAIN | Keyword |
| Small Open Chemical Systems Theory and Its Implications to Darwinian Evolutionary Dynamics, Complex Self-Organization and Beyond | Study |
| Understanding and Modelling the Complexity of the Immune System: Systems Biology for Integration and Dynamical Reconstruction of Lymphocyte Multi-Scale Dynamics | Study |
The majority of the most influential nodes were STUDIES relating to complex systems and nodes relating to pain pathways. This suggests there are significant intersections between the study of complex systems and pain.
As seen in Table 8, a larger number of nodes were leaders among two metrics, and could be considered part of a larger, defining pattern between the fields of pain management and mind-body therapies.
**Table 8**
| Label | Type |
| --- | --- |
| A Mechanism-Based Approach to the Management of Osteoarthritis Pain | Study |
| ADOLESCENT | Keyword |
| ADULT | Keyword |
| AGED | Keyword |
| ANXIETY | Keyword |
| Assessing for unique immunomodulatory and neuroplastic profiles of physical activity subtypes: a focus on psychiatric disorders | Study |
| CATASTROPHIZATION | Keyword |
| CENTRAL NERVOUS SYSTEM SENSITIZATION | Keyword |
| CHRONIC PAIN | Keyword |
| Common Brain Mechanisms of Chronic Pain and Addiction | Study |
| Complex System | Topics |
| CROSS-SECTIONAL STUDIES | Keyword |
| DEPRESSION | Keyword |
| EXERCISE | Keyword |
| FEMALE | Keyword |
| HUMANS | Keyword |
| Immediate preoperative outcomes of pain neuroscience education for patients undergoing total knee arthroplasty: A case series | Study |
| Influence of a periodized circuit training protocol on intermuscular adipose tissue of patients with knee osteoarthritis: protocol for a randomized controlled trial | Study |
| Low- Versus High-Intensity Plyometric Exercise During Rehabilitation After Anterior Cruciate Ligament Reconstruction | Study |
| MALE | Keyword |
| MEDITATION | Keyword |
| MIDDLE AGED | Keyword |
| Pain and Inflammation | Topics |
| PAIN MEASUREMENT | Keyword |
| PAIN RESPONSES | Topics |
| PAIN THRESHOLD | Keyword |
| Psychological processing in chronic pain: a neural systems approach | Study |
| QUALITY OF LIFE | Keyword |
| RANDOMIZED CONTROLLED TRIALS AS TOPIC | Keyword |
| SURVEYS AND QUESTIONNAIRES | Keyword |
| TREATMENT OUTCOME | Keyword |
| YOGA | Keyword |
| Yoga and Pain Response | Topics |
| Yoga Efficacy | Topics |
| YOUNG ADULT | Keyword |
Setting aside demographic identifier KEYWORDS, the nodes that were leading in two separate metrics tend to involve pain management, chronic pain, neuroscience, pain education, sensitization/catastrophization, and yoga or meditation. This suggests that the broader literature review highlights the close connections between chronic pain and mind-body therapies.
Combining the blocks of synthesized findings provides evidence that the fields of study regarding complex systems, pain management, and mind-body therapies share many of the same topics, keywords, and published studies. The literature review suggests the fields share significant patterns.
## Functional systems mapping results
Figure 4 reflects a basic map of the essential functions and components of a complex adaptive system. Any potential CAS should possess elements that fulfill every function and operate at a net energy loss. 1.Energy and information: the external environment interacting with the system2.Input: a means of absorbing this energy or information transfers it into the interconnected system3.Detectors: identify and react to the new input, changing the behavior of the system4.Dynamic networks: changing relationships in response to the detector’s signals throughout the system, these networks include a.Multi scalar interactions: simple mechanics on many different scalesb. Stochastic dynamics: unpredictable, highly sensitive reactions and feedback loops5.Agents: mechanisms by which the system actively modulates and adjusts behavior in response to the changes in the dynamic network6.Nonlinear effects: a secondary feature of the dynamic network interactions includes nonlinear effects, systemic and often disproportionate reactions to changes7.Emergent behavior: the combination of active adjustments by agents and unpredictable nonlinear effects results in emergent behavior that is inextricably part of a holistic pattern and cannot be reliably modeled in isolation8.Output: the behavioral changes of the system often interact with environment outside the system and modify its relationship with it a.Feedback loops: the changes in output can shift the way the initial input is received, altering the entire set of reactions in either positive or negative feedback loopsb. Energy loss: the agency and behavior of the system expends energy, usually back into the outside environment, in what is considered an open system
**Figure 4:** *Simplified functional map of a complex adaptive system. Grey-scale nodes: outside the system, orange: input stimuli, red: functional detection, blue: multi-scale networks, light green: unpredictable effects, violet: dynamic networks, purple: active agents of change, dark teal: effects of system change, brown: output leading to input in a feedback loop.*
## Pain response map
The array of pain response pathways comprise every function of a complex adaptive system and the overall mechanisms are far from energetic equilibrium. 1.Energy and information: events related to heat, itch, or damage affect the system from the outside environment2.Input: the peripheral nervous system is involved with receiving the initial input signals (Figure 5)3.Detectors: nociceptors identify the sensations and sends signals toward the central nervous system4.Dynamic networks: cascading effects across nociceptive, sensory, and other networks in reaction to the pain responses throughout the body, these networks include a.Multi scalar interactions: an example of a multi-scale network influenced by pain signals is the hypothalamic-pituitary-adrenal axis (HPA axis), which regulates neuroendocrine responses that range from digestive networks to immune responsesb. Stochastic dynamics: one of the many examples of unpredictable reactions can be neuro-tagging, when interoceptive and exteroceptive data combine with nociceptive information to classify sensory signals to the rest of the body5.Agents: the central nervous system is a clear vehicle serving as an agent in pain response systems6.Nonlinear effects: the inflammatory load and reaction of the body has a highly significant influence on pain outcomes, even small differences in inflammatory states can cause chain reactions with enormous implications7.Emergent behavior: sensitization to pain signaling is influenced by a large number of complex variables and in turn produces numerous outputs in the body’s pain management and healing response that cannot be precisely modeled without taking the entire pain response system into account8.Output: the body’s various pain reactions are both internal and external-facing a.Feedback loops: combinations of inflammatory reactions and catastrophization can increase sensitization to input and thereby increase pain reaction, inflammatory responses, and catastrophization in systemic feedback loopsb. Energy loss: the functioning of the pain response system relies on cellular energy and caloric expenditure, both of which are indicative of an energetically open system
**Figure 5:** *Simplified functional map of pain response system. Grey-scale nodes: outside the system, orange: input stimuli, red: functional detection, blue: multi-scale networks, light green: unpredictable effects, violet: dynamic networks, purple: active agents of change, dark teal: effects of system change, brown: output leading to input in a feedback loop.*
## Yoga therapy map
The components of yogic practice comprise every function of a complex adaptive system and the overall mechanisms are far from energetic equilibrium.
The array of pain response pathways comprise every function of a complex adaptive system and the overall mechanisms are far from energetic equilibrium. 1.Energy and information: the practice of yoga involves both physical movement and mental activities, comprising both energy and information (Figure 6)2.Input: the peripheral nervous system is involved with receiving the initial input signals3.Detectors: interoceptors identify the internal sensations and sends signals toward the central nervous system4.Dynamic networks: cascading effects across interoceptive, nociceptive, sensory, and other networks in reaction to the pain responses throughout the body, these networks include a.Multi scalar interactions: an example of a multi-scale network influenced by yoga is the hypothalamic-pituitary-adrenal axis (HPA axis), which regulates neuroendocrine responses that range from digestive networks to immune responses, and the tone of the vagal nerve, both of which impact networks on multiple scalesb. Stochastic dynamics: one of the many examples of unpredictable reactions can occur during cognitive reframing, when new interoceptive and exteroceptive data combine with existing nociceptive information to reclassify sensory signals to the rest of the body5.Agents: the central nervous system is a clear vehicle serving as an agent in reactions to the physical and mental activities of yoga, while alterations to the brain’s neural structure through repeated practice serve as agents of change6.Nonlinear effects: any alterations in neuro-tagging have a highly significant influence on pain management, even small differences in sensitization signals can cause chain reactions with enormous implications7.Emergent behavior: sensitization to pain signaling is influenced by a large number of complex variables and in turn produces numerous outputs in the body’s pain management and healing response, meaning yoga’s full impact on pain cannot be precisely modeled without taking the entire mind-body therapeutic system into account8.Output: the body’s various reactions to yoga practice are both internal and external-facing a.Feedback loops: combinations of reductions in inflammatory and catastrophization can decrease sensitization to input and thereby pain reactions, inflammatory responses, and catastrophization in systemic feedback loopsb. Energy loss: the practice of yoga relies on cellular energy and caloric expenditure, both of which are indicative of an energetically open system
**Figure 6:** *Simplified functional map of neurobiological effects of yoga practice. Grey-scale nodes: outside the system, orange: input stimuli, red: functional detection, blue: multi-scale networks, light green: unpredictable effects, violet: dynamic networks, purple: active agents of change, dark teal: effects of system change, brown: output leading to input in a feedback loop.*
## Analysis
The examination of the complexity of yoga’s role in pain management was conducted in three parts. •Evidence overview: a textual discussion of the published research relating yoga and pain responses within the framework of a complex system•Network analysis: determining the strength of connection between different topics and research articles related to yoga, pain, and complex systems•Functional systems mapping: cross-referencing the core components of a complex system with the behavior of pain responses and yoga as pain management
## Evidence overviews
The efficacy and operational pathways of yoga are sufficiently documented to allow comparisons to the established pathways of the pain response system. A review of existing literature suggests that both pain stimuli and the practice of yoga interact with many of the same systems in the peripheral and central nervous system, with the therapeutic effects of yoga often addressing the most detrimental side-effects of pain responses.
From a systems perspective the evidence overview supports the case that pain responses and yoga practices are each complex adaptive systems. Each system involves a range of physiological and neurological interactions that lead to reactions exhibiting all the defining characteristics of a complex adaptive system. Pain responses have been well established as a complex adaptive system, the evidence overview confirms this. Yoga's definition as a CAS is novel but as well evidenced as pain responses’. Due to the multimodal nature of yoga, this definition appears to apply both in connection with pain management and in isolation.
From a functional perspective this information indicates yoga to be a complex system that is an effective method of treating pain. Given the complex manifestations of pain in neurological, psychological, and inflammatory contexts, a systemic intervention could be of particular value.
## Network analyses
The network analyses of the selected body of studies and their associated keywords sought to identify patterns in the findings relating to pain, yoga, and complex systems. The frequency of keyword use in studies across various topics will be used to extrapolate the predominance of different topics in the research. Findings from the network analysis were compiled into a cluster graph and the relevant metrics emphasized. •Betweenness: as seen in Figure 7, given the relatively isolated nature of the keywords related to complex systems, it is unsurprising that a metric tracking traffic would exhibit high values related to complex systems, much like a bottleneck increases pressure. The themes covered in the highest betweenness values include (due to nodes that are irrelevant or apply to two or more themes, percentages may not total $100\%$): •Complex systems $33.3\%$: This suggests that while complex systems are not central to most studies relating to pain, the existing connections between the topics are heavily trafficked•Pain responses $30\%$: This topic was central to a significant number of connections, reflecting the importance of pain to many topics•Pain management $30\%$: This topic was central to a significant number of connections, reflecting the importance of pain management to many topics•Yoga $13.3\%$: Yoga was not as significant in betweenness, possibly due to the wide-ranging, less centralized nature of the studies.•Closeness: as seen in Figure 8, this metric often identifies the dominant tendencies within a network and can be used to determine the most interdependent factors of the literature review. Predictably the highest values are clustered near the center of the graph, close to the highest concentration of connections. The themes covered in the highest closeness values include (due to nodes that are irrelevant or apply to two or more themes, percentages may not total $100\%$): •Complex systems $3.3\%$: Low values indicate that the concept of complex systems is not frequently incorporated into the most common studies•Pain responses $63.3\%$: *As this* is a widely studied and well-established field, it is expected that pain responses would dominate the trends of most common keywords•Pain management $30\%$: This topic was central to significant numbers of connections, for many of the same reasons as pain responses•Yoga $23.3\%$: Yoga exhibited significant values in closeness, possibly for the same reasons it scored low in betweenness: the broad, decentralized nature of many yoga studies touch on large numbers of trending topics•Degree centrality: as seen in Figure 9, degree centrality is a simple quantitative metric and is prone to overvaluing diagnostic data. Many of the leading values in degree involved irrelevant terms and keywords, but the fact pain responses and management still ranked highly reinforces their importance to this network. The themes covered in the highest degree values include (due to nodes that are irrelevant or apply to two or more themes, percentages may not total $100\%$): •Complex systems $0\%$: No values indicate that the concept of complex systems is not directly connected to most studies•Pain responses $40\%$: *As this* is a widely studied and well-established field, it is expected that pain responses would be frequently connected to the most common keywords•Pain management $20\%$: This topic was central to significant numbers of connections, for many of the same reasons as pain responses•Yoga $16.6\%$: Yoga exhibited small values in degree centrality, suggesting that while certain aspects of yoga are highly connected, these aspects are often disparate and separated among studies•Eigenvector: as seen in Figure 10, measurements of eigenvector values are particularly useful for identifying systems. This metric reveals the parts of a network that have the greatest nonlinear representation, suggesting topics that may not be the dominant trends but underpin and amplify them. •Complex systems $83.3\%$: The extremely high eigenvector value for topics related to complex systems reinforces its utility as a predictor of systems influence. Much like complex systems themselves, studies about complex systems are often disproportionately influential and relevant to multidisciplinary fields.•Pain responses $16.6\%$: The majority of the topics related to pain were also related to systems like the immune system or inflammatory response system•Pain management $0\%$: The lack of any pain management in the top eigenvector values was unexpected, given that it is a response to pain itself, which was represented•Yoga $0\%$: The lack of any yoga topics in the top eigenvector values was unexpected, given that it is a response to pain itself and a multidisciplinary approach in itself•Reach efficiency: as seen in Figure 11, combining the qualitative analysis of eigenvector metrics with the simple quantitative metrics of degree centrality results in reach efficiency, which can provide mitigate some of the outliers in either approach. This is reflected in the more well-rounded findings of this study’s network analysis. •Complex systems $56.6\%$: The high reach efficiency value for topics related to complex systems suggests strong connections within just two degrees, as well as across the network•Pain responses $40\%$: The majority of the topics related to pain were also related to complex systems, suggesting systems approaches are increasingly relevant to the field•Pain management $10\%$: *As a* subset of pain research, it is logical that pain management would be represented to a significant but smaller degree than pain responses as a whole•Yoga $0\%$: The lack of any yoga topics in the top reach efficiency values was unexpected, given its close relationship to pain research•Synthesis: Selecting the nodes that consistently ranked in the top metrics for two or three different metrics could provide insights to important topics and themes that are not obvious from a single measurement. •3 metrics: Out of 12 nodes that were high ranking in at least 3 separate metrics, $75\%$ were related to complex systems and $25\%$ were related to pain responses, suggesting that at the highest levels complex systems are closely intertwined with pain research•2 metrics: Out of 35 nodes that were high ranking in 2 separate metrics $20\%$ were related to pain responses, $20\%$ related to nodes similar to yoga, $17.1\%$ related to pain management, and $3\%$ related to complex systems. A notable observation is that nodes related to yoga and pain responses are equally represented, despite pain response nodes being far more common across each individual metric. Network analysis showed pervasive patterns connecting pain responses, yoga, and complex system research. The most prevalent keywords in all three fields of study have numerous, strong associations.
**Figure 7:** *Betweenness network analysis visualization, demonstrating nodes and connections between TOPICS, STUDIES, and KEYWORDS. Nodes with higher values are larger and darker in color.* **Figure 8:** *Closeness network analysis visualization, demonstrating nodes and connections between TOPICS, STUDIES, and KEYWORDS. Nodes with higher values are larger and darker in color.* **Figure 9:** *Degree network analysis visualization, demonstrating nodes and connections between TOPICS, STUDIES, and KEYWORDS. Nodes with higher values are larger and darker in color.* **Figure 10:** *Eigenvector network analysis visualization, demonstrating nodes and connections between TOPICS, STUDIES, and KEYWORDS. Nodes with higher values are larger and darker in color.* **Figure 11:** *Reach efficiency network analysis visualization, demonstrating nodes and connections between TOPICS, STUDIES, and KEYWORDS. Nodes with higher values are larger and darker in color.*
## Functional systems maps
The functional mapping of pain responses and yoga practice was conducted to identify whether there were functions possessed by either system analogous to the minimum requirements to be considered a complex adaptive system. Any potential CAS should possess elements that fulfill every one of these functions and operates at a net energy loss. 1)Energy and information2)Input3)Detectors4)Dynamic networks (a)Multi scalar interactions (b)Stochastic dynamics5)Agents6)Nonlinear effects7)Emergent behavior8)Output (a)Feedback loops(b)Energy lossBoth the practice of yoga and the pain response system met every major definition of a complex adaptive system.
## Conclusion
Three distinct investigative methods were used to examine the potential of yoga as a complex system of pain management. All three demonstrated evidence that the practice of yoga for pain is effective and behaves like a complex system. •Evidence overview: yoga and pain operate along many of the same sensory pathways, making yoga a systemically effective form of pain management. Both pain responses and yoga practice demonstrate complex system behaviors.•Network analysis: among 7 different metrics tracking research into pain, yoga, and complex systems, 3 metrics showed connections between all three, 3 showed connections between complex systems and pain, and 1 showed connections between yoga and pain. No metric demonstrated isolated values or a lack of connection. This suggests an interconnected, complex system.•Functional system mapping: overlaying the mechanisms of pain responses and yoga onto a map of the core mechanisms required for a complex system revealed a consistent overlap. Yoga as pain management fulfills every requisite function to be considered a complex system, and regulates many of the same mechanisms affected by pain responses.
## Limitations
The study of complex systems is necessarily nuanced and multifaceted. There are numerous aspects of complex systems that were not examined due to either a lack of data or expertise. This includes determining the presence and significance of strange attractors, functional systems models or modules, and detailed statistical analysis. Further examination of this topic by specialists in the field of complex systems is warranted and necessary to verify this study's broad implications.
Further, the practice of yoga is varied and cannot be generalized. Any metrics tracking the effects of yoga are informed by the particular tradition being practiced, the adherence of the practitioner, and the amount of time spent practicing. These factors make interpolation of the discrete features of yoga problematic.
## Discussion
Evidence overviews established the practice of yoga as a viable pain management therapy that shared many of the characteristics of a complex adaptive system. Network analysis of 433 studies and 1,639 keywords identified pain responses and yoga-related topics as comparable across numerous metrics, suggesting a strong relationship and interconnected system. The greatest concentration of highly influential keywords indicate complex systems are the dominant, if indirect, connecting feature across studies, providing further evidence that pain response systems and yoga practice are both complex systems. Mapping the essential functions of complex adaptive systems onto pain responses and yoga practice demonstrated that both systems met every requirement of operational complex adaptive systems. It is notable that the functional mapping of yoga demonstrated interactions with nearly every one of the body's systems that pain impacted.
Recent reviews have supported the role of yoga as a pain management intervention, but since most research has focused on isolated, usually physical components of yoga rather than systemic mind-body effects, multiple forms of analysis were considered necessary to examine the novel hypothesis of this study [68]. These diverse methods all support considering yoga a complex adaptive system that exhibits unique interactions with the pain response system. Much like the consequences of pain can have pervasive, unpredictable effects on homeostasis, it should be considered that the practice of yoga could likewise have systemic, indirect impacts. This is especially relevant when considering chronic pain, long-term interventions, and quality of life.
## Implications
Designation as a complex adaptive system entails significant changes in how the effects of an intervention are tracked and interpreted. Complex adaptive systems are emergent phenomenon that cannot be reduced to simple, linear interactions.
In regards to research, an understanding of this dynamic could significantly improve study of mind-body therapies like yoga, shifting the attention from the presumed mechanism in isolation to the emergent effect on the total health of the patient. This nonlinear perspective may address the often-cited unpredictability in yoga research and shift methodologies from short-term metrics to measuring long-term systemic changes. The wide-ranging benefits of yoga for pain management and similarity in function to broader behavioral health interventions suggest a similar approach to other mind-body therapies is warranted.
At the level of direct patient interventions, this study provides an overview of the evidence indicating yoga is a viable option for pain management. Further, yoga may be uniquely suited to treat systemic chronic issues as a result of operating as a holistic rather than discrete intervention. Another intervention-centric benefit to this may lie in reorienting recommendations by health professionals away from simple calisthenics and focusing on broader multimodal approaches like yoga.
## 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.
## Ethics statement
Ethical review and approval was not required for this study in accordance with the local legislation and institutional requirements.
## Author contributions
DC: conception or design of the work, drafting the article, critical revision, final approval. ES: data collection, drafting the article, final approval. WB: data analysis, critical revision. RC: design of work, data collection, data analysis, drafting the article, critical revision, final approval. 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/fpain.2023.1075866/full#supplementary-material.
Supplement A to Yoga and Pain: a Mind-Body Complex System Deepak Chopra1†, Eddie Stern2, William C Bushell3, Ryan D Castle4†
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|
---
title: Transcriptome analysis of Ganoderma lingzhi (Agaricomycetes) response to Trichoderma
hengshanicum infection
authors:
- Tiantian Wang
- Xiaobin Li
- Chunlan Zhang
- Jize Xu
journal: Frontiers in Microbiology
year: 2023
pmcid: PMC9996313
doi: 10.3389/fmicb.2023.1131599
license: CC BY 4.0
---
# Transcriptome analysis of Ganoderma lingzhi (Agaricomycetes) response to Trichoderma hengshanicum infection
## Abstract
Green mold caused by Trichoderma spp. has become one of the most serious diseases which threatening the production of Ganoderma lingzhi. To understand the possible resistance mechanism of the G. lingzhi response to T. hengshanicum infection, we examined the G. lingzhi transcript accumulation at 0, 12, and 24 h after T. hengshanicum inoculation. *The* gene expression analysis was conducted on the interaction between G. lingzhi and T. hengshanicum using RNA-seq and digital gene expression (DGE) profiling methods. Transcriptome sequencing indicated that there were 162 differentially expressed genes (DEGs) at three infection time points, containing 15 up-regulated DEGs and 147 down-regulated DEGs. Resistance-related genes thaumatin-like proteins (TLPs) (PR-5s), phenylalanine ammonia-lyase, and Beta-1,3-glucan binding protein were significantly up-regulated. At the three time points of infection, the heat shock proteins (HSPs) genes of G. lingzhi were down-regulated. The down-regulation of HSPs genes led to the inhibition of HSP function, which may compromise the HSP-mediated defense signaling transduction pathway, leading to G. lingzhi susceptibility. Pathway enrichment analyses showed that the main enriched pathways by G. lingzhi after infection were sphingolipid metabolism, ether lipid metabolism, and valine, leucine and isoleucine degradation pathway. Overall, the results described here improve fundamental knowledge of molecular responses to G. lingzhi defense and contribute to the design of strategies against Trichoderma spp.
## Introduction
Ganoderma lingzhi S. H. Wu, Y. Cao, and Y. C. Dai (Ganodermataceae, Agaricomycetes) is a traditional Chinese medicinal mushroom in Asia that has been used for thousands of years (Cao et al., 2012). It has good medicinal, health, and ornamental value (Wagner et al., 2003; Cao et al., 2012). The pharmacological action of G. lingzhi is based on its solid immune modulation and immune potential. The main medicinal ingredients include polysaccharides, triterpenes, peptides, proteins, adenosine, which have anti-androgen, anti-cancer, anti-diabetes, anti-hypertension, anti-melanocyte, anti-virus, and other health functions (Sanodiya et al., 2009; Qian et al., 2013; Satria et al., 2019). Currently, the main cultivation methods of G. lingzhi are substitute cultivation and basswood cultivation in China, mainly distributed in the Northeast region, the Dabie Mountains, the Southeast coast, and other places (Jin et al., 2016; Zhou et al., 2017). In recent years, the market demand for G. lingzhi has increased by 18–$30\%$ every year. In 2015, China was the world’s primary producer and exporter of G. lingzhi, with 10,000 hm2 of G. lingzhi yield and 120,000 t of spore powder, accounting for approximately 75 and $30\%$ of the world, respectively (Jin et al., 2016; Ma, 2017; Li et al., 2021). Due to the increase of the cultivation years, some diseases of G. lingzhi have also been successively discovered. Among them, Trichoderma spp. has the characteristics of wide distribution, more kinds, rapid incidence, and strong concealment (Xu et al., 2019). Trichoderma spp. has become one of the most harmful pathogens in the cultivation of G. lingzhi, resulting in a decline in the yield and quality decline of G. lingzhi, which have caused severe economic losses to growers and restricted the development of the G. lingzhi industry (Xie and Tan, 2015; Huang et al., 2018).
Following the emergence of G. lingzhi, it is primarily controlled through the use of chemical reagents such as pesticides, which cause severe pollution to the environment and make drug resistance more likely (Fu et al., 2013; Xie et al., 2018). Based on six chemical reagents, Yan [2011] screened the bacteriostasis of different fungicides on edible fungi and Trichoderma spp. by preliminary screening, inhibition of spore germination, and mycelial germination tests. A total of $50\%$ hymexazol has a strong bacteriostatic effect on Trichoderma spp. and a little destructive effect on various edible fungi mycelia, which can be recommended for production. Luković et al. [ 2021] conducted identification and fungicide screening tests on 22 isolation of Trichoderma strains. In fungicide susceptibility tests, all examined Trichoderma strains were found to be highly sensitive to prochloraz (ED 50 < 0.4 mg ⋅ mL–1) and considerably susceptible to metrafenone (ED 50 < 4 mg ⋅ mL–1). Hence, metrafenone might also be recommended to control the green mold of mushrooms. In aspects of biological control, among 50 bacterial strains isolated from mushroom compost, *Bacillus subtillis* B-38 inhibited *Trichoderma harzianum* T54 ($48.08\%$) and *Trichoderma aggressivum* f. europaeum T77 ($52.25\%$) mycelium growth in vitro. In plot trials, the incidence of the plots inoculated with the Trichoderma strains and treated with B. subtillis B-38 and B. subtilis QST 713 presented significantly lower disease incidence compared to the control, and results for disease control and yield harvested were comparable to the plots treated with prochloraz-Mn, indicating that B. subtilis B-38 and B. subtilis QST 713 could be used as suitable substitutes for chemical fungicides (Milijašević-Marčić et al., 2017; Gea et al., 2021). Although these methods can play a preventive role to a certain extent, the control effect is limited, so screening and breeding resistant varieties of G. lingzhi is the most economical and effective way to control green mold. Therefore, exploring the defense mechanism of G. lingzhi against T. hengshanicum will help to speed up the breeding process of disease-resistant G. lingzhi.
With the development of second-generation sequencing technology and molecular biology, many researchers have explored the molecular regulation mechanisms of rice bacterial blight (Sana et al., 2010), Phytophthora capsici disease (Fan et al., 2022), *Nerium indicum* witches’ broom disease (Wang et al., 2022), *Phytophthora sojae* disease (Zhu, 2018), apple alternaria blotch disease (Zhu et al., 2017), *Chrysanthemum morifolium* black spot (Liu L. N. et al., 2021), and wheat leaf spot (Ye et al., 2019). Bailey et al. [ 2013] found that the pathogen (Lecanicillium fungicola) and the host (Agaricus bisporus) changed the expression of their respective genes during the interaction through transcriptome analysis, which initially revealed the host’s defense response mechanism. Ma et al. [ 2021] found that the expression level of LeTLP1 was strongly induced in response to T. atroviride infection in the resistant Y3334 by transcriptome analysis and quantitative real-time polymerase chain reaction (qRT-PCR) detection. The function of LeTLP1 was verified by gene overexpression and gene silencing technology. Compared with the parent strain Y3334, LeTLP1-silenced transformants had reduced resistance relative to T. atroviride. These findings suggest that overexpression of LeTLP1 is a major mechanism for the assistance of Lentinula edodes to T. atroviride. This molecular basis provides a theoretical foundation for breeding resistant L. edodes strains. A. bisporus brown blotch disease caused by *Pseudomonas tolaasii* infection mainly activates the arginine and proline metabolism, cysteine and methionine metabolism, jasmonic acid (JA) biosynthesis, methane metabolism, phenylpropanoid metabolism, shikimate pathway, sulfur metabolism and signaling pathways, as well as oxidative phosphorylation pathways. Transcriptomics data combined with qPCR verification indicated that 10 differentially expressed genes (DEGs), including PIP1, MET3, AGX, PAL1, GCL, LOX $\frac{1}{3}$, PR-like, MYB3R, UCR, and SDHB, were the most potential genes involved in the early defense. These results revealed the early defense response of A. bisporus against P. tolaasii (Yang et al., 2022). However, there has yet to be a report on how G. lingzhi responds to the pathogen Trichoderma spp. In this study, RNA-seq technology was used to analyze the transcriptome of G. lingzhi in response to T. hengshanicum infection, which has a great significance for the breeding of resistant varieties of G. lingzhi and the prevention and control of soil pollution.
## Trichoderma hengshanicum and Ganoderma lingzhi cultures, and inoculation method
Trichoderma hengshanicum “1009” and G. lingzhi “11GL-16” were used in all experiments and preserved in the Development and Utilization Laboratory of Fungi Resource of Jilin Agricultural Science and Technology College. G. lingzhi was grown in an edible fungus base. For G. lingzhi-back inoculation, the fruiting bodies of some growth vigor were strictly selected. T. hengshanicum isolated on potato dextrose agar (PDA) was propagated in a constant temperature incubator at 25°C for 4 days. The G. lingzhi fruiting bodies were inoculated with 5-mm-diameter mycelial blocks (from PDA culture plates). The control group inoculated PDA agar blocks without mycelium. At 0, 2, 12, and 24 h after inoculation, the G. lingzhi fruiting bodies were cut off with a sterile scalpel at a distance of 5 mm from the lesions and stored at −80°C after quick freezing with liquid nitrogen. At 0, 2, 12, and 24 h after inoculation, three fruiting bodies were taken from each replicate of each treatment group. The frozen samples were used for RNA sequencing.
## RNA extraction, library construction, and sequencing
After 0, 2, 12, and 24 h of infection, more than 500 mg of fruiting bodies were collected for RNA extraction. Total RNA was extracted using a Trizol reagent kit according to the manufacturer’s protocol. RNA quality was assessed on an Agilent 2100 Bioanalyzer and checked using RNase-free agarose gel electrophoresis. Illumina MiSeq library construction was performed according to the manufacturer’s instructions (Illumina, San Diego, CA, USA). To separate the mRNA from the total RNA, magnetic beads with poly T oligos were used. Then the enriched mRNA was fragmented into short fragments using a fragmentation buffer and reverse transcribed into cDNA with random primers. The cDNA fragments were purified with QIAquick PCR (Qiagen, Venlo, The Netherlands). Extraction Kit, end-repaired, and A base added and ligated to Illumina sequencing adapters. The ligation products were size selected by agarose gel electrophoresis, PCR amplified and sequenced using Illumina MiSeq by Personal Biotechnology Co., Ltd. (Shanghai, China).
## Data filtering, de novo assembly, and gene function annotation
Reads obtained from the sequencing machines included raw reads containing adapters or low-quality bases, which would affect the following assembly and analysis. The clean reads were retrieved after trimming adapter sequences and removal of low quality (containing >$50\%$ bases with a Phred quality score < 20) using the FastQC tool. Transcriptome de novo assembly was performed with the short reads assembling program-Trinity (Grabherr et al., 2011). Firstly, a short sequence library of K-mer length was constructed using high-quality sequences. Then the short sequence was extended by the overlap of K-mer-1 length between short sequences to obtain the preliminary spliced contig sequences. Next, Chrysalis clusters related contigs that correspond to portions of alternatively spliced transcripts or otherwise unique portions of paralogous genes and then builds Bruijn graphs for each cluster of related contigs. Finally, these Bruijn graphs were processed to find the path based on the reads and paired reads in the graphs to obtain the transcripts. To comprehensively obtain gene annotation information, genes were compared with six databases, including NR (NCBI non-redundant protein sequences), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genome (KEGG), eggNOG (evolutionary genealogy of genes: Non-supervised Orthologous Groups), Swiss-Prot, and Pfam, and the annotation situation of each database was counted.
## Differentially expressed genes (DEGs) and enrichment analysis
RSEM estimated gene expression levels for each sample (Li and Dewey, 2011). *The* gene abundances were calculated and normalized to Reads Per kb per Million reads (RPKM). Three pairwise comparisons were made from RNA-seq data, including inoculation 0 h (CK) vs. inoculation 2 h (GT2), inoculation 0 h vs. inoculation 12 h (GT12), and inoculation 0 h vs. inoculation 24 h (GT24). DEGs was performed using DESeq2 (Love et al., 2014) software between two groups. *The* genes with the parameter of P-value < 0.05 and | log2FoldChange| > 1 were considered DEGs. All DEGs were mapped to GO terms in the GO database,1 gene numbers were calculated for every term, and significantly enriched GO terms in DEGs compared to the genome background were defined by hypergeometric test. The criterion for significant enrichment of GO function was P-value < 0.05. Pathway enrichment analysis identified significantly enriched metabolic pathways or signal transduction pathways in DEGs compared with the whole genome background. The corrected P-values adopted 0.05 as the threshold, and KEGG pathways meeting the above conditions were defined as significantly enriched pathways in DEGs.
## Quantitative real-time polymerase chain reaction (qRT-PCR) verification
RNA-seq results were validated by selecting six DEGs to examine the consistency of their expression profiles. Total RNAs were extracted from collected G. lingzhi materials using the Trizol (Invitrogen, USA) kit according to the manufacturer’s instructions. First-strand cDNAs were synthesized by the PrimeScript™ 1st stand cDNA Synthesis Kit. The internal transcribed spacer (ITS) gene was used as an internal control. Volume for all the reactions was 20 μL; 1 μL cDNA, 10 μL 2 × SYBR real-time PCR (Applied Biosystem, Carlsbad, CA, USA), and 0.4 μL of each primer. The PCR procedure was 5 min at 95°C, followed by 40 cycles of 15 s at 95°C and 30 s at 60°C. Three biological replicates were performed per sample. The formula of 2–ΔΔCT was used to calculate gene relative expression levels.
## Summary of transcriptome analysis
Four sample sets, each with three biological replicates, were subjected to RNA-seq, and 12 cDNA libraries were generated: GT0-1, GT0-2, GT0-3, GT2-1, GT2-2, GT2-3, GT12-1, GT12-2, GT12-3, GT24-1, GT24-2, GT24-3. Samples were inoculated for 0 h (GT0-1, GT0-2, GT0-3) as a control. The sequencing raw data set has been deposited in National Center for Biotechnology Information Sequence Read Archive database (accession number PRJNA917261). Approximately 4.169∼5.386 million raw reads were produced for each sample, with the percentages of Q20 and Q30 being over 97.64 and $93.75\%$, respectively. A total of 79.7 Gb of clean data was obtained and the clean data of each sample reached more than 6.0 Gb. A transcriptome database containing 36,870,053 unigene of total length was obtained using Trinity software, with a mean length of 1,850.35 bp and GC content of $57.06\%$. All unigenes and transcripts obtained by transcriptome assembly were aligned with six major databases (Nr, Swiss-prot, Pfam, COG, GO, and KEGG databases).
## Differentially expressed gene (DEG) analysis in Ganoderma lingzhi
Differentially expressed genes in susceptible G. lingzhi at different time points were identified using the thresholds $p \leq 0.05$ and | log2FoldChange| > 1. In response to the fungal stimulus, a total of 1,978 genes showed differential expression at three-time points after inoculation (Figure 1). A total of 754 (347 up-regulation and 407 down-regulation), 620 (259 up-regulation and 361 down-regulation), and 604 (79 up-regulation and 525 down-regulation) differential genes were observed in CK vs. GT2, CK vs. GT12, and CK vs. GT24, respectively. The Venn diagram (Figure 1) showed that both shared and unique DEGs were identified between different combinations. There were 162 shared DEGs in CK vs. GT2, CK vs. GT12, and CK vs. GT24.
**FIGURE 1:** *Differentially expressed genes (DEGs) between samples. Left: numbers of DEGs compared between two samples (CK vs. GT2, CK vs. GT12, and CK vs. GT24). DEGs are shown in red (up-regulated) and blue (down-regulated). Right: Venn diagram analysis of the DEGs in Ganoderma lingzhi after inoculation with Trichoderma hengshanicum.*
## Functional annotation of differentially expressed genes (DEGs)
Gene Ontology classification analysis of DEGs between CK vs. GT2, CK vs. GT12, and CK vs. GT24 was shown in Figure 2. GO had three ontologies, describing the molecular function, cellular component, and biological process of genes. At 2 h after infection, GO enrichment analysis of DEGs showed that 396, 47, and 128 GO terms were identified in biological processes, cellular components, and molecular functions, respectively. The most significant enrichment of DEGs in the biological process ontology was heterochromatin assembly by small RNA, mRNA cleavage involved in gene silencing, Wnt signaling pathway-calcium modulating pathway, and transcription (RNA-templated), whereas RNA-induced silencing complex (RISC)-loading complex and mitochondrial permeability transition pore complex occupied important positions in the cellular component ontology. The most significantly enriched molecular function ontology was catalytic activity, RNA-directed 5′-3′ RNA polymerase activity, miRNA binding, oxidoreductase activity, cofactor binding, regulatory RNA binding, nicotinamide adenine dinucleotide phosphate (NADP) binding, siRNA binding, endoribonuclease activity (cleaving siRNA-paired mRNA), endoribonuclease activity (cleaving miRNA-paired mRNA), oxidoreductase activity (acting on the CH-OH group of donors), monooxygenase activity, and iron ion binding. At 12 h after infection, GO analysis of DEGs revealed 360 entries related to biological processes, 52 cellular components, and 150 molecular functions. According to the GO annotations analysis at Level 2, the most significant enrichment of DEGs was in the oxidation-reduction process, heterochromatin assembly by small RNA, isoprenoid biosynthetic process ontology, transcription, and RNA-templated in the biological process ontology. The most significantly enriched molecular function ontology was oxidoreductase activity, catalytic activity, oxidoreductase activity (acting on CH-OH group of donors), cofactor binding, oxidoreductase activity (acting on the CH-OH group of donors, NAD or NADP as acceptor), RNA-directed 5′-3′ RNA polymerase activity, miRNA binding, coenzyme binding, and regulatory RNA binding, while myelin sheath, an intrinsic component of the membrane and integral component of membrane occupied the important positions in the cellular component ontology. At 24 h after infection, DEGs were divided into 390 functional categories according to 275 biological processes, 34 cellular components, and 81 molecular functions. The most enriched DEGs in the biological processes belonged to heterochromatin assembly by small RNA, cytoplasmic translation, response to sucrose, response to disaccharide and transcription, and RNA-templated. There were 34 terms related to cellular components, among which the most significantly enriched were cytosolic ribosome, ribosome, ribosomal subunit, cytosolic small ribosomal subunit, and small ribosomal subunit; 81 GO terms related to molecular function were identified, of which, RNA-directed 5′-3′ RNA polymerase activity and structural constituent of ribosome were the most significantly enriched metabolic pathways. In the biological process ontology, CK vs. GT2, CK vs. GT12, and CK vs. GT24 had the two most significantly enriched terms, namely, heterochromatin assembly by small RNA and transcription, RNA-templated. RNA-directed 5′-3′ RNA polymerase activity was the most significantly enriched cellular component ontology term in CK vs. GT2, CK vs. GT12, and CK vs. GT24. Five of the most significantly enriched terms in CK vs. GT2 and CK vs. GT12 were related to molecular function ontology, which were catalytic activity, oxidoreductase activity, oxidoreductase activity (acting on the CH-OH group of donors) and regulatory RNA binding, and were also enriched in CK vs. GT24.
**FIGURE 2:** *Gene Ontology (GO) functional enrichment analyzes of differentially expressed genes (DEGs).*
To elucidate the main metabolic pathways involved in the DEGs responding to T. hengshanicum stress in G. lingzhi, the KEGG enrichment analysis was conducted in CK vs. GT2, CK vs. GT12, and CK vs. GT24. The top 20 KEGG pathways with the lowest false discovery rate (FDR) values are shown in Figure 3. The greater the richness factor, the greater the enrichment. DEGs annotated 93 metabolic pathways in CK vs. GT2, CK vs. GT12, and CK vs. GT24. Sphingolipid metabolism, ether lipid metabolism, steroid biosynthesis, and valine, leucine and isoleucine degradation were the most significantly enriched metabolic pathways in CK vs. GT2. Terpenoid backbone biosynthesis, glycine, serine and threonine metabolism, tryptophan metabolism, ascorbate, and aldarate metabolism, fatty acid degradation, arachidonic acid metabolism, glycolysis/gluconeogenesis, pyruvate metabolism, lysine degradation, steroid biosynthesis, glycerolipid metabolism, oxidative phosphorylation, phenylalanine metabolism, tyrosine metabolism, linoleic acid metabolism, histidine metabolism, valine, leucine and isoleucine degradation, folate biosynthesis, methane metabolism, and hippo signaling pathway-multiple species were the most significantly enriched metabolic pathways in CK vs. GT12. The most significantly enriched metabolic pathways in CK vs. GT24 belonged to the ribosome, sphingolipid metabolism, glycerophospholipid metabolism, ether lipid metabolism, and biosynthesis of unsaturated fatty acids. Valine, leucine and isoleucine degradation was significantly enriched in CK vs. GT2, and CK vs. GT12, and also enriched in CK vs. GT24. There were two significant metabolic pathways in CK vs. GT2 and CK vs. GT24: sphingolipid metabolism and ether lipid metabolism, which were also enriched in CK vs. GT12.
**FIGURE 3:** *Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analyzes of differentially expressed genes (DEGs).*
## qRT-PCR analysis
To confirm the reliability of the generated RNA-seq data, the expression of six DEGs was analyzed using qRT-PCR validation. Genescloud tools2 were used to visualize the results (Figure 4). Although the magnitude of differences in expression detected by qRT-PCR was not identical to those of DEGs detected by the RNA-seq results from the samples at three infection time points, the direction of the change in DEG expression was consistently using the two approaches, indicating that the results of transcriptome sequencing were highly reliable.
**FIGURE 4:** *The relative expression level change of six selected genes from differentially expressed genes (DEGs) by quantitative real-time polymerase chain reaction (qRT-PCR).*
## Sphingolipid metabolism
The basic structure of biomembranes comprises various lipids, such as glycerophospholipids, sphingolipids, and sterols, and proper homeostasis of the composition of these lipids in biomembranes is extremely important for the maintenance of multiple cellular functions (Tani and Funato, 2018). Sphingolipids are essential biomembrane lipids for eukaryotic organisms. They commonly have a long-chain base (LCB) backbone. Ceramide (Cer), the hydrophobic portion of sphingolipids, comprises an LCB and a fatty acid (Dickson et al., 2006; Tani and Funato, 2018). In this study, the sphingolipid metabolism pathway of G. lingzhi was significantly enriched after T. hengshanicum infection (Figure 5). As shown in the Figure 6, two genes were up-regulated and three genes were down-regulated in the phospholipid metabolic pathway at 2 h after infection. At 12 h after infection, one gene was up-regulated and two genes were down-regulated in the phospholipid metabolic pathway. At 24 h after infection, one gene was up-regulated and four genes were down-regulated in the phospholipid metabolic pathway. This indirectly showed that T. hengshanicum infection would affect sphingolipid metabolism in G. lingzhi.
**FIGURE 5:** *Differentially expressed genes (DEGs) comparison between two samples (CK vs. GT2, CK vs. GT12, and CK vs. GT24) mapped to the sphingolipid metabolism (map00600; red for up-regulated, green for down-regulated).* **FIGURE 6:** *Heatmaps of differentially expressed genes (DEGs) involved in sphingolipid metabolism pathways. The log2Foldchange was colored using Genescloud tools (green for up-regulated, brown for down-regulated).*
## Pathogenesis-related proteins
Pathology-associated proteins are a class of proteins induced by plants in pathological or pathology-associated environments, initially detected from tobacco mosaic virus infection in tobacco leaves (Li, 2020). Pathogenesis-related (PR) proteins are comprised of 17 families that are normally expressed at low levels in healthy tissues but rapidly accumulate to significant levels in response to biotic or abiotic stress (Van Loon and Van Strien, 1999; Van Loon et al., 2006; Zhu et al., 2017). It was known that the timing of PR gene expression was a crucial determinant of pathogenesis. The accumulation of PR proteins is usually associated with systemic acquired resistance to a wide range of pathogens. G. lingzhi PR genes were induced in response to T. hengshanicum infection in our experiment. Our results showed that G. lingzhi up-regulated PR thaumatin-like proteins (TLPs) (PR-5s) after infection with T. hengshanicum. The PR-5s genes were up-regulated 8.6-fold at 2 h post-infection (Figure 7).
**FIGURE 7:** *Left: Heatmaps of differentially expressed genes (DEGs) encoding thaumatin-like proteins (TLPs); right: Heatmap of DEGs encoding heat shock proteins (HSPs). The log2Foldchange was colored using Genescloud tools (green for up-regulated, brown for down-regulated), each horizontal row represents a DEG with its gene ID.*
Heat shock proteins (HSPs) are a subset of molecular chaperones, best known because they are rapidly induced in large numbers by stress (Neumann et al., 1994; Wang et al., 2004; Scarpeci et al., 2008). These proteins are implicated in a wide variety of cellular processes as molecular chaperons, including the protection of the proteome from stress, the folding and transport of newly synthesized polypeptides, the activation of proteolysis of misfolded proteins, and the formation and dissociation of protein complexes, plays a pivotal role in the protein quality control system, ensuring the correct folding of proteins, the re-folding of misfolded proteins, and controlling the targeting of proteins for subsequent degradation. The results showed that HSPs in the G. lingzhi displayed a range of responses to T. hengshanicum. At the three time points of infection, the HSPs genes of G. lingzhi were down-regulated (Figure 7).
## Discussion and conclusion
Ganoderma lingzhi has high application value in the prevention and treatment of nephritis, hypertension, and bronchitis, and has remarkable antitumor properties, and is deeply loved by people (Wu et al., 2011; Nie et al., 2013; Vitak et al., 2015; Paterson, 2016; Yan et al., 2019). With the rapid expansion of G. lingzhi cultivation, however, green mold has become one of the severe diseases threatening the production of G. lingzhi. After the fruiting bodies were infected with Trichoderma spp., lesions appeared and were covered by green mycelium. The infected fruiting bodies became deformed and withered as the disease progressed (Yan et al., 2019; Cai et al., 2020). Therefore, it is important to use disease-resistant variety to control disease that damage the quantity and quality of G. lingzhi. Currently, there are many studies on the interaction between plants and pathogens. In response to external biotic stresses, plants induce a series of immune responses, including the production of physical barriers (keratin, wax, lignin, and special stomatal structures), chemical barriers (secondary metabolites with antimicrobial properties), and molecular responses (hypersensitivity, production of reactive oxygen species, and expression of pathogen-associated genes) (Ding and Yang, 2016). Therefore, it is inevitable that macrofungi will also produce various defensive responses to resist the pathogen infection. For breeding disease-resistant G. lingzhi, it is necessary to have information on genetic variation in the hosts reaction to disease infestation. In this research, we studied the transcription of G. lingzhi at 2, 12, and 24 h after infection by T. hengshanicum, and explored the resistance genes and metabolic pathways induced by T. hengshanicum.
In this study, we selected six DEGs related to the T. hengshanicum infection of G. lingzhi for qRT-PCR verification. Our results revealed the same trend in DEG expression as established by transcriptome sequencing results, indicating high reliability of the transcriptome sequencing results in reflecting the proper expression levels of genes in G. lingzhi infected by T. hengshanicum. The six DEGs verified by qRT-PCR were the TLPs (PR-5s) gene, the phenylalanine ammonia-lyase gene, the cyanide hydratase gene, the Beta-1,3-glucan binding protein, the polyketide synthase gene, and the 6-phosphofructokinase gene. TLPs have antifungal and osmotic adjustment activities or act as an elicitor of other antifungal proteins and play an important role in the growth and development of the host and the process of stress resistance (Menu-Bouaouiche et al., 2003; Guo et al., 2016; Faillace et al., 2019; Sun et al., 2020; Liu Y. et al., 2021; Liu et al., 2022). Previous studies have shown that PR-5s is involved in the plant defense response induced by diseases and insects (Hou et al., 2018). For example, overexpression of the rice TLPs gene significantly increased the resistance of rice, and wheat to related diseases (Chen et al., 1999; Datta et al., 1999). As a result, up-regulation of PR5 expression at 2 h after infection benefits G. lingzhi in preventing a violation by T. hengshanicum. The phenylalanine ammonia-lyase gene was also up-regulated after infection. As the first rate-limiting enzyme in the phenylpropanoid metabolism pathways, phenylalanine ammonia-lyase (PALs) can catalyze the deamination of L-phenylalanine to form trans-cinnamic acid, which is a precursor of lignin, salicylic acid (SA), flavonoids, phytoalexins, as well as bioactive phenolamides via specific branch pathways, playing important roles in plant growth, development, and stress responses (Dixon et al., 2002; Zhang and Liu, 2015; You et al., 2020). For example, enhanced deposition of lignin can reinforce the plant cell wall, providing a structural barrier to pathogen spread, and the toxic phenolic precursors produced during lignin biosynthesis or polymerization can directly inhibit pathogen multiplication and movement (Tonnessen et al., 2015). The up-regulation of phenylalanine ammonia-lyase gene expression in G. lingzhi might activate the phenylpropanoid metabolic pathway in G. lingzhi and then produce some or specific related secondary metabolites to resist the infection of T. hengshanicum. Moreover, the Beta-1,3-glucan binding protein (LGBP) molecule was reported to have antibacterial, anti-biofilm, anti-inflammatory, and antioxidant properties (Iswarya et al., 2017). Many studies have shown that β-1,3-glucan binding proteins are host pattern recognition receptors (PRRs) that recognize conserved surface ligands in microorganisms designed the pathogen-associated molecule patterns (PAMPs) and have a strong affinity toward the –glucans present on the surface of bacteria and fungi, thereby activating the prophenoloxidase (proPO) activating system to elicit the invertebrate innate defense system (Zhang et al., 2016; Anjugam et al., 2017; Li S. S. et al., 2022). The results of this study revealed that T. hengshanicum infection of G. lingzhi not only up-regulated β-1,3-glucan binding proteins but also tyrosinase, and the infection site of G. lingzhi turned brown 24 h after T. hengshanicum infection, which could be due to activation of the prophenoloxidase system in G. lingzhi, resulting in the production of melanin or other secondary metabolite deposition. Intermediate sphingolipid metabolic pathways are important signal molecules closely related to cell growth, apoptosis, differentiation, senescence, stress resistance, and signaling transduction (Li J. et al., 2022). Free sphingosine binds to different substances in organisms to form Cer, sphingomyelin, and glycosphingolipids (Dickson et al., 2006; Harrison et al., 2018). In the study of Saccharomyces cerevisiae, long-chain sphingolipid bases are signaling molecules that regulate growth, responses to heat stress, cell wall synthesis and repair, endocytosis, and dynamics of the actin cytoskeleton (Dickson et al., 2006). In plants, sphingolipids were not only the main components of the plant plasmalemma, tonoplast membrane, and intima but also participated in various plant stress responses as the second messenger of plant defense mechanisms (Markham et al., 2013; Shan et al., 2019). Changes in sphingolipid content and sphingolipid/phosphorylated derivative balance in plants infected with microorganisms regulated plants to produce a resistance response (Bi et al., 2014; Yanagawa et al., 2017). Therefore, in the interaction between G. lingzhi and T. hengshanicum, G. lingzhi sphingolipids might be the signal molecules that could induce resistance G. lingzhi.
These data provided a better understanding of the mechanisms and identified potential DEGs involved in the early disease defenses of G. lingzhi against T. hengshanicum. These DEGs could be used as a screening indicator for identifying or selecting strains with high-disease-resistant properties. However, the results of this study were slightly different from those of plant-pathogen interactions. No DEGs related to plant hormones (JA, SA, and ABA, etc.) signaling transduction pathways were found in G. lingzhi infected by T. hengshanicum. In plants, SA and JA were endogenous plant hormones, that could induce the expression of pathogenicity-related proteins and systemic acquired resistance in plants, and were also recognized as a signal of plant responses to abiotic and biotic stresses. In A. bisporus, after P. tolaasii infection, JA biosynthesis and signaling transduction pathways were significantly enriched, and JA content was also detected to increase (Ma et al., 2021). Furthermore, no differential changes in genes related to cell wall synthesis were found in this study. In plants, the cuticle is the first cell wall layer encountered by a pathogen, plant pathogens must overcome the physical barrier presented by the cuticle and plant cell wall, so that the plant cell wall undergoes very large cell wall remodeling. Therefore, the changes in genes related to plant hormones and cell wall synthesis enzymes must be further studied.
In conclusion, transcriptomic analysis detected 620, 754, and 604 DEGs at 2, 12, and 24 h after infection with T. hengshanicum. Transcriptome sequencing indicated that there were 162 DEGs at three infection time points, containing 15 up-regulated DEGs and 147 down-regulated DEGs. After G. lingzhi was infected by T. hengshanicum, most of the DEGs were down-regulated at three-time points, indicating that G. lingzhi may resist the infection of T. hengshanicum mainly by down-regulating gene expression. Resistance-related genes TLPs (PR-5s) gene, phenylalanine ammonia-lyase gene, Beta-1,3-glucan binding protein were significantly up-regulated. At the three-time points of infection, the HSPs genes of G. lingzhi were down-regulated. The down-regulation of HSPs genes led to the inhibition of HSP function, which may compromise the HSP-mediated defense signaling transduction pathway, leading to G. lingzhi susceptibility. We performed GO and pathway enrichment analysis of DEGs at 2, 12, and 24 h for susceptible G. lingzhi, respectively. Four different gene sets were enriched for GO classification and KEGG enrichment, the main GO enrichment included heterochromatin assembly by small RNA and transcription, RNA-templated, RNA-directed 5′-3′ RNA polymerase activity, catalytic activity, oxidoreductase activity, oxidoreductase activity (acting on CH-OH group of donors), and regulatory RNA binding and the enriched pathways included sphingolipid metabolism, ether lipid metabolism, and valine, leucine and isoleucine degradation pathway. Although the T. hengshanicum pathogens induced resistance in G. lingzhi, such resistance could not completely prevent pathogen invasion, thereby showing disease symptoms. In conclusion, our results revealed the DEGs and metabolic pathways in the early defense response of Trichoderma spp. and provided a theoretical basis for the breeding of resistant strains of G. lingzhi.
## Data availability statement
The data presented in the study are deposited in the National Center for Biotechnology Information Sequence Read Archive repository, accession number PRJNA917261.
## Author contributions
TW wrote the manuscript. TW and CZ carried out experiments and collecting specimens. JX revised the manuscript and designed experiments. XL revised the manuscript and submitted the transcriptome data. 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.
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|
---
title: Single-cell transcriptome dynamics of the autotaxin-lysophosphatidic acid axis
during muscle regeneration reveal proliferative effects in mesenchymal fibro-adipogenic
progenitors
authors:
- Osvaldo Contreras
- Richard P. Harvey
journal: Frontiers in Cell and Developmental Biology
year: 2023
pmcid: PMC9996314
doi: 10.3389/fcell.2023.1017660
license: CC BY 4.0
---
# Single-cell transcriptome dynamics of the autotaxin-lysophosphatidic acid axis during muscle regeneration reveal proliferative effects in mesenchymal fibro-adipogenic progenitors
## Abstract
Lysophosphatidic acid is a growth factor-like bioactive phospholipid recognising LPA receptors and mediating signalling pathways that regulate embryonic development, wound healing, carcinogenesis, and fibrosis, via effects on cell migration, proliferation and differentiation. Extracellular LPA is generated from lysophospholipids by the secreted hydrolase—ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2; also, AUTOTAXIN/ATX) and metabolised by different membrane-bound phospholipid phosphatases (PLPPs). Here, we use public bulk and single-cell RNA sequencing datasets to explore the expression of Lpar 1–6, Enpp2, and *Plpp* genes under skeletal muscle homeostasis and regeneration conditions. We show that the skeletal muscle system dynamically expresses the Enpp2-Lpar-*Plpp* gene axis, with Lpar1 being the highest expressed member among LPARs. Lpar1 was expressed by mesenchymal fibro-adipogenic progenitors and tenocytes, whereas FAPs mainly expressed Enpp2. Clustering of FAPs identified populations representing distinct cell states with robust Lpar1 and Enpp2 transcriptome signatures in homeostatic cells expressing higher levels of markers Dpp4 and Hsd11b1. However, tissue injury induced transient repression of *Lpar* genes and Enpp2. The role of LPA in modulating the fate and differentiation of tissue-resident FAPs has not yet been explored. Ex vivo, LPAR$\frac{1}{3}$ and ENPP2 inhibition significantly decreased the cell-cycle activity of FAPs and impaired fibro-adipogenic differentiation, implicating LPA signalling in the modulation of the proliferative and differentiative fate of FAPs. Together, our results demonstrate the importance of the ENPP2-LPAR-PLPP axis in different muscle cell types and FAP lineage populations in homeostasis and injury, paving the way for further research on the role of this signalling pathway in skeletal muscle homeostasis and regeneration, and that of other organs and tissues, in vivo.
## Introduction
Striated skeletal muscle is an endocrine organ regulating whole-body metabolism, heat, posture, and movement. This highly plastic tissue changes and adapts its function throughout an organism’s lifespan, making it an essential organ to maintain whole-body homeostasis. Mammalian adult skeletal muscle regeneration remains one of the most captivating and remarkable faculties in mammals (Baghdadi and Tajbakhsh, 2018). Although regenerative muscle capability relies on tissue-resident adult unipotent muscle stem cells (MuSCs, also known as satellite cells) (Lepper et al., 2011; Murphy et al., 2011; Sambasivan et al., 2011; Fry et al., 2015), recent discoveries have demonstrated that successful muscle regeneration requires a complex interplay between different cell types (reviewed in Theret et al., 2021). Although significant progress has been made in understanding skeletal muscle regeneration, there is a need to identify novel, potentially druggable, targets to boost muscle repair in myopathies, neuromuscular disorders, muscle trauma and unhealthy aging. Fibro-adipogenic progenitors (FAPs) have recently emerged as essential stromal cells for maintaining skeletal muscle homeostasis, mass, neuromuscular integrity, and proper tissue regeneration (reviewed in Giuliani et al. [ 2021] and Theret et al. [ 2021]). However, FAPs have also been proven to drive muscle degeneration, mediating exacerbated fibrous-adipose-bone ectopic deposition in severe trauma and myopathies (reviewed in Contreras et al. ( 2021b) and Molina et al. [ 2022]).
Lysophosphatidic acid (LPA, also known as lysophosphatidate) is a small circulating bioactive phospholipid (430–480 Da, equivalent to four to five amino acids) with a core that has a phosphate group, glycerol, and a fatty acid chain (Moolenaar, 1995; Okudaira et al., 2010). LPA can act as an extracellular signalling molecule via autocrine, paracrine, or endocrine processes (Moolenaar, 1995; Geraldo et al., 2021). Among its wide range of biological functions, LPA regulates platelet aggregation, smooth muscle cell contraction, cell differentiation, cell proliferation and survival, chemotaxis, carcinogenesis, and stem cell biology (van Corven et al., 1989; 1992; Fang et al., 2000; Binder et al., 2015; Lidgerwood et al., 2018; Magkrioti et al., 2018; Geraldo et al., 2021; Xu et al., 2021). LPA signalling is mediated by at least six different receptors (LPA1–6) encoded by individual genes, which recognise extracellular LPA species (Kihara et al., 2014; Geraldo et al., 2021). These receptors are members of the seven-transmembrane G protein-coupled receptors (GPCRs) superfamily, Class A rhodopsin-like and lipid-like receptors (Kihara et al., 2014). As such, LPARs signal through several intracellular effector pathways activated by heterotrimeric G proteins, including Gi/o, G$\frac{12}{13}$, Gq/11, and Gs (reviewed in Geraldo et al. [ 2021]).
Extracellular levels of LPA are mainly controlled by the lysophospholipase D activity of the secreted enzyme ENPP2 (also known as AUTOTAXIN/ATX) (Akira et al., 1986; Tokumura et al., 2002; Okudaira et al., 2010). ENPP2 generates LPA by hydrolysis of lysophospholipids (lysophosphatidylcholine, lysophosphatidylserine, and lysophosphatidylethanolamine), making it an essential enzyme for production and maintenance of extracellular and serum LPA (Umezu-Goto et al., 2002; Benesch et al., 2015). ENPP2 is required for proper mammalian development and Enpp2-null mice die around embryonic day 10 (Tanaka et al., 2006). Although ubiquitously expressed in adult tissues (Ninou et al., 2018), recent studies have shown ENPP2 expression in adipose tissue as a major source of circulating and extracellular levels of this enzyme (Dusaulcy et al., 2011; Nishimura et al., 2014), suggesting that ENPP2 could act as an essential long and short distance adipokine (Funcke and Scherer, 2019).
Extracellular LPA is primarily metabolized by the ecto-activities of at least three plasma membrane-bound magnesium-independent lipid phosphate phosphatases or phospholipid phosphatases: PLPP1, PLPP2, and PLPP3, encoded by their respective *Plpp* genes (reviewed in Brindley et al. [ 2009] and Tang et al. [ 2015]). However, other magnesium-independent phospholipid phosphatases with broader substrate specificity can also metabolize LPA, including PLPP4, PLPP5, and PLPP6 (Tang and Brindley, 2020). PLPP7 has no demonstrable enzymatic activity, and little information is available (Tang and Brindley, 2020). PLPPs catalyze the dephosphorylation of various glycerolipid and sphingolipid phosphate esters, regulating their bioavailability (Brindley et al., 2009). Because of their crucial role in metabolizing LPA, gaining knowledge about the gene expression dynamics and regulation of ENPP2 and PLPPs, and their associated genes, could bring novel interventional strategies for treating disease.
Accumulative evidence suggests the participation of the ENPP2-LPA-LPAR axis in skeletal muscles. Yoshida et al. provided the first evidence demonstrating that LPA acts in skeletal muscle cells. These authors showed in vitro that LPA induced C2C12 myoblast proliferation and cell growth while inhibiting myotube differentiation through Gi proteins (Yoshida et al., 1996). Interestingly, structurally related lipids did not exert the same pro-proliferative and anti-fusion effects as LPA or phosphatidic acid (PA) (Yoshida et al., 1996). Initial evidence suggested the expression of some *Lpar* genes in C2C12 myogenic cells in which LPA activates two known pro-mitogenic signalling pathways, ERK$\frac{1}{2}$ and AKT (Jean-Baptiste et al., 2005). Other supporting studies have shown that LPA increases intracellular Ca2+ concentration and induces DNA synthesis (Xu et al., 2008), reinforcing that LPA modulates myogenic cell growth and proliferation (Bernacchioni et al., 2018). Recently, Gomez-Larrauri et al. reported that PA induces DNA synthesis in C2C12 myoblast via LPAR1/LPAR2 and downstream ERK$\frac{1}{2}$-AKT signalling at similar concentrations to LPA (Gomez-Larrauri et al., 2021). Pharmacological inhibition of ENPP2 or Enpp2 knockdown reduces myogenic differentiation, via a mechanism whereby Enpp2 is a direct target gene of WNT/RSPO2-mediated TCF/LEF/β-CATENIN signalling (Sah et al., 2020). The authors also showed that whole-body conditional deletion of Enpp2 impairs muscle regeneration upon acute BaCl2-induced damage (Sah et al., 2020). Reasoning in favour of a myogenic and pro-regenerative role for ENPP2, Ray et al. recently revealed that the ENPP2 axis regulates skeletal muscle regeneration in a satellite cell-specific manner (Ray et al., 2021). Thus, cumulative evidence shows that the ENPP2-LPAR axis is active in striated muscles modulating muscle stem cell function, adult myogenesis, hypertrophic muscle growth, and regeneration.
Because the exploration of the ENPP2-LPAR-PLPP network in muscles has been highly limited to myogenic and satellite cells, there is a current lack of knowledge about the gene expression dynamics of this axis in other muscle cells in response to injury. Here, utilizing publicly available bulk RNA-seq and single-cell transcriptomic (scRNA-seq) datasets, we studied for the first time the gene expression and pathway dynamics of the ENPP2-LPAR-PLPP network and its changes in numerous cell types in adult muscle homeostasis and regeneration, including subsets of immune cells, muscle stem cells, tenocytes, and fibro-adipogenic progenitors. In addition, we compared the effects of two specific pharmacological inhibitors of LPAR$\frac{1}{3}$ (Ki16425) and ENPP2 (PF-8380) in modulating cell growth, proliferation, and fibro-adipogenic differentiative fate on adult mesenchymal FAPs and satellite cells.
## Skeletal muscle differentially expresses ENPP2-LPAR-PLPP coding genes
To study ENPP2-LPAR-PLPP pathway gene expression dynamics in adult skeletal muscle tissue, we utilized public bulk transcriptomic data (Scott et al., 2019) and evaluated ENPP2-LPAR-PLPP gene expression in different samples: whole muscle, lineage+ cells (CD31+/CD45+), lineage− cells (CD31−/CD45−), and Lineage−/SCA1+ FAPs (Figure 1A). In whole muscle tissue, genome-wide transcriptomic profiling showed differential expression of LPAR members. Lpar1 was the most expressed member, followed by Lpar6 and Lpar4, whereas Lpar2, Lpar3, and Lpar5 levels were very low (Figure 1B). Limb muscle also expresses Enpp2 (∼8 FPKM or fragments per kilobase of exon per million mapped fragments) (Figure 1B). Moreover, we evaluated *Plpp* gene expression dynamics in skeletal muscle tissue. Plpp1, Plpp3, and Plpp7 were highly expressed compared to Plpp2, Plpp4, Plpp5, and Plpp6. Interestingly, Lpar6, Plpp2, and Plpp6 were highly enriched in the lineage+ fraction, suggesting they may be expressed by endothelial or hematopoietic lineage (Figure 1A). Thus, most ENPP2-LPAR-PLPP pathway components are present in healthy adult skeletal muscle.
**FIGURE 1:** *Bulk RNAseq transcriptomics analysis revealed differential gene expression of the ENPP2-LPAR-PLPP network. (A) Heat map showing gene expression levels of Lpar, Enpp2, and Plpp genes in whole skeletal muscle tissue, Lineage+, Lineage−, and Sca1+ FAPs from a bulk RNAseq library (Scott et al., 2019). Gene expression is shown as fragments per kilobase of exon per million mapped fragments (FPKM). (B) Quantification of Lpar, Enpp2, and Plpp genes transcript abundance (FPKM) in muscle tissue. (C) Heat map showing gene expression levels of Lpar, Enpp2, and Plpp genes in Lineage−Sca1+ FAPs and Hic1+ tdTomato expressing cells (Scott et al., 2019). (D) Quantification of Lpar
(1–6) genes transcript abundance (FPKM) in Sca1+ FAPs. (E) Quantification of Enpp2 and Plpp genes transcript abundance (FPKM) in Sca1+ FAPs.*
## SCA1+ fibro-adipogenic progenitors abundantly express ENPP2-LPAR-PLPP pathway genes in resting state
Since FAPs have a crucial role in regulating muscle and neuromuscular tissue integrity, we evaluated gene expression of the ENPP2-LPAR-PLPP gene network in uninjured muscle-resident SCA1+ FAPs and Hic1-lineage+ (tdTomato+) mesenchymal stromal cells (Scott et al., 2019). As observed in skeletal muscle tissue, LPA receptors were differentially expressed in resting FAPs (Figures 1C,D). Lpar1 was the most expressed family member, followed by Lpar4 and Lpar6, respectively (Figures 1C,D). However, Lpar2, Lpar3, and Lpar5 were not significantly expressed in FAPs (Figures 1C,D). These results indicate that Lpar1 is the highest expressed LPAR member in stromal FAPs, as seen in fibroblast lineages in other tissues (Supplementary Figure S1).
FAPs express relatively high levels of Enpp2 (∼77 FPKM) (Figure 1E), suggesting FAPs could be a significant cell source of extracellular LPA. Of the *Plpp* genes, Plpp3 was the highest expressed member, followed by Plpp1 and Plpp5. Plpp2, Plpp4, Plpp6, and *Plpp7* genes were very low expressed (Figure 1E). The trend of ENPP2-LPAR-PLPP pathway gene expression is similar between SCA1+ FAPs and Hic1-lineage+ FAP cells (Supplementary Figure S2). These results show that ENPP2-LPAR-PLPP pathway genes are significantly expressed in FAPs and, therefore, suggest a role for the bioactive phospholipid LPA and LPA-mediated signalling in skeletal muscle and stromal progenitor cells in homeostasis.
## Analysis of ENPP2-LPAR-PLPP network gene expression in skeletal muscle using single-cell transcriptomics
To gain more detailed insights into the role of the LPA axis in adult skeletal muscle cells, we further evaluated the relative expression of its network genes in skeletal muscle cells using scRNA-seq data (Oprescu et al., 2020). The single-cell data was derived from uninjured and injured muscle sampled at different time points from early hours post-injury to damage resolution (Figure 2A; Supplementary Figure S3). Here, we identified cells with discrete lineage identities and transcriptional states, performing unbiased clustering on an aggregate of cells using the Seurat R package (Butler et al., 2018) (Figure 2A; Supplementary Figure S3). We initially obtained 29 distinct clusters across different conditions (Supplementary Figures S3A–C). We visualize distinct cell populations in UMAP dimensionality reduction plots (Materials and methods), representing a total of 19 cell populations and 7 distinct cell lineages across uninjured and injured conditions (Figure 2A; Supplementary Figure S4).
**FIGURE 2:** *Analysis of Lpar-Enpp2-Plpp gene expression at single-cell resolution. (A) Uniform manifold approximation and projection (UMAP) plot of scRNA-seq public data (Oprescu et al., 2020) showing 19 distinct cell lineages in single cells across skeletal muscle homeostasis and regeneration. Detected major cell lineages were colored by the predominant cell type(s) that composes each cluster. (B) Violin plots showing the expression level of several marker genes across the different populations depicted in (A). (C) Violin plots showing the gene expression level of LPA receptors, Enpp2, and Plpp family members across the different populations or cell clusters. (D) Dot plot showing gene expression levels of LPA receptors, Enpp2, and Plpp family members. Dot plots help to visualize two values across two dimensions: color and size. The color gradient of the dot approximates average gene expression (light grey: low expression; navy blue: high expression).*
Major cell types and their defining marker signatures comprised fibro-adipogenic progenitors (FAPs; Pdgfra + Pi16 + Smoc2 + Hsd11b1 + Cxcl14 +), differentiated fibroblasts (DiffFibroblasts; Pdgfra − Meg3 + Lum + Col1a1 + Dlk1 +), muscle stem cells/satellite cells (MuSCs; Cdh15 + Pax7 + Myog + Megf10 +), myonuclei (Ttn + Ckm + Myh1 +), pericytes (Rgs5 +, which also includes markers of smooth muscle cells, i.e., Myh11), endothelial cells (Pecam1 + Cdh5 + Kdr + Cd36 +), tenocytes (Tnmd + Mkx + Fmod + Kera +), CD8 + T cells (Cd8a +), natural killer T cells (NKTcells; Nkg7 +), natural killer cells (NKcells; Ccr7 + Ccl5 +), neutrophils (S100a8 + S100a9 + Irg1 + Tnf +), antigen presenting cells (i.e., B cells, among others) (APCs; H2-Eb1 +), dendritic cells (DCs; Cd209a + H2-Eb1 + Ccr7 +), proliferative immune cells (ProlifICs; Stmn1 + Birc5 + Mki67+Acp5 +), Mrc1 macrophages (Mrc1MO; Mrc1 + C1qc + Cx3cr1 − Gpnmb −), M1 macrophages (M1MO; Cx3cr1 - Pf4 + Arg1 + Cd36 +), monocytes (F10 + Chil3 + Tnf+), and two related M2 macrophages (M2MO)—M2MOCx3cr1hi (C1qc+Cx3cr1 hi Tnfaip8l2 hi Gpnmb low) and M2MOCx3cr1lo (C1qc + Cx3cr1 low Tnfaip8l2 low Gpnmb hi) (Figures 2A,B; Supplementary Figure S4).
Within the FAP lineage, we observed high transcriptional variation and identified several cluster subtypes (Supplementary Figures S3A–C, S4). However, for preliminary analyses involving major cell lineages we intentionally grouped the 7 main FAP subclusters (clusters 12, 8, 2, 9, 4, 20, and 21) and kept differentiated fibroblasts (DiffFibroblasts, cluster 15) aside (Figures 2A, B; Supplementary Figures S3, S4). DiffFibroblasts have a differentiated fibroblasts/myofibroblast-like gene signature, highly expressing markers of activation and differentiation, and loss of stemness markers (Pdgfra − Pi16 − Lum + Col1a1 + Dlk1 + Col3a1 + Col6a3 +) (Figure 2B; Supplementary Figure S4). Specifically, downregulation of Pdgfra has been shown to be a sign of a differentiated FAP phenotype and correlates with their loss of stemness (Contreras et al., 2019b; 2020; Soliman et al., 2020).
Our analysis shows that fibro-adipogenic progenitors, tenocytes and MuSC/satellite cells predominantly express Lpar1, but its expression was higher in tenocytes and FAPs than MuSCs (Figures 2C, D). Lpar1 expression has not previously been shown in tenocytes or FAPs, although it has been reported that MuSCs express functional LPAR1 (Ray et al., 2021). Lpar4 was expressed in FAPs but not highly expressed in other cell types (Figures 2C, D). Lpar2, Lpar3, and *Lpar5* genes were virtually absent in FAPs (Figures 2C, D), which corroborates our previous results exploring bulk RNAseq data of SCA1+ FAPs (Figure 1D). On the contrary, Lpar6 had a broader cell type-dependent expression, including in different populations of immune cells (e.g., monocytes and APCs) and endothelial cells (Figures 2C,D). Interestingly, Lpar6 is the only LPAR gene member expressed in the immune cell lineage, suggesting that LPA or related phospholipids may also modulate immune cell function. These findings better define the bulk RNAseq analyses shown in Figure 1 for lineage+ cells.
FAPs and tenocytes expressed high levels of Enpp2, which was barely detected in other cell types (Figures 2C, D). This suggests FAPs and tenocytes as the two major cell types responsible for extracellular LPA production in skeletal muscles. With relation to LPA catabolizing enzymes, FAPs highly expressed Plpp1, followed by Plpp3 and Plpp5 (Figures 2C, D). MuSCs expressed Plpp1 and Plpp2, but no other members, whereas pericytes only Plpp3 (Figures 2C, D). Endothelial cells highly expressed Plpp3 and, to a lesser degree, Plpp1 (Figures 2C, D). Tenocytes also highly expressed Plpp3 and Plpp1, and less Plpp5 (Figures 2C, D). M2-like MCR1+ macrophages specifically expressed Plpp3. Myonuclei only expressed Plpp7 (Figures 2C, D). Intriguingly, we could not detect Plpp4 in the analyzed data, which could be due to the very low expression of this *Plpp* gene as seen exploring bulk RNAseq data (Figure 1). Hence, our analysis reveals for the first time the detailed landscape of Enpp2-Lpar-*Plpp* gene expression in several muscle cell types in homeostasis and regeneration at single-cell resolution, suggesting an active role of FAPs and tenocytes in producing LPA.
## Analysis of ENPP2-LPAR-PLPP axis in fibro-adipogenic progenitor subpopulations in response to skeletal muscle regeneration
Fibro-adipogenic progenitors and their descendant lineages are the primary cell types responsible for ectopic fibrosis, fatty tissue, and bone formation and deposition in severe myopathies, degenerative disorders and neuromuscular disease (Contreras et al., 2021b). Thus, we explored the temporal gene expression dynamics of Enpp2-Lpar-*Plpp* gene members in FAPs in homeostasis and in response to acute injury. We performed unbiased clustering on an aggregate of the initial clusters 12, 8, 2, 9, 4, 20, and 21 to increase the resolution of our fibro-adipogenic progenitor analyses (Supplementary Figure S3). We decided to include tenocytes [initial cluster 17 (Supplementary Figures S3, S4)] in our clustering analysis since these cells share a mesenchymal origin and highly express Lpar1 and Enpp2.
Our unbiased subcluster analysis retrieved 10 distinct clusters (Figure 3A). Using the FindAllMarkers Seurat function on these clusters and determining the top 8 marker genes, we assigned different names for the eight FAP subtypes obtained and tenocytes (Figures 3B, C). All FAP subpopulations showed expression of canonical FAP markers Pdgfra and Sparcl1, albeit at varying proportions and levels (Supplementary Figures S5A–C), and major changes in cell proportions were seen between conditions or days of injury (Figures 3D, E). We named these cells Sparcl1 FAPs, Csrp2 FAPs, Dlk1 FAPs, Dpp4 FAPs, Hsd11b1 FAPs, Tyrobp FAPs (previously named as DiffFibroblasts), Cycling FAPs and Ccl2 FAPs, starting from the most numerous subpopulations to the less abundant (Figures 3B, C). We also observed noticeable transcriptomic changes in the top eight expressed genes following muscle injury (Figure 3D). The top eight expressing genes of Sparcl1 FAPs were Sparcl1, Abca8a, Col15a1, Hmcn2, Htra3, Ltbp4, Penk and Cfh (Figure 3E). Csrp2 FAPs highly expressed Csrp2, Sfrp2, Ltbp2, Lrrc15, Tnc, 1500015O10Rik, Acta2 and Tagln, whereas Dlk1 FAPs highly expressed Dlk1, Igf2, Plagl1, Mest, Zim1, H19, Nrk and Agtr2. The Dpp4 FAP top eight expressed genes were Efhd1, Pcolce2, Dpp4, Sema3c, Cd55, Pi16, Efemp1 and Stmn4. Hsd11b1, Ccl11, Crispld2, Vwa1, Enpp2, G0s2, Nmb and *Inmt* genes distinguished Hsd11b1 FAPs from other FAP subtypes, although these also highly express Cxcl14 (Figure 3E). Tyrobp FAPs expressed Tyrobp, Fcer1g, Ctss, Lyz2, Laptm5, Slfn2, Cd52 and Srgn, whereas Cycling FAPs were characterized by high expression of genes related to survival and cell cycle, including 2810417H14Rik, Stmn1, Birc5, Mki67, Cks2, Tpx2, Cenpa and Top2a. Finally, Ccl2 FAPs high expressed Cxcl5, Ddx21, Ccl2, Rdh10, Slco2a1, Prg4, Lif and Mmp3, highlighting a pro-inflammatory state of these cells at 12 h post-injury (Figure 3E). The tenocyte cluster highly expressed Tnmd, Fmod, Thbs4, Col11a1, Cilp2, Scx, Kera and Chodl, as previously described (Harvey et al., 2019; Scott et al., 2019).
**FIGURE 3:** *Resting fibro-adipogenic progenitors predominantly express LPA receptors and LPA-producing enzyme ENPP2. (A) UMAP plot showing 10 distinct clusters across skeletal muscle homeostasis and regeneration when subclustering FAPs, DiffFibroblasts, and tenocytes subclusters. Detected major cell lineages and states were colored by the predominant cell type(s) that comprise each cluster (0–9). (B) UMAP plot showing nine distinct clusters (eight clusters for FAP lineage and one for tenocytes), which are named based on the most highly expressed gene in the heat maps shown below (C, E). (C) Heat map plot showing top eight expressed genes in each individual initial cluster shown in (A). (D) Heat map plot showing top eight expressed genes in the grouped nine distinct clusters under different conditions [undamaged and days post-injury (DPI)]. (E) Heat map plot showing the named clusters as described in the text, having its name because of one of the top eight expressed genes in each subset. (E) UMAP plot showing individual cells grouped based on the different conditions (uninjured and injured muscle) at different time points. DPI, days post-injury. (F) Violin plots showing the gene expression level of LPAR, Enpp2, and Plpp family members across the different FAPs and tenocytes subclusters. (G) Dot plot showing gene expression levels of Lpar, Enpp2, and Plpp family members. Note that Dpp4 and Hsd11b1 FAPs highly express Enpp2 gene.*
In uninjured conditions, we could distinguish two distinct FAP populations based on scRNA-seq, named Hsd11b1 FAPs, and Dpp4 FAPs after their highest upregulated genes (Figures 3A–E; Supplementary Figure S5), as previously described (Scott et al., 2019; Oprescu et al., 2020). Early in the injury process, Ccl2 FAPs appear and relate to an activated immune-like pro-inflammatory FAP subpopulation mostly present at 12 h post-injury (Figures 3A, B). Cycling FAPs uniquely expressed a potent cell cycle gene signature, representing the most abundant FAP subtype found at 2 days post-injury (Figures 3A–E; Supplementary Figure S5). Cycling FAPs can also be found at 3.5- and 5-day post-injury but to a lesser extent (Figures 3A–E; Supplementary Figure S5). Csrp2 FAPs are more abundant at 3.5- and 5-day post-injury, whereas Dlk1 FAPs were present at 10 days (Figures 3A–E; Supplementary Figure S5). The final captured stage of skeletal muscle regeneration, corresponding to day 21, mostly identified Sparcl1 FAPs together with Tyrobp FAPs (corresponding to DiffFibroblasts in our initial clustering) and, to a lesser extent, Dpp4 FAPs (Figures 3A–E; Supplementary Figure S5).
Next, we further identified the expression profiles of the ENPP2-LPAR-PLPP axis in the different FAP subpopulations. Most major FAP subtypes expressed Lpar1, including Dpp4, Dlk1 and Sparcl1 FAPs at high levels (Figures 3F, G). Lpar1 was also expressed in Hsd11b1, Csrp2, Ccl2 and Cycling FAPs, although to a lesser extent (Figures 3F, G). Lpar1 was also highly expressed by tenocytes (Figures 3F, G). Noticeable, *Lpar1* gene expression was undetectable in Tyrobp FAPs compared to the other 7 FAPs subtypes, suggesting LPAR1-dependent signalling may be downregulated in day 21 differentiated fibroblasts-like FAPs (Figures 3F, G). No significant gene expression was detected for Lpar2, Lpar3, Lpar5, and Lpar6 in FAPs or tenocytes (Figures 3F, G). Lpar4 was primarily expressed in Dlk1 and Sparcl1 FAPs and to a less extent in uninjured Hsd11b1 FAPs and Dpp4 FAPs (Figures 3F, G). These results show that different FAP subpopulations that exist in homeostasis and those that appear following acute damage express different levels of LPA receptors, suggesting that LPA modulates FAP activation, survival, and fate primarily throughout LPAR1 and LPAR4. Also, the absence of LPA receptor gene expression in the Tyrobp FAPs subtype compared to their counterparts at day 21 (e.g., Sparcl1 and Dpp4 FAPs) suggests Tyrobp FAPs may be refractory to LPA actions (Figures 3E–G). Thus, our single-cell exploration reports highly dynamic gene expression of LPA receptors in fibro-adipogenic progenitors in homeostasis and skeletal muscle regeneration.
*Enpp2* gene expression was higher in uninjured Hsd11b1 FAPs and Dpp4 FAPs than Sparcl1 FAPs, however, was repressed in other FAP subpopulations (Figures 3E–G). Remarkably, Enpp2 was among the top 5 markers expressed in Hsd11b1 FAPs (Figures 3C, E). Dpp4 FAPs also highly expressed Enpp2 (Figures 3C–G), suggesting a role for the encoded LPA extracellular-producing enzyme in resting FAPs, yet to be discovered. Tenocytes expressed Enpp2 at levels comparable to Sparcl1 FAPs (Figures 3E–G). Among *Plpp* genes, *Plpp3* gene expression was the most broadly distributed (Figures 3E–G), although its expression was higher in Dpp4 FAPs, followed by Hsd11b1 and Sparcl1 FAPs, but was downregulated in other FAP subpopulations (Figures 3E–G). Plpp1 and *Plpp5* gene expression patterns were similar, except for Ccl2 FAPs that did not express Plpp5, only Plpp3 (Figures 3E–G). Among *Plpp* genes, Tyrobp FAPs only expressed one family member, Plpp3 (Figures 3E–G). As previously suggested, Plpp2, Plpp6, and *Plpp7* genes were absent in most FAP subtypes, with a small percent of Ccl2 FAPs expressing Plpp2 and Hsd11b1 FAPs expressing Plpp7 (Figures 3E–G). Thus, our single-cell transcriptomics analysis showed enrichment for transcripts encoding the extracellular LPA-producing enzyme ENPP2 in two FAP subpopulations, Hsd11b1 and Dpp4, suggesting resting FAPs as a significant source of LPA in skeletal muscles.
## Downregulation of LPA receptors in fibro-adipogenic progenitors in response to acute injury
To better understand the single-cell gene expression patterns of the LPA receptor family in adult FAPs during muscle regeneration, we grouped all FAP clusters. We then evaluated *Lpar* gene expression in response to injury (Supplementary Figure S5E). It was evident that skeletal muscle injury triggers a rapid but transient downregulation of LPA receptors in FAPs, including Lpar1, Lpar4, and Lpar6 (Figure 4A; Supplementary Figure S5E). Lpar1, Lpar4, and *Lpar6* gene expression was most noticeably downregulated at 12 h and stayed low up to 48 h post-muscle injury (Figure 4A; Supplementary Figure S5E). Then, their expression increased towards pre-injury levels from 3.5 to 10 days post-injury (Figure 4A). Since changes happen when FAPs are activated and commit to proliferation and expand their numbers (Figure 3; Supplementary Figure S5D), these data suggest an association between FAP activation and cell cycle dynamics during the period of repression of LPAR family genes. Finally, we determined the relative expression of LPA receptors genes at the genome-wide transcript level in quiescent and injury-activated Hic1-lineage+ (tdTomato+) mesenchymal stromal cells in muscle, found to be enriched in FAPs (Scott et al., 2019) (Figures 4B, C). Lpar1 was highly and preferentially expressed over Lpar4 and Lpar6 in quiescent and injury-activated Hic1-lineage+ cells (Figures 4B, C). Lpar2, Lpar3, and Lpar5 were more lowly expressed (Figures 4B, C). *Lpar1* gene expression was early repressed in Hic1-lineage+ cells following acute damage, reaching its lowest on day 3 but recovering from day 4 post-injury onwards. Hence, two independent datasets demonstrate that the expression of LPAR gene family members is dynamically downregulated in injury-activated FAPs and Hic1-lineage+ cells but recovers later as muscle damage resolves through regeneration.
**FIGURE 4:** *Muscle injury triggers a fast and strong downregulation LPA receptors in fibro-adipogenic progenitors. (A) Violin plots showing the gene expression level of LPAR family members and dynamics in response to injury. DPI, Days post-injury. (B) Heat map showing gene expression levels of Lpar, Enpp2, and Plpp genes in Hic1+ tdTomato expressing cells upon acute muscle damage (Scott et al., 2019). (C) Quantification of Lpar
(1–6) genes transcript abundance (FPKM) in Hic1+ tdTomato expressing cells and dynamics in response to injury.*
## Repression of Enpp2 and Plpp genes in fibro-adipogenic progenitors in response to acute injury
Next, we evaluated Enpp2 expression in Hic1-lineage+ cells. Enpp2 was highly expressed in quiescent Hic1-lineage+ cells but then sharply downregulated 1-day post-injury, before increasing again up to day 3, then reducing again until day 5 post muscle injury (Figure 5A; Supplementary Figure S5E). Expression increased again from day 5 to day 10 post-injury (Figure 5A). At single-cell resolution, Enpp2 showed an expression pattern in FAPs similar to that in Hic1-lineage+ cells (Figures 5A, B), which is expected since Hic1-expressing cells mainly comprise FAPs in adult skeletal muscles (Scott et al., 2019; Contreras, 2020). Hence, Enpp2 is downregulated to almost undetectable levels in FAPs at regenerative time points that associate with cell activation and proliferation (Supplementary Figures S5D, E). Levels remained very low up to 5 days, then recovered from day 10 to day 21 post-injury (Figure 5B; Supplementary Figure S5E).
**FIGURE 5:** *Quick and pronounced gene repression of LPA-producing and -catabolizing enzymes in fibro-adipogenic progenitors following muscle damage. (A) Quantification of Enpp2 transcript abundance (FPKM) in Hic1+ tdTomato expressing cells in response to injury. (B) Violin plots showing the gene expression level of Enpp2 in FAPs in response to injury. DPI, Days post-injury. (C) Quantification of different Plpps transcript abundance (FPKM) in Hic1+ tdTomato expressing cells in response to injury. (D) Violin plots showing the expression level of the seven Plpp genes in FAPs in response to injury.*
Hic1-expressing cells repressed Plpp3 expression immediately following injury, whereas Plpp1 and Plpp5 expression were transiently increased (Figure 5C). These changes largely align with data derived from our single cell results of FAPs, however with some differences potentially accounted for by the pooling of FAP subsets. In pooled FAPs, all expressed *Plpp* genes were transiently downregulated early with expression recovering at later regenerative time points (Figure 5D). Overall, the trend is towards an initial downregulation of transcripts for Enpp2, that produces extracellular LPA, and different *Plpp* genes, although Plpp1 and Plpp5 show different kinetics in Hic1-expressing cells (i.e., FAPs).
## Single cell cross-validation of the ENPP2-LPAR-PLPP axis in skeletal muscle cells
We next aimed to validate our previous single-cell transcriptomics findings using three public scRNAseq datasets (McKellar et al., 2021; Yang et al., 2022; Zhang et al., 2022). Using the dataset of Zhang et al., we first observed that Lpar1, Lpar4, Enpp2 and Plpp3 were expressed by muscle FAPs, whereas tenocytes also expressed Lpar1 and Plpp3 (Supplementary Figures S6A, B). Again, FAPs did not express Lpar2, Lpar3, and *Lpar5* genes. Lpar6, Plpp1, and Plpp3 were present in endothelial cells and pericytes (Supplementary Figures S6A, B). Some satellite cells express Lpar1 and Lpar4, but not much of other LPA axis components (Supplementary Figures S6A, B). Neuron cells have a similar Enpp2-Lpar-Plpp expression profile to that of tendon cells (Supplementary Figures S6A, B). In addition, another two recently published skeletal muscle single-cell datasets further corroborated our previous findings (Supplementary Figures S6C, D) (McKellar et al., 2021; Yang et al., 2022). Of note, the dataset of Yang et al. used forelimb triceps brachii skeletal muscle, which supports our findings exploring the dataset of Oprescu et al. using hindlimb tibialis anterior muscle (Oprescu et al., 2020; Yang et al., 2022). Furthermore, Yang et al.s’ scRNAseq dataset from subcutaneous white adipose tissue also supports the notion of progenitor adipose stromal cells (ASCs), as cells highly expressing Lpar1, Lpar4, Enpp2 and Plpp3 (Supplementary Figure S6E), implying that the ENPP2-LPAR-PLPP gene axis is enriched in stromal cells from different tissue origins. These data, coupled with the observation that Lpar1 and Enpp2 are specific to skeletal muscle FAPs, supports a model whereby LPA could be involved in modulating the fate and behaviour of stromal cells, potentially through an autocrine signalling. Moreover, these gene expression profiles are consistent with our previous data analysis and conclusions, validating the dataset for further analysis.
## Extracellular LPA and LPAR-mediated downstream signalling are essential for fibro-adipogenic progenitor colony formation, growth, and proliferation
Given that the ENPP2-LPA-LPAR gene axis shows differential gene regulation in resting versus activated and proliferative subsets of FAPs, we hypothesized that the pathway could regulate fibro-adipogenic cell proliferation. Thus, we sought to evaluate FAP proliferation and cell cycle parameters in response to LPAR1 and LPAR3 subtype-selective antagonist Ki16425 (Ohta et al., 2003) and the potent ENPP2 inhibitor PF-8380 (Gierse et al., 2010) under conditions of colony formation and growth in vitro. FAPs have colony-forming units-fibroblast (CFU-Fs) properties, which reflects the presence of immature in vivo progenitors with proliferative, self-renewal and multi-lineage differentiation potential (Joe et al., 2010; Uezumi et al., 2010; 2014; Contreras et al., 2019b; Reggio et al., 2020; Farup et al., 2021). We evaluated the effects of Ki16425 and PF-8380 on SCA1+/PDGFRα+ FAP CFU-F formation and growth (Supplementary Figure S7A) and assessed colony numbers and self-renewal properties in vitro (Figure 6A). Treatment of FAPs with Ki16425 significantly reduced FAP cell growth (Figure 6B), suggesting that extracellular LPA, contained either in the bovine serum used for culture or endogenously produced by FAPs as they highly express Enpp2, has pro-proliferative effects. Consistently, PF-8380 treated cells formed only few colonies (Figure 6B), suggesting that the LPA-producing activity of ENPP2 is essential for FAP proliferation and growth. We next utilized immunofluorescence and flow cytometric analyses to evaluate the percentage of DNA replicating cells, based on the incorporation of 5-ethynyl-2′-deoxyuridine (EdU) and its detection by click chemistry (Salic and Mitchison, 2008) (Figures 6C–F). First, we evaluated the percentage of EdU+ FAPs at 24 h of inhibitor treatment in $10\%$ FBS, after a short 2 h pulse with EdU. Our data show that Ki16425 significantly reduced the proportion of cycling FAPs by half, as determined by the percentage of EdU+ cells (Figures 6D, E). ENPP2 pharmacological inhibition with PF-8380 reduced the proportion of replicating FAP cells even more than Ki16425 inhibition of LPARs (Figures 6D, E), corroborating our previous CFU-F findings. Quantitative detection of EdU+ FAPs using single-cell flow cytometry further corroborated our results (Figure 6F; Supplementary Figure S7B). In addition, using Ki67 protein immune-labelling and flow cytometric detection, we show that Ki16425, and more profoundly PF-8380, decreased the proportion of Ki67+ cycling-competent cells (Supplementary Figure S7C). LPA addition at 20 µM did not rescue the reduction of cycling FAPs by Ki16425, and it only partially rescued the proliferation deficits induced by ENPP2 inhibitor (Figure 6F; Supplementary Figure S7C), likely due to the presence of PLPPs. Further experiments showed that PF-8380 strongly blocks the progression of the G1-to-S phase transition of the FAP cell cycle (Figure 6F), indicating that the ENPP2-LPA-LPAR axis regulates the cycling activity of FAPs.
**FIGURE 6:** *Pharmacological inhibition of LPA receptors and Autotaxin reduces fibro-adipogenic progenitor cell growth and proliferation affecting their fibro-fatty fate. (A) Outline of colony-forming units assay using muscle FAPs. Representative images of FAPs control-treated (DMSO) or treated with Ki16245 (10 μM, LPAR1/3 inhibitor) and PF-8380 (10 μM, ATX inhibitor) as shown, and then stained with Crystal Violet. Scale bar: 1 cm. (B) Quantification of the number of cells per area as shown in (A) from four independent experiments. ****p < 0.0001 by one-way ANOVA with Tukey’s multiple comparison post-test; n = 4. (C) EdU assay outline. (D) Representative laser confocal images of FAPs after the different treatments [DMSO as control, Ki16245 (10 μM) and PF-8380 (10 μM)] at 24 h post treatment. EdU staining in shown in cyan hot, nuclear staining with Hoechst in magenta, and αSMA is shown in light green. Scale bar: 500 μm. (E) Quantification of the % of EdU labelled cells at 24 h post treatments. **p < 0.001 by one-way ANOVA with Tukey’s post-test; n = 3. (F) Flow cytometry determination of EdU labelled cells in combination with DNA fluorescence at 24 h of treatments. (G) Outline of neutral lipid staining assay in muscle FAPs treated with adipogenic differentiation media (ADM). (H) Representative laser confocal images of FAPs after the different treatments [DMSO as control, Ki16245 (10 μM) and PF-8380 (10 μM)] at day 3 post treatment. BODIPY staining is shown in light green, nuclear staining in magenta, and αSMA in cyan hot. Scale bar 500 μm. (I) Quantification of the % of BODIPY labelled cells. ****p < 0.0001 by one-way ANOVA with Tukey’s multiple comparison post-test; n = 4. (J) Myofibroblast index of αSMA labelled cells. **p < 0.0021 by one-way ANOVA with Tukey’s multiple comparison post-test; n = 3.*
Given that ENPP2 has been suggested as an adipose tissue-derived LPA generator, and we have shown that the ENPP2-LPAR-PLPP axis regulates skeletal muscle stromal FAP cell cycle and division, we next evaluated whether Ki16425 and PF-8380 could also impair cell growth and proliferation of visceral adipose stromal cells (ASCs) ex vivo. Our results show that both Ki16425 and PF-8380 inhibited the formation of ASC CFU-F (Supplementary Figures S7D, E). As found for FAP CFU-Fs, PF-8380-treated ASCs exhibited no cell growth (Supplementary Figures S7D, E). Both Ki16425 and PF-8380 treatments reduced the proportion of ASCs that can be detected in S-phase (i.e., EdU+), indicating defects on the G1-S phase transition (Supplementary Figures S7F, G). Flow cytometric analyses of DNA replicating cells indicated that LPA treatment (20 µM) without inhibitors induced a slight increase of EdU+ ASCs (Supplementary Figure S7G). LPA co-treatment with PF-8380 showed a small but significant rescue of the proportion of EdU+ ASCs compared to PF-8380 alone (Supplementary Figure S7G). Overall, these results support the notion that the ENPP2-LPA-LPAR axis regulates the proliferation and cell division of stromal cells from different tissue origins.
## Pharmacological inhibition of LPA1/3 receptors and ENPP2 impairs the differentiative fate of fibro-adipogenic progenitors
Finally, we evaluated the adipogenic differentiation of FAPs in vitro, scoring for FAP-derived adipocytes positive for neutral lipophilic molecule BODIPY staining at day 3 of induction using confocal tile image reconstruction (Figure 6G). We observed that both LPAR and ENPP2 inhibition, from the beginning of differentiation, reduced the proportion of BODIPY+ adipocytes; however, only the more pronounced effect of ENPP2 inhibitor PF-8380 was statistically significant when normalized to total cell number (Figures 6H,I). Both inhibitors also showed a strong anti-proliferative effect in adipogenic media, which reinforced our previous results (Figure 6H). Since FAPs can also differentiate into activated fibroblasts and myofibroblasts, we evaluated alpha smooth muscle actin (αSMA)-positive stress fiber labelling as a proxy for myofibroblastic differentiation (Figure 6H). Our results show that Ki16425 treatment leads to smaller sized FAPs and significantly reduced αSMA+ myofibroblast differentiation (Figures 6H, I). However, whereas the ENPP2 inhibitor PF-8380 reduced myofibroblast differentiation of FAPs overall, this was not statistically significant when normalized to the total number of cells (Figures 6H, J), highlighting the stronger anti-proliferative effect. Taken together, these results suggest that inhibition of LPA receptors and ENPP2 impairs the proliferative and fibro-fatty fate of fibro-adipogenic progenitors.
## Downregulation of Lpar1 and Lpar4 is associated with dividing and committed muscle stem cell states
Ray et al. recently reported that ENPP2-LPA-LPAR1 signalling is a crucial pro-regenerative axis in skeletal muscle (Ray et al., 2021). The authors also reported that Lpar1 expression increased in myotubes compared to proliferative myoblasts, suggesting a pivotal role of LPA in modulating adult satellite cell differentiation.
To better understand the single-cell gene expression dynamics of the LPAR family in adult MuSCs, we again used scRNA-seq data and performed unsupervised sub-clustering on the MuSC metacluster, as previously shown (Oprescu et al., 2020; Contreras et al., 2021a). Six different subsets of MuSCs resulted from our analysis, consistent with previous findings (Oprescu et al., 2020; Contreras et al., 2021a) (Supplementary Figure S8A). Quiescent (QSC) adult MuSCs expressed Lpar1 (about $50\%$ of MuSCs) and Lpar4 ($20\%$ of MuSCs), but no other LPAR gene family members (Supplementary Figure S8B). Lpar1 and Lpar4 remained relatively stable in activated MuSCs (ASC), but decreased in dividing (DIV), committed (COM), immunomyoblasts (IMB) and differentiated (DIF) MuSCs (Supplementary Figure S8B), suggesting in fact that both LPA receptors are downregulated as muscle stem cells proliferate, commit, and differentiate to form mature myofibers.
Among phospholipid phosphatases expressed in quiescent MuSCs, Plpp3 was the most highly expressed member of the family with expression progressively decreasing as these cells become activated, committed and differentiated (Supplementary Figure S8B). In contrast, Plpp1 was significantly higher in activated, committed, and differentiating MuSCs, whereas Plpp2 increased only in immunomyoblasts, and activated and dividing MuSCs (Supplementary Figure S8B). Plpp5 and Plpp6 were not detectably expressed in MuSCs (Supplementary Figure S8B). The non-enzymatic member, Plpp7, was absent in each of the six MuSCs subpopulations except for differentiated MuSCs (Supplementary Figure S8B). Enpp2 expression was detected in ∼$20\%$ percent of quiescent MuSC (Supplementary Figure S8B) and this further decreased in ASC, DIV, IMB, COM and DIF MuSCs subpopulations (Supplementary Figure S8B). Thus, our results suggest that Enpp2 is expressed in at least some MuSCs, and functional data of Ray et al. suggest that this is sufficient to have biological relevance for muscle regeneration. These single cell transcriptomic analyses suggest that the expression of Enpp2-Lpar-*Plpp axis* genes is dynamic in muscle stem cells in homeostasis and injury. They illustrate also the potentially complex cell communication networks mediated by the bioactive phospholipid LPA in skeletal muscle and the MuSC niche.
## Pharmacological inhibition of ENPP2 inhibits satellite cell proliferation and myotube differentiation
Because our results show that Lpar1 and Lpar4 receptor genes are downregulated as muscle satellite cell proliferate, commit, and differentiate, we evaluated whether inhibiting LPAR$\frac{1}{3}$ receptors or ENPP2 would affect MuSC proliferation and differentiation. Our flow cytometric results showed that 10 µM of the ENPP2 inhibitor PF-8380 reduced by half the proportion of EdU+ satellite cells (Figures 7B, C; Supplementary Figure S9A), as well as and the proportion of mitotic pH3S10+ cells (Figure 7D). However, Ki16425 pharmacological inhibition of LPAR$\frac{1}{3}$ did not affect the proportion of EdU+ or pH3S10+ satellite cells (Figures 7C, D), perhaps other receptors than LPAR$\frac{1}{3}$ may be involved. These data show that ENPP2 pharmacological inhibition impaired the number of replicating satellite cells. Next, we studied whether pharmacological inhibition of LPAR or ENPP2 would alter myotube differentiation of satellite cells. By evaluating myotube differentiation at day 3 (Figure 7E), we observed that inhibition of LPAR with 10 µM Ki16425 did not affect myotube differentiation of satellite cells (Figure 7F). By contrast, 10 µM of the ENPP2 inhibitor PF-8380 resulted in a significant reduction of sarcomeric α-ACTININ+ myotubes and MF20+ myotubes (Figure 7F; Supplementary Figure S9B). Remarkably, we observed that 1 µM of PF-8380 also inhibited myotube differentiation and formation (Supplementary Figure S9C). Hence, ENPP2 catalytic activity is required for proper myotube differentiation and maturation, as previously suggested using a higher concentration of PF-8380 (25 µM) ex vivo (Ray et al., 2021). 20 μM LPA treatment alone significantly increased myotube (α-ACTININ+ and MF20+) number and thickness compared to untreated (control) cells (Figure 7F; Supplementary Figure S9B). Overall, our data suggest that the LPA pathway is indispensable for myogenic differentiation of satellite cells.
**FIGURE 7:** *PF-8380 pharmacological inhibition of ENPP2 impairs satellite cell proliferation and myotube differentiation. (A) Brightfield images of cultured muscle stem cells (i.e., satellite cells). (B) Outline of EdU assay in muscle satellite cells. (C) Flow cytometry detection of EdU labelled cells in combination with DNA fluorescence at 24 h of treatments. (D) Flow cytometry detection of mitotic (phospho-H3S10) labelled cells in combination with DNA fluorescence at 24 h of treatments. (E) Outline of satellite cell differentiation protocol. GM, growth media; DM, differentiation media. (F) Representative laser confocal images of day 3 myotubes after the different treatments. α-Actinin staining is shown in hot yellow, nuclear staining in hot blue, and MF20 in magenta. Scale bar: 100 μm.*
## Discussion
LPA is a signalling lipid with multiple biological functions and roles in health and disease. Collectively, our study provides insights into the presence and dynamic expression of the ENPP2-LPAR-PLPP gene axis in different muscle cells, cell lineages and states in homeostasis, injury and regeneration at single cell resolution. We first showed that Lpar1 is the highest expressed member among other LPAR genes in tibialis anterior limb muscle, followed by Lpar6 and Lpar4. We also found that Lpar2, Lpar3, and Lpar5 were almost unexpressed. Enpp2 is a relatively abundant gene in tibialis anterior, and several *Plpp* genes were also expressed, including Plpp1, Plpp3, and Plpp7. Thus, we report most ENPP2-LPAR-PLPP pathway gene components are present in healthy adult skeletal muscle in mice. FAPs highly express Lpar1 and Enpp2, suggesting that stromal cells may be the primary source of extracellular LPA and LPA-mediated signalling and functions in the muscle niche. We additionally validated these findings utilising other scRNAseq datasets.
By sub-clustering stromal fibro-adipogenic progenitors (FAPs), we identified different subpopulations representing distinct cell states with robust LPAR and ENPP2 transcriptome signatures in homeostasis. Lpar1 was expressed mainly by different subset of FAPs and tenocytes, whereas Enpp2 was mostly expressed by resting FAPs. We also showed that tissue injury triggered transient repression of LPA receptors and Enpp2. Hence, uniquely activated FAP cell states are partly defined by a downregulation of Lpar and *Enpp2* gene expression. In addition, our ex vivo experiments indicate that the LPAR$\frac{1}{3}$ Ki16425 receptor antagonist and ENPP2 inhibitor PF-8380 impaired cell cycle progression and proliferation of muscle FAPs and visceral ASCs, although PF-8380 had a stronger effect. Since Lpar3 is not expressed by resting or activated FAPs, we speculate that most Ki16425-driven effects are mediated by inhibition of LPAR1 in FAPs. Here, in PF-8380 treated cells, we also found decreased adipogenic differentiation of FAPs, in part due to the proliferative deficits induced by this potent ENPP2 inhibitor. On the contrary, although Ki16425 treatment did not significantly impair adipocyte differentiation of FAPs, it did reduce the proportion of αSMA+ myofibroblasts. Thus, pharmacological inhibition of LPAR1 and ENPP2 reduced the growth and proliferation of stromal cells, affecting their differentiation potential.
Finally, focusing on different MuSCs subtypes that emerge following acute damage we also observed differential ENPP2-LPAR-PLPP axis gene expression, although in general terms the axis was more lowly expressed compared to FAPs and tenocytes. In this study, using pharmacological inhibition, we found that ENPP2 was essential for satellite cell proliferation and myotube differentiation. We also reported that exogenous LPA is sufficient to enhance the efficiency of satellite cell differentiation and myotube maturation, indicating that even low transcript abundance of LPA receptors in satellite cells is enough to elicit relatively strong cellular responses to extracellular LPA ex vivo. Related to this finding, Ray et al. recently showed that myogenic differentiation induces Enpp2 expression, suggesting an increase in the extracellular abundance of ENPP2, and Enpp2 knockdown reduced fusion and myotube differentiation (Ray et al., 2021). Remarkably, the cell-specific deletion of Enpp2 in MuSCs impairs acute injury-induced muscle regeneration in mice, resulting in reduced muscle fiber caliber (Ray et al., 2021). These results were supported by utilizing the pharmacological ENPP2 inhibitor PF-8380, which also caused reduced muscle regeneration. Furthermore, Enpp2 transgenic mice overexpressing circulating and extracellular ENPP2 levels, and expectedly increasing serum LPA, showed signs of muscle hypertrophy via ribosomal S5K signalling and accelerated recovery post-acute damage (i.e., faster muscle regeneration) (Ray et al., 2021). In support of this, intramuscular injections of both ENPP2 and LPA into healthy muscles resulted in muscle hypertrophy. These results provide significant data in favour of a pro-regenerative role of the ATX-LPA-LPAR axis in skeletal muscles. Additionally, inhibition of ATX using PF-8380 at 25 µM impaired satellite cell differentiation into myotubes but did not affect satellite cell proliferation using 5-bromo-2′-deoxyuridine (BrdU) uptake (Ray et al., 2021). In contrast, our results showed that 10 µM PF-8380 was sufficient to alter EdU uptake and the mitotic mark H3 phosphorylated in Serine10 in satellite cells ex vivo, suggesting that ENPP2 regulates MuSC proliferation. Lower concentrations of the ENPP2 inhibitor PF-8380 than that used by Ray et al. [ 2021] also impaired satellite cell myotube differentiation, supporting the notion that ENPP2 activity is key for proper skeletal myogenesis. Overall, future studies are needed to understand the role of LPA on skeletal myogenesis and muscle regeneration. However, due to the pleiotropic effects LPA might have on different cell types and cell states, addressing these questions on models of muscle damage remain challenging.
Kienesberger et al. showed that the ENPP2-LPA axis is involved in obesity-induced insulin resistance in muscles, affecting mitochondrial respiration in differentiated myotubes (D’Souza et al., 2018), validating the previously suggested key role of ENPP2-LPA axis in healthy and obese adipose tissue (Ferry et al., 2003; Boucher et al., 2005; Federico et al., 2012). The authors also showed that partial genetic reduction of ENPP2 levels ameliorated obesity and systemic insulin resistance in a high-fat diet mouse model (D’Souza et al., 2018). These results suggest that the ENPP2-LPA axis could contribute to the development of obesity-related disorders and tissue malfunction in metabolically altered states. Our analysis shows that adipose tissue stromal cells highly express Enpp2, and do respond to ENPP2 inhibitor PF-8380.
We and others have demonstrated that LPA induces the gene expression and protein levels of biologically active Connective Tissue Growth Factor (CTGF/CCN2) in C2C12 myoblasts (Vial et al., 2008; Riquelme-Guzmán et al., 2018). CTGF is a matricellular regulatory protein that modulates skeletal muscle repair, muscular dystrophy pathophysiology, and fibrosis (Morales et al., 2013; Petrosino et al., 2019; Rebolledo et al., 2019; Chen et al., 2020). LPA-mediated CTGF induction has been reported in different cell types, including embryonic and adult fibroblasts, mesothelial cells, in human and mouse models (Heusinger-Ribeiro et al., 2001; Stortelers et al., 2008; Sakai et al., 2013). Remarkably, several ubiquitous signalling pathways mediate LPA-mediated CTGF induction in myogenic cells, including αvβ3 and αvβ5 Integrins, TGF-β receptor kinase activity, JNK, ECM components, and FAK (Cabello-Verrugio et al., 2011; Riquelme-Guzmán et al., 2018). These studies reveal an intricate network of signalling molecules that may tune LPA-driven responses in cells and tissues. Remarkably, Chen et al. showed that LPA, which has been previously identified to increase upon myocardial infarction (Chen et al., 2003), promotes proliferation and apoptosis of cardiac fibroblasts depending on its concentrations, suggesting that LPA has dual roles in fibroblasts (Chen et al., 2006). Our flow cytometry and imaging data, highlighting cell cycle and proliferation analyses, show that LPA does not cause fibro-adipogenic progenitor cell death but, on the contrary, supports a pro-proliferative role of the ENPP2/LPA/LPAR axis in muscle and adipose tissue-derived stromal cells.
Recently, Córdova-Casanova et al. showed that intramuscular injections of LPA induced CTGF protein levels and a few ECM proteins (Córdova-Casanova et al., 2022). Using LPAR inhibitor Ki16425 or LPAR1 knockout mice the authors observed an inhibition of these effects. They also showed increase muscle cellularity, i.e., total number of nuclei, and number of PDGFRα+ FAPs in response to LPA intramuscular injections. These results indicate LPA could have fibrotic-like properties in vivo in damaged muscles as previously suggested using cell culture (Vial et al., 2008; Riquelme-Guzmán et al., 2018) or in vivo models (Davies et al., 2017). Periodic intraperitoneal injections of LPA worsen the inflammatory milieu of rotator cuff tears (RCTs) in adult rats, increasing Tnfa and Tgfb1 at 6 weeks post tear and the number of inflammatory cells within the affected muscles (Davies et al., 2017). Rotator cuff tears (RCTs) are a highly prevalent form of muscle trauma and tissue degeneration (Agha et al., 2021). Since severe intramuscular fibrosis and fatty infiltration are key morphological features of RCTs, significant research suggests FAPs as critical mediators of RCT onset, pathology, and progression (Theret et al., 2021). The authors also showed that enhanced systemic LPA worsens the fibrotic and adipogenic phenotype of RCTs (Davies et al., 2017). Thus, their study is the first of its kind to demonstrate a pro-fibrotic and pro-adipogenic role of systemic LPA in damaged muscles. Although forced intramuscular injections with LPA may not reflect proper physiological or pathophysiological conditions, the results of Córdova-Casanova et al. together with those of Davis et al., offer a new avenue to start exploring the relevance of LPA-mediated signalling pathways and their role in muscle disease and physio-pathophysiology.
Since FAPs are the main mediators of ectopic fibrous and fatty tissue, and because our results show resting FAPs highly express Lpar1 and Enpp2, we speculate that LPA acts on these stromal cells early after muscle injury to promote cell proliferation and survival, therefore, resulting in a fibrotic and adipogenic phenotype in severely damaged muscles. Our functional analyses demonstrated that LPA regulates the proliferative status of FAPs and ASCs, impacting also the differentiative fate of these cells. Owing that FAPs highly express Enpp2 and Lpar1, we propose that an autocrine LPA signalling regulates the activation and proliferation of FAPs. Due our analysis also showed that endothelial cells and several immune cell types, including monocytes, M2 Cxc3cr1hi macrophages, and APCs express Lpar6, we could also consider that intramuscular or intraperitoneal injections of LPA target endothelial and immune cells. In this regard, LPA promotes the development of macrophages from monocytes through a mechanism that may involve PPARγ (Ray et al., 2021). Hence, our results suggest that injury-induced LPA could act on monocyte to promote their maturation and differentiation into macrophages via LPAR6.
The involvement of the ENPP2-LPA-LPAR axis in inflammation and fibrosis is not new and several studies have shown its crucial participation (Tager et al., 2008; Castelino et al., 2011; Gan et al., 2011; Sakai et al., 2013; Ohashi and Yamamoto, 2015; He et al., 2018; Ninou et al., 2018); however, the mechanisms and cellular targets have been underexplored. Several ongoing studies suggest the ENPP2-LPA-LPAR axis as a prognostic indicator of injury- or radiation-induced fibrosis (NCT05031065), with some studying the safety, tolerance, and effectiveness of orally available ENPP2 inhibitors [BBT-877: NCT03830125; GLPG1690 (ziritaxestat): NCT02738801 and NCT03798366] or LPAR antagonists [BMS-986020: NCT01766817 (Decato et al., 2022); BMS-986278: NCT04308681], as a means of reducing tissue fibrosis and improving organ function in different patients cohorts. Because stromal cells of mesenchymal origin, e.g., FAPs and ASCs, highly express Lpar1, Enpp2, and key Plpp members, upcoming research should focus on better understanding the role of LPA axis in muscle homeostasis, inflammation, fibrosis, repair, and regeneration. This understanding would potentially offer new druggable avenues for devastating muscle diseases like myopathies, severe muscle trauma, or neuromuscular disorders.
In summary, applying bulk and single cell transcriptomic data analyses we zoom in on skeletal muscle tissue at single cell resolution and provide for the first time a detailed view of the ENPP2-LPA-LPAR-PLPP axis for future insights in how to target LPA-driven signalling and functions. Furthermore, using ex vivo FAP and adipose stromal cell cultures and pharmacological inhibition of LPARs and ENPP2, we demonstrate, for the first time, that the ENPP2-LPA-LPAR axis regulates the cell cycle activity and proliferation of these cells. Hence, our data analysis highlights LPA signalling in different muscle cells and fibro-adipogenic progenitor lineages after muscle injury and provides an entry point for more profound research of the role of LPA signalling in homeostasis, inflammation, fibrosis, repair, and regeneration.
## Limitations of the study
This study has certain limitations. First, our transcriptomics analyses cannot address the protein levels of the ENPP2-LPA-LPAR-PLPP network, noting, however, there is currently a limitation of validated and working antibodies of axis components. Second, although we detected a downregulation of LPARs in FAPs in response to injury, we did not evaluate LPA receptor protein levels in FAPs upon injury-induced activation. The development of high-quality and validated LPAR antibodies should help answer these and related questions. Third, commonly used tissue disaggregation strategies, flow cytometry, and droplet-based scRNAseq does not efficiently capture certain cell types (e.g., adipocytes) because of their high propensity to rupture and buoyancy. Furthermore, large cells (e.g., myofibers, nerves, and adipocytes) do not effectively fit into a droplet and are often underrepresented in scRNAseq studies. Fourth, we have not evaluated or measured the effects of exogenous LPA or pharmacological inhibitors of LPARs or ENPP2 on modulating the fate of immune, tenocytes, or endothelial cells. Subsequent studies should also focus on understanding the influence and role of the ENPP2-LPA-LPAR-PLPP network and its effects on the fate of different muscle cells. Nevertheless, our study represents the first of its kind since in exploring the ENPP2-LPA-LPAR-PLPP network at single-cell resolution, and the proliferative and differentiated fate of fibro-adipogenic progenitors with altered LPA signalling.
## scRNA-seq data processing and analyses
We extracted the single-cell RNA sequencing data used in this paper from Gene Expression Omnibus (GEO; GSE138826) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE138826; GSE138826_expression_matrix.txt) (Oprescu et al., 2020). The preliminary analyses of processed scRNA-seq data were analysed using the Seurat suite (version 4.0.3) standard workflow in RStudio Version 1.2.5042 and R version 4.0.3. First, we applied initial quality control to Oprescu et al., 2020 dataset. We kept all the features (genes) expressed at least in five cells and cells with more than 200 genes detected. Otherwise, we filtered out the cells. Second, we verified nUMIs_RNA (>200 and < 4,000) and percent.mt. ( less than $5\%$) Third, UMIs were normalized to counts-per-ten-thousand log-transformed (normalization.method = LogNormalize). The log-normalized data were then used to find variable genes (FindVariableFeatures) and scaled (ScaleData). Finally, Principal Component Analysis (PCA) was run (RunPCA) on the scaled data using highly variable features or genes. Elbowplot were used to decide the number of principal components (PCs) to use for unsupervised graph-based clustering and dimensional reduction plot (UMAP) embedding of all cells or further subclustering analyses (i.e., FAPs) using the standard FindNeighbors, FindClusters, and RunUMAP workflow. We used 30 PCs and a resolution of 0.6 to visualize a Uniform manifold approximation and projection (UMAP) dimensionality reduction plot generated on the same set of PCs used for clustering. We decided the resolution value for FindClusters on a supervised basis after considering clustering output from a range of resolutions (0.4, 0.6, 0.8, and 1.2). We used a resolution of 0.6. Our initial clustering analysis returned 29 clusters (clusters 0–28). We identified cell populations and lineage-specific marker genes for the analyzed dataset using the FindAllMarkers function with logfc.threshold = 0.25, test.use = “wilcox,” and max.cells.per.ident = 1,000. We then plotted the top 10 expressed genes, grouped by orig.ident and seurat_clusters using the DoHeatmap function. We determine cell lineages and cell types based on the expression of canonical genes. We also inspected the clusters (in Figures 2, 3) for hybrid or not well-defined gene expression signatures. Clusters that had similar canonical marker gene expression patterns were merged.
For Mesenchymal Clusters (group of FAPs + DiffFibroblasts + Tenocytes obtained in Figure 2) we used PCs 20 and a resolution of 20 to visualize on the UMAP plot. Our mesenchymal subclustering analysis returned 10 clusters (clusters 0–9). Cell populations and lineage-specific marker genes were identified for the analyzed dataset using the FindAllMarkers function with logfc.threshold = 0.25 and max.cells.per.ident = 1,000. We then plotted the top eight expressed genes, grouped by orig.ident and seurat_clusters using the DoHeatmap function. The identity of the returned cell clusters was then annotated based on known marker genes (see details about cell type and cell lineage definitions in the main text, Results section). Individual cell clusters were grouped to represent cell lineages and types better. Finally, figures were generated using Seurat and ggplot2 R packages. We also used dot plots because they reveal gross differences in expression patterns across different cell types and highlight moderately or highly expressed genes.
To validate our initial skeletal muscle single-cell analysis, we explored three publicly available scRNAseq datasets (McKellar et al., 2021; Yang et al., 2022; Zhang et al., 2022). Zhang et al. dataset was explored using R/ShinyApp (https://mayoxz.shinyapps.io/Muscle), McKellar et al. [ 2021] using their web tool developed http://scmuscle.bme.cornell.edu/, and Yang et al. using their Single Cell Metab Browser http://scmetab.mit.edu/. All the figures used were downloaded from the websites (Supplementary Figure S6).
The scRNAseq pipeline used for MuSC subclustering was developed following previous studies (Oprescu et al., 2020; Contreras et al., 2021a). To perform unsupervised MuSC subclustering, we used Seurat’s subset function FindClusters, followed by dimensionality reduction and UMAP visualization (DimPlot) in Seurat.
## Bulk RNA-seq data processing and analyses
Bulk RNA-seq data was extracted as FPKM values from a previously processed dataset extracted from GEO (GSE110038) (Scott et al., 2019). No further RNA-seq processing was performed to that of Scott et al. *We* generated the heat maps shown in Figures 1, 2 with Morpheus (https://software.broadinstitute.org/morpheus/) using previous transcriptomic available RNA-seq data (Scott et al., 2019).
## Reagents
We used oleoyl-L-α-lysophosphatidic acid sodium salt, LPA (L7260-1MG, Sigma-Aldrich), Ki16425 (potent antagonist of the lysophosphatidic acid receptors LPA1 and LPA3, SML0971-5MG, Sigma-Aldrich) and PF-8380 (Autotaxin inhibitor, Cat. No. HY-13344, MedChemExpress). LPA was reconstituted according to the supplier’s instructions. Ki16425 and PF-8380 were reconstituted in cell culture grade Dimethyl sulfoxide (Hybri-Max DMSO, D2650, Sigma-Aldrich) at 10 mM stock according to the supplier’s instructions and used as indicated in the corresponding figures. DMSO was used as a control when these inhibitors were added. Ki16425 and PF-8380 were added at 15 min prior being co-incubated with LPA, when indicated. Other reagents, unless otherwise is indicated, were purchased from Sigma-Aldrich.
## Mice
Wild type mice (Inbred C57BL/6J, Stock No: 000664, Jackson Laboratory) were bred and housed in the BioCORE facility of the Victor Chang Cardiac Institute. Rooms were temperature and light/dark cycle controlled, and standard food was provided ad libitum. Two-to four-month-old female mice were used in experiments regarding ex vivo culture of fibro-adipogenic progenitors and satellite cells.
## Skeletal muscle fibro-adipogenic progenitors and muscle stem cell isolation, ex vivo culture, and FAP CFU-F
One-step digestion of skeletal muscle tissue for fibro-adipogenic progenitor isolation was performed as described before with few modifications (Contreras et al., 2020). Briefly, skeletal muscles from both hindlimbs of female wild type mice were carefully dissected, washed with ice-cold DMEM, and cut into small pieces with blades until a homogeneous, paste-like slurry was formed. Seven ml of digestion solution containing collagenase type II (265 Unit/mL, Worthington, DC, United States), 0.5 U of Dispase (Cat. No. 07913, STEMCELL™ Technologies, Canada), 0.05 mg/mL of DNaseI (Cat. No. 10104159001, Roche/Sigma-Aldrich, 100 mg from bovine), and $1\%$ BSA (Sigma-Aldrich Pty Ltd., A3311-50G) dissolved in DMEM (Cat. No. 10566016) was added to two hindlimbs and the preparation was placed on a water bath with constant rotation at 37°C for 45 min and intermittent vortexing every 15 min. Tissue preparations were gently pipetted up and down 5–10 times to enhance muscle dissociation with a 10 mL Stripette® serological pipette on ice. Ice-cold FACS buffer was added to make the final volume up to 30 mL volume and samples were then passed through 100 μm cell strainer sequentially after gentle mixing. Following centrifugation at 600 g for 6 min at 4°C, the pellet was resuspended in 10 mL of growth media (20 ng/mL of basic Fibroblast Growth Factor (Milteny Biotec, Cat. No. 130-093-843) and $10\%$ heat-inactivated fetal bovine serum (v/v) (FBS; Hyclone, UT, United States) in DMEM (Cat. No. 10566016) and supplemented with antibiotics (Penicillin-Streptomycin Cat. no. 15140122, Gibco by Life Technologies) and cells were pre-plated onto 100 mm plastic tissue culture dish for 2 h and grown at 37°C in $5\%$ CO2 as previously described (Contreras et al., 2019a). After 2 h of FAP pre-plating the supernatant media was removed to culture muscle stem cells (see Muscle stem cell enrichment and myotube differentiation protocol below) and replaced with fresh growth media. FAP CFU-F assay was performed with cells seeded at a density of 250/cm2 in growth media in a 12-well plate coated with Corning Matrigel Matrix hESC qualified (Cat. No. 354277) prepared in cold DMEM/F-12 as per the provider’s instructions. Cultured FAPs were allowed to grow for about 7 days before splitting them. CFU-F experimental outline is shown in Figure 6B. FAPs were used not further than passage 1. CFU-F averages were obtained from three technical replicates/samples using three female mice. CFU-F photos were taken using an iPhone XR 12MP Wide camera.
## Muscle stem cell enrichment and myotube differentiation
After 2 h of fibro-adipogenic progenitors pre-plating (as described above), muscle stem cells were enriched by transferring the muscle preparation supernatant into a new 100 mm plastic tissue culture dish coated with Corning Matrigel Matrix (as described above) and further cultured for 2 h. Then, the supernatant was carefully replaced with 10 mL of MuSC growth media (20 ng/mL of basic Fibroblast Growth Factor (Milteny Biotec, Cat. No. 130-093-843) and $10\%$ heat-inactivated fetal bovine serum (v/v) (FBS; Hyclone, UT, United States) in DMEM (Cat. No. 10566016). The MuSC growth media was replaced every second day and the cells were allowed to growth for 4–5 days before splitting them. Muscle stem cells were used not further than passage 1. MuSCs were platted at 75,000 cells per cm2 when EdU (at 10 µM final concentration) or pH3S10 labelling (Alexa Fluor® 594 anti-Histone H3 Phospho (Ser10) Antibody, 1:250 dilution, clone 11D8, Cat. No. 650810, Biolegend) was performed as indicated in Figures 7B–D. Hoechst 33342 was used at 10 μg/mL final concentration. For MuSC-into-myotube differentiation, MuSCs at passage 0 were split using MuSC growth media at 200,000 cells per cm2 and cultured into 48-well plates coated with hESC-qualified Corning Matrigel Matrix for 24 h. Then, 500 µL of myotube differentiation media ($5\%$ of *Horse serum* (H1270-100ML, Sigma-Aldrich) in DMEM (Cat. No. 10566016) were added to 500 µL of MuSC growth media. The mixed media was then changed every day using myotube differentiation media. Cells were fixed in $4\%$ PFA for 15 min and kept in PBS1x at 4°C until myotube staining was performed. Myotubes were permeabilized in 1× saponin-based permeabilization and wash buffer ($0.2\%$ (w/v) saponin containing $4\%$ (v/v) FBS (v/v), $1\%$ (w/v) BSA and $0.02\%$ (v/v) Sodium Azide in PBS) for 10 min. Cells were then stained for 2 h using sarcomeric α-Actinin antibody (α-Actinin (Sarcomeric) Antibody, anti-human/mouse/rat, Vio® R667, REAfinity™, clone REA402, 130-128-698, 1:100 dilution) and MF20 (MF 20 was deposited to the DSHB by Fischman, D.A. (DSHB Hybridoma Product MF 20), 1:20 dilution, Uniprot ID: P13538 [Myosin heavy chain, sarcomere (MHC)] and Hoechst 33342 at 10 μg/mL. Confocal laser scanning microscopy of stained myotubes was performed using a LSM900 Inverted confocal laser scanning microscope that comprises an upright Zeiss Axio Observer 7, four laser lines, two Gallium Arsenide Phosphide photomultiplier tubes (GaAsP-PMT), and a motorised stage. 4 × 4 tile images were acquired on a Zeiss Axio Observer 7 fitted with an LSM 900 confocal scan head, using a ×10 objective, 0.45 numerical aperture with a z-step size of 4 μm, 1,024 μm × 1,024 μm, WD 2.0, Plan-APO UV-VIS–NIR, and PBS immersion.
## Flow cytometry of fibro-adipogenic progenitors using stromal markers
Flow cytometry analyses of FAP markers were performed in day 6–7 growing FAPs at passage 0 at $70\%$–$80\%$ confluence using a BD LSRFortessa™ X-20 Cell Analyzer. We used freshly TrypLE-dissociated FAPs. Briefly, FAPs were dissociated in 1 mL (6-well plate) of TrypLE as described before. After TrypLE incubation, 0.5 mL of cold FACS Buffer was added, cells were fully dissociated, samples collected in 2 mL tubes and centrifuged at 500 g for 5 min. The supernatant was carefully discarded, and the pellet of cells resuspended thoroughly with 1 mL ul of cold FACS Buffer. Total protein labelling (Supplementary Figure S7A) was determined by flow cytometry through fixing the dissociated cells in $2\%$ PFA for 10 min at 4°C. After fixation, cells were washed three times, with 3 mL of PBS 1×. Cells were stained with primary antibodies for 30 min at RT in BD perm/wash buffer at ∼2.5 × 105 cells per 100 μL of cell suspension. The following antibodies were used: FITC Rat Anti-Mouse Ly-6A/E (SCA-1) (Clone E13-161.7), BD Bioscience (1:200 dilution, Cat. No. 561077), PE anti-mouse CD140a (PDGFRA) Monoclonal Antibody (APA5), BioLegend (1:200 dilution, Cat. No. 135905, Lot: B244566), APC anti-mouse CD140b Rat IgG2a, κ Antibody (APB5), BioLegend (1:200 dilution, Cat. No. 136007, Lot: B306888) and APC/Cyanine7 anti-mouse CD90.2 Rat IgG2b, κ Antibody, BioLegend (Clone 30-H12) (1:200 dilution, Cat. No. 105327, Lot: B353527). The isotypes control antibodies used were as follows: FITC Rat IgG2a, κ Isotype Control (Clone R35-95, BD Bioscience, Cat. No. 553929), PE Rat IgG2a, κ Isotype Ctrl Antibody (BioLegend, Clone RTK2758, Cat. No. 400507), APC Rat IgG2a, κ Isotype Ctrl Antibody (BioLegend, Clone RTK2758, Cat. No. 400511), and APC/Cyanine7 Rat IgG2b, κ Isotype Ctrl Antibody (BioLegend, Clone RTK4530, Cat. No. 400623). After staining, cells were washed three times after staining using BD perm/wash buffer, and analyzed by flow cytometry. All flow cytometry data were analyzed using FlowJo software (version 10.8.1, BD).
## Adipogenic differentiation of fibro-adipogenic progenitors and adipocyte assessment
After 6–8 days of cell growth, passage 0 FAPs were dissociated in 2 mL (100 mm culture dish) of pre-warmed TrypLE for 10 min. 10,000 FAPs per cm2 were added into each well using a 48-well plate and cells were allowed to grow for an additional of 1 up to 2 days using FAP growth media until the cells reached $95\%$–$100\%$ confluence. Adipogenic differentiation was induced for 3 days using an in-house adipogenic induction media (ADM; $5\%$ FBS, 1XPenStrep, 1 µM Dexamethasone, 0.5 mM IBMX, 1 μg/mL Human Insulin and 1 µM Rosiglitazone in high-glucose DMEM + GlutaMax). Then, cells were fixed in $4\%$ PFA for 10 min at room temperature, washed with PBS and permeabilized in 1× saponin-based permeabilization and wash buffer ($0.2\%$ (w/v) saponin containing $4\%$ (v/v) FBS (v/v), $1\%$ (w/v) BSA and $0.02\%$ (v/v) Sodium Azide in PBS) for 10 min. Cells were incubated for 1 h with 200 nM of BODIPY $\frac{493}{503}$ (Cat. No. 25892, Cayman Chemical), αSMA-Cy3™ (1:200 dilution, clone 1A4, Cat. No. C6198, Sigma-Aldrich) and Hoechst 33342 (10 μg/mL in PBS, B2261-25MG, Sigma-Aldrich) in permeabilization and wash buffer at room temperature. Images were acquired on a LSM900 confocal laser microscope as detailed before. In brief, 4 × 4 tiled images were acquired (2.65 mm2 area at the center point of the well) and the total cell number and the percentage of BODIPY+ cells were quantified using Fiji software. Total cell number was determined using StarDist 2D plugin using the nuclei Hoechst staining layer. BODIPY+ adipocytes were counted manually using the Cell Counter plugin, and the values expressed as the % of BODIPY+ cells. Myofibroblast index was calculated quantifying the fluorescence intensity of αSMA-Cy3, normalized by the total number of cells per area.
## Cell cycle S-phase analysis of fibro-adipogenic progenitors and muscle stem cells using Click-iT EdU flow cytometry assay
5-Ethynyl-2 deoxyuridine (EdU) flow cytometry analysis was determined in fibro-adipogenic progenitors and MuSCs as previously described (Contreras et al., 2021a). Briefly, 22 h after DMSO, LPA 20 µM or LPAR$\frac{1}{3}$ (Ki16425) or ATX (PF-8380) inhibitors treatments, EdU (10 μM final concentration) was added to the culture medium and incubated for 2 h. For negative staining controls, we included DMSO-treated cells that have not been exposed to EdU. Once each experimental condition and treatment was finished, cells were washed with PBS and dissociated in 1 mL (6-well plate) of pre-warmed TrypLE (TrypLE™ Express Enzyme (1×), no phenol red, Cat. No. 12604013, ThermoFisher Scientific). TrypLE was incubated for 7 up to 10 min at 37°C. After TrypLE incubation, 0.5 mL of cold FACS Buffer (PBS 1×, $2\%$ FBS v/v, 2 mM EDTA pH 7.9) was added, cells were fully dissociated by pipetting, and samples collected in 2 mL tubes. Samples were centrifuged at 500 g for 5 min. Then, the supernatant was discarded, and the pellet of cells resuspended with 0.4 mL of cold PBS 1×. Cells were fixed by adding 0.5 mL of $4\%$ PFA into ∼0.5 mL of cell suspension. Cells were incubated for 10 min at room temperature, and then $2\%$ PFA was washed three times with abundant PBS. When cells were ready to work with, they were distributed into 1.5 mL tubes, 500 µL of $0.1\%$ BSA in PBS added, and pelleted. Pelleted cells were flicked and 400 μL of a 1× saponin-based permeabilization and wash reagent ($0.2\%$ (w/v) saponin containing $4\%$ (v/v) FBS (v/v), $1\%$ (w/v) BSA and $0.02\%$ (v/v) Sodium Azide in PBS) was added and incubated for 15 min. Then the cells were centrifuged (500 ×g for 5 min). After the last centrifuge, EdU detection was performed using an in-house developed Click-iT EdU reaction cocktail made of 200 nM AZDye™ 488 Azide (Cat. No. 1275, Click Chemistry Tools, Scottsdale, AZ), 800 µM Copper (II) sulfate, and 5 mM Ascorbic acid in PBS1x. In brief, 400 μL per sample of the Click-iT reaction cocktail was added to the pellet, and the cells resuspended and incubated for 45 min at room temperature, protected from light. After Click-iT EdU reaction cocktail incubation, the cells were washed twice with 0.5 mL of 1× saponin-based permeabilization and wash reagent and pelleted at 500 g for 5 min, leaving 50 µL of pellet per tube which was resuspended by flicking. Then, 50 µL of conjugated antibodies prepared in perm/wash buffer (Ki-67 Antibody, anti-human/mouse, Vio® R667, REAfinity™, order no. 130-120-562, clone REA183, 1:100 dilution, Miltenyi Biotech) and/or (Alexa Fluor® 594 anti-Histone H3 Phospho (Ser10), clone 11D8, Mouse IgG2b, κ, 1:250 dilution, BioLegend) were added and incubated at RT for 1 h. After antibody incubation, 800 µL of $0.1\%$ BSA in PBS was added to each tube, and samples were spun down at 500 g for 6 min. The supernatants were removed and 50 µL pellets resuspended by flicking and incubated with 300 μL of Hoechst 33342 (10 μg/mL final concentration, B2261-25MG, Sigma-Aldrich) for 10 min at room temperature in 1× saponin-based permeabilization and wash reagent. Samples were analyzed by flow cytometry for DNA content and EdU labelled cells using a BD LSR Fortessa Laser Cell Analyser (BD Biosciences, Erembodegem, Belgium) equipped with five excitation lasers (UV 355 nm, Violet 405 nm, Blue 488 nm, Yellow/Green 561 nm, and Red 633 nm). EdU-AZDye™ 488 Azide, Ki67-Vio® R667, and pH3S10-Alexa Fluor® 594 fluorescence were detected with logarithmic amplification using the B530 ($\frac{530}{30}$), R670 ($\frac{670}{14}$), and YF610 ($\frac{610}{20}$), detectors, respectively, whereas Hoechst fluorescence was detected with linear amplification using the V450 (V$\frac{450}{50}$) detector. Flow cytometry measurements were run at a mid-flow rate, and the core stream allowed to stabilize for 5 s prior acquisition. Data were collected using FacsDIVA 8 software. For optimal Hoechst signal detection and cell cycle progression analyses, an event concentration of <800 events/s was used, and 20,000 events were captured. All flow cytometry data were analyzed using FlowJo Portal [version 10.8.1, Becton Dickinson & Company (BD)] using Mac OS X operating system.
## Statistical analysis
Mean ± SEM values, as well as the number of experiments performed, are indicated in each figure. Bulk RNAseq data were collected in Microsoft Excel, and statistical analyses were performed using GraphPad Prism 9.4.0 software for macOS Monterey. All bulk RNAseq datasets used to determine gene expression were analyzed for normal distribution using the Shapiro-Wilk test with a significant level set at alpha = 0.05. Statistical significance of the differences between the means was evaluated using the One-Way ANOVA test followed by post hoc Dunnett or Tukey’s multiple comparisons tests, and the significance level was set at $p \leq 0.05$ ($95\%$ confidence interval). p-value style: GP: 0.0332 (*), 0.0021 (**), 0.0002 (***), <0.0001 (****).
## Summary
Our reanalysis of single-cell transcriptomics revealed the involvement and temporally dynamic expression of the ENPP2-LPAR-PLPP axis in response to skeletal muscle regeneration.
## 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.
## Ethics statement
The animal study was reviewed and approved by the Garvan Institute/St. Vincent’s Hospital Animal Experimentation Ethics Committee (Nos. $\frac{13}{02}$, $\frac{16}{10}$, $\frac{19}{14}$) and performed in strict accordance with the National Health and Medical Research Council (NHMRC) of Australia Guidelines on Animal Experimentation. All efforts were made to minimize suffering.
## Author contributions
OC: Conceptualization, data curation, formal analysis, investigation, methodology, project administration, resources, software, supervision, validation, visualization, writing—original draft, writing—review and editing. RH: Resources, supervision, visualization, review and editing. The authors gave final approval for publication and agreed to be held accountable for the work performed therein.
## 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.2023.1017660/full#supplementary-material
## Abbreviations
ASCs, adipose stromal cells; ATX, autotaxin; CTGF/CCN2, connective tissue growth factor; ENPP2, ectonucleotide pyrophosphatase/phosphodiesterase 2; FAPs, fibro-adipogenic progenitors; GPCRs, G protein-coupled receptors; LPARs, LPA receptors; LPA, lysophosphatidic acid; MSCs, mesenchymal stromal cells; MuSCs, muscle stem cells; PA, phosphatidic acid; PLPPs, phospholipid phosphatases; scRNAseq, single-cell RNA sequencing.
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|
---
title: Identification of hsa_circRNA_100632 as a novel molecular biomarker for fulminant
type 1 diabetes
authors:
- Wenfeng Yin
- Shuoming Luo
- Junlin Qiu
- Zilin Xiao
- Ziwei Zhang
- Zhiguo Xie
- Xia Li
- Zhiguang Zhou
journal: Frontiers in Immunology
year: 2023
pmcid: PMC9996325
doi: 10.3389/fimmu.2023.1075970
license: CC BY 4.0
---
# Identification of hsa_circRNA_100632 as a novel molecular biomarker for fulminant type 1 diabetes
## Abstract
### Objective
Circular RNAs (circRNAs) are associated with diabetes, but their role in fulminant type 1 diabetes (FT1D) is unclear. Thus, we characterized the role of circRNAs in FT1D.
### Research design and methods
CircRNA expression profiles were detected in peripheral blood mononuclear cells (PBMCs) of five FT1D patients and five controls using a circRNA microarray. An independent cohort comprised of 40 FT1D cases, 75 type 1 diabetes (T1D) cases, and 115 controls was used to verify the circRNAs using quantitative real-time polymerase chain reaction (qRT-PCR). Spearman’s correlation analysis and receiver operating characteristic (ROC) curve analysis were performed to determine the clinical diagnostic capability of circRNAs. Bioinformatics was used to identify potential biological functions and circRNA–miRNA–mRNA interactions.
### Results
There were 13 upregulated and 13 downregulated circRNAs in PBMCs of patients with FT1D. Five circRNAs were further verified in a second cohort. Hsa_circRNA_100632 was significantly upregulated in the FT1D and T1D groups. Hsa_circRNA_100632 was differentiated between patients with FT1D and controls [area under the curve (AUC) 0.846; $95\%$ CI 0.776–0.916; $P \leq 0.0001$] as well as between patients with FT1D and patients with T1D (AUC 0.726; $95\%$ CI 0.633–0.820; $P \leq 0.0001$). Bioinformatics analysis showed that hsa_circRNA_100632 may be involved in 47 circRNA–miRNA–mRNA signaling pathways associated with diabetes.
### Conclusions
CircRNAs were aberrantly expressed in PBMCs of patients with FT1D, and hsa_circRNA_100632 may be a diagnostic marker of FT1D.
## Introduction
Fulminant type 1 diabetes (FT1D) is a novel subtype of type 1 diabetes (T1D) first identified by Imagawa et al. in 2000 [1, 2]. FT1D is characterized by aggressive disease onset, dramatic loss of C-peptide concentration, nearly normal levels of hemoglobin A1c (HbA1c) at onset, and diabetic ketoacidosis (DKA) [3, 4]. Failure to accurately diagnose FT1D can result in death [5, 6]. Diagnosis of FT1D requires measurement of plasma glucose (PG) at onset, measurement of HbA1c, and onset of clinical symptoms of DKA within 1 week after onset. Then, fasting C-peptide (FCP) and 2 h postprandial C-peptide (PCP) must be measured after complete remission of ketoacidosis, which typically occurs within 1 or 2 weeks, to confirm the diagnosis [7, 8]. These criteria make the diagnosis of FT1D difficult and complex, preventing precise intervention and worsening prognosis. Therefore, it is of great clinical significance to explore markers for identification of FT1D to allow for simple or early diagnosis and effective intervention.
Circular RNAs (circRNAs), a new class of non-coding RNAs, are characterized by a closed-loop structure, resistance to degradation, and excellent stability [9, 10]. Several studies have shown that circRNAs are involved in many physiological and pathological disease processes, and circRNAs can serve as diagnostic and prognostic molecular biomarkers [10, 11]. Our previous study showed that several circRNAs are differentially expressed in the peripheral blood of T1D patients and may contribute to T1D disease progression through interactions with miRNA and circRNA-derived peptides [12]. Other studies have shown that circRNAs promote the onset and progression of T1D [13, 14]. However, the expression profiles of circRNAs and the value of circRNAs as biomarkers of FT1D have not been characterized.
In the present study, we investigated the circRNA expression profiles in peripheral blood mononuclear cells (PBMCs) from patients with FT1D using microarray data, analyzed their biological functions using bioinformatics, and validated their expression levels using quantitative real-time polymerase chain reaction (qRT-PCR). As a result, a circRNA that can be used for diagnosis and differential diagnosis of FT1D was identified using a receiver operating characteristic (ROC) curve. The present study demonstrated that circRNAs have potential to aid in the diagnosis and treatment of patients with FT1D.
## Participants
A total of 240 individuals were placed into two cohorts, and Table 1 shows the clinical and demographic data of these individuals. We selected five patients with new onset FT1D (median diabetes duration one month) and five age- and sex-matched control individuals for analysis in the first cohort. In the second cohort, 115 patients with FT1D ($$n = 40$$) or T1D ($$n = 75$$) and 115 controls were recruited to validate differentially expressed circRNAs identified in the first cohort.
**Table 1**
| Variable | Control (N = 120) | Fulminant type 1 diabetes (N = 45) | Type 1 diabetes (N = 75) | P value |
| --- | --- | --- | --- | --- |
| Age (years) | 33.00 (26.25–41.75) | 31.00 (26.50–44.00) | 31.00 (22.0–39.00) | NS |
| Sex (male/female) | 53/67 | 22/23 | 32/43 | NS |
| BMI (kg/m2) | 22.00 ± 2.68 | 21.23 ± 2.49 | 21.24 ± 2.66 | NS |
| TG (mmol/L) | 1.03 (0.69–1.59) | 0.72 (0.59–1.03) | 0.64 (0.52–0.88) | < 0.001 |
| TC (mmol/L) | 4.65 (4.13–5.17) | 4.33 (3.91–4.71) | 4.27 (3.66–4.84) | NS |
| LDL-C (mmol/L) | 2.70 (2.29–3.31) | 2.32 (1.91–2.90) | 2.33 (1.73–3.00) | < 0.01 |
| HDL-C (mmol/L) | 1.38 (1.15–1.67) | 1.52 (1.29–1.82) | 1.57 (1.26–1.84) | < 0.05 |
| FPG (mmol/L) | 4.74 ± 0.42 | 9.83 ± 4.15 | 7.94 ± 3.58 | < 0.001 |
| FCP (ng/mL) | — | 0.05 (0.05–0.08) | 0.12 (0.05–0.44) | < 0.0001 |
| HbA1c (%) | 5.40 (5.20–5.60) | 7.20 (6.40–8.08) | 7.20 (6.30–8.25) | < 0.001 |
| HbA1c (mmol/mol) | 36 (33–38) | 55 (46–65) | 55 (45–67) | < 0.001 |
Patients with diabetes met the diagnostic criteria of the American Diabetes Association [15]. Individuals with T1D were recruited based on the following criteria: [1] acute onset of ketosis or ketoacidosis requiring insulin replacement therapy; [2] at least six months of continuous insulin dependence after diagnosis; and [3] positive glutamic acid decarboxylase antibodies (GADAs). Diagnosis of FT1D was performed according to the criteria of the Committee of the Japan Diabetes Society in 2012 [16]. The inclusion criteria for FT1D were as follows [1]: occurrence of diabetic ketosis or ketoacidosis within a week after onset of hyperglycemic symptoms; [2] initial PG level > 16.0 mmol/L and HbA1c < $8.7\%$ (72 mmol/mol) at onset; and [3] serum FCP < 100 pmol/L (0.3 ng/ml) and PCP < 170 pmol/L (0.5 ng/ml) at disease onset. Controls had fasting plasma glucose (FPG) < 5.6 mmol/L and HbA1c < $6.1\%$. The exclusion criteria for control subjects were infectious diseases, pregnancy, malignant disease, or a family history of diabetes.
All data were collected from patients from the Second Xiangya Hospital of Central South University (Changsha, Hunan, China). The study was approved by the Ethical Committee of the Second Xiangya Hospital of Central South University (LYF 2021100), and all subjects provided written informed consent prior to inclusion in the study.
## Data collection and clinical measurements
Sex, age, body height, and weight were recorded for all subjects. Fasting venous blood samples were tested for triglycerides (TG), total cholesterol (TC), high density lipoprotein-cholesterol (HDL-C), low density lipoprotein-cholesterol (LDL-C), FPG, and HbA1c using standard procedures in the hospital clinical laboratory. GADAs were detected as previously described [17].
## Peripheral blood mononuclear cell collection
Fasting peripheral blood was collected from patients and controls into EDTA blood tubes, and PBMCs were isolated using density gradient centrifugation. Ficoll-Paque PLUS (GE Healthcare, USA) was added to the blood samples with an equal amount of phosphate buffered saline (PBS; Invitrogen, USA). Finally, PBMCs were stored in TRIzol reagent (Invitrogen, USA) at –80 °C.
## Total RNA extraction and quality control
Total RNA was extracted from PBMCs of each participant using TRIzol reagent (Invitrogen, USA) according to the manufacturer’s instructions. The RNA purity and concentration were determined using a NanoDrop 2000 instrument (Thermo Scientific, USA). The range of mean A260/A280 was 1.8 to 2.0.
## CircRNA microarray expression profiling
Sample preparation and microarray hybridization were performed according to the manufacturer’s instructions (Arraystar Human circRNA Array V2.0, 8 ×15K). Total RNA was digested with Rnase R (Epicentre, USA) to remove of linear RNA and enrich circRNA. The enriched circRNAs were then amplified and transcribed into fluorescent cRNA according to the Arraystar Super RNA Labeling protocol (Arraystar, USA). Finally, the hybridized arrays were washed, fixed, and scanned using an Agilent Scanner G2505C. The details of the method have been previously described [12]. Agilent Feature Extraction software (version 11.0.1.1) was used to analyze the acquired array images. Quantile normalization and subsequent data processing were performed using the limma package in R software. Significantly differentially expressed circRNAs between the FT1D group and control group were identified using volcano plot filtering. CircRNAs with absolute fold differences ≥ 1.5 and $P \leq 0.05$ were considered significantly differentially expressed. Hierarchical clustering was performed to show the distinguishable circRNA expression pattern among samples.
## GO and KEGG pathway enrichment analyses
OmicShare tools was used to perform Gene Ontology (GO) and Kyoto Encyclopedia Genes and Genomes (KEGG) analyses. GO analysis was conducted based on the parent genes of circRNAs to investigate the properties of aberrantly expressed circRNAs. Pathways were analyzed based on differentially expressed circRNAs using KEGG analysis.
## QRT-PCR assay
One microgram of total RNA was converted into cDNA using a GoScript™ Reverse Transcription System (Promega, USA) according to the manufacturer’s instructions. *Target* gene expression levels were determined using a SYBR Green kit (Promega, USA) in 10-μl samples using an ABI PRISM Step One Sequence Detection System (Applied Biosystems, USA). The thermocycler conditions were as follows: 95°C for 10 min followed by 45 cycles of 95°C for 15 s and 60°C for 1 min. Internal standard was β-actin, and all reactions were performed in triplicate. The designed and optimized primers for circRNA transcripts are shown in Supplemental Table S1.
## CircRNA–miRNA–mRNA network analysis
CircRNA can sponge miRNA, and mRNA can be targeted by miRNA, which may play critical roles in disease onset and progression. To elucidate the relationship between circRNAs and miRNAs, TargetScan and miRanda prediction software were used to predict the interactions of circRNAs–miRNAs–mRNAs. The circRNA–miRNA–mRNA network was constructed using the Cytoscape bioinformatics software.
## Statistical analysis
Data are presented as the mean ± SD (standard deviation) or as the median (25th–75th percentile). The 2-ΔΔCT method was used to calculate the relative expression levels of selected circRNAs analyzed using qRT-PCR. Normality of the data was checked using the Shapiro–Wilk test. One-way analysis of variance (ANOVA) was performed to compare groups if the data were normally distributed, and a rank test (Mann–Whitney U test or Kruskal–Wallis H test) was used if the data were not normally distributed. The Chi-square test was used to analyze categorical data among groups. Generalized linear regression model analysis was used to identify the associations between circRNA expression levels and FT1D after adjusting the confounders. Spearman’s correlation coefficient was used to evaluate the relationship between circRNAs and clinical characteristics of FT1D. A ROC curve was used to assess the diagnostic and differential diagnostic performance of circRNA for FT1D. Statistical analyses were performed using SPSS 21.0 (IBM SPSS, Inc., Chicago, IL, USA) and GraphPad Prism 5.01 (GraphPad Software, San Diego, CA, USA). $P \leq 0.05$ was considered statistically significant.
## Identification of aberrantly expressed circRNAs in PBMCs of patients with FT1D
We detected 13,563 circRNAs in PBMCs from patients with FT1D. The classification of circRNAs according to expression level was performed by hierarchical clustering (Figure 1A). In the volcano plots (Figure 1B), separately expressed circRNAs were grouped by absolute fold changes ≥ 1.5 and $P \leq 0.05$, resulting in 26 differentially expressed circRNAs in patients with respect to controls, of which 13 were upregulated and 13 were downregulated (Table 2).
**Figure 1:** *Expression profiles of significantly altered circRNAs in peripheral blood mononuclear cells (PBMCs) of 5 patients with fulminant type 1 diabetes and 5 controls. (A) Heatmap of 26 differentially expressed circRNAs in PBMCs of patients with fulminant type 1 diabetes and controls. (B) Twenty-six significantly upregulated and downregulated circRNAs according to volcano diagram analysis (absolute fold changes ≥ 1.5, $P \leq 0.05$).* TABLE_PLACEHOLDER:Table 2
## Bioinformatics analysis of differentially expressed circRNAs
To predict the potential biological functions of the 26 differentially expressed circRNAs, we used their parent genes for GO enrichment and KEGG pathway analyses. The GO terms for parent genes of differentially expressed circRNAs were classified and summarized according to biological process (BP), cellular component (CC), and molecular function (MF). The terms with the most genes in BP, CC, and MF were cellular process, binding, and cell, respectively (Figure 2A). For BP, the term with the highest enrichment score was negative regulation of phosphate metabolic process (Figure 2B), and the term with the highest enrichment score for CC was lamellar body membrane and alveolar lamellar body membrane (Figure 2C). For MF, the term with the highest enrichment score was D-ribulokinase activity (Figure 2D).
**Figure 2:** *Gene Ontology terms of parent genes of differentially expressed circRNAs. (A) Gene Ontology categories of significantly differentially expressed circRNAs. (B) Biological process, (C) cellular component, and (D) molecular function of the top 20 genes in GO enrichment analysis.*
The enriched pathways of the parent genes of the 26 differentially expressed circRNAs were analyzed using KEGG pathway annotation. The non-homologous end-joining and 2-oxocarboxylic acid metabolism terms had the highest correlations with the enrichment scores (Figure 3A). Furthermore, KEGG pathway annotation showed that most genes were involved in cell growth and death as well as the immune system (Figure 3B).
**Figure 3:** *KEGG pathway gene enrichment analysis for differentially expressed circRNAs. (A) Enriched circle map of the transcripts of 26 significantly differentially expressed circRNAs. The first circle indicates the top 20 KEGG pathways. The second circle indicates the gene number for the specified signaling pathway. The third circle indicates the total number of prospective genes. The fourth circle indicates the enrichment factor of each KEGG pathway. (B) KEGG pathway enrichment analysis of genes that produced up- and downregulated circRNAs.*
## Validation of differentially expressed circRNAs
To further validate the microarray data, five differentially expressed circRNAs, including three upregulated circRNAs (hsa_circRNA_100246, hsa_circRNA_100245, and hsa_circRNA_100632) and two downregulated circRNAs (hsa_circRNA_005528 and hsa_circRNA_406299), were selected for qRT-PCR verification. The following four inclusion criteria were used: [1] circRNA with a high differential expression fold; [2] circRNA length between 200 bp and 1000 bp; [3] circRNA average raw intensity > 100; and [4] exonic-circRNAs. As shown in Figure 4, patients with FT1D and T1D had significantly increased expression of hsa_circRNA_100632 ($P \leq 0.0001$) and significantly decreased expression of hsa_circRNA_005528 ($P \leq 0.01$) compared to controls. Moreover, patients with FT1D showed higher expression of hsa_circRNA_100632 than patients with T1D ($P \leq 0.0001$) and hsa_circRNA_100632 has a significant difference in FT1D and controls ($P \leq 0.0001$) regardless of before or after adjusting for age (Table S2).
**Figure 4:** *QRT-PCR analysis of five differentially expressed circRNAs among patients with fulminant type 1 diabetes, patients with type 1 diabetes, and controls. (fulminant type 1 diabetes, N = 40; type 1 diabetes, N = 75; controls N = 115). **
P < 0.01 and ****
P < 0.0001.*
## Association of significantly differentially expressed circRNAs with clinical parameters
To analyze whether significantly differentially expressed circRNAs are associated with clinical parameters, Spearman’s correlation analysis was performed in the FT1D group, the T1D group, the control group, and for all subjects. In patients with T1D, the expression levels of hsa_circRNA_100632 were positively correlated with FCP ($P \leq 0.05$), and the expression levels of hsa_circRNA_005528 were positively associated with sex ($P \leq 0.01$). In all subjects, hsa_circRNA_100632 was negatively correlated with age ($P \leq 0.05$), BMI ($P \leq 0.01$), TG ($P \leq 0.05$), TC ($P \leq 0.01$), and LDL-C ($P \leq 0.01$), but hsa_circRNA_100632 was positively associated with FPG ($P \leq 0.01$) and HbA1c ($P \leq 0.01$). In addition, hsa_circRNA_005528 was negatively correlated with HbA1c in all subjects ($P \leq 0.01$) (Table 3).
**Table 3**
| Unnamed: 0 | Age | Sex | BMI | TG | TC | LDL-C | HDL-C | FPG | FCP | HbA1c |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Total subjects | Total subjects | Total subjects | Total subjects | Total subjects | Total subjects | Total subjects | Total subjects | Total subjects | Total subjects | Total subjects |
| hsa_circRNA_100632 | –0.169* | 0.019 | –0.175** | –0.195* | –0.205** | –0.241** | 0.045 | 0.200** | 0.042 | 0.329** |
| hsa_circRNA_005528 | –0.097 | 0.059 | 0.046 | –0.021 | 0.031 | 0.099 | –0.146 | –0.073 | 0.035 | –0.200** |
| Control group | Control group | Control group | Control group | Control group | Control group | Control group | Control group | Control group | Control group | Control group |
| hsa_circRNA_100632 | –0.251** | 0.063 | –0.159 | –0.002 | –0.117 | –0.178 | –0.186 | 0.046 | – | –0.185 |
| hsa_circRNA_005528 | –0.094 | 0.004 | –0.035 | –0.017 | 0.005 | –0.009 | –0.049 | 0.039 | – | –0.183 |
| Fulminant type 1 diabetes group | Fulminant type 1 diabetes group | Fulminant type 1 diabetes group | Fulminant type 1 diabetes group | Fulminant type 1 diabetes group | Fulminant type 1 diabetes group | Fulminant type 1 diabetes group | Fulminant type 1 diabetes group | Fulminant type 1 diabetes group | Fulminant type 1 diabetes group | Fulminant type 1 diabetes group |
| hsa_circRNA_100632 | –0.196 | –0.035 | 0.136 | 0.028 | 0.125 | –0.023 | 0.120 | –0.126 | –0.084 | 0.170 |
| hsa_circRNA_005528 | –0.302 | –0.165 | –0.158 | –0.084 | 0.170 | 0.275 | –0.154 | –0.099 | 0.245 | 0.065 |
| Type 1 diabetes group | Type 1 diabetes group | Type 1 diabetes group | Type 1 diabetes group | Type 1 diabetes group | Type 1 diabetes group | Type 1 diabetes group | Type 1 diabetes group | Type 1 diabetes group | Type 1 diabetes group | Type 1 diabetes group |
| hsa_circRNA_100632 | –0.142 | –0.027 | –0.196 | –0.220 | –0.235 | –0.205 | 0.006 | –0.067 | 0.263* | 0.172 |
| hsa_circRNA_005528 | –0.098 | 0.305** | 0.160 | –0.097 | –0.199 | –0.074 | –0.194 | –0.057 | –0.030 | –0.048 |
## Diagnostic value of hsa_circRNA_100632 as a biomarker of FT1D
To further evaluate whether has_circRNA_100632 has molecular diagnostic value for FT1D, we generated a ROC curve to evaluate its sensitivity and specificity. As shown in Figure 5, the area under the curve (AUC) for hsa_circRNA_100632 was 0.846 ($95\%$ CI 0.776–0.916) with a sensitivity of $75.0\%$ and specificity of $79.1\%$ between patients with FT1D and controls ($P \leq 0.0001$). Furthermore, the AUC of hsa_circRNA_100632 was 0.726 ($95\%$ CI 0.633–0.820) with a sensitivity of $82.5\%$ and specificity of $58.8\%$ between patients with FT1D and patients with T1D ($P \leq 0.0001$).
**Figure 5:** *Receiver operating characteristic (ROC) plot of hsa_circRNA_100632 in fulminant type 1 diabetes. The blue line indicates the ROC curve for hsa_circRNA_100632 distinguishing fulminant type 1 diabetes from controls, and the black line represents the ROC curve for hsa_circRNA_100632 distinguishing fulminant type 1 diabetes from type 1 diabetes.*
## Construction of circRNA–miRNA–mRNA interaction network
CircRNAs can function as competing endogenous RNA that can sponge corresponding miRNAs and affect mRNA expression in various diseases, including diabetes. Hsa_circRNA_100632 was predicted by circBank, a comprehensive database of human circRNA, to sponge 70 miRNAs. Five miRNAs related to diabetes were selected (hsa-miR-1-3p, hsa-miR-27a-3p, hsa-miR-221-3p, hsa-miR-222-3p, and hsa-miR-503-5p) for miRNA–mRNA prediction. According to TargetScan and miRanda software, these 5 miRNAs may bind to 333 mRNAs. We next selected 41 mRNAs related to diabetes to form a specific circRNA–miRNA–mRNA network related to diabetes. The results showed that hsa_circRNA_100632 may be involved in 47 circRNA–miRNA–mRNA signaling pathways associated with diabetes (Figure 6). For example, hsa_circRNA_100632 sponges hsa-miR-221-3p and hsa-miR-222-3p, resulting in modulation of the thrombospondin 1 (THBS1), solute carrier family 40 member 1 (SLC40A1), and aldehyde dehydrogenase 1 family, member A1 (ALDH1A1) target genes.
**Figure 6:** *Hsa_circRNA_100632–miRNA–mRNA network. Blue ellipses, pink hexagons, and red octagons represent mRNAs related to diabetes, diabetes-associated miRNAs, and hsa_circRNA_100632, respectively. The orange solid lines indicate the relationship between miRNAs and mRNAs, and the dotted lines represent the relationship between circRNAs and miRNAs.*
## Discussion
In the present study, we profiled circRNA expression in PBMCs of patients with FT1D. We verified that hsa_circRNA_100632 was differentially upregulated and that hsa_circRNA_005528 was differentially downregulated among patients with FT1D, patients with T1D, and controls. We found that hsa_circRNA_100632 levels were associated with islet β-cell destruction in patients with T1D. Previous studies have suggested that circRNAs may play a role in T1D or type 2 diabetes (T2D) through regulation of islet β-cell dysfunction [18, 19]. CircGlis3, a β-cell-derived exosomal circRNA, has been shown to play a role in lipotoxicity-induced β-cell disorder and development of diabetes through suppression of insulin secretion and cell proliferation [20]. In the present study, we identified hsa_circRNA_100632 as an important circRNA in FT1D. Has_circRNA_100632 is derived from sterile alpha motif domain containing 8 (SAMD8), which is a ceramide phosphoethanolamine synthase in the endoplasmic reticulum. This circRNA may act as a ceramide sensor, and its N-terminal SAM structural domain controls endoplasmic reticulum ceramide levels and inhibits ceramide-induced mitochondrial apoptosis [21]. High glucose can lead to mitochondrial dysfunction through oxidative stress [22]. We hypothesized that hsa_circRNA_100632 may promote progression of T1D through induction of β-cell failure. Future studies that include functional experiments using immune cells and animal models are needed.
The present study indicated that hsa_circRNA_100632 in PBMCs may be a diagnostic marker of FT1D. Increasing evidence has indicated that circRNAs may be diagnostic indicators of T1D and T2D [23]. Hsa_circ_0054633, a diagnostic biomarker of pre-diabetes and T2D, shows diagnostic capability with AUC values of 0.751 and 0.793, respectively [24]. In addition, hsa_circ_0063425 and hsa_circ_0056891 may be valuable markers for early detection of T2D [25]. A recent study has suggested that the expression level of hsa_circ_0060450 is upregulated in patients with T1D and may represent a novel therapeutic target for T1D [14]. Furthermore, our previous study found that hsa_circ_0072697 is involved in 50 signaling pathways related to diabetes, indicating that it may be a diagnostic indicator of T1D [12]. However, the role of circRNA in FT1D has not been characterized. The present study is the first to indicate that circRNAs may play a key role in FT1D, suggesting that circRNAs may be valuable diagnostic and differential diagnostic markers for FT1D. ROC analysis showed that hsa_circRNA_100632 had a sensitivity of $75.0\%$ and specificity of $79.1\%$ for distinguishing between patients with FT1D and control subjects (AUC = 0.846). Moreover, hsa_circRNA_100632 distinguished between FT1D and T1D with $82.5\%$ sensitivity and $58.8\%$ specificity (AUC = 0.726). Several diagnostic markers have been previously explored in smaller cohorts, and models have been built from data derived from multiple clinical studies (5, 26–28). For example, the glycated albumin (GA)/HbA1c ratio has been verified as a sensitive marker for glucose excursion of FT1D in a cohort comprised of 56 outpatients [27]. The fulminant index shows an ability to identify FT1D based on DKA [5]. In addition, the serum 1,5-anhydroglucitol/GA index has been shown to be a suitable indicator for early differential diagnosis between FT1D and T1D when HbA1c < $8.7\%$ with an optimal cut-off point of 0.3 [28]. In contrast, the present study focused on the expression levels of hsa_circRNA_100632, which may simplify and reduce the cost associated with diagnosis of FT1D.
In addition, KEGG analysis showed that differentially expressed circRNAs may be associated with cell growth and death as well as the immune system. FT1D has a rapid onset, severe symptoms, and a high mortality rate [29, 30]. Initially, FT1D was considered related to non-autoimmune pathogenesis due to the absence of islet autoantibodies, such as GADAs [1]. Over the past 20 years, FT1D has gained increasing attention. Studies have shown that immunity plays a critical role in the initiation and progression of FT1D [29, 31]. Cellular and humoral immunity may play an important role in FT1D [29]. Viral infection and drug induction may lead to β-cell loss in FT1D by triggering an immune response [32, 33]. The present results indicated that circRNAs were abnormally expressed in peripheral immune cells in FT1D, and bioinformatics analysis indicated that circRNAs may be involved in the immune response. Furthermore, the present results showed that hsa_circRNA_100632 may be involved in 47 diabetes-associated circRNA–miRNA–mRNA interaction signaling pathways. In the hsa_circRNA_100632 network, hsa_circRNA_100632 may sponge hsa-miR-27a-3p and modulate the nuclear factor of activated T cells 5 (NFAT5) target gene. NFAT5 activates various immune cells, especially T lymphocytes [34]. These findings suggest that hsa_circRNA_100632/hsa-miR-27a-3p/NFAT5 may play a key role in FT1D by regulating the immune response. However, it is not known whether the upregulated expression levels of hsa_circRNA_100632 are due to increased expression of specific leukocytes in PBMCs of FT1D patients or whether the increasing hsa_circRNA_100632 can affect specific leukocyte expression. Further studies are needed to verify this finding.
The major strength of the present work was that it included a large validation cohort considering the rarity of FT1D. Furthermore, the protocols for recruitment and examination of subjects were highly standardized. However, the present study had several limitations. The sample size of the discovery cohort was small and the multiple-testing correction was not performed in the discovery analysis, indicating that the relevance of hsa_circRNA_100632 to the pathogenesis of FT1D requires further immune cell and animal studies. In addition, the present study focused on FT1D in a Chinese population, and further studies are needed to determine if these results regarding hsa_circRNA_100632 can be generalized to other ethnic groups.
In summary, we characterized the circRNA transcriptome in PBMCs derived from patients with FT1D for the first time. We then predicted the biological functions of differentially expressed circRNAs using GO and KEGG pathway analyses. The results indicated that hsa_circRNA_100632 may be a molecular biomarker for FT1D. Longitudinal studies are needed to validate hsa_circRNA_100632 as a molecular biomarker of FT1D progression.
## Data availability statement
The original contributions presented in the study are publicly available. This data can be found here: https://ngdc.cncb.ac.cn/omix.(OMIX repository, accession number OMIX002351).
## Ethics statement
The studies involving human participants were reviewed and approved by the ethical committee of the Second Xiangya Hospital of Central South University. 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.
## Author contributions
WY contributed to the experiments and data analysis and wrote the first draft of the manuscript. SL and ZZho proposed the project, designed the study, provided critical suggestions, and revised the manuscript. JQ, ZXia, and ZZha collected the study data and samples. JQ, ZXie, and XL reviewed the manuscript and contributed to the discussion. 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.2023.1075970/full#supplementary-material
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|
---
title: Phenotypic diversity of human adipose tissue-resident NK cells in obesity
authors:
- Martha E. Haugstøyl
- Martin Cornillet
- Kristina Strand
- Natalie Stiglund
- Dan Sun
- Laurence Lawrence-Archer
- Iren D. Hjellestad
- Christian Busch
- Gunnar Mellgren
- Niklas K. Björkström
- Johan Fernø
journal: Frontiers in Immunology
year: 2023
pmcid: PMC9996326
doi: 10.3389/fimmu.2023.1130370
license: CC BY 4.0
---
# Phenotypic diversity of human adipose tissue-resident NK cells in obesity
## Abstract
Natural killer (NK) cells have emerged as key mediators of obesity-related adipose tissue inflammation. However, the phenotype of NK cell subsets residing in human adipose tissue are poorly defined, preventing a detailed understanding of their role in metabolic disorders. In this study, we applied multicolor flow cytometry to characterize CD56bright and CD56dim NK cells in blood and adipose tissue depots in individuals with obesity and identified surface proteins enriched on adipose tissue-resident CD56bright NK cells. Particularly, we found that adipose tissue harbored clusters of tissue-resident CD56bright NK cells signatured by the expression of CD26, CCR5 and CD63, possibly reflecting an adaptation to the microenvironment. Together, our findings provide broad insights into the identity of NK cells in blood and adipose tissue in relation to obesity.
## Introduction
The expansion of adipose tissue in obesity is accompanied by accumulation of pro-inflammatory immune cells and cytokines, causing a state of chronic, low-grade inflammation that may disrupt insulin signaling both in the adipose tissue and systemically [1]. Natural killer (NK) cells have been proposed as important mediators of adipose tissue inflammation. NK cells are innate lymphoid cells that can sense different types of stress through the expression of activating and inhibitory receptors, including killer-cell immunoglobulin-like receptors (KIRs), NKG2A, NKG2D and NKp46 [2]. Upon activation, NK cells secrete cytotoxic perforin and granzyme molecules to facilitate killing of tumor cells and virus-infected cells. In addition, they secrete cytokines that may influence other immune cells [3]. Studies from mice have demonstrated a homeostatic role of NK cells in lean adipose tissue, with increased accumulation and altered phenotype and functionality in obesity [4, 5]. Obesity-induced NK cell dysregulations are characterized by increased secretion of interferon (IFN)-γ that can mediate polarization of macrophages towards a pro-inflammatory state. This, combined with reduced NK cell cytotoxicity that hampers their ability to kill specific macrophage populations [6], has been shown to lead to systemic insulin resistance.
In humans, NK cells are broadly divided into two subsets, cytotoxic CD56dimCD16+ (hereby CD56dim) NK cells and cytokine-secreting CD56brightCD16- (hereby CD56bright) NK cells. In blood, CD56dim NK cells represent around $90\%$ of the total NK cell population, whereas in tissues CD56bright NK cells dominates, where they also express the canonical residency marker CD69. Importantly, the local microenvironment in each tissue contributes to shape unique phenotypic and functional features of these cells, with diverse subset distributions of tissue-resident NK cells present in various tissues with important local immunological functions [7, 8].
Adipose tissue is divided into subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT). VAT is generally found to harbor more pro-inflammatory immune cells and presents increased risk of developing metabolic disease. In line with these observations, data from murine studies demonstrate higher accumulation of NK cells in VAT compared with SAT in obesity, largely caused by proliferation of tissue-resident NK cells [5, 6, 9]. In humans less is known about the distribution and protein signatures of CD56bright and CD56dim NK cells between different adipose tissue depots, and how these features are affected by obesity. Some studies also have investigated NK cells in human adipose tissue (10–13), but the tissue-resident NK cell population is poorly defined, and it is uncertain to what extent these cells display a CD56bright phenotype and whether they can be identified by markers additional to CD69. The lack of information regarding phenotypic markers defining adipose tissue CD56bright and CD56dim NK cells and how they may differ between SAT and VAT, has so far represented a limitation for a detailed understanding of how adipose tissue NK cells may contribute to metabolic disturbances in obesity. Thus, a more comprehensive characterization of these cells is warranted.
In this study, we performed a surface proteome screening to identify novel proteins expressed by NK cells in human adipose tissue. Next, we applied multicolor flow cytometry to evaluate the expression of the novel proteins in combination with established NK cell markers on CD56bright and CD56dim NK cells in blood, SAT, and VAT of a sizeable cohort of individuals with obesity. The findings reveal marked NK cell diversity in adipose tissue and identify multiple clusters that may potentially represent NK cell subsets involved in adipose tissue inflammation.
## Surface proteome screening reveals distinct proteins expressed by adipose tissue NK cells
As a first approach to phenotype NK cells in adipose tissue, we performed LEGENDScreen™ surface proteomic screening to evaluate 315 surface receptors and compare their expression on NK cells in blood (peripheral blood mononuclear cells; PBMCs) and SAT from donors (Figure 1A). Substantial overlap in protein expression was observed between total NK cells in blood and adipose tissue, displaying high expression levels of CD11a, CD47, CD48, and CD50 (Figure 1B). Several proteins were identified as either blood- or adipose tissue-enriched, such as higher expression of CD181, CD274, and CD137L on SAT NK cells and higher CD99, NKp80, CD81, and GPR56 on blood NK cells (Figure 1C). The CD56bright NK cells are normally considered the tissue-resident subset, and to identify potential adipose tissue-resident NK cell markers, we investigated proteins that were more highly expressed by these cells relative to the CD56dim NK cells. Interestingly, levels of several proteins, such as CD63, CD54, and CD183, were expressed by higher proportions of the CD56bright cells in SAT (Figure 1C). Taken together, we identified surface proteins enriched on NK cells in blood and adipose tissue, respectively. Moreover, we defined surface proteins characteristic of CD56bright NK cells in adipose tissue that may potentially define adipose tissue-resident NK cell subsets. Of note, the tissue residency marker, CD69, did not emerge as tissue-specific from the proteomic analysis (data not shown), emphasizing certain limitations with this approach and the need to validate our findings.
**Figure 1:** *Surface proteome screening of human subcutaneous adipose tissue reveals distinct proteins expressed by adipose tissue NK cells. (A) Experimental overview of LEGENDScreen™ surface proteome screening on NK cells in subcutaneous adipose tissue (SAT) and peripheral blood (PBMC). Created with BioRender.com (B) Heatmap depicting the percentage of total, CD56dim and CD56bright NK cells expressing 315 individual surface proteins in SAT and PBMC. (C) Flow histograms showing a selection of proteins enriched on SAT NK cells (top), blood NK cells (middle) and SAT CD56bright NK cells (bottom).*
## Deep-phenotyping of CD56bright and CD56dim NK cells in adipose tissue depots and blood
To validate the LEGENDScreen™ findings and to further characterize the CD56bright and CD56dim NK cells in blood and adipose tissue, we generated a 27-parameters flow cytometry panel that combined established NK cell-associated markers (e.g., granzyme B, perforin, T-bet, Eomes, KIRs, NKp46, NKG2A, and NKG2D) with the adipose tissue-enriched CD56bright NK cell proteins identified from the screening (CD26, CD54, CD63, CCR5, and Sialyl Lewisx). CD69 was also included in the panel as an indicator of tissue residency. The protein expression was measured on CD56bright and CD56dim NK cells from matched blood, SAT, and VAT samples of 43 individuals with obesity, undergoing bariatric surgery (Table 1) and the gating strategy is presented in Figure 2A.
The proportion of total NK cells was significantly higher in adipose tissue relative to blood and also higher in SAT than in VAT (Figure 2B). Similarly, depot-specific differences were found for the relative abundance of CD56bright and CD56dim subsets (Figure 2C). As expected, the CD56bright NK cells constituted on average around $10\%$ of the NK cell population in blood, a distribution pattern that was also seen in SAT. In VAT, however, the CD56bright NK cells made up on average almost $50\%$ of the NK cell compartment.
The expression levels of several proteins differed significantly between the CD56bright and CD56dim subsets (Figures 2D, E, Supplementary Figures 1, 2). As expected, the CD56dim NK cells displayed a cytotoxic- and maturation profile, indicated by consistently higher levels of perforin, granzyme B, T-bet, and KIRs. Interestingly, a proportion of the CD56bright cells was also positive for these markers, suggesting some level of cytotoxic potential for this subset. Additionally, CD56bright cells in the adipose tissue expressed higher levels of NKG2A and CD69, which supports the assumption that CD56bright NK cells represent the major tissue-resident population in adipose tissue. Interestingly, not all CD56bright NK cells were positive for CD69, indicating that there are subsets that do not display classical tissue residency phenotypes (Figure 2D). The proteins identified from LEGENDScreen™ also displayed differential expression between the two subtypes, with significantly higher levels of CD26, CD54, CD63, CCR5, and Sialyl Lewisx detected on the CD56bright NK cells in adipose tissue compared with CD56dim NK cells (Figure 2D).
Substantial variations in NK cell protein expression were observed between the various tissue compartments (Figure 2D). This was particularly evident for the CD56bright subset, of which CD54, CD63, CD69, CCR5, Eomes, and NKG2D, were not only enriched in adipose tissue compared with blood but also significantly higher in VAT than in SAT. Conversely, CD26 and Sialyl Lewisx emerged as SAT-enriched proteins, although CD26 was also expressed by nearly $50\%$ of the CD56bright NK cells in VAT. Perforin, granzyme B, KIRs, T-bet, and CD49e displayed the opposite tissue expression pattern on the CD56bright NK cells, with significantly reduced levels in adipose tissue compared to blood, and lower in VAT than in SAT. The CD56dim NK cells also showed tissue-specific phenotypes, although less striking. Similar to the CD56bright subset, NKG2D was increased on the CD56dim NK cells in adipose tissue compared with blood, also displaying a higher VAT/SAT ratio (Figure 2D). Furthermore, a small fraction of the CD56dim NK cells in adipose tissue, particularly in VAT, were positive for CD69, indicating that subsets of CD56dim NK cells also exhibit signs of tissue residency. In summary, our data suggests the existence of tissue-resident CD56bright NK cells enriched in VAT, characterized by the expression of additional surface markers that may potentially reflect adaptations to the microenvironment in this tissue depot.
## NK cell proteins in adipose tissue associate weakly with systemic insulin resistance
Since adipose tissue NK cells are suggested contributors for the development of insulin resistance and metabolic disease, we performed a correlation analysis between the individual proteins expressed by the CD56bright and CD56dim NK cell subsets and glucose and lipid parameters related to cardiometabolic disease. Interestingly, no significant associations were found between proteins on NK cells in blood, SAT, or VAT and HOMA-IR, as a measure of systemic insulin resistance (Figure 3A). However, we did observe several, mostly negative, statistically significant associations between glucose parameters and protein expression in blood, such as between the long-term glucose indicator HbA1c and CD38 on both NK cell subsets, and between glucose and NKG2D on the CD56bright NK cells (Figure 3A). In adipose tissue, the few identified correlations with nominal significance were found with lipid parameters, such as positive correlations between both LDL and total cholesterol and CCR5 expression on the CD56bright NK cells in SAT (Figure 3A). Given the observed associations of HbA1c and fasting glucose with NK cell proteins, we further investigated potential differences in NK cells in patients with and without T2D. However, in our material we did not find significant differences between the two groups, neither the NK cell abundance nor NK cell protein expression, which may possibly be due to the relatively low number of people with T2D in our cohort ($$n = 9$$) (Figure 3B, Supplementary Figure 3). In summary, we observed some significant associations between NK cell proteins and circulating glucose- and lipid parameters, however, the weak associations with systemic insulin resistance and T2D in our cohort suggest that expression of individual proteins may not provide sufficient power to identify such link.
**Figure 3:** *Associations between NK cell proteins and metabolic dysfunction. (A) Pearson correlations between NK cell proteins and circulating clinical parameters. Black squares indicate significant at adjusted FDR <0.1. (B) Box-and-whisker plots depicting the percentages of total NK cells out of viable CD45+ lymphocytes and CD56bright and CD56dim NK cells out of total NK cells from peripheral blood (PBMC), subcutaneous (SAT) and visceral (VAT) adipose tissue from individuals with obesity with type 2 diabetes (T2D, n=9) and without type 2 diabetes (n=34).*
## High-dimensional analysis identifies subsets of adipose tissue-resident CD56bright NK cells
For a higher resolution depiction of NK cell diversity, we employed the high dimensional reduction technique, Uniform Manifold Approximation and Projection (UMAP), on CD56bright and CD56dim NK cells from blood, SAT, and VAT to identify protein combinations on a single cell level. UMAP identified multiple clusters of CD56dim and CD56bright NK cells deriving from the different tissue compartments (Figure 4A). The CD56dim NK cells from both blood, SAT, and VAT primarily co-localized in two clusters, whereas the CD56bright NK cells widely dispersed into multiple SAT and VAT-derived clusters, suggesting higher degree of phenotypic diversity among this subset (Figure 4A). The UMAP plots display variation in expression levels of each of the proteins in the flow cytometry panel across the different clusters within both the CD56dim and CD56bright NK cell populations (Figure 4B). CD38 and Sialyl LewisX in CD56bright cells from VAT and SAT, respectively, and variation of KIR expression in CD56dim cells from all compartments were examples of surface proteins showing high variation (Figure 4B). Thus, the analysis identified multiple clusters of CD56bright and CD56dim NK cells with different levels of protein expression, pointing to the existence of several subpopulations of NK cells.
**Figure 4:** *High-dimensional analysis reveals distinct NK cell subclusters. (A) UMAP plot showing the distribution of CD56bright and CD56dim NK cells from peripheral blood mononuclear cells (PBMC), subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) of 33 individuals with obesity. The NK cell subsets from each sample were barcoded, down sampled to 5000 cells per sample and concatenated. UMAP projection colored according to the cell type and tissue of origin. (B) Expression intensities of 21 proteins within the UMAP plot. (C) UMAP plot overlaid with 18 identified PhenoGraph clusters. (D) Donut plot showing proportions of each cluster out of the total concatenated NK cell population. (E) Selected PhenoGraph clusters displayed over UMAP embeddings showing clusters enriched in VAT, SAT, and blood. (F) Stacked bars showing relative abundance of CD56dim and CD56bright NK cells (left) and tissue of origin (right) within each cluster. (G) Heatmap displaying Z-score transformed median fluorescence intensity (MFI) expression values for each of the proteins across the 18 clusters. Color scale is determined for each column separately, based on the lowest and highest Z-score value for that protein. (H) Flow histograms showing expression of proteins within the indicated clusters.*
Indeed, PhenoGraph clustering analysis identified 18 defined clusters of NK cells within the UMAP plot with various size proportions (Figures 4C-E, Supplementary Figure 4). Most clusters were enriched with either CD56dim or CD56bright NK cells, with the exception of cluster 6, containing equal proportion of both subsets (Figure 4F, left). The majority of clusters contained cells deriving from all tissue compartments (Figure 4F, right), but as expected, the VAT-enriched (#9, #16, #18) and the SAT-enriched (#8, #11) clusters were dominated by CD56bright NK cells, and the blood-enriched cluster (#2) were dominated by CD56dim NK cells (Figure 4E, F). A hierarchical clustering, summarizing protein expression patterns across the 18 clusters, confirmed the similarities between the VAT-enriched CD56bright NK cell clusters (#9, #16, #18), all displaying considerably higher levels of CD63, CD69, and NKG2D compared with the other clusters (Figures 4G, H). However, differences were also observed across these clusters, such as higher levels of Eomes, NKG2A, and CD38 in #9 and CCR5 and KIRs in #16, revealing proteins that may define the different subclusters of CD56bright NK cells in VAT. As expected, the clusters enriched with CD56dim NK cells (e.g., #1, #2, #10) shared phenotypic signatures, such as higher levels of perforin, granzyme B and T-bet (Figure 4G).
Given the few associations observed between the single proteins and metabolic parameters (Figure 3A), we next investigated the relationship between the NK cell clusters and clinical traits. To this end, the patients were stratified into “low” and “high” based on the median value of the clinical parameters from all patients (Table 1). Overall, the clusters shared relatively similar distribution of NK cells deriving from “low” and “high” patients for most of the parameters, with exception of cluster #14 that contained higher proportions of cells from patients with “high” BMI and HOMA-IR ($77\%$ and $78\%$, respectively) and cluster #16 that contained higher proportions of cells from patients with “low” BMI, HOMA-IR, and LDL ($74\%$, $80\%$, $79\%$, respectively) (Supplementary Figure 5).
Taken together, by employing high dimensional clustering analysis, we identified protein signatures defining distinct subsets of CD56bright and CD56dim NK cells in blood, SAT, and VAT from people with obesity. Importantly, we discovered potential subpopulations of tissue-resident CD56bright NK cells, mainly residing in VAT, defined by the co-expression of CD63, CD69, and NKG2D.
## Discussion
The main function of immune cells is to combat pathogens and infections. However, it has become increasingly clear that immune cells are also involved in maintaining tissue homeostasis, mainly exerted by local immune cell populations that reside in the tissues and do not enter the circulation [14]. The concept of tissue-residency has rapidly evolved over the last years, and it is now evident that also NK cells constitute a widely heterogeneous population of resident cells across various tissues [8, 15]. Since the proteins that define specific NK cell populations are dependent on the tissue type, the discovery of tissue-specific markers has been important to study the human NK cell compartment during homeostatic and pathological conditions.
In adipose tissue, resident NK cells have recently gained increased interest as mediators of pro-inflammatory signaling that links obesity to insulin resistance [4, 5]. Thus, targeting specific adipose tissue NK cell population to reduce inflammatory signaling may represent a future pharmacological strategy to treat obesity-related co-morbidities. However, one major shortcoming to achieve this goal is the gap in knowledge of the phenotypic markers that define NK cell subsets residing in human adipose tissue in the setting of obesity. In this study, we analyzed CD56bright and CD56dim NK cells in blood and two adipose tissue depots from a cohort of individuals with obesity, using a high-dimensional flow cytometry panel that combined established NK cell tissue residency-, maturation-, and effector proteins with proteins from a broad surface receptor screening. We identified NK cell subsets with unique protein signatures and distribution patterns between blood and between adipose tissue depots, thus providing novel insight of NK cell heterogeneity within the adipose tissue.
As expected, our flow cytometry data revealed that the proportion of CD56bright NK cells in blood was low. Interestingly, this was also true for SAT, suggesting a low frequency of tissue-resident NK cells in this adipose depot. By contrast, the proportion of CD56bright NK cells was higher in VAT, which is in accordance with previous reports and supports that obesity promotes proliferation of tissue-resident NK cells in VAT [13, 16]. Moreover, since CD56bright NK cells are the main cytokine-producing subset, the CD56bright enrichments in VAT possibly reflects more pro-inflammatory signaling in this depot.
Phenotypic differences between the CD56bright and CD56dim NK cells in adipose tissue resembled the phenotypes described in other tissues. Notably, the increased levels of granzyme B and perforin on the CD56dim NK cells manifested their cytotoxic potential and the elevated KIRs expression on the CD56dim cells and NKG2A expression on the CD56bright NK cells confirmed their distinct maturation profiles [17, 18]. Furthermore, the inverse expression patterns of CD69 and CD49e on the CD56bright cells supported the tissue residency nature of this subset [19]. Tissue-resident CD56bright NK cells may also be shaped by the tissue microenvironment and can be characterized by the expression of distinct tissue-specific proteins [8, 14]. In the liver, such adaptations have been reflected by the identification of CXCR6, CCR5 and Eomes on resident CD56bright NK cells [20, 21], whereas NK cells residing in the uterus express CD9 [22]. In our study, we identified increased levels CD26, CD54, CD63, CCR5, and Sialyl Lewisx on the resident CD56bright NK cells in adipose tissue, with most proteins found enriched in VAT compared with SAT. Several of these proteins are linked to activation and have previously been shown to be expressed by tissue-resident NK cells in other organs, including the chemokine receptor CCR5 in the liver [20] and the adhesion molecule CD54 in lymphoid tissues [23]. Also enriched on adipose CD56bright NK cells in the adipose tissue was the metabolic protein CD26, previously shown upregulated on NK cells upon activation to facilitate cytokine production [24], and the exosome marker CD63, which has been related to NK cell effector functions [25]. To what extent these proteins play an active role in tissue retention or execute other relevant functions on the tissue-resident CD56bright NK cells remains to be established. It should be noted that the lack of CD127 in our gating strategy prevents us from formally excluding ILCs from the CD56bright NK cell population. However, since the majority of CD56bright cells were positive for Eomes, a protein not expressed by ILCs, we can assume that the CD56bright NK cell population is primarily made up of bona fide NK cells. Our data suggests that CD56bright NK cells share similar phenotypic features across different tissues, although the relative expression of the different surface proteins may vary, possibly depending on differences in the microenvironment [7]. The differences in protein expression that we observed between SAT and VAT may reflect adaptations by such microenvironmental signals, including differences in levels of hormones, fatty acids, and adipokines, that are known to fluctuate in the adipose tissue to meet increased energy requirements in obesity [26]. Structural differences between SAT and VAT, such as the extracellular matrix (ECM) composition, may potentially also influence the immune cell phenotypes within the distinct depots [27]. The UMAP and PhenoGraph analyses indicated the existence of multiple adipose tissue NK cell clusters, underscoring recent evidence of NK cell heterogeneity in adipose tissue [28]. In our analysis, VAT was enriched with clusters of resident CD56bright NK cells defined by the co-expression patterns of CD69, CD63, and NKG2D, with CCR5 and Eomes defining specific subclusters. Although their functional relevance requires further evaluation, the observed co-expression of these proteins might define specific NK cell subsets relevant for inflammation in VAT and potentially for the development of insulin resistance.
A relationship between NK cells and insulin resistance have been reported by several [11, 29]. However, such associations could not be found in our cohort when examining the individual proteins and by using HOMA-IR as a measure of insulin resistance. There may be several explanations for this lack of association. Firstly, it should be noted that our analyses included biological material from patients with class III obesity only, and inclusion of lean controls may have given a different result. Also, HOMA-IR does not provide the same accuracy as an hyperinsulinemic-euglycemic clamp measurements, considered the gold standard of insulin resistance measurement [30]. Furthermore, HOMA-IR measures systemic insulin resistance and does not reflect insulin resistance in adipose tissue, which ideally requires measurements of adipose tissue lipolysis [31]. In addition, it is important to acknowledge the fact that pro-inflammatory immune cells should not merely be considered pathological, since they also have protective effects in the obese adipose tissue. This was recently demonstrated for a subset of crown-like structure, pro-inflammatory macrophages that were in fact essential for adipose tissue homeostatic functions during nutrient excess [32]. Thus, the co-existence of pro-inflammatory adipose tissue immune cell subtypes with potentially both beneficial and dysregulatory functions may, at least partly, disrupt potential correlations with the metabolic parameters.
Taken together, we have performed a high dimensional profiling of NK cells in blood and adipose tissue in human obesity and identified depot-specific proteins defining adipose tissue-resident NK cell subsets. The observed heterogeneity of NK cells residing in adipose tissue underlines the importance of identifying such proteins to distinguish between subsets contributing to metabolic homeostasis and subsets mediating the pathological signaling related to metabolic dysfunction.
## Clinical cohorts
Several clinical cohorts were included in the current study. This was approved by the Regional Committees for Medical and Health Research Ethics in Bergen (REK: $\frac{2010}{502}$ and $\frac{2015}{2343}$) and written consent was obtained from all participants. First, subcutaneous adipose tissue from liposuction aspirates of individuals undergoing plastic surgery was used for proteomic screening. Buffy coats from anonymous blood donors were used as blood samples and internal controls for the cell surface screening. For the 27-color flow cytometry analysis, matched fasting blood samples and subcutaneous- and visceral adipose tissue biopsies were obtained from 43 patients with obesity undergoing bariatric surgery at Voss Hospital. The clinical characteristics and biochemical measurements of this cohort are presented in Table 1.
## Isolation of stromal vascular fraction and peripheral blood mononuclear cells
Subcutaneous adipose tissue and visceral adipose tissue biopsies obtained from bariatric surgery were immediately stored in Krebs-Ringer Phosphate (KRP) buffer until processing. The biopsies were cut into pieces and enzymatically digested with collagenase Type I (Life Technologies) for 1 hour at 37°C with constant shaking. The subcutaneous adipose tissue from liposuction was washed in $0.9\%$ NaCl, diluted in KRP buffer and digested with Liberase (Roche). The dissolved tissues were filtered, and the stromal vascular cells (SVC) were isolated from the mature adipocytes and washed with Phosphate Buffered Saline (PBS). The liposuction SVC was freshly stained, whereas the SVC from biopsies were preserved in freezing media containing FBS and $10\%$ DMSO and stored in liquid nitrogen until further flow cytometry experiments. Peripheral blood mononuclear cells (PBMC) were isolated from blood of bariatric surgery patients and healthy donors using density gradient centrifugation, as previously described [33].
## Surface proteomic screening
LEGENDScreen™ Human PE kit (Biolegend, Cat#700007) was used to screen the cell surface proteome. Fresh SVC from subcutaneous liposuction aspirates and PBMC from buffy coat blood were used in the screening as previously described [33]. In brief, the tissue-derived and blood-derived cells were CD45 barcoded and stained with a backbone antibody panel allowing identification of the cells of interest. The cells were added to the plates provided in the LEGENDScreen™ kit and the staining was performed according to the manufacture protocol. Plates were run on an 18-color flow cytometer (LSR Fortessa, BD Biosciences) with 407, 488, 561 and 640 lasers using the BD FACSDiva™ Software (BD Biosciences). Flow cytometry data was analyzed using FlowJo v10 (Treestar, USA). Two manual gating approaches were performed to identify the NK cell subsets, a “broad” gating followed by a “fine” gating. Several adipose-enriched proteins identified from both gating approaches were validated for expression in additional adipose tissue samples, and a selection of these proteins was included in the 27-parameter flow cytometry experiment.
## 27-parameters flow cytometry staining
Frozen PBMC and SVC were thawed and stained with an extracellular primary antibody panel containing the following antibodies from BD Biosciences: BUV496 CD16 (Cat# 612944), BUV563 CD56 (Cat# 565704), BUV661 CD38 (Cat# 612969), BUV737 CD69 (Cat# 612817), BUV805 CD45 (Cat# 612891), BB700 NKG2A (Cat# 747926), APC Vio770 CCR5 (Cat# 557755), V500 CD14 (Cat# 561391), BV510 CD19 (Cat# 562947), BV650 CD98 (Cat# 744505), BV711 Sialyl Lewis x (Cat# 563910), PE-Cy5 CD54 (Cat# 555512). From Biolegend: Biotin NKp46 (Cat# 331906), A700 CD63 (Cat# 353024), BV421 Bcl-2 (Cat# 658709), BV510 CD123 (Cat# 306022), BV750 CD3 (Cat# 344845), BV785 HLA-DR (Cat# 307642), PE CD26 (Cat# 302706), PE-Cy7 NKG2D (Cat# 320812), PE-Cy7 CD162 (Cat# 328816), BB515 CD49e (Cat# 130-110-534, Miltenyi), PE-Cy5.5 KIR2DL1/S1 (Cat# A66898, Beckman Coulter), PE-Cy5.5 KIR2DL2/L3/S2 (Cat# A66900, Beckman Coulter). Staining was performed in FACS buffer (PBS with 2mM EDTA (Cat# AM9260G, Ambion), $2\%$ FCS (Cat# F7524, Sigma). After 20 min incubation at room temperature (RT) and in the dark, the cells were washed twice with FACS buffer and further stained with a secondary antibody panel containing streptavidin (Cat# 624294, BD Biosciences) for 15 min at RT in dark. Cells were washed twice with FACS buffer before adding Fix/perm Buffer (diluted ¼ with eBioscience reagents: Fix/perm diluent (Cat#00.5223.56) and Fix/perm concentrate (Cat#00.5123.43)) and incubated for 45 min RT in dark. Cells were then washed twice with perm/MQ buffer (Perm Buffer 10X (Cat#00.8333.56) diluted $\frac{1}{10}$ with MQ water). Further, the cells were stained with an intracellular antibody panel containing the following antibodies from BD Biosciences: BUV395 Ki67 (Cat# 564071), BB755-P Perforin (Cat# 624391, BD Horizon custom reagents), BB790-P Granzyme B (Cat# 624296, BD Horizon custom reagents), and eF660 Eomes (Cat# 50-4877-41, eBioscience), PE-Dazzle594 T-bet (Cat# 644828, Biolegend). Live/dead Fixable Aqua Dead Cell stain kit (Invitrogen, 1:100 dilution) was used to distinguish between dead and live cells. After 30 min incubation, the cells were washed twice with perm/MQ buffer and kept in FACS buffer for immediate flow cytometry analysis. Samples were run on a 29-color Symphony (BD Biosciences) with 405, 488, 561 and 639 lasers using the BD FACSDiva™ software (BD Biosciences). Flow cytometry data generated was analyzed using FlowJo V10 (Treestar, USA).
## Dimension reduction analysis
The FlowJo plugins Uniform Manifold Approximation and Projection (UMAP) and PhenoGraph were performed to visualize the single cell flow cytometry data in a high dimensional structure. To this end, CD56bright and CD56dim NK cells from PBMC, SAT, and VAT for each patient ($$n = 33$$) were electronically barcoded for cell subset, tissue of origin and several biochemical parameters (either “low” or “high” based on median values of each parameter). The patient samples containing the same cell subset and tissue type were concatenated (6 samples in total), which was downsampled to 5118 events/file for an equal number of cell input. The samples were concatenated to a final file that was analyzed for UMAP and Phenograph. UMAP was run using the default settings (Euclidean distance function, nearest neighbors: 15 and minimum distance: 0.5). PhenoGraph was run using the default number of nearest neighbors ($K = 30$). The parameters included in both analyses were the phenotypic markers of interest ($$n = 21$$) and excluded the proteins used to gate on the NK cell subsets.
## Statistical analysis
Flow cytometry data was analyzed using Prism version 9.2.0 (GraphPad). D’Agostino & *Pearson omnibus* normality test was used to determine normality of the data. For normally distributed data, one-way ANOVA with Holm-Šídák’s multiple comparisons test was used. When data was not normally distributed, Friedman test with Dunn’s multiple comparisons test was used. A p-value of < 0.05 was considered statistically significant. Correlation analysis was performed in R using the Pearson correlation coefficient and considered significant at adjusted FDR <0.1.
## Data availability statement
The original contributions presented in the study are included in the article/ Supplementary Materials. Further inquiries can be directed to the corresponding author.
## Ethics statement
The studies involving human participants were reviewed and approved by Regional Committees for Medical and Health Research Ethics in Bergen (REK: $\frac{2010}{502}$ and $\frac{2015}{2343}$). The patients/participants provided their written informed consent to participate in this study.
## Author contributions
MEH designed panel, planned and performed experiments, acquired and analyzed data and wrote the manuscript. MC designed panel, planned experiments, analyzed data and reviewed/edited the manuscript. KS planned and performed experiments and analyzed data. NS planned experiments. DS and LL-A analyzed data. IDH acquired data. CB sampled adipose tissue. GM contributed to the discussion and reviewed/edited the manuscript. NB and JF designed the study, oversaw its conduction, provided funding, contributed to the discussion and reviewed/edited the manuscript. 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.2023.1130370/full#supplementary-material
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|
---
title: 'Renal outcomes of rivaroxaban compared with warfarin in Asian patients with
nonvalvular atrial fibrillation: A nationwide population-based cohort study'
authors:
- So-Ryoung Lee
- Eue-Keun Choi
- Sang-Hyun Park
- Kyung-Do Han
- Seil Oh
- Khaled Abdelgawwad
- Gregory Y. H. Lip
journal: Frontiers in Cardiovascular Medicine
year: 2023
pmcid: PMC9996329
doi: 10.3389/fcvm.2023.1040834
license: CC BY 4.0
---
# Renal outcomes of rivaroxaban compared with warfarin in Asian patients with nonvalvular atrial fibrillation: A nationwide population-based cohort study
## Abstract
### Background
Further studies are needed to expand the evidence for the association of rivaroxaban with a lower risk of adverse renal outcomes in patients with atrial fibrillation (AF) as compared with warfarin, especially in Asians.
### Objectives
To determine whether there are differences in adverse renal outcomes between rivaroxaban and warfarin-treated AF patients.
### Methods
Using the Korean nationwide claims database partly linked to laboratory results, patients with AF who initiated warfarin or rivaroxaban from 1 January 2014 to 31 December 2017 were identified. Inverse probability of treatment weighting (IPTW) was used to balance the baseline characteristics of the two groups. The primary outcome (kidney failure) was defined as the need for maintenance dialysis or having kidney transplantation. For the exploratory analysis in a subset of patients with baseline and follow-up laboratory results, the composite of renal outcomes, including estimated glomerular filtration rate (eGFR) lower than 15 ml/min/1.73 m2 at follow-up measurement, starting dialysis, or having kidney transplantation, ≥ $30\%$ decline in eGFR, doubling of serum creatinine level, and acute kidney injury (AKI) were evaluated. The two groups were compared using Cox proportional hazards regression in the weighted population.
### Results
We identified 30,933 warfarin users and 17,013 rivaroxaban users ($51\%$ of low dose rivaroxaban). After IPTW, the mean age was 70 years, and the mean CHA2DS2-VASc score was 3.9 in both groups. During a median follow-up of 0.93 (interquartile ranges 0.23–2.10) years, weighted incidence rates of kidney failure for warfarin and rivaroxaban were 0.83 and 0.32 per 100 person-years, respectively. Compared with the warfarin group, the rivaroxaban group was associated with a lower risk of kidney failure (hazard ratio [HR] 0.389, $95\%$ confidence interval [CI] 0.300–0.499, $p \leq 0.001$). In patients with preexisting chronic kidney disease or eGFR ≤ 60 ml/min/1.73 m2, rivaroxaban was more beneficial than warfarin in reducing the risk of kidney failure. For the composite of five renal outcomes in the exploratory analysis, the rivaroxaban group showed a lower risk than warfarin (HR 0.798, $95\%$ CI 0.713–0.892, $p \leq 0.001$).
### Conclusion
Rivaroxaban was associated with lower risks of renal adverse outcomes than warfarin in Korean patients with AF.
## Introduction
Atrial fibrillation (AF) has been related to an increased risk of chronic kidney disease (CKD) later in life [1]. For several decades, warfarin was the only oral anticoagulation (OAC) therapy in preventing thromboembolic events in AF patients. Warfarin-related nephropathy, including the rapid development of renal function decline in CKD patients and the prevalence of acute kidney injury (AKI), has been described among warfarin-treated patients [2, 3].
Since the introduction of non-vitamin K antagonist oral anticoagulants (NOACs), there has been some evidence that NOACs might be associated with improved renal function preservation compared with warfarin [4, 5]. According to a post-hoc analysis of the Randomized Evaluation of Long-Term Anticoagulation Therapy (RE-LY) trial, dabigatran was linked to a reduced risk of creatinine clearance reduction compared with warfarin [4]. Several observational studies found that NOACs had similar results to warfarin, but there was variance in the outcomes across NOACs (5–9). Firstly, rivaroxaban and dabigatran consistently outperformed warfarin regarding kidney preservation (5–9). On the other hand, apixaban did not produce consistent findings with statistical significance [5], and there was no information on edoxaban. Secondly, the relationship between NOAC and the likelihood of unfavorable renal outcomes varied depending on the patients’ baseline kidney function [10]. Finally, between non-Asians and Asians, the protective effect of NOACs versus warfarin on renal outcomes was slightly different [9].
Renal function deterioration is widespread in AF patients treated with OAC [5]. As decreased renal function is associated with an increased risk of stroke and bleeding, it is critical to maintain renal function in patients treated with OAC [11, 12]. Further studies are needed to examine whether NOACs bring consistent results for preventing progressive renal function decline, especially in Asians who had poor treatment quality of warfarin therapy [13].
This study aims to determine whether there are differences in adverse renal outcomes between rivaroxaban and warfarin-treated AF patients utilizing a nationwide population-based study in South Korea.
## Data source
This retrospective observational nationwide population-based cohort study was conducted using administrative claims data from the Korean National Health Insurance Service (NHIS) and the linked health check-up database of the National Health Insurance Corporation (NHIC) [14, 15]. The Korean NHIS provides comprehensive medical care coverage for the entire Korean population (approximately 50 million people). The analysis was based on a randomly selected $50\%$ sample cohort from the Korean NHIS. Supplementary methods provide additional information about the data source. All data have been provided publicly available through the National Health Insurance Data Sharing Service (accessed at: http://nhiss.nhis.or.kr/bd/ab/bada000eng.do). After permission to use the data was obtained, the analysis was performed at the Korean NHIS Big Data Center, Seoul, Republic of Korea.
## Study population and study design
The study period was from 1 January 2013 to 31 December 2018. The study’s enrollment period ran from 1 January 2014 to 31 December 2017, to allow for at least a 12-month follow-up period. Study enrollment flow is presented in Figure 1. Firstly, we identified adult AF patients prescribed OAC during the enrollment period. AF was defined as at least one hospitalization or outpatient visit with relevant diagnostic codes (I48.0–I48.4, I48.9). To compare the renal outcome between two treatment groups (rivaroxaban versus warfarin), we included patients who were OAC new users (who had no record of OAC use in the prior 12 months) and were newly initiated on rivaroxaban or warfarin. Patients with valvular AF, alternative indications of OAC including pulmonary embolism, deep vein thrombosis, recent joint surgery, and end-stage renal disease (ESRD) were excluded.
**Figure 1:** *Study enrollment flow. AF, atrial fibrillation; ESRD, end-stage renal disease; OAC, oral anticoagulant.*
The primary analysis included all eligible patients. Additionally, we designed the exploratory analysis to assess renal outcomes estimated by laboratory data, including a subset of patients who received at least two health examinations during the study period. These patients had baseline and follow-up eGFR measurements. As a baseline eGFR, we collected the results of the health examination performed within 2-year from the index date. Among patients with a baseline eGFR value, we included patients with at least one follow-up health examination data during follow-up.
## Covariates
Age, sex, co-morbidities including hypertension, diabetes, dyslipidemia, heart failure, prior stroke, prior myocardial infarction, peripheral artery disease, chronic kidney disease, chronic obstructive pulmonary disease (COPD), and cancer, CHA2DS2-VASc score, Charlson Comorbidity Index (CCI), and concomitant use of antiplatelet agents were evaluated as covariates. The operational definitions of co-morbidities were based on diagnostic codes, drug dispensing records, and inpatient/outpatient hospital visits within 3 years prior to the index date. Complete definitions of each covariate are presented in Supplementary Tables S1 and S2 [5, 15, 16].
Among the total study population, $67.4\%$ of patients had the data from the baseline national health examination, and $23.4\%$ had the data from both baseline and at least one follow-up national health examination. From the health examination data, body weight, body mass index (kg/m2), serum creatinine (mg/dL) and eGFR (mL/min/1.73 m2) were collected. eGFR was calculated by a creatinine-based equation used from Modification of Diet in Renal Disease. In addition, smoking status (never smoker, ex-smoker, or current smoker), alcohol consumption (heavy drinker, ≥ 30 g/day), and physical activity were also evaluated from the self-reported questionnaires of health examination. Regular exercise was defined as performing moderate-intensity exercise ≥ 5 times per week or vigorous-intensity exercise ≥ 3 times per week [17].
## Study outcomes and follow-up
The index date was defined as the time when rivaroxaban or warfarin was newly initiated. To evaluate the comparative risk of renal outcome between the two groups, the primary outcome was incident kidney failure, defined as the need for maintenance dialysis or having kidney transplantation (Supplementary Table S3) [5, 18]. Secondary outcomes were incident ischemic stroke, intracranial hemorrhage, major gastrointestinal bleeding, major bleeding, and all-cause death (Supplementary Table S3) [16]. To assess the outcomes, patients were followed up until 31 December 2018. Patients were censored at the occurrence of each outcome, the end of the study period (31 December 2018), or death, whichever came first. In addition, the main analysis followed the on-treatment approach; therefore, patients were also censored at the discontinuation of index treatment for more than 30 days. The date of discontinuation was defined as the end of exposure, and patients were censored.
For the exploratory analysis, five renal outcomes were assessed; [1] eGFR lower than 15 ml/min/1.73 m2 at follow-up measurement, [2] starting dialysis or having kidney transplantation, [3] ≥ $30\%$ decline in eGFR, [4] doubling of serum creatinine level, and [5] AKI (Supplementary Table S3) [5]. The $30\%$ decline in eGFR and doubling of serum creatinine defined as changes from baseline (using measurement closest to index date) at any time point during follow-up [5]. Because [1, 3, 4] relied entirely on laboratory data, when examining these three outcomes, patients were censored at their last laboratory measurement. AKI was defined as an emergency department visit or hospitalization with a diagnostic code of AKI (N17 ×) [5, 9]. The composite of five renal outcomes was also evaluated.
## Statistical analysis
Patients were described at treatment initiation in terms of demographic and clinical variables. Continuous variables are presented as means and standard deviations or medians and interquartile ranges (IQR). The numbers and proportions of patients in each category are presented for categorical variables. Person-years of follow-up were calculated from the index date to the outcome event of interest, discontinuation of the index treatment, death, or the end of the study period, whichever comes first. Incidence rates were calculated as the number of events over the observed person-time and presented as per 100 person-years.
We used the propensity score (PS) methods to compare the rivaroxaban and warfarin groups [19]. We utilized stabilized inverse probability of treatment weighting (IPTW) approach based on the PS to adjust for potential confounding resulting from imbalances in baseline patient characteristics. The objective of IPTW is to create a weighted sample for which the distribution of either the confounding variables or the prognostically important covariates is approximately the same between comparison groups [20]. PS is the patient’s probability of receiving a treatment under investigation (rivaroxaban) given a set of known patients’ baseline characteristics. PS was calculated using multiple logistic regression on all the available covariates, including demographics, co-morbidities, CHA2DS2-VASc score, Charlson Comorbidity Index, and concomitant medication. For the exploratory analysis, health examination variables such as body weight, body mass index (BMI), eGFR, smoking, alcohol consumption, and physical activity were additionally included for PS calculation. Detailed methods of IPTW are described in Supplementary methods. After IPTW, we assessed the balance of the two treatment groups by using absolute standardized differences (ASDs). The PSs and stabilized weights distributions were inspected for initial and synthetic samples. An ASD of 0.1 or less was considered as a negligible difference between the two groups. The weighted event numbers and incidence rates were calculated. We compared treatments using weighted Cox proportional hazards regression with IPTW. Results of Cox analyses are reported as hazard ratios (HRs) with $95\%$ confidence intervals (CIs). Each Cox regression was checked to see if the model assumptions were fulfilled. For the exploratory analysis set, weighted cumulative incidences of the composite of five renal outcomes were estimated by the Kaplan–Meier method and log-rank test.
All statistical analyses were performed using SAS 9.3 (SAS Institute Inc., Cary, NC, United States).
## Subgroup analyses
In the main analysis set, for the primary outcome, subgroup analyses were performed for age strata (< 65, 65–74, and ≥ 75 years), sex, hypertension, diabetes, heart failure, CKD (defined by diagnostic codes), CHA2DS2-VASc score (< 3, and ≥ 3) and Charlson Comorbidity Index (< 3, and ≥ 3). Among patients with baseline eGFR measurements, subgroup analyses were performed for eGFR ranges (> 60 and ≤ 60 ml/min/1.73 m2). Subgroup analyses were performed using a multivariable Cox proportional hazards regression model. The variables used in the multivariable *Cox analysis* were identical to those used in the PS calculation for the main analysis set. Tests for interaction were conducted to evaluate statistically significant ($p \leq 0.1$) subgroup differences in treatment.
The benefit of rivaroxaban compared with warfarin on the risk of kidney failure was consistently observed across almost all of the examined subgroups (Figure 3). However, wide CI was observed in patients without hypertension due to the small number of patients and low event rates. There were no significant interactions between treatment and all subgroups, except in the subgroup stratified by CKD and eGFR. Rivaroxaban was associated with a greater reduction in the risk of kidney failure in patients with underlying CKD, as defined by diagnostic codes, compared with those without (value of p for interaction < 0.001). There was also a strong trend towards a reduction in the risk of kidney failure in patients with CKD defined as eGFR less than 60 ml/min/1.73 m2, compared with those with an eGFR greater than 60 ml/min/1.73 m2.
**Figure 3:** *Subgroup analyses. CCI, Charlson comorbidity index; CI, confidence interval; CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; IR, incidence rate.*
## Sensitivity analyses
To provide complementary analyses, we performed sensitivity analyses for the primary outcome as follows: [1] IPTW following the intention-to-treat (ITT) approach, which was not censoring patients at discontinuation or switching the index treatment), [2] multivariable Cox proportional hazards regression models in the study population before IPTW following the on-treatment approach, [3] multivariable *Cox analysis* following the ITT approach, [4] $5\%$ trimmed IPTW following the on-treatment approach, [5] $5\%$ trimmed IPTW following the ITT approach, [6] a sensitivity analysis among patients with a 6-month or longer follow-up period to evaluate whether the main results are consistent in those who had neither drug discontinuation nor any renal outcome during the first 6 months, [7] a sensitivity analysis restricting the follow-up within 12 months, and [8] an analysis in the subset of patients with baseline eGFR measurements. The sensitivity analyses of [2, 3, 6, 7] were performed using a multivariable Cox proportional hazards regression model, and the variables used in the multivariable *Cox analysis* were identical to those used in the PS calculation for the main analysis set. For [8], baseline eGFR values were additionally adjusted. In addition, although we included the CHA2DS2-VASc score and CCI in the final multivariable Cox analysis, there is a possibility of model overfitting. Therefore, we conducted a sensitivity analysis excluding the CHA2DS2-VASc score, CCI, or both of these in the final model. Also, we performed a competing risk analysis with the Fine-Gray methods as a sensitivity analysis [21].
For the primary outcome we performed various sensitivity analyses that demonstrated results consistent with the main analysis. Rivaroxaban was associated with significant reductions in the risk for kidney failure in all analyses (Supplementary Results, Supplementary Figure S2, and Supplementary Table S5). The results were consistent with the primary findings when we conducted a competing risk analysis that was adjusted for the competing risk of death rather than a censoring event (HR 0.447, $95\%$ 0.344–0.582, $p \leq 0.001$).
## Baseline characteristics
This study comprised a total of 47,946 individuals (mean age 70.1 ± 11.7 years, mean CHA2DS2-VASc score 3.9 ± 1.9), with 30,933 patients taking warfarin and 17,013 taking rivaroxaban. Supplementary Table S2 shows the baseline characteristics of the total, warfarin, and rivaroxaban groups. Before PS matching, the rivaroxaban group was older, more likely to be women, and had a higher mean CHA2DS2-VASc score than the warfarin group. Co-morbidities such as hypertension, diabetes, dyslipidemia, heart failure, peripheral artery disease, and cancer were more common in the rivaroxaban group. In contrast, prior stroke, prior myocardial infarction, chronic kidney disease, and chronic obstructive pulmonary disease were more prevalent in the warfarin group. Antiplatelet co-use was more common in the warfarin group than in the rivaroxaban group. In the rivaroxaban group, standard dose rivaroxaban (20 mg once daily) was prescribed to $49\%$ of patients, whereas low-dose rivaroxaban (15 mg once daily) was prescribed to $51\%$.
## Primary and secondary outcomes
In the main analysis set, a median follow-up duration was 0.93 (IQR 0.23–2.10) years. Rivaroxaban group showed longer median follow-up duration than warfarin group (1.27 [IQR 0.27–2.35] vs. 0.75 [0.21–1.85], $p \leq 0.001$). Supplementary Table S4 shows crude event numbers, incidence rates, and unadjusted HRs for primary and secondary outcomes. In Table 1, all baseline variables were well-balanced in the two groups after PS weighting, and all ASDs for the two groups were less than 0.1 (Table 1). PS distribution after weighting is presented in Supplementary Figure S1.
**Table 1**
| Unnamed: 0 | Before IPTW | Before IPTW.1 | Before IPTW.2 | After IPTW | After IPTW.1 | After IPTW.2 |
| --- | --- | --- | --- | --- | --- | --- |
| | Warfarin | Rivaroxaban | ASD | Warfarin | Rivaroxaban | ASD |
| n | 30933 | 17013 | | 30946 | 17006 | |
| Age, years | 69.0 ± 12.3 | 72.1 ± 10.1 | 0.277 | 70.2 ± 11.9 | 70.4 ± 11.2 | 0.015 |
| < 65 years | 9,944 (32.2) | 3,468 (20.4) | | 87,223 (28.2) | 4,523 (26.6) | |
| 65 to < 75 years | 9,412 (30.4) | 5,974 (35.1) | | 9,700 (31.3) | 5,701 (33.5) | |
| ≥ 75 years | 11,577 (37.4) | 7,571 (44.5) | | 12,524 (40.5) | 6,782 (39.9) | |
| Sex, male | 18,260 (59.0) | 9,605 (56.5) | 0.052 | 17,985 (58.1) | 9,909 (58.4) | 0.003 |
| CHA2DS2-VASc | 3.8 ± 2.0 | 4.1 ± 1.7 | 0.125 | 3.9 ± 1.9 | 3.9 ± 1.9 | 0.010 |
| CHA2DS2-VASc ≥ 3 | 22,494 (72.7) | 13,779 (81.0) | 0.197 | 23,446 (75.8) | 12,949 (76.2) | 0.013 |
| Charlson comorbidity index | 4.0 ± 2.5 | 4.0 ± 2.4 | 0.006 | 4.0 ± 2.5 | 4.0 ± 2.5 | 0.015 |
| Charlson comorbidity index ≥ 3 | 21,444 (69.3) | 11,978 (70.4) | 0.023 | 21,668 (70.0) | 11,900 (70.0) | 0.009 |
| Hypertension | 25,023 (80.9) | 14,582 (85.7) | 0.129 | 25,572 (82.6) | 14,050 (82.6) | <0.001 |
| Diabetes | 8,067 (26.1) | 4,617 (27.1) | 0.023 | 8,213 (26.5) | 4,558 (26.8) | 0.005 |
| Dyslipidemia | 16,290 (52.7) | 9,357 (55.0) | 0.046 | 16,563 (53.5) | 9,150 (53.8) | 0.005 |
| Heart failure | 12,550 (40.6) | 7,592 (44.6) | 0.082 | 12,988 (42.0) | 7,104 (41.8) | 0.003 |
| Prior stroke | 9,511 (30.8) | 4,315 (25.4) | 0.120 | 8,964 (29.0) | 5,017 (29.5) | 0.011 |
| Prior myocardial infarction | 2026 (6.6) | 1,004 (5.9) | 0.026 | 1955 (6.3) | 1,079 (6.3) | 0.001 |
| Peripheral artery disease | 6,948 (22.5) | 4,355 (25.6) | 0.073 | 7,301 (23.6) | 4,025 (23.7) | 0.001 |
| Chronic kidney disease | 1899 (6.1) | 724 (4.3) | 0.084 | 1,699 (5.5) | 976 (5.7) | 0.010 |
| COPD | 2,975 (9.6) | 1,372 (8.1) | 0.054 | 2,814 (9.1) | 1,578 (9.3) | 0.006 |
| Cancer | 2003 (6.5) | 1,368 (8.0) | 0.060 | 2,177 (7.0) | 1,219 (7.2) | 0.005 |
| Antiplatelet use | | | | | | |
| | 18,790 (60.7) | 12,679 (74.5) | 0.235 | 20,305 (65.6) | 11,114 (65.4) | <0.001 |
| Aspirin only | 6,562 (21.2) | 2,137 (12.6) | 0.235 | 5,611 (18.1) | 3,090 (18.2) | <0.001 |
| P2Y12 only | 1889 (6.1) | 902 (5.3) | 0.235 | 1800 (5.8) | 1,002 (5.9) | <0.001 |
| Both | 3,701 (12.0) | 1,295 (7.6) | 0.235 | 3,230 (10.4) | 1800 (10.6) | <0.001 |
| Rivaroxaban dose | | | | | | |
| 20 mg once daily | | 8,354 (49.1) | | | 8,022 (47.2) | |
| 15 mg once daily | | 8,659 (50.9) | | | 8,984 (52.8) | |
Figure 2 shows weighted incidence rates and weighted HRs for primary and secondary outcomes. Compared with the warfarin group, the rivaroxaban group was associated with a lower risk of kidney failure (HR 0.398, $95\%$ CI 0.300–0.499). For the secondary outcomes, the rivaroxaban group was associated with lower risks of ischemic stroke (HR 0.887, $95\%$ CI 0.797–0.986), intracranial hemorrhage (HR 0.699, $95\%$ CI 0.550–0.883), and all-cause death (HR 0.807, $95\%$ CI 0.751–0.867) than the warfarin group. The two groups had comparable outcomes for major gastrointestinal bleeding (HR 1.092, $95\%$ CI 0.930–1.279) and major bleeding (HR 0.966, $95\%$ CI 0.858–1.086).
**Figure 2:** *Weighted event numbers, incidence rates, and hazard ratios for the primary and secondary outcomes between warfarin and rivaroxaban groups. Incidence rate, per 100 person-years. CI, confidence interval; GIB, gastrointestinal bleeding; ICH, intracranial hemorrhage; IPTW, inverse probability of treatment weighting; IR, incidence rate; R, rivaroxaban; W, warfarin.*
## Exploratory analysis in patients with baseline and follow-up eGFR measurements
Among the total study population, 11,210 ($23.4\%$) patients were included in the exploratory analysis. Baseline characteristics of the total population, warfarin, and rivaroxaban group are presented in Supplementary Table S6. After IPTW, the two groups were well-balanced in all variables (all ASDs < 0.1). Mean baseline eGFR was 81.6 ml/min/1.73 m2 in the two groups (ASD < 0.001). The duration from baseline eGFR to index date and baseline eGFR to follow-up eGFR of the two groups did not show statistically significant differences.
During a median follow-up of 2.28 (IQR 1.42–3.19) years, five renal outcomes and the composite of renal outcomes were evaluated in the two groups. Weighted event numbers, incidence rates, and HRs are shown in Figure 4A. Compared with warfarin, the rivaroxaban group was associated with significant 72, 20 and $39\%$ reductions in the risks of developing eGFR lower than 15 ml/min/1.73 m2 at follow-up measurement, $30\%$ decline in eGFR, and incidence of AKI, respectively. Although there was no statistically significant difference in the risk of serum creatinine doubling, the rivaroxaban group had a lower chance than the warfarin group. During the follow-up period, none of the patients in this exploratory analysis started dialysis or had kidney transplantation. For the composite of five renal outcomes, the rivaroxaban group showed a lower risk than warfarin (HR 0.798, $95\%$ CI 0.713–0.892, $p \leq 0.001$; Figures 4A,B).
**Figure 4:** *Exploratory analysis in patients with baseline and follow-up eGFR measurements. (A). Weighted event numbers, incidence rates, and hazard ratios for five renal outcomes and composite of renal outcomes between rivaroxaban and warfarin groups (B). Weighted Kaplan–Meier curves for the composite of renal outcomes between rivaroxaban and warfarin groups. Incidence rate, per 100 person-years. AKI, acute kidney injury; CI, confidence interval; Cr, creatinine; eGFR, estimated glomerular filtration rate; HR, hazard ratio; KTPL, kidney transplantation; IPTW, inverse probability of treatment weighting; IR, incidence rate; R, rivaroxaban; W, warfarin.*
## Discussion
In this large-scale observational cohort, we observed very consistent findings that rivaroxaban was associated with a lower risk of renal adverse outcomes than warfarin in Korean patients with AF. Also, consistently with the general consensus, we confirmed that rivaroxaban was associated with a lower risk of ischemic stroke, intracranial hemorrhage, and all-cause death than warfarin. The effect of rivaroxaban on renal preservation was more accentuated in patients with underlying renal function impairment. The strength of this study included a large number of patients with AF treated in diverse clinical practice settings who had linked insurance claims and laboratory results. Also, this analysis allowed us to examine multiple renal outcomes to evaluate the consistency of results across a variety of renal outcomes.
Patients with AF should be aware of the potential deterioration in renal function. Renal impairment puts individuals with AF at greater risk of thromboembolism and bleeding [22]. Also, the dose of NOACs may need to be adjusted with renal function decline, or the prescription of NOACs should be discontinued if significant renal impairment develops [23]. Since anticoagulation therapy should be continued throughout a patients’ entire life for those with AF, preserving renal function has become an important issue for optimal care in patients with AF. From the post-hoc analysis of the RE-LY trial, dabigatran, a direct thrombin inhibitor, firstly showed a protective effect from the progressive renal function decline compared with warfarin [4]. Interestingly, warfarin with an increased international normalized ratio (INR) out of the therapeutic range showed a significantly rapid progression of renal function decline than dabigatran. In contrast, warfarin with mainly below therapeutic INR rage showed similar renal function decline to dabigatran [4]. Considering poor INR control of Asians, mainly with lower INR than therapeutic ranges [13, 24], we needed additional *Asian data* to provide a comprehensive comparison of the risk of renal outcome caused by NOAC versus warfarin. Two previous reports from the Taiwanese population were based on the nationwide administrative claims database [6, 9]. According to these studies, dabigatran, rivaroxaban, and apixaban were associated with a lower risk of AKI [6, 9]. Although these studies included many patients, approximately 6,000–28,000 patients in each NOAC group, the study outcome was only defined by diagnostic codes of AKI without laboratory measurements. The present study, including many Asian patients, showed consistent findings with previous observational studies of non-Asians [5, 7, 8] and Asians [6, 9]. Furthermore, in a subset of patients with laboratory results, we first confirmed that rivaroxaban benefited renal preservation in various definitions of renal outcomes in Asian patients with AF.
In previous studies, including three NOACs (rivaroxaban, dabigatran, and apixaban), the results were slightly different among studies [5, 9, 10]. Compared with warfarin, rivaroxaban was associated with lower risks of a $30\%$ decline in eGFR, doubling of serum creatinine, and AKI, but dabigatran was only associated with a $30\%$ decline in eGFR and AKI, and apixaban did not show significant risk reduction for the any of the renal outcomes [5]. With AKI defined by diagnostic codes, rivaroxaban and dabigatran were associated with a lower risk of AKI than warfarin, but apixaban showed comparable results with warfarin [10]. In Asian patients with AF, all three NOACs showed a similar risk reduction of AKI defined by diagnostic codes to warfarin [9]. NOACs’ renal preservation compared with warfarin is often attributed to warfarin’s hazardous effects, such as glomerular microhemorrhage, vascular inflammation, or calcification [4]. Further studies are required to discover the difference among NOACs on the renal protection effect, especially edoxaban, and consider the dose–response relationship.
This study highlighted that rivaroxaban reduced the risk of renal failure in patients with CKD compared with those without. In the subgroup analyses, patients with underlying CKD and those with baseline eGFR ≤ 60 ml/min/1.73 m2 showed greater relative risk reduction with rivaroxaban than warfarin. Patients who are more vulnerable to the risk of kidney failure might get more benefit from rivaroxaban’s kidney protection effect. Kidney failure due to acute tubular injury with microhemorrhage might be more critical in patients with a smaller reservoir because of underlying renal impairment. This finding was consistently observed in previous studies [6, 7, 9]. Careful selection of the anticoagulation agent and close follow-up of kidney function should be emphasized in this population. From Korean AF patients with mildly impaired renal function (creatinine clearance 50–60 ml/min), we previously reported that rivaroxaban 15 mg once daily was associated with a lower risk of ischemic stroke, intracranial hemorrhage, and hospitalization for major bleeding than warfarin. Additionally, rivaroxaban 15 mg once daily showed a comparable risk of ischemic stroke, intracranial hemorrhage, and hospitalization for major bleeding with rivaroxaban 20 mg once daily [25].
Recently, consistent results have been updated in various subsets of patients with elderly [26] and those with diabetes [7], and even a meta-analysis has been reported [27]; thus, it is quite evident that NOAC is superior to warfarin for renal preservation. Our study supported its reasoning using data from large-scale Asian patients and laboratory data.
## Limitations
First, despite careful adjustment using IPTW, our study may still be subject to residual confounding. In database analysis where randomization is not possible, such PS-based methods as matching or IPTW serve to harmonize comparison groups concerning patient characteristics. However, residual confounding was caused by unmeasured factors such as laboratory values (e.g., time in the therapeutic window for warfarin), missing data, miscoding, or tactical coding issues. Second, the application of both on-treatment and ITT analysis in non-randomized studies has different limitations as follows: an on-treatment method leads to a loss of information on the reasons for treatment discontinuation, while an ITT approach would not reflect changes on treatments affecting the primary outcome. In our study, the primary purpose of this study was to compare warfarin and rivaroxaban for the risk of kidney failure in anticoagulated patients with AF. In real-world clinical practice, many patients changed their OAC agents from warfarin to NOAC [28]. The clinical impact of warfarin might widely mix with various NOACs in patients who changed their OAC agents from warfarin to NOAC in ITT analysis. Therefore, we believe it is more appropriate for the main analysis to be an on-treatment manner rather than ITT manner. Furthermore, we analyzed an ITT analysis for a sensitivity analysis. Although there was a slight attenuation on the HRs, the results were largely consistent with the main analysis in an on-treatment manner. Third, to control the possible effect of prior use of warfarin, we only include OAC new users from 1 January 2014. This could result in an overall short-term follow-up duration for both groups. Fourth, in the present study, we did not perform a comprehensive comparison among different NOACs for the risk of kidney failure because of the limitation of dataset. Comparative analysis among DOACs on the risk of kidney failure might be a valuable topic foe patient care. Further research is needed to elucidate the relative risk difference of different NOACs on the risk of kidney failure compared to warfarin or NOACs. Fifth, because of an inherent limitation of the data source, we could not analyze the treatment quality of warfarin using the time in therapeutic range of INR. Furthermore, the results can be generalized only to Korean patients with AF. Informative censoring might exist in patients who discontinued the index treatment. This was evaluated by a sensitivity analysis that follows the ITT approach.
## Conclusion
In Korean patients with AF, rivaroxaban was associated with a lower risk of renal adverse outcomes than warfarin. The renal preservation effect of rivaroxaban compared with warfarin was particularly pronounced in patients with preexisting renal impairment. Rivaroxaban should be explored for anticoagulation therapy in AF patients at high risk of renal function decline.
## Data availability statement
Publicly available datasets were analyzed in this study. This data can be found at: http://nhiss.nhis.or.kr/bd/ab/bada000eng.do.
## Ethics statement
Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
## Author contributions
S-RL contributed to the design of the study, interpretation of the results and prepared the manuscript. E-KC, SO, KA, and GL contributed to the design of the study, interpretation of the results and critical revision of the manuscript. S-HP and K-DH contributed to the analysis of data and interpretation of results. All authors contributed to the article and approved the submitted version.
## Conflict of interest
E-KC: Research grants or speaking fees from Bayer, Biosense Webster, BMS/Pfizer, Chong Kun Dang, Daiichi Sankyo, Dreamtech Co., Ltd., Jeil Pharmaceutical Co. Ltd., Medtronic, Samjinpharm, Seers Technology, and Skylabs. GL: Consultant and speaker for BMS/Pfizer, Boehringer Ingelheim, and Daiichi Sankyo. No fees are received personally. KA was employed by Bayer AG. This study was funded by Bayer AG. Bayer AG contributed to the design and conduct of the study; management and interpretation of the data; preparation, review, and approval of the manuscript; and the decision to submit the manuscript for publication.
## 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.2023.1040834/full#supplementary-material
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|
---
title: Differences in clinical features and gut microbiota between individuals with
methamphetamine casual use and methamphetamine use disorder
authors:
- Li He
- Bao-Zhu Yang
- Yue-Jiao Ma
- Li Wen
- Feng Liu
- Xiao-Jie Zhang
- Tie-Qiao Liu
journal: Frontiers in Cellular and Infection Microbiology
year: 2023
pmcid: PMC9996337
doi: 10.3389/fcimb.2023.1103919
license: CC BY 4.0
---
# Differences in clinical features and gut microbiota between individuals with methamphetamine casual use and methamphetamine use disorder
## Abstract
### Background
The transition from methamphetamine (MA) casual use (MCU) to compulsive use is enigmatic as some MA users can remain in casual use, but some cannot. There is a knowledge gap if gut microbiota (GM) play a role in differing MCU from MA use disorder (MUD). We aimed to investigate the clinical features and GM differences between individuals with MCU and MUD.
### Method
We recruited two groups of MA users –MCU and MUD – and matched them according to age and body mass index ($$n = 21$$ in each group). Participants were accessed using the Semi-Structured Assessment for Drug Dependence and Alcoholism, and their fecal samples were undergone 16S ribosomal DNA sequencing. We compared the hosts’ clinical features and GM diversity, composition, and structure (represented by enterotypes) between the two groups. We have identified differential microbes between the two groups and performed network analyses connecting GM and the clinical traits.
### Result
Compared with the casual users, individuals with MUD had higher incidences of MA-induced neuropsychiatric symptoms (e.g., paranoia, depression) and withdrawal symptoms (e.g., fatigue, drowsiness, and increased appetite), as well as stronger cravings for and intentions to use MA, and increased MA tolerance. The GM diversity showed no significant differences between the two groups, but four genera (Halomonas, Clostridium, Devosia, and Dorea) were enriched in the individuals with MUD ($p \leq 0.05$). Three distinct enterotypes were identified in all MA users, and Ruminococcus-driven enterotype 2 was dominant in individuals with MUD compared to the MCU ($61.90\%$ vs. $28.60\%$, $$p \leq 0.03$$). Network analysis shows that *Devosia is* the hub genus (hub index = 0.75), which is not only related to the counts of the MUD diagnostic criteria (ρ=0.40; $$p \leq 0.01$$) but also to the clinical features of MA users such as reduced social activities (ρ=0.54; $p \leq 0.01$). Devosia is also associated with the increased intention to use MA (ρ=0.48; $p \leq 0.01$), increased MA tolerance (ρ=0.38; $$p \leq 0.01$$), craving for MA (ρ=0.37; $$p \leq 0.01$$), and MA-induced withdrawal symptoms ($p \leq 0.05$).
### Conclusion
Our findings suggest that Ruminococcus-driven enterotype 2 and the genera Devosia might be two influential factors that differentiate MA casual use from MUD, but further studies are warranted.
## Introduction
Illicit drug use continues to be a significant public health concern worldwide. With an estimated 27 million users worldwide, amphetamine-type stimulants (ATSs) remain among the world’s most popular illicit drugs ((UNODC) TUNOoDaC, 2021). Methamphetamine (MA) is the most popular ATS, and its recreational use has increased over the past decade, particularly in East and Southeast Asia. In China, synthetic drug users (mainly MA) accounted for $55\%$ of the nearly 2.2 million registered drug users in 2019, and the proportion of MA users has been increasing since the early 2000s ((UNODC) TUNOoDaC, 2021). The powerfully addictive nature of MA is one of the significant factors contributing to its misuse and addiction. The transition from casual use to habitual and compulsive use is commonly seen in individuals with MA use disorder (MUD). MA casual users can control drug-seeking and -taking behaviors, whereas individuals with MUD experience strong cravings for MA and lose self-control. Intriguingly, some MA casual users can stay casual use and never become MUD, while some MA casual users will transit to MUD. MA casual users and individuals with MUD have significant differences in their responses to MA; however, the differences in their clinical features are not entirely clear. Furthermore, the biological mechanism of MA casual use (MCU) to MUD transition has not yet been fully elucidated.
The human gut microbiota (GM) contains a genome of approximately 10 million genes, about 150-fold larger than the human genome (Meckel and Kiraly, 2019). With the emergence of the “gut-brain axis,” the influence of intestinal flora on the brain’s physiological, behavioral, and cognitive functions has become a trendy research topic (Wang and Wang, 2016). Recent studies have demonstrated a strong link between MA use and gut microbiome (Ning et al., 2017; Cook et al., 2019; Angoa-Pérez et al., 2020; Forouzan et al., 2020; Chen et al., 2021; Deng et al., 2021; Wang et al., 2021; Yang et al., 2021; Yang et al., 2021; Lai et al., 2022; Wang et al., 2022; Zhang et al., 2022). Clinical research has shown significant differences in the GM composition between MA users and non-MA users (Cook et al., 2019) and marked changes in GM among individuals with MUD related to their inflammatory markers and clinical characteristics (Deng et al., 2021; Yang et al., 2021). In animal studies, MA treatment disrupted the gut microbiome balance (Angoa-Pérez et al., 2020; Forouzan et al., 2020; Chen et al., 2021), and MA-induced conditioned place preference (CPP) resulted in dramatic changes in the diversity and composition of the GM (Ning et al., 2017; Wang et al., 2021; Yang et al., 2021). The GM continued to evolve during different phases of CPP, including acquisition, extinction, and reinstatement (Wang et al., 2021). These various lines of evidence suggest a potential link between GM and MUD development.
MA could disrupt the intestinal barrier and induce inflammatory responses, altering the gut microenvironment and potentially affecting GM structure (Persons et al., 2018; Wang et al., 2022). MA’s destruction of intestinal barrier integrity also leads to intestinal bacterial metabolites entering the circulation, potentially impacting the host (Wang et al., 2022; Zhang et al., 2022). It is also fascinating that GM consistently interacts with the host, and it is conceivable that the interactions between GM and the MA-using host are different in individuals with MCU and MUD. The altered microbiota structure might reflect the hosts’ MA use states, as clinical studies of individuals with MUD have suggested (Deng et al., 2021; Yang et al., 2021). Our previous animal study on MA use (Yang et al., 2021) suggested a link between GM and susceptibility to MA addiction. However, the differences in GM among individuals with different MA use statuses have rarely been studied. Building on previous findings, we aimed to investigate further the relationship between the GM, MCU, and MUD status, and specific clinical characteristics of MA users.
## Study participants
We recruited the study participants from the Compulsory Detoxification Center of Changsha Public Security Bureau in Changsha, Hunan, China, between October 2018 and October 2019. All the study subjects had used MA within the previous 12 months. We recruited two groups of participants – one with MCU and the other with MUD. According to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM‐5), MUD participants fulfilled at least 2 of 11 DSM-5 MUD diagnostic criteria, whereas MCU participants fulfilled 0 or 1 and could also control MA use. We assessed the participants using the Semi-Structured Assessment for Drug Dependence and Alcoholism (SSADDA), Chinese Version (Ma et al., 2021). The exclusion criteria of participants were as follows: 1) medical condition related to intestinal dysbacteriosis such as gastrointestinal disease, liver disease, or infection; 2) current neurological and psychiatric disorders not due to MA use; 3) antibiotics use in the previous three months; 4) other illicit substances used in the previous 12 months other than MA. The participants were given a complete explanation of the study before their invitation to participate. All participants signed written informed consent. The study was approved by the Ethics Committee of the Second Xiangya Hospital of Central South University.
Sixty-six participants (21 MCU and 45 MUD (42 males and 3 females)) were recruited for this study. None of the participants reported using antipsychotic medication or other prescription medications. We excluded the three female MUDs to avoid the confounding effect of sex and retained only the male participants in the subsequent investigation. To avoid the effects of age and obesity on GM, we conducted a propensity-matched analysis of the eligible participants by age and body mass index (BMI). To this end, 42 age- and BMI-matched participants (21 MCU and 21 MUD) were included in the current study.
## Fecal sample collection and DNA preparation
The detoxification center provided the same meals for all the participants during their stay. After two weeks of detoxication, fecal samples from the participants were collected in sterile containers and immediately stored at -80°C until further processing. Bacterial DNA was extracted using the E.Z.N.A.® Stool DNA Kit (Omega Bio-Tek, Norcross, GA, USA) according to the manufacturer’s protocols. The V4 regions of the bacteria 16S rRNA gene were amplified by a polymerase chain reaction (PCR) in triplicates with barcoded primers as previously described (Yang et al., 2021). PCR products were purified using the AxyPrep Mag PCR Clean-Up Kit (Axygen Biosciences, Union City, CA, USA), and the DNA concentration of each sample was assessed by Qubit 2.0 Fluorometer (Life Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s guidelines.
## 16S rRNA gene sequencing and data processing
Purified PCR products were pooled to generate a library and sequenced on the Illumina MiSeq platform according to the standard protocols described by Genergy Biotechnology (Shanghai, China). Raw fastq files were demultiplexed, quality-filtered, and merged using the “DeBlur” algorithm in the QIIME2. Raw count data were filtered to remove low-expressed features with less than ten counts. Mitochondria and chloroplast were also filtered according to the taxonomy. Absolute Sequence Variants were generated using QIIME2 software program. The taxonomical assignment of absolute sequence variants was analyzed by QIIME2 against the GreenGenes database (gg_13_8) using a confidence threshold of $70\%$. The sequencing depth was unified to 20,000 counts for all samples to obtain relative abundance based on the alpha rarefaction curves (Supplementary Figure 1). The counts were converted to relative abundances by dividing total counts for further analysis. The raw sequencing data is available on the Sequence Read Archive (PRJNA910806).
## Analysis of intestinal microbiota diversity
Alpha-diversity indexes were assessed using Wilcoxon rank sum tests, including Faith’s phylogenetic diversity, Shannon’s diversity, observed features, and Pielou’s evenness. The beta-diversity indexes, including Jaccard distance, Bray-Curtis distance, unweighted UniFrac distance, and weighted UniFrac distance, were calculated by performing a Permutational Multivariate Analysis of Variance (PERMANOVA).
## Comparison of intestinal microbiota differences
Microbial differences between the MA casual and compulsive users were determined using Wilcoxon rank-sum tests and Linear discriminant analysis effect size (LEfSe). Linear discriminant analysis (LDA) values>2.0 at a $p \leq 0.05$ were considered significantly enriched.
## Analysis of enterotypes
An enterotype is a class of living microbes clustered according to their GM’s bacteriological composition (Arumugam et al., 2011). We analyzed enterotypes using the R packages ade4 (Chessel et al., 2004; Dray and Dufour, 2007; Dray et al., 2007; Bougeard and Dray, 2018; Thioulouse et al., 2018), cluster (Maechler et al., 2022), and clusterSim (Walesiak and Dudek, 2020). Enterotypes were identified based on the relative genus abundances using the Jensen-Shannon Distance (JSD) and the Partitioning Around Medoids (PAM) clustering algorithm and visualized by between-class analysis (BCA) (Arumugam et al., 2011).
We assessed the enterotypes using Between-class analysis (BCA). The results showed that the genus taxa clustered into three groups based on the Jensen-Shannon divergence (JSD) among the MA users (Figure 3A), suggesting three different enterotypes were present. The percentage of enterotype 2 was higher in the MUD group compared to the MCU group ($61.9\%$ vs. $28.6\%$, $$p \leq 0.03$$), while enterotype 1 ($19.0\%$ vs. $33.3\%$, $$p \leq 0.27$$) and enterotype 3 ($19.0\%$ vs. $38.1\%$, $$p \leq 0.16$$) were not significantly different between the two groups (Figure 3B). To further understand the features of enterotypes identified in all of the MA participants, we analyzed the abundances of three representative genera (Bacteroides, Prevotella, and Ruminococcus) among these different enterotypes (Arumugam et al., 2011). Prevotella ($$p \leq 0.01$$), Ruminococcus ($p \leq 0.01$), and Bacteroides ($p \leq 0.01$) were enriched in enterotype 1, enterotype 2, and enterotype 3, respectively (Figure 3C).
**Figure 3:** *The enterotype characteristics of methamphetamine (MA) users. (A) The result of Between-class analysis. The genus taxa clustered into three groups based on the Jensen-Shannon divergence among the MA users. (B) Distribution of enterotypes in MCU and MUD. The percentage of enterotype 2 was higher in the MUD compared to the MCU (61.9% vs. 28.6%, p=0.03). (C) The abundance of representative genera in different enterotypes. Prevotella (p=0.01), Ruminococcus (p<0.01), and Bacteroides (p<0.01) were enriched in enterotype 1, enterotype 2, and enterotype 3, respectively. *p<0.05; **p<0.01; MCU, individuals with MA casual use; MUD, individuals with MA use disorder; the red color represents enterotype 1; the green color represents enterotype 2; the blue color represents enterotype 3. ns, no significance.*
## Network analysis
Network analysis for the top 50 genera (accounting for approximately $99.9\%$ of the abundance of all genera), enterotypes, and clinical traits was performed using the R package, psych (Revelle, 2022), and the p-values were adjusted by the false discovery rate. We defined an edge between two nodes to have statistically significant Spearman’s rank correlations (adjusted $p \leq 0.05$) with a magnitude of >= 0.35 or<= -0.35. We constructed undirected and directed network graphs to display the potential relationship between GM and MA-related clinical features. The network reconstruction and property measurements were conducted by Gephi 0.9.7 (Bastian. et al., 2009). The betweenness centrality index (BCI) is defined as in which V is a set of nodes; σ st (v) denotes the number of shortest paths from node “s” to node “t” that a node v lies on, and σ st indicates the number of shortest paths from s to t (Brandes, 2001). BCI was used to assess the importance of a node in the undirected network, and a node with a high BCI indicates its centrality in the network. The authority index and hub index are defined as in which E represents a set of edges; x p denotes authority index by summing y q, for all q pointing to p. On the other hand, y p denotes hub index by summing x q for all q pointing to p) (Kleinberg, 1999). These two indexes were used to compare the magnitude of in- and out-degree in the directed network. In the directed network, GM having higher hub indexes is more important in relation to MA-induced clinical features, while clinical features having higher authority indexes were more critical in association with GM. The Fruchterman and Reingold algorithms are used in Gephi to create the graph layout (Thomas Fruchterman, 1991).
Figure 4 shows the directed network. The node size reflected the number of ‘degree,’ in which ‘degree’ is defined as the number of edges directly connected to the nodes in the network. Based on the hub index, Devosia was the hub genus, which was related to many of the clinical features we identified to be significantly increased in the MUD (hub index = 0.75). These clinical features include reduced social activities (ρ=0.54; $p \leq 0.01$), increased intention to use MA (ρ=0.48; $p \leq 0.01$), neglect of responsibilities (ρ=0.46; $p \leq 0.01$), MA use daily (ρ=0.41; $$p \leq 0.01$$), increased MA tolerance (ρ=0.38; $$p \leq 0.01$$) and craving for MA (ρ=0.37; $$p \leq 0.01$$), as well as MA-induced withdrawal symptoms, such as drowsiness (ρ=0.51; $p \leq 0.01$), craving during withdrawal (ρ=0.41; $$p \leq 0.01$$), fatigue (ρ=0.40; $$p \leq 0.01$$), and depressed (ρ=0.37; $$p \leq 0.02$$). Besides, the genus Devosia was also related to the counts of MUD diagnostic criteria (ρ=0.40; $$p \leq 0.01$$) and MA use status, i.e., MCU or MUD (ρ=0.35, $$p \leq 0.02$$). The clinical features most associated with GM are craving for MA and MA withdrawal-induced drowsiness, all of which yield an authority index of 0.34. To explore the relationships of specific GM to GM, clinical traits to clinical traits, and GM to clinical traits, we also constructed an undirected network (see Supplementary Figure 2). We found three hub nodes in this network, i.e., enterotype, genus Devosia, and genus Halomonas, and the genus Devosia was highly associated with the genus Halomonas (ρ=0.91, $p \leq 0.01$). Besides, in the undirected network, the enterotype only connected to microbiota nodes, particularly two nodes, Bacteroides (ρ=0.7, $p \leq 0.01$) and Prevotella (ρ=-0.38, $$p \leq 0.01$$), which were enriched in enterotype 3 and enterotype 1, respectively.
**Figure 4:** *The clinical-microbial network in methamphetamine (MA) users. A directed network from the microbiota to clinical traits of MA users was built. The node size reflected the number of ‘degree,’ in which ‘degree’ is defined as the number of edges directly connected to nodes in the network. Yellow node indicates the hub genus or clinical trait in the clinical-microbial network. Brown node indicates the intestinal flora at the genus level. Blue node indicates the clinical traits of MA users (c.f.
Supplementary Table 1
for the details). The green line indicates a positive correlation, while the red line indicates a negative correlation. The width of the line represents the magnitude of the absolute value of Spearman’s correlation coefficient between the two nodes; the broader the width, the greater the correlation coefficient. The exact Spearman’s correlation coefficient values are also shown in the graph, in the same color as the line to which they belong.*
## Statistical analyses
Statistical analyses were performed using SPSS 26.0. For demographic and clinical characteristics, differences between the two groups were assessed using the chi-square tests for categorical variables, Wilcoxon rank-sum tests for continuous variables, and the Z-test for comparison of percentages. When sample sizes were small (n<=5), the Fisher exact test was employed instead of the chi-square test. Continuous variables were represented by the median (interquartile range), and categorical variables were described as the number. $P \leq 0.05$ was considered statistically significant.
## Characteristics of participants
Table 1 shows the baseline information for the MCU and MUD participants. There were no significant differences between the two groups in age, age of the first MA use, BMI, education levels, or life status, including adoptee, employment, marriage, or having children. However, compared to the MCU group, the MUD group had significantly more MA withdrawal episodes (median counts: 1 vs. 0, $p \leq 0.01$) and longer durations of overall MA use (median years: 2 vs. 0, $p \leq 0.01$), of daily MA use (median months: 3 vs. 0, $p \leq 0.01$), and of heaviest use (median months: 4 vs. 1, $p \leq 0.01$). The MUD group also spent more time (median days: 30 vs. 3, $p \leq 0.01$) and money (median yuan: 300 vs. 100, $p \leq 0.01$) on MA during the heaviest use periods, and their withdrawal symptoms lasted longer (median days: 3 vs. 0, $p \leq 0.01$).
**Table 1**
| Characteristics | MA casual use(n=21) | MA use disorder(n=21) | p-value |
| --- | --- | --- | --- |
| Age, median (IQR) | 37 (33, 41) | 36 (29, 44) | 0.70 |
| Body mass index (kg/m2), median (IQR) | 24.22 (21.1, 24.9) | 24.06 (21.2, 25.7) | 0.73 |
| Year of education, median (IQR) | 9 (8, 12) | 9 (9, 12) | 0.41 |
| Adoption, n (%) No | 20 (95.2) | 20 (95.2) | 1 |
| Yes | 1 (4.8) | 1 (4.8) | |
| Employment, n (%) Have jobs | 8 (38.1) | 9 (42.9) | 0.75 |
| Have no job | 13 (61.9) | 12 (57.1) | |
| Marriage, n (%) Married | 9 (42.9) | 7 (33.3) | 0.52 |
| Widowed | 0 (0) | 1 (4.8) | |
| Divorced | 7 (33.3) | 5 (23.8) | |
| Never married | 5 (23.8) | 8 (38.1) | |
| Children, n (%) 0 | 5 (23.8) | 6 (30.0) | 0.74 |
| 1 | 12 (57.1) | 9 (45.0) | |
| ≥2 | 4 (19.0) | 5 (25.0) | |
| Counts of MUD criteria, median (IQR) | 0 (0, 0) | 7 (4, 10) | 0.00* |
| Age of first MA use, median (IQR) | 30 (27, 36) | 31 (22, 37) | 0.44 |
| Year of MA use, median (IQR)† | 0 (0, 0) | 2 (0, 3) | 0.00* |
| MA withdrawal episodes, median (IQR)‡ | 0 (0, 0) | 1 (0, 1) | 0.00* |
| Maximum duration of daily MA use (month), median (IQR) | 0 (0, 0) | 3 (0, 12) | 0.00* |
| Duration of heaviest use (month), median (IQR) | 1 (1, 2) | 4 (2, 12) | 0.00* |
| Days of MA use per month, median (IQR)# | 3 (2, 5) | 30 (8, 30) | 0.00* |
| Daily cost for MA (yuan), median (IQR)# | 100 (100, 200) | 300 (200, 350) | 0.00* |
| Number of times MA withdrawal symptoms occur together, median (IQR) | 0 (0, 0) | 0 (0, 11) | 0.13 |
| Maximum duration of the withdrawal symptoms occurred together (day), median (IQR) | 0 (0, 0) | 3 (1, 6) | 0.00* |
To further compare the differences in clinical features between the two groups, we summarized the relevant yes/no answers from the MA section of the SSADDA Chinese version, including 19 facets and 74 questions (Supplementary Table 1). In addition to the MUD group that had more frequent use of MA and more cravings, they uniquely exhibited a loss of control over MA use, as evidenced by a higher incidence of inability to stop MA use ($52.4\%$ vs. $0.0\%$, $p \leq 0.01$). The individuals in the MUD group also had a greater intention to use MA ($57.1\%$ vs. $0.0\%$, $p \leq 0.01$) and higher tolerance to MA ($47.6\%$ vs. $0.0\%$, $p \leq 0.01$). Moreover, the MUD group showed higher symptom rates of paranoia ($23.8\%$ vs. $0.0\%$, $p \leq 0.05$) and depression ($23.8\%$ vs. $0.0\%$, $p \leq 0.05$) during MA use, while they experienced a higher incidence of fatigue ($81.0\%$ vs. $14.3\%$, $p \leq 0.01$), drowsiness ($81.0\%$ vs. $14.3\%$, $p \leq 0.01$), and increased appetite ($66.7\%$ vs. $4.8\%$, $p \leq 0.01$) during withdrawal. In further contrast to MCU, MUD participants were more likely to engage in risky behaviors under the influence of MA ($57.1\%$ vs. $9.5\%$, $p \leq 0.01$) and demonstrated neglecting major work of job, household, and family responsibilities ($57.1\%$ vs. $0.0\%$, $p \leq 0.01$). Furthermore, individuals in the MUD group showed a reduction in essential activities ($57.1\%$ vs. $0.0\%$, $p \leq 0.01$), which was accompanied by significantly greater damage to their social lives, such as reducing contact with friends or family ($57.1\%$ vs. $4.8\%$, $p \leq 0.01$) and having problems with friends or family ($85.7\%$ vs. $52.4\%$, $$p \leq 0.02$$) because of MA use.
## Composition and diversity of the microbial community
After size filtering, quality control, chimera removal, and rarefying, a total of 840,000 high-quality sequence reads were acquired from the fecal samples of 42 participants. These sequences were clustered into 1,721 absolute sequence variants, followed by assigning them to 8 phyla, 15 classes, 23 orders, 49 families, and 94 genera. Figure 1 shows the average bacterial compositions of MCU and MUD participants at the phylum and genus levels. Firmicutes (average relative abundance $69.4\%$ vs. $72.5\%$), Bacteroidetes ($24.8\%$ vs. $17.2\%$), Proteobacteria ($3.5\%$ vs. $7.7\%$), and Actinobacteria ($1.9\%$ vs. $2.4\%$) were the four dominant phyla for the MCU and MUD groups, respectively, accounting for more than $90\%$ of the intestinal flora. Faecalibacterium (average relative abundance $14.5\%$ vs. $15.4\%$), Bacteroides ($17.9\%$ vs. $11.8\%$), Roseburia ($15.9\%$ vs. $12.6\%$), Ruminococcus ($5.8\%$ vs. $11.4\%$), Megamonas ($9.4\%$ vs. $4.0\%$), Prevotella ($4.5\%$ vs. $3.7\%$), Lachnospira ($2.6\%$ vs. $3.8\%$), Blautia ($1.8\%$ vs. $1.8\%$), Coprococcus ($1.4\%$ vs. $2.0\%$), and Dialister ($1.7\%$ vs. $1.6\%$) were the top ten genera among the MCU and MUD groups, respectively. However, we did not find significant differences in the GM’s alpha and beta diversity between MCU and MUD groups (Table 2).
**Figure 1:** **Gut microbiota* composition of methamphetamine (MA) users at phylum and genus level. (A) The average bacterial compositions at the phylum level. (B) The top 30 genera in terms of average relative abundance. MCU, individuals with MA casual use; MUD, individuals with MA use disorder.* TABLE_PLACEHOLDER:Table 2
## Analysis of intestinal microbiota differences between the MCU and MUD groups
We used Wilcoxon rank-sum tests to identify differential taxa between the MCU and MUD groups (Figure 2A). The analysis revealed a significant increase in the relative abundance of some taxa in the MUD group. At the class level, the relative abundance of Alphaproteobacteria was significantly higher in the MUD group compared with the MCU group ($$p \leq 0.03$$). At the order level, Oceanospirillales, Xanthomonadales, and Rhizobiales were significantly higher in the MUD group than in the MCU group ($$p \leq 0.02$$, $$p \leq 0.02$$, and $$p \leq 0.03$$, respectively). Further, four families, Clostridiaceae, Halomonadaceae, Hyphomicrobiaceae, and Xanthomonadaceae, were significantly higher in the MUD group than in the MCU group ($$p \leq 0.01$$, $$p \leq 0.02$$, $$p \leq 0.02$$, and $$p \leq 0.02$$, respectively). *The* genera of Halomonas, Clostridium, Devosia, and Dorea were significantly higher in the MUD group than in the MCU group ($$p \leq 0.02$$, $$p \leq 0.02$$, $$p \leq 0.02$$, and $$p \leq 0.04$$, respectively). We further confirmed these results using the LEfSe analysis (Figures 2B, C), consistent with those using Wilcoxon rank-sum tests. For validation, we compared the 21 MCUs to the 21 unmatched MUDs (those excluded by the propensity-matched analysis) using the LEfSe analysis. Still, these two groups differed significantly in age (median age 37 vs. 29, $p \leq 0.01$). The genus *Clostridium remained* significantly more abundant in the unmatched MUD group than in the MCU group (LD score = 3.00, $$p \leq 0.03$$). In other words, we replicated one of the four genera showing the difference in abundance between the MCU and MUD groups. However, we could not exclude the confounding effects of age in this validation analysis. An in-depth elaboration is presented in the discussion.
**Figure 2:** *Differences in gut microbiota between individuals with methamphetamine (MA) casual use and MA use disorder. (A) The taxa with significant differences (p<0.05) between MCU and MUD were determined by the Wilcoxon rank-sum test. (B) The taxa significant differences (LDA score>2.0 and p<0.05) between MCU and MUD were detected by the LEfSe analysis. (C) The cladogram shows the differential taxa between the MCU and MUD found in the LEfSe analysis. *p<0.05; MCU, individuals with MA casual use; MUD, individuals with MA use disorder; the green color represents the MCU group; the red color represents the MUD group.*
## Discussion
There is a significant distinction in response to MA between MA casual users and individuals with MUD. Our study found substantial differences in clinical features between the two groups. In addition to more significant cravings, higher intention to use MA, and increased MA tolerance, MUD patients had higher incidences of MA-induced neuropsychiatric symptoms, withdrawal symptoms, social damage, and neglecting responsibilities. Notably, MCU participants showed better self-control and the ability to limit MA use than individuals with MUD. Besides, we also found differences in the composition of GM, including specific microbes in various classes, orders, and families, between the two groups of MA use. Our study also suggests a link between GM and the clinical features of MA users and indicates a potential role of GM in whether a transition occurs from MCU to MUD.
MA users were found to have relatively longer transitions from the onset of drug use to compulsive use than heroin users (85.0 vs. 50.0 days) (Wang et al., 2017). Although the core factors that cause the transition from MCU to MUD are unclear, several lines of evidence suggest that the dose of MA, frequency of use, and biological factors influence the response to MA (Zhang et al., 2020; McKetin et al., 2013; Rau et al., 2016; Mayo et al., 2019).. Our study showed that MUD patients spent more time and money on MA and had more prolonged MA use than MCU participants, supporting earlier findings that repetitive drug use could develop MUD (Tien and Ho, 2011; Mizoguchi and Yamada, 2019). Moreover, our study found that individuals with MUD had more obvious MA-induced neuropsychiatric and withdrawal symptoms than the MCU group. MA-dependent users had a high incidence of substance-induced psychotic disorders ($23.8\%$), particularly delusions ($16.4\%$) and hallucinations ($14.8\%$) (Salo et al., 2011), and MA-related psychiatric symptoms were associated with the MA dose and duration of MA use (McKetin, 2018). Withdrawal symptoms are closely associated with MA relapse and low treatment compliance (Zorick et al., 2010; Pennay and Lee, 2011). Depression and psychotic symptoms were most prevalent among MA-dependent individuals during MA abstinence (Zorick et al., 2010). MA withdrawal symptoms were also associated with more frequent MA use ($p \leq 0.05$) (Zhao et al., 2021). Recent studies have shown that biological factors, such as genetics or neuroimmunology, are associated with MA addiction (Zhang et al., 2020; Mayo et al., 2019; Shi et al., 2022). However, the field of treating MUD still as yet lacks biological intervention targets (Shoptaw et al., 2009; Morley et al., 2017). Given the increased intensity of MUD patients’ MA cravings, withdrawal symptoms, and ensuring knock-on effects on treatment efficacy, investigating the differences between MCU and MUD participants is a crucial step to elucidate and thus target the biological mechanisms of MUD’s development.
Gut flora is a promising target for addiction treatment, as clinical studies have shown that antibiotics, probiotics, and fecal transplants effectively reduce alcohol-induced somatic symptoms and cravings (Kirpich et al., 2008; Zuo et al., 2017; Bajaj et al., 2021). In addition, a growing number of studies have found that using non-intestinally absorbed antibiotics could affect the development of addiction in various animal models (Kiraly et al., 2016; Chen et al., 2020; Ezquer et al., 2021). Recent studies have demonstrated that the composition of intestinal microflora was altered in patients with alcohol, opioids, cocaine, or MA use disorders (Mutlu et al., 2012; Volpe et al., 2014; Acharya et al., 2017; Cook et al., 2019). Moreover, these changes were related to the behavioral changes of experimental animals in substance-induced animal models (Ning et al., 2017; Scorza et al., 2019). However, GM’s role in developing substance use disorders remains unclear. To clarify the association between GM and the development of MUD, we conducted a comparative analysis of intestinal microflora between individuals with MCU and MUD. We found that four genera (Halomonas, Clostridium, Devosia, and Dorea) dramatically increased with MUD and stated their appealing relevance to our study as follows: The species of the genus Halomonas are Gram-negative aerobic bacteria with salt tolerance (Kim et al., 2013; Gasperotti et al., 2018). Genus Halomonas with pathogenic potential could cause bacteremia, particularly in a dialysis setting (Kim et al., 2010; Stevens et al., 2013). To our knowledge, we are the first to report the link between Halomonas and substance use disorder. Previously, Halomonas was found to be significantly elevated in the intestines of HIV-infected patients and was associated with sexual transmission of HIV (Xu et al., 2021).
Species of the genus *Clostridium are* Gram-positive and anaerobic bacteria that could produce short-chain fatty acids (SCFAs) (Dürre, 2014). Of note, SCFAs are often beneficial to health. As another layer of complexity differs in the impact, some strains of the genus *Clostridium produce* short-chain fatty acids and are considered probiotics (e.g., Clostridium butyricum) (Chen et al., 2020). In contrast, some strains are considered pathogenic (e.g., Clostridium difficile) (Kuehne et al., 2010). Thus, the effects of the genus *Clostridium are* not exclusively related to SCFA. Our study found an association between *Clostridium and* MUD with higher abundance in the MUD than in the MCU group. Other studies (Peterson et al., 2017; Fulcher et al., 2018) have found *Clostridium also* to be related to alcohol and marijuana use, which suggested a role in substance use. Note that the genus *Clostridium expression* remained significantly higher in the MUD group than in the MCU group for the validation analysis using the unmatched MUDs. We further analyzed the demographic traits of the two MUD groups (i.e., age-matched and -unmatched). We found no differences between these two groups in BMI ($$p \leq 0.59$$), years of MA use ($$p \leq 0.58$$), MA withdrawal episodes ($$p \leq 0.50$$), and counts of DSM-5 MUD diagnostic criteria ($$p \leq 0.09$$), except for significant differences in age ($p \leq 0.01$) and age of first MA use ($p \leq 0.01$). The above new evidence suggests that the relationship between the genus *Clostridium and* MUD may be independent of the age and age of first MA use. However, we cannot exclude the confounding effects of age and age of first MA use in the genera Devosia, Dorea, and Halomonas between MCU and unmatched MUD group. Intriguingly, *Clostridium was* reported to play a role in metabolizing MA to amphetamine (Caldwell et al., 1972; Caldwell and Hawksworth, 1973). In sum, our findings suggest *Clostridium differs* MUD from MCU and might trigger the transition from MCU to MUD.
Species of the genus Devosia are Gram-negative and aerobic bacteria (Yoon et al., 2007). A recent study reported a significant increase in Devosia in patients with colorectal cancer, which might be a promising biomarker for the early detection of colorectal cancer (Zhang et al., 2019). Our study revealed a significant increase in Devosia in individuals with MUD, and the abundance of Devosia was correlated to the counts of the DSM-5 MUD diagnostic criteria. Furthermore, the analysis of the clinical-microbial network shows that *Devosia is* the hub genus and is associated with the clinical features that we identified to be significantly increased in the MUD.
Another associated gut microbe we identified is Dorea. Previously, Dorea was positively correlated with obesity (Zeng et al., 2019; Pinart and Dötsch, 2021). Dorea also increased significantly in patients with irritable bowel syndrome (Maharshak et al., 2018; Liu et al., 2021). One study has shown a significantly lower level of Dorea in patients with MUD compared with a healthy control (Deng et al., 2021), whereas our study showed an increase of Dorea in individuals with MUD compared with MA casual users. These indicated that Dorea might play a more complex role in various MA use statuses.
In addition, our network analysis showed that two of the clinical features of MA users, craving for MA and MA withdrawal-induced drowsiness, were significantly associated with the altered gut flora, suggesting a potential role of gut flora in the development of MA addiction. Our previous animal studies (Yang et al., 2021) showed a relationship between gut flora and the MA-induced CPP scores (i.e., measurements of animals’ MA liking). The altered gut flora by non-intestinal absorbable antibiotics could affect the CPP scores, suggesting an effect of gut flora on susceptibility to MA addiction.
Enterotypes are clusters of gut microbial communities that share similar bacteria compositions, which are not always stable and can be affected by diet, age, and antibiotics (Arumugam et al., 2011; Cheng and Ning, 2019). Research has found associations between enterotypes and opioid agonists (Gicquelais et al., 2020). Our study identified three distinct enterotypes (Bacteroides, Prevotella, and Ruminococcus) in MA users. Enterotype 2, in which Ruminococcus dominates, was present in more than half of our participants with MUD. The Ruminococcus-driven enterotype was not widespread and might not exist in some populations, in contrast to the Bacteroides-driven and Prevotella-driven enterotypes (Arumugam et al., 2011). A Han Chinese and Muslim (Li et al., 2018) study found only two different enterotypes (Bacteroides and Prevotella) (Fulcher et al., 2018). In a Taiwanese cohort, the Ruminococcus-driven enterotype was absent, while the Bacteroides- and Prevotella-driven enterotypes were present (Liang et al., 2017). Ruminococcus-driven enterotype predominates in certain diseases, such as Parkinson’s disease and obstructive sleep apnea-hypopnea syndrome (Ko et al., 2019; Zhang et al., 2022). Moreover, the enterotypes are influenced by the intestinal flora’s richness and diversity and are related to the absorption and metabolism of substances (Liang et al., 2017; Vandeputte et al., 2017; Brial et al., 2021). Several studies have shown that the Bacteroides-driven enterotype is related to a protein and animal fat diet, while the Prevotella-driven enterotype is associated with a carbohydrate diet (Wu et al., 2011). Our network analysis suggests that gut flora, such as Prevotella and Bacteroides, are associated with enterotypes, although we did not find any MA users’ clinical features directly correlated with enterotypes. Further study is needed to determine the defined effects of these enterotypes on the hosts.
The limitations of the current study are as follows. First, all the study participants are men, so we cannot generalize the influence of intestinal flora on MUD to the different sex. As noted earlier, the sample size of this study is limited. Ideally, multicenter studies and extensive sample recruitment will be needed to prove the role of intestinal microflora changes in developing MUD. To exclude the effects of diet, age, and obesity on the intestinal flora, in our current study, we collected stool samples from age- and BMI- matched participants after two weeks of a common diet source in a compulsory drug rehabilitation center. Also, the detoxification treatment (i.e., no MA intake while the participants were residing in the detoxification center) may have altered some extent of the MA’s effects on intestinal flora (Forouzan et al., 2020; Wang et al., 2021).
In summary, our study investigated the differences in clinical characteristics and GM between individuals with MCU and MUD. We found that they differed significantly in drug use patterns and MA-induced symptoms. We showed that the composition of GM significantly differed in individuals with MUD, and these differences were associated with the clinical characteristics of MA users. Our study may suggest a potential link between the microbiota and the progression from MCU to developing MUD.
## Data availability statement
The data presented in the study are deposited in the Sequence Read Archive, accession number PRJNA910806.
## Ethics statement
The studies involving human participants were reviewed and approved by Ethics Committee of the Second Xiangya Hospital of Central South University. The patients/participants provided their written informed consent to participate in this study.
## Author contributions
LH conducted the data analysis and drafted and revised the manuscript. B-ZY guided the methodology and interpreted the result; she also wrote and revised the manuscript. Y-JM recruited the study participants and curated the data. LW commented on and revised the manuscript. FL provided the resources during the participant recruitment. X-JZ designed and supervised the study. T-QL is the principal investigator for the study, supervising and providing resources for the current study. 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/fcimb.2023.1103919/full#supplementary-material
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|
---
title: Oral arsenic administration to humanizedUDP-glucuronosyltransferase1 neonatal
mice induces UGT1A1 through a dependence on Nrf2 and PXR
authors:
- Xiaojing Yang
- André A. Weber
- Elvira Mennillo
- Miles Paszek
- Samantha Wong
- Sabrina Le
- Jia Ying Ashley Teo
- Max Chang
- Christopher W. Benner
- Robert H. Tukey
- Shujuan Chen
journal: The Journal of Biological Chemistry
year: 2023
pmcid: PMC9996368
doi: 10.1016/j.jbc.2023.102955
license: CC BY 4.0
---
# Oral arsenic administration to humanizedUDP-glucuronosyltransferase1 neonatal mice induces UGT1A1 through a dependence on Nrf2 and PXR
## Body
Inorganic arsenic (iAs) is a naturally occurring environmental element [1, 2]. Based on the maximum permissible limit of 10 ppb recommended by the World Health Organization [3, 4], nearly 108 countries are affected by iAs contamination in groundwater. The most affected countries are in Southeast Asia, including Bangladesh, India, and Pakistan [5, 6]. In the United States, approximately 13 million Americans are exposed to elevated levels of iAs. Even if iAs-safe drinking water is supplied, iAs in the soil and water used for irrigation can enter the food chain through crops and fodders [7]. Many foods, including apple juice, chicken, wine, beer, and rice have been documented to have detectable levels of iAs. Rice accumulates more iAs than other food crops, with high levels of iAs detected in many rice-based products, such as rice milk, rice-based breakfast cereal, and infant rice cereal [8, 9, 10]. Rice consumption has become the single biggest food source of human exposure to iAs [11]. Chronic iAs exposure has been associated with several cancers, such as lung, bladder, skin, and liver [12], along with many noncancer diseases, including diabetes, atherosclerosis, and nonalcoholic fatty liver disease [13, 14, 15]. These events indicate that oral absorption of iAs can influence key pathophysiological events at the site of absorption such as the intestinal tract in addition to additional tissues where the properties of iAs can induce oxidative stress.
Human UDP-glucuronosyltransferase 1A1 (UGT1A1) is the sole enzyme capable of conjugating and detoxifying bilirubin [16]. Humanized UGT1 mice (hUGT1) express the entire human UGT1 locus under a mouse Ugt1-null background (TgUGT1/Ugt1−/− mice) [17]. The absence of murine Ugt1a1 and the delayed expression of the human UGT1A1 transgene results in severe neonatal hyperbilirubinemia in hUGT1 mice during the suckling period [18, 19]. In hUGT1 mice, activation of the pregnane X receptor (PXR), constitutive androstane receptor (CAR), peroxisome proliferator-activator receptor alpha (PPARα), nuclear factor erythroid derived 2-like 2 (Nrf2), aryl hydrocarbon receptor, liver X receptor-α, and farnesoid X receptor (FXR) have been confirmed to regulate the UGT1A1 gene in either the intestinal tract or liver in neonates leading to the metabolism of serum bilirubin [20, 21, 22, 23, 24, 25, 26].
The intestinal tract displays a unique physiology through repression that has been shown to play an important role in regulation of the UGT1A1 gene. Gene repression is largely achieved by forming corepressor complexes, which are recruited to DNA by nuclear receptors and act through epigenetic modifications to limit nucleosomal DNA accessibility and transcriptional activation [27]. Nuclear receptor corepressor 1 (NCoR1) was first discovered by its repressive function and highlighted by its important role in development [28, 29]. When NCoR1 was selectively deleted in the intestinal tract of hUGT1 mice (hUGT1/Ncor1ΔIEC), this event stimulated intestinal epithelial cell (IEC) maturation in newborns that led to derepression (i.e., induction) of the UGT1A1 gene and the resulting metabolism of serum bilirubin [20]. In addition, obeticholic acid, an FXR agonist, when given orally to newborn hUGT1 mice activates FXR target genes in both the intestinal tract and liver [26]. The induction of UGT1A1 by obeticholic acid in intestines was dependent upon CAR as well as IEC maturation [26]. Other prooxidants, such as cadmium and isothiocyanates [25, 30], activate CAR in the intestines of hUGT1 neonates while inducing UGT1A1 [25], with isothiocyanates also inducing IEC maturation. While it has become clear that the processes leading to neonatal IEC maturation are linked to induction of intestinal UGT1A1, the generation of reactive oxygen species (ROS) and the activation of IEC maturation may be linked to a family of selective nuclear receptors.
One of the more potent producers of oxidative stress and ROS is iAs [31, 32]. Of importance, as we will address, acute oral iAs exposure has a profound effect on the expression of proteins both in the intestinal tract and liver that have been linked to the metabolism of circulating bilirubin and induction of UGT1A1. Analysis of these events has uncovered an important linkage between iAs-induced IEC maturation and the role of Nrf2 and PXR in induction of intestinal and liver UGT1A1.
## Abstract
Inorganic arsenic (iAs) is an environmental toxicant that can lead to severe health consequences, which can be exacerbated if exposure occurs early in development. Here, we evaluated the impact of oral iAs treatment on UDP-glucuronosyltransferase 1A1 (UGT1A1) expression and bilirubin metabolism in humanized UGT1 (hUGT1) mice. We found that oral administration of iAs to neonatal hUGT1 mice that display severe neonatal hyperbilirubinemia leads to induction of intestinal UGT1A1 and a reduction in total serum bilirubin values. Oral iAs administration accelerates neonatal intestinal maturation, an event that is directly associated with UGT1A1 induction. As a reactive oxygen species producer, oral iAs treatment activated the Keap-Nrf2 pathway in the intestinal tract and liver. When Nrf2-deficient hUGT1 mice (hUGT1/Nrf2−/−) were treated with iAs, it was shown that activated Nrf2 contributed significantly toward intestinal maturation and UGT1A1 induction. However, hepatic UGT1A1 was not induced upon iAs exposure. We previously demonstrated that the nuclear receptor PXR represses liver UGT1A1 in neonatal hUGT1 mice. When PXR was deleted in hUGT1 mice (hUGT1/Pxr−/−), derepression of UGT1A1 was evident in both liver and intestinal tissue in neonates. Furthermore, when neonatal hUGT1/Pxr−/− mice were treated with iAs, UGT1A1 was superinduced in both tissues, confirming PXR release derepressed key regulatory elements on the gene that could be activated by iAs exposure. With iAs capable of generating reactive oxygen species in both liver and intestinal tissue, we conclude that PXR deficiency in neonatal hUGT1/Pxr−/− mice allows greater access of activated transcriptional modifiers such as Nrf2 leading to superinduction of UGT1A1.
## Tissue-specific and developmental-dependent induction of human UGT1A1 by iAs in hUGT1 neonatal mice
Humanized UGT1 mice at ∼12 to 13 days old were administered a single oral dose of iAs at 10 mg/kg, which led to a time-dependent reduction in total serum bilirubin (TSB) levels at 4, 24, and 48 h post treatment (Fig. 1A). When we treated hUGT1 neonates with a range of iAs concentrations from 1 to 10 mg/kg, a dose-dependent reduction of TSB levels was observed after 24 h (Fig. S1A). Our previous studies have linked serum TSB levels in hUGT1 mice directly with the expression of hepatic and/or intestinal human UGT1A1 [17, 18]. Oral iAs exposure led to the induction of UGT1A1 only in intestinal tissue (Figs. 1, B and C and S1B). Other human UGT1A isoforms quantitated by reverse transcription–quantitative polymerase chain reaction (RT-qPCR) confirmed that the UGT1A3, -$\frac{4}{5}$, -6, -7, and -$\frac{9}{10}$ genes were also induced in the small intestine (Fig. S1C). It has been demonstrated that induction of intestinal UGT1A1 expression in neonatal hUGT1 mice is positively correlated with IEC growth and enterocyte maturation [20, 26]. When we performed RNA sequencing (RNA-seq) analysis on intestinal RNA from vehicle- and iAs-treated mice, genes expressed predominantly in fetal and newborn mice were all repressed to adult levels, while underexpressed adult IEC marker genes were all induced (Fig. 1D). Several of these maturation markers were confirmed by RT-qPCR analysis (Fig. 1, E and F). In addition, it has been demonstrated that selective induction of intestinal UGT1A1 by oral iAs is developmentally regulated. When adult hUGT1 mice (8 weeks) were orally treated with iAs, baseline levels of intestinal UGT1A1 expression were not altered (Fig. 1, G–I). However, liver UGT1A1 expression was induced. The selective induction of liver UGT1A1 in adult mice and not neonates confirms that liver UGT1A1 gene expression in newborn mice is repressed and this repression is developmentally regulated. Figure 1Inorganic arsenic (iAs) exposure lowers total serum bilirubin (TSB) levels with intestinal UGT1A1 induction in hUGT1 neonates. A–F, for time-dependent studies, neonatal hUGT1 mice at 12 or 13 days old were orally treated with 10 mg/kg iAs. Mice were sacrificed at 4, 24, or 48 h post treatment. Liver, small intestine, and blood samples were collected. For dose-dependent studies, 13-day-old mice were orally treated with 1, 2, 5, or 10 mg/kg iAs. Tissue samples were collected at 24 h after treatment. A, TSB levels in time-dependent studies ($$n = 10$$, 3, 7, 9). B, RT-qPCR of UGT1A1 in both liver and small intestine (SI) ($$n = 3$$). C, Western blot analysis of UGT1A1. GAPDH served as a loading control. D, RNA-Seq study. Small intestines were collected 24 h after iAs treatment. Samples from three mice were combined as one sample, with four samples from each group. Heatmap comparing genes related to small intestine maturation. E, RT-qPCR of small intestinal maturation markers, Sis and Krt20 ($$n = 4$$). F, RT-qPCR of Nox4 and Lrp2 ($$n = 4$$). G–I, adult hUGT1 mice at 8 weeks old were orally treated with 10 mg/kg iAs. Twenty-four hours later, tissue and blood were collected. G, TSB levels ($$n = 4$$). H, RT-qPCR of UGT1A1 in both liver and small intestine ($$n = 3$$). I, Western blot analysis. Results are described as mean ± SD (n ≥ 3). ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, ∗∗∗$p \leq 0.001$, ∗∗∗∗$p \leq 0.0001$, Student’s t test. Individual p values were listed in Table S2. RT-qPCR, reverse transcription–quantitative polymerase chain reaction.
## iAs treatment alters nuclear receptor target genes
An important property of iAs and one of the most intensely studied mechanisms leading to carcinogenesis and other health effects is the production of ROS. The production of ROS can lead to the activation of Nrf2, a master regulator that responds to oxidative stress. The production of oxidative stress and activation of the Keap1-Nrf2 pathway promotes the overexpression of antioxidant enzymes following binding of Nrf2 to antioxidant response elements, resulting in transcriptional activation [31, 33]. Following iAs treatment, a large panel of genes regulated by Nrf2 were significantly induced (Fig. 2A). Further confirmation for induction of antioxidant responsive genes known to be activated following ROS production, heme oxygenase I (Hmox1), NAD(P)H quinone dehydrogenase 1 (Nqo1), and glutathione S-transferase alpha 1 (Gsta1) were induced in both liver and small intestines (Fig. 2, B–E). The induction of these three genes followed a remarkably similar pattern in liver and intestinal tissues, although the induction of Gsta1 was higher in small intestine ($p \leq 0.05$) (Fig. 2D). HMOX1 and NQO1 were induced in both liver and small intestines (Fig. 2E). In addition to the expression of Nrf2 target genes, expression of the Cyp2b10 gene, a target of activated CAR, was induced in both liver and intestinal tissues, with a much higher induction at approximately 44-fold in small intestine in comparison with a 6-fold increase in liver (Fig. 2F). The induction of Cyp2b10 is indicative of oxidative stress, since it has previously been confirmed that agents such as isothiocyanates and cadmium, which produce oxidative stress, can activate CAR in a non-Nrf2-dependent fashion [25]. Further analysis of RNA-seq studies revealed that nuclear receptors PXR and PPARα target genes that are uniquely repressed following iAs treatment (Fig. 2A). This can also be observed with RT-qPCR analysis of intestinal Cyp3a11 expression, which is regulated by PXR, and Cyp4a10, which is regulated by PPARα (Fig. 2, G and H). Clearly, iAs can differentially regulate downstream nuclear receptor (NR) target genes either by activation of the receptors by oxidative stress or by inhibition. Figure 2iAs exposure induces oxidative stress and alters genes encoding drug metabolism enzymes. A, RNA-seq analysis. Neonatal hUGT1 mice were treated with 10 mg/kg iAs and samples collected at 24 h. Heatmap analysis comparing genes that are regulated by different nuclear receptors. B–D, reverse transcription–quantitative polymerase chain reaction of oxidative stress markers including Hmox1, Nqo-1, and Gsta1 ($$n = 6$$, 3, 6, 6). E, Western blot analysis of HMOX1 and NQO1 in both liver (L) and small intestine (SI). F–H, RT-qPCR of Cyp2b10, Cyp3a11, and Cyp4a10 in both liver and small intestine ($$n = 6$$, 3, 6, 6). Results are described as mean ± SD. ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, ∗∗∗$p \leq 0.001$, ∗∗∗∗$p \leq 0.0001$, Student’s t test. Individual p values were listed in Table S3.
## The role of Nrf2 in iAs-induced UGT1A1 gene expression
Newborn hUGT1/Nrf2+/− and hUGT1/Nrf2−/− mice develop severe neonatal hyperbilirubinemia like that observed in newborn hUGT1 mice. hUGT1/Nrf2−/− and hUGT1/Nrf2+/− mice at 13 days old were treated with iAs by oral gavage. In both liver and intestinal tissue, gene expression of the Nrf2 target genes, Nqo1 and Gsta1, were either abolished or significantly reduced in small intestine and liver of hUGT1/Nrf2−/− mice (Fig. 3, A–C). In the small intestine, UGT1A1 gene and protein expression was induced following iAs treatment in hUGT1/Nrf2+/− mice but was greatly reduced in iAs-treated hUGT1/Nrf2−/− mice (Fig. 3, D and E). The other genes associated with the UGT1 locus were also repressed following iAs treatment in hUGT1/Nrf2−/− mice (Fig. S2A). The pattern of intestinal UGT1A1 expression was reflected in TSB values, with a reduction in hUGT1/Nrf2+/− mice followed by little change in hUGT1/Nrf2−/− mice (Fig. 3F). It should also be noted that Nrf2 deficiency led to reduced iAs-stimulated intestinal maturation as measured by a reduction in intestinal maturation gene expressions (Fig. 3G). The reduction in intestinal maturation can be reflected in reduced intestinal UGT1A1 expression. Figure 3The dependency of Nrf2 toward iAs-mediated induction of UGT1A1.hUGT1/Nrf2+/− and hUGT1/Nrf2−/− neonates at 13 days old were orally treated with vehicle or 10 mg/kg iAs. Small intestines and blood samples were collected 24 h post treatment. A, RT-qPCR of Nrf2 target genes including Nqo-1 and Gsta1 in small intestines (SI, $$n = 4$$, 6, 4, 4) and liver (L, $$n = 5$$, 4, 4, 5). B and C, following iAs treatment, Western blot analysis of NQO1 in SI and liver L. D, RT-qPCR of UGT1A1 gene expression in SI ($$n = 4$$, 6, 4, 3). E, Western blot analysis of UGT1A1 in SI and L. F, total serum bilirubin (TSB) levels ($$n = 6$$, 6, 6, 11). G, RT-qPCR of Sis and *Krt20* gene expression in SI ($$n = 4$$, 6, 4, 3). H, RT-qPCR analysis of Cyp2b10 gene expression in SI ($$n = 4$$, 6, 4, 3). Results are described as mean ± SD. ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, ∗∗∗$p \leq 0.001$, ∗∗∗∗$p \leq 0.0001$, one-way ANOVA. Individual p value were listed in Table S4. RT-qPCR, reverse transcription–quantitative polymerase chain reaction.
There was little impact of Nrf2 deficiency on the inhibition of Cyp4a10 and Cyp3a11 in the small intestines (Fig. S2B), leading us to conclude that iAs and not ROS production inhibits directly PXR and PPARα. However, iAs treatment and ROS production are linked to induction of the Cyp2b10 gene through an Nrf2-dependent process (Fig. 3H). iAs-initiated induction of Cyp2b10 gene expression is greatly reduced in hUGT1/Nrf2−/− mice.
To determine if this process is associated with CAR, we treated neonatal hUGT1/Car−/− and WT mice with iAs. Expression of the Cyp2b10 gene in small intestines was blocked in hUGT1/Car−/− mice, with undetectable levels of induced protein (Fig. 4, A and B). This result establishes an important link between iAs exposure, Nrf2, and CAR. Although UGT1A1 can be induced by activated CAR [30], deletion of CAR had no impact on UGT1A1 gene or protein expression in the small intestines following iAs exposure (Fig. 4, B–D). We can speculate that activation of Nrf2 by iAs production of ROS is sufficient for maximal transcriptional activation of the UGT1A1 gene. Alternatively, induction of UGT1A1 in hUGT1/Car−/− mice may result from intestinal maturation and ROS as indicated by induction of the enterocyte Sis (Fig. 4E) and *Gsta1* genes (Fig. 4F), both in WT and CAR-deficient mice. Figure 4iAs-mediated UGT1A1 induction is independent from CAR.hUGT1/Car+/− and hUGT1/Car−/− neonatal mice at 12 days old were orally treated with 10 mg/kg iAs for 48 h. A, RT-qPCR of Cyp2b10 gene expression ($$n = 3$$, 4, 3, 3). B, Western blot analysis of UGT1A1 and CYP2B10 in the small intestine (SI). C, RT-qPCR analysis of UGT1A1 gene expression in SI ($$n = 3$$, 3, 3, 4). D, total serum bilirubin (TSB) levels ($$n = 4$$, 5, 6, 6). E and F, RT-qPCR of Sis and *Gsta1* gene expression ($$n = 3$$). Results are described as mean ± SD. ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, ∗∗∗$p \leq 0.001$, ∗∗∗∗$p \leq 0.0001$, one-way ANOVA. Individual p values were listed in Table S5. RT-qPCR, reverse transcription–quantitative polymerase chain reaction.
## Contribution of PXR and PPARα toward iAs-induced expression of UGT1A1
RNA-seq analysis of intestinal RNA following iAs exposure revealed that target genes regulated by PXR and PPARα are inhibited (Fig. 2A). Activation of both PXR and PPARα in hUGT1 mice leads to induction of UGT1A1 in liver and intestines of adult mice [18, 23]. To examine the contribution of PXR and PPARα toward expression of UGT1A1 following iAs exposure, we deleted PXR (hUGT1/Pxr−/−) and PPARα (hUGT1/Pparα−/−) followed by exposure of 13-day-old neonates to oral iAs. iAs exposure of hUGT1/Pparα+/− and hUGT1/Pparα−/− neonates resulted in UGT1A1 induction in both strains (Fig. S3, A–C), demonstrating that PPARα expression is not tied to UGT1A1 induction.
However, PXR plays an important repressive role in both the intestinal tract and liver. In hUGT1/Pxr−/− mice, TSB levels in newborn mice are $50\%$ lower than in hUGT1/Pxr+/− neonates (Fig. 5A), resulting from elevated constitutive expression confirmed by RT-qPCR levels (Fig. 5B) and Western blot analysis of intestinal UGT1A1 (Fig. 5, C and D). In hUGT1/Pxr+/− neonates, there is no induction of UGT1A1 in liver tissue (Fig. 5, E–G). However, in hUGT1/Pxr−/− mice, the actions of iAs lead to induction of liver UGT1A1 (Fig. 5, E–G). Analysis of small intestine UGT1A1 expression following iAs treatment in hUGT1/Pxr−/− mice shows superinduction compared with induction in hUGT1/Pxr+/− neonates (Fig. 5, C and D). Other human UGT1A isoforms quantitated by RT-qPCR confirmed that UGT1A3, -$\frac{4}{5}$, -6, and -$\frac{9}{10}$ were also induced in the small intestine in hUGT1/Pxr−/− mice (Fig. S4). These findings confirm that PXR expression in both liver and intestinal tract in neonatal mice serves to repress UGT1A1 gene expression. The absence of PXR leads to derepression, and in the presence of iAs, the epigenetic changes associated with the UGT1A1 gene in both liver and intestines provides access to activated transcriptional events induced by oxidative stress. Figure 5PXR repression contributes to iAs-mediated induction of UGT1A1.hUGT1/PXR+/− and hUGT1/PXR−/− 12-day-old neonates were orally treated with 10 mg/kg iAs. Small intestines, liver, and blood samples were collected at 48 h after treatment. A, total serum bilirubin (TSB) levels ($$n = 6$$, 3, 9, 4). B, RT-qPCR analysis of intestinal UGT1A1 gene expression ($$n = 4$$, 3, 4, 4). C, Western blot analysis of intestinal UGT1A1. D, quantification of the Western blot in C. E, RT-qPCR analysis of liver UGT1A1 gene expression ($$n = 4$$, 5, 4, 5). F, Western blot analysis of liver UGT1A1 G, quantification of Western blot in F. Results are described as mean ± SD. ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, ∗∗∗$p \leq 0.001$, ∗∗∗∗$p \leq 0.0001$, one-way ANOVA. Individual p values were listed in Table S6. RT-qPCR, reverse transcription–quantitative polymerase chain reaction.
## Discussion
In newborn children and hUGT1 neonatal mice, the UGT1A1 gene is repressed in the liver leading to increases in TSB levels, which results in varying degrees of hyperbilirubinemia [19, 34]. In humans, there are few reports of the developmental properties of UGT1A1 in neonatal liver and virtually no information on the importance of intestinal UGT1A1 expression as a modulator of neonatal hyperbilirubinemia. The expression of UGT1A1 and the other UGT1A genes is regulated in a tissue-specific fashion similar to expression patterns confirmed in human tissues [35, 36, 37, 38, 39, 40]. Of importance, the role of the intestinal tract and activation of the UGT1A1 gene in controlling the onset and development of neonatal hyperbilirubinemia has been clearly documented [26, 41]. While there is significant information on the mechanistic events leading to induction and developmental expression of UGT1A1 in hUGT1 mice following activation of the family of NRs [22, 23, 24, 42], we have linked the production of intracellular ROS by iAs exposure to neonatal mice as a catalyst for intestinal UGT1A1 gene expression. Although iAs-induced ROS is produced both in the intestinal tract and liver of neonates, induction of UGT1A1 could only be detected in the intestinal tract. This finding indicates that unique physiological events associated with the developing intestinal tract underlie the mechanisms associated with iAs induction of intestinal UGT1A1.
Along with induction of intestinal UGT1A1, iAs treatment drove the induction of adult intestinal maturation markers from their resting fetal levels to adult levels, while inhibiting the induction of fetal maturation markers. The increase in IEC maturation following iAs treatment and the induction of UGT1A1 resembles similar results when intestinal NCoR1 is selectively deleted from the intestinal tract in hUGT1 neonates. During development of the intestinal tract in neonatal mice, NCoR1 serves to suppress IEC maturation [20]. The repressive nature of NCoR1 and the NRs is controlled through specific phosphorylation events [43, 44, 45, 46], with a host of different kinases implicated in NCoR1 regulation. Phosphorylation of NCoR1 uncouples it from the NRs, resulting in derepression of transcription. Oral iAs treatment of neonatal hUGT1 mice activates stress-linked proteins such as p38-mitogen-activated protein kinase and extracellular signal-regulated kinase (ERK) $\frac{1}{2}$ [47, 48, 49]. If NCoR1 is targeted in the intestinal tract by activated p38 or ERK$\frac{1}{2}$, its phosphorylation would result in derepression of IEC maturation as well as the UGT1A1 gene. While the functional role of NCoR1 may be important in iAs-induced IEC maturation and expression of UGT1A1, part of the actions of the intestinal tract following exposure can be linked to regulation of NRs, several of which are associated with repression of UGT1A1.
The actions of oral iAs exposure of hUGT1 neonatal mice clearly resulted in induction of ROS-dependent Nrf2 target genes in addition to that of CAR-dependent target genes. In Nrf2-deficient mice, induction of intestinal and liver Nqo1/Nqo1 expression by iAs was completely suppressed, verifying that iAs-induced ROS drives oxidative stress. In the small intestines, there is dramatic but not total reduction in iAs-induced UGT1A1, implicating that additional Nrf2-independent processes are in play to regulate UGT1A1 gene expression. It is also clear that deletion of Nrf2 reduces iAs induction of the intestinal maturation markers, a finding that ties oxidative stress and Nrf2 activation to control of IEC maturation in neonates. Our findings also demonstrate that iAs exposure induces intestinal Cyp2b10/CYP2B10, which is highly dependent upon Nrf2. Interestingly, when we deleted CAR in hUGT1 mice (hUGT1/Car−/−), iAs induction of CYP2B10 was virtually eliminated since CAR plays a major role in the regulation of Cyp2b10. However, CAR deficiency had no impact on the induction of UGT1A1/UGT1A1. While activated CAR and Nrf2 can induce both UGT1A1 and Cyp2b10 genes in hUGT1 mice, the lack of iAs-initiated induction of CYP2B10 in hUGT1/Car−/− mice demonstrates that the generation of ROS catalyzes an important association between CAR and Nrf2 that is required to induce the Cyp2b10 gene.
From RNA-seq analysis and reverse genetic studies, induction of oxidative stress by iAs exposure links activation of Nrf2 and IEC maturation to events leading to induction of intestinal UGT1A1. Also apparent in these studies was an inhibitory pattern that impacted PXR and PPARα target genes. This finding was relevant since we had previously demonstrated that deletion of PXR in hUGT1 mice led to derepression of hepatic UGT1A1 in neonates [23], while NCoR1-deficient hUGT1 neonates resulted in an activated gene expression profile linked to PPARα [20]. Thus, inhibition of NRs such as PXR and PPARα by iAs exposure may directly impact expression of UGT1A1. To examine this possibility, PXR and PPARα were deleted in hUGT1, and the hUGT1/Pxr−/− and hUGT1/Pparα−/− neonates exposed to iAs. While there was little impact on iAs-induced expression of UGT1A1 in hUGT1/Pparα−/− mice, there was a dramatic impact in hUGT1/Pxr−/− mice that influenced expression of UGT1A1 in both the intestinal tract and liver. Deletion of PXR led to significant derepression of UGT1A1 in the small intestine. While iAs treatment of hUGT1/Pxr+/− led to induction of UGT1A1 in the intestines, exposure of PXR knockout mice led to far greater UGT1A1 induction or superinduction. The treatment of wildtype or hUGT1/Pxr+/− mice with iAs did not result in UGT1A1 induction in liver, although it did result in hepatic oxidative stress and induction of Nrf2 target genes. When we examined liver tissue from iAs-treated hUGT1/Pxr−/− mice, there was significant induction of UGT1A1. It is worth noting that the PXR binding site (PXRE) and the Nrf2 binding antioxidant response elements are located within a 280-bp DNA region on the UGT1A1 promoter [18, 42]. The close proximity of these binding sites coupled with findings that iAs can induce hepatic UGT1A1 in hUGT1/Pxr−/− mice allows us to conclude that PXR represses the UGT1A1 gene by masking Nrf2-binding sites, and removal of PXR heightens the sensitivity toward ROS-activated Nrf2.
Oral exposure of neonatal hUGT1 mice to iAs has uncovered unique signaling events that control developmental expression of the UGT1A1 gene in intestines and liver but by uniquely different mechanisms. We have confirmed that the single commonality of iAs treatment between liver and intestines is the production of ROS and the activation of Nrf2. Although the mechanism is unclear, iAs exposure and the activation of Nrf2 in the intestines helps direct IEC maturation, which is directly linked to induction of UGT1A1. The activation of Nrf2 by iAs exposure in liver has minimal impact on UGT1A1 expression. However, in liver and intestines, PXR participates actively in repressing the developmental expression of UGT1A1. Since UGT1A1 is abundantly expressed in adult liver and intestinal tissue and is solely responsible for the metabolism and clearance of serum bilirubin, the developmental delay in liver and intestinal tissue can be directly linked to PXR, although the mechanism leading to the repressive actions toward the UGT1A1 gene during development are unknown.
## Animals and treatment
Transgenic mice expressing the human UGT1 locus in a Ugt1−/− background (hUGT1) were developed previously [17]. The Car-null (Car−/−) mice were a gift from Dr Masahiko Negishi (National Institute of Environmental Health Sciences) and were crossed with hUGT1 mice to generate hUGT1/Car−/− mice. The Pparα−/−, Pxr−/−, and Nrf2−/− mice were purchased from The Jackson Laboratory and were used to generate hUGT1/Pparα−/−, hUGT1/Nrf2−/−, and hUGT1/Pxr−/− mice. All mouse strains were housed in a pathogen-free UCSD Animal Care Facility and received food and water ad libitum. All animal protocols were reviewed and approved by the UCSD Animal Care and Use Committee. In neonatal studies, male and female pups at ∼12 to 13 days old with body weight between 6.0 g and 9.0 g were used. Each experiment result was obtained from at least two different litters. In each litter, mice were randomly divided into control and treated groups. Littermate controls were used for all experiments. The sample size calculation was based on serum TSB levels from vehicle and treated mice at the beginning stage of the experiment. Experimental design assistant (https://eda.nc3rs.org.uk/eda/login/auth) was used as the power calculator. The power of the experiment was set to $90\%$, and the calculated N value for each group is 3. Therefore, we used at least three mice in each group in the following experiments.
Time- and dose-dependent studies were performed. For time-dependent studies, 12-day-old hUGT1 neonatal mice were treated by oral gavage with water (vehicle) or 10 mg/kg sodium arsenite (iAs, Sigma-Aldrich, S7400) dissolved in vehicle. Blood and tissues were collected at 4, 24, or 48 h after administration. For dose-dependent studies, 13-day-old hUGT1 mice were treated by oral gavage with water or 1, 2, 5, 10 mg/kg iAs. Blood and tissues were collected at 24 h after treatment. For the above transgenic neonatal mice, 12- or 13-day-old neonatal mice were treated by oral gavage with water or 10 mg/kg iAs. Blood and tissues were collected at 24 or 48 h after treatment. In adult studies, vehicle or 10 mg/kg iAs was orally administrated to 8-week-old male mice and tissues were collected at 24 h after iAs treatment. For tissue collection, liver and small intestine tissues were washed with ice-cold phosphate-buffered saline and then snap frozen in liquid nitrogen immediately. Tissue samples were stored at −80 °C until analysis.
## Reverse transcription–quantitative-PCR and RNA sequencing
Total RNA was extracted from the collected liver or small intestinal tissue samples using TRIzol reagent (Thermo Fisher Scientific, 15596026). The cDNA sample was generated using the iScript complementary DNA synthesis kit (Bio-Rad, 1708891) according to the manufacturer’s instructions. RT-qPCR was performed on a CFX96 qPCR system (Bio-Rad, C1000 Touch Thermal Cycler, CFX96 Real-Time System) using Ssoadvanced SYBR Green reagent (Bio-Rad, 1725274). All primers used in this study were purchased from Integrated DNA technology and reported in Table S1.
For RNA-seq, 13-day-old hUGT1 mice were first treated with vehicle or iAs (10 mg/kg) for 24 h, and then small intestines were collected for total RNA isolation. The extracted RNA samples from three mice were combined to represent a single sample. For both vehicle and iAs treatment, RNA-*Seq analysis* was run on four samples (12 mice for each treatment). The sequencing library for each sample was prepared using the Illumina Stranded mRNA Saple Prep Kit (20040534; Illumina) with 1 μg of RNA. Using an Illumina NovaSeq6000 sequencer in the IGM Genomics Center at UCSD, samples were sequenced with Paired End 100 base pair sequencing, with base calling performed using bcl2fastq (v2.20; Illumina). STAR (v2.7.9a) was used to align the sequencing reads to the mouse genome (mm10) and to generate gene-level counts from uniquely aligned reads. Differentially expressed genes were calculated with DESeq2 (1.34.0).
## Bilirubin measurement
Blood samples were collected from the submandibular vein and centrifuged at 14,000g for 2 min to obtain serum. The total serum bilirubin (TSB) levels were measured using a Unistat Bilirubinometer (Reichert, Inc).
## Protein preparation and Western blot analysis
Tissue samples (100 mg) were homogenized in 0.4 ml 1 × RIPA lysis buffer (EMD Millipore, 20-188) containing a protease and phosphatase inhibitor cocktail (Thermo Fisher Scientific, 87786 and 78420) and incubated on ice for 30 min. The resultant mixture was centrifuged at 16,000g for 20 min at 4 °C, and the supernatants were transferred to a new tube and stored at −80 °C until analyzed.
Electrophoresis was performed by using NuPAGE 4 to $12\%$ BisTris-polyacrylamide gels (Thermo Fisher Scientific, NW04127BOX) following the manufacturer’s instructions. Protein (30 μg) was subject to electrophoresis at 170 V for 1 h and transferred at 20 V for 1 h to PVDF membranes (EMD Millipore, IPVH00010). After washing, membranes were blocked with $5\%$ nonfat milk at room temperature for 1 h and incubated with primary antibodies at 4 °C overnight. The following antibodies were used: rabbit antihuman UGT1A1 (Abcam, Ab-170858), mouse anti-GAPDH (Santacruz, sc-47724), rabbit anti-NQO1 (Cell Signaling Technology, CS62262), goat anti-SIS (Santacruz, sc-27603), rabbit anti-CYP2B10 (a kind gift from Dr Masahiko Negishi, NIEHS), and rabbit anti-HO1 (Cell Signaling Technology, 5853). After incubation with primary antibody, membranes were exposed to HRP-conjugated secondary antibodies (anti-mouse IgG, anti-rabbit IgG and anti-goat IgG, Cell Signaling Technology). All primary antibodies were diluted 1:1000 and secondary antibodies were diluted 1:3000. Protein was detected by the ECL Plus Western blotting detection system (Bio-Rad) and was visualized by the Bio-Rad Chemidoc Touch Imaging System. Band density was quantified by using Image Lab 5.2.1 software.
## Statistics
All results were subjected to statistical analysis. Student t test analyses (nonparametric Mann–Whitney test) were performed for two-group comparison. For comparison among multiple groups, one-way ANOVA with Tukey’s multiple comparison test was used. All statistics and graphs were generated using GraphPad Prism software (GraphPad Software). Data are expressed as means ± SD, and p values smaller than 0.05 were considered statistically significant. The results of all statistical comparisons between experimental groups are clearly indicated in each figure legend, with exact p values showed in Tables S2–S10.
## Data availability
All data are contained within the article.
## Supporting information
This article contains supporting information.
Supplemental Figures S1–S4 and Tables S1–S10
## Conflict of interest
The authors declare that they have no conflicts of interest with the contents of this article.
## Author contributions
R. H. T., S. C. conceptualization; X. Y., A. A. W., E. M., M. P., S. W., S. L., J. Y. A. T. investigation; X. Y. resources; M. C., C. W. B. data curation; R. H. T. writing – original draft; X. Y. writing – review & editing; R. H. T. supervision; R. H. T., S. C. project administration; R. H. T., S. C. funding acquisition.
## Funding and additional information
This work was supported by $\frac{10.13039}{100007197}$US Public Health Service grants R21-ES031849, P42-ES010337, R01-GM126074 (R. H. T.), R21-ES034630 (R. H. T., S. C.), and R21-AI35677 (S. C.).
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|
---
title: 'Design Principles in mHealth Interventions for Sustainable Health Behavior
Changes: Protocol for a Systematic Review'
journal: JMIR Research Protocols
year: 2023
pmcid: PMC9996417
doi: 10.2196/39093
license: CC BY 4.0
---
# Design Principles in mHealth Interventions for Sustainable Health Behavior Changes: Protocol for a Systematic Review
## Abstract
### Background
In recent years, mHealth has increasingly been used to deliver behavioral interventions for disease prevention and self-management. Computing power in mHealth tools can provide unique functions beyond conventional interventions in provisioning personalized behavior change recommendations and delivering them in real time, supported by dialogue systems. However, design principles to incorporate these features in mHealth interventions have not been systematically evaluated.
### Objective
The goal of this review is to identify best practices for the design of mHealth interventions targeting diet, physical activity, and sedentary behavior. We aim to identify and summarize the design characteristics of current mHealth tools with a focus on the following features: [1] personalization, [2] real-time functions, and [3] deliverable resources.
### Methods
We will conduct a systematic search of electronic databases, including MEDLINE, CINAHL, Embase, PsycINFO, and Web of Science for studies published since 2010. First, we will use keywords that combine mHealth, interventions, chronic disease prevention, and self-management. Second, we will use keywords that cover diet, physical activity, and sedentary behavior. Literature found in the first and second steps will be combined. Finally, we will use keywords for personalization and real-time functions to limit the results to interventions that have reported these design features. We expect to perform narrative syntheses for each of the 3 target design features. Study quality will be evaluated using the Risk of Bias 2 assessment tool.
### Results
We have conducted a preliminary search of existing systematic reviews and review protocols on mHealth-supported behavior change interventions. We have identified several reviews that aimed to evaluate the efficacy of mHealth behavior change interventions in a range of populations, evaluate methodologies for assessing mHealth behavior change randomized trials, and assess the diversity of behavior change techniques and theories in mHealth interventions. However, syntheses on the unique features of mHealth intervention design are absent in the literature.
### Conclusions
Our findings will provide a basis for developing best practices for designing mHealth tools for sustainable behavior change.
### Trial Registration
PROSPERO CRD42021261078; https://tinyurl.com/m454r65t
### International Registered Report Identifier (IRRID)
PRR1-$\frac{10.2196}{39093}$
## Introduction
One of the most significant achievements in human health in the past century was the extension of life expectancy from 45 to >75 years, resulting in an expanding, aging global population [1]. Concurrently, lifestyles have changed with industrialization, inducing dramatic shifts in the global disease burden, which is now dominated by chronic diseases [2,3]. Globally, the leading modifiable risk factors for chronic diseases are poor diet, alcohol consumption, smoking, and a lack of physical activity [4]. More importantly, these lifestyle behaviors are also modifiable factors for chronic disease management [5]. Hence, sustaining healthy lifestyle behaviors has been recommended by the World Cancer Research Fund [6], the International Society of Hypertension [7], the International Diabetes Federation [8], and many others [9-12].
Since the 1950s, the harm of smoking has been embedded in medical training with policy support to promote smoking cessation [13]. Coupled with individual behavior change strategies, smoking rates have seen a continuous decline since the 1960s [14]. In sharp contrast, the inclusion of diet and physical activity as topics in medical training was only initiated as recently as the 21st century [15-18]. Unique to quitting smoking, which removes one behavior, initiating and maintaining healthy dietary patterns and an active lifestyle (ie, reducing sedentary behavior and increasing physical activity) requires sustained behavior changes throughout life. Therefore, responsibility for these behaviors ultimately falls on patients, who must self-manage their chronic diseases in the long term, even with the availability of system and policy supports [19].
Theory-based behavior change interventions are highly efficacious in controlled experiments. Multiple behavior change theories have been tested in selected populations (eg, the Theory of Planned Behavior and the Social Cognitive Theory), targeting individual knowledge and cognitive and affective determinants of behavior. To reduce the complexity of using multiple theories, the Theoretical Domains Framework was developed in 2005 by bringing together 33 models of behavior change [20]. In 2011, the Behaviour Change Wheel was created as a causal “behavior system” to guide intervention design through mapping Theoretical Domains Framework–based behavior determinants to the Behaviour Change Technique Taxonomy [21].
Despite high efficacy in experimental settings, the real-world application of behavior theories has had limited effectiveness at the population level in achieving sustainable changes in diet, physical activity, and sedentary behavior [22,23]. For models that emphasize the interaction between individuals and the environment within a social system (eg, the Ecological Model), environmental influences and policy context often become the primary target [24], risking disparities within population subgroups (ie, by creating urban-rural disparities). Human behavior is dynamic, and sustained behavior change following interventions is dependent on the individual’s ability to adopt and continuously use behavior change techniques [25-27]. Intervention fidelity (ie, the delivery-receipt-enactment chain) is the key process measure of the mechanism linking intervention to outcome [28]. The process of enactment is the most sensitive to potential breakdowns in the delivery-receipt-enactment chain, occurring when individual-level factors and contextual resources are not properly aligned to support enactment [25]. Hence, real-time personalized interventions are desired to improve self-enactment and fidelity by facilitating the continuous alignment of individual and contextual factors.
The growing computing capabilities of mobile phones have enabled us to monitor and deliver health metrics continuously in real time [29,30]. Therefore, mobile health (mHealth) has the potential to enable personalized, real-time feedback and monitoring of targeted behaviors. The term “mHealth” describes the practice of medicine and public health supported by mobile devices [31]. The World Health Organization has recommended mHealth as a key health promotion strategy to improve global health across low- to middle- and high-income countries [32]. In recent years, mHealth has increasingly been used as a method in behavioral research for disease prevention and self-management through supporting positive changes in diet, physical activity, and sedentary behavior [33].
It is important to note that technology-enabled mHealth tools are twofold, including the active ingredients of behavior change interventions as the intervention content and the mHealth tools themselves as the intervention delivery strategy [34]. The computational power (ie, how fast a system can process data and perform a computational task) of mHealth tools can provide unique functions compared to conventional interventions, such as providing personalized behavior change recommendations and delivering them in real time with the support of dialogue systems. It is also worth noting that mHealth interventions differ from in-person interventions in the resources they deliver. For instance, mHealth interventions may deliver behavior change techniques as an end product with virtual resources [35], whereas conventional interventions may be able to deliver direct physical resources, such as in-person group interventions, exercise equipment, or access to facilities [36]. However, design principles for mHealth interventions to incorporate personalization, real-time functions, and deliverable resources have not been systematically evaluated.
The goal of this review is to identify best practices for the design of mHealth interventions targeting diet, physical activity, and sedentary behavior changes for chronic disease prevention and self-management. We aim to review and identify specific designs in current mHealth tools that feature personalization, real-time functions, and deliverable resources. Our findings will provide a basis for developing best practice guidelines for designing mHealth tools targeting sustained behavioral change.
## Prospective Registration and Reporting Guidelines
This systematic review has been registered with PROSPERO (CRD42021261078), an international database of prospectively registered systematic reviews. Conduct of this review will be guided by the 2022 updated PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [37].
## Eligibility Criteria
We will focus on randomized controlled trials that have reported using mHealth tools for interventions targeting diet, physical activity, or sedentary behavior for chronic disease prevention or management purposes among adults aged 18 years or older. The inclusion criteria are as follows: [1] the study must include a behavior change intervention for chronic disease prevention and self-management; [2] the mHealth tool must include a personalization feature (eg, personalized intervention content and dose, delivery process, or feedback) or a real-time feature (eg, real-time behavioral monitoring or a dialogue system); [3] the behavior change need not have been the primary aim of the study or the primary outcome measure of the study, but a specific measure of behavior change must be reported; [4] a deliverable resource does not have to have been included in the mHealth intervention, although we will review and summarize deliverable resources (eg, virtual social support, access to facilities, diet recipes, or exercise videos) in the mHealth tool; [5] the study must be peer-reviewed and must also have an English abstract, even if it is written in a different language. We will screen non-English abstracts for inclusion and leverage international colleagues for full-text screening and data extraction.
## Information Sources
Two reviewers will independently search electronic databases, including MEDLINE, CINAHL, Embase, PsycINFO, and Web of Science, for studies published since 2010 that report findings from mHealth interventions. The year 2010 was chosen because it was when several national and international health organizations identified mHealth as a health promotion strategy with funding opportunities [32,38].
## Search Strategy
We will use keywords for mHealth, behavior change, interventions, and self-management and combine them using the “AND” term (Multimedia Appendix 1). Literature found with the search will discuss mHealth interventions targeting lifestyle behavior changes for chronic disease prevention and self-management. Next, we will use keywords for diet, physical activity, and sedentary behavior to limit the search to mHealth interventions targeting these behaviors. Finally, we will use keywords for personalization and real-time functions, respectively, to limit the identified interventions to ones that have reported these design features.
## Selection Process
The search strategy will be applied to all databases and aggregated in Endnote reference management software (Clarivate LLC). One reviewer will remove obviously irrelevant references by screening the titles and abstracts, with a $5\%$ sample of these decisions being verified by another reviewer. All remaining abstracts will be assessed for inclusion by one reviewer, with all those selected for exclusion being checked by the other reviewers before final exclusion. The full text of all remaining studies will be obtained and assessed independently for inclusion by 2 reviewers, with any discrepancies resolved in discussion with a third reviewer. The process of study selection will be reported in PRISMA flow diagrams [37] and the reasons for exclusion will be noted. Reference lists of the included studies will also be reviewed to further identify relevant studies.
## Data Collection Process
The lead reviewer will develop a data extraction form. Two reviewers will independently pilot the data extraction form on 5 studies. Extracted data will be reviewed by the entire review team to refine the data extraction form. At the commencement of data extraction, 2 reviewers will each extract the data from half of the included studies and perform a cross-check to verify the extracted data. Any discrepancies will be recorded and resolved by discussion.
## Data Items
The following data will be extracted for each included study: first author name, year of publication, journal, country, setting, and objective; study design and content of the mHealth intervention (ie, the targeted behavior, behavior change theory used, personalization features, real-time functions, and deliverable resources); procedures for defining, recruiting, and sampling from the intervention and control groups; characteristics and sample size of the study population; frequency and duration of follow-up; definition and measures of behavior change; reference group in any statistical modeling and results of any statistical tests reported; and subgroup analyses or any evidence relating to effects on other health outcomes.
## Study Risk of Bias Assessment
Study quality will be assessed using the Risk of Bias 2 assessment tool, an update to the original Cochrane risk of bias tool [39]. The Risk of Bias 2 tool evaluates the following domains in randomized controlled trials: randomization process, deviations from the intended interventions, missing outcome data, measurement of the outcome, and selection of the reported result. Two reviewers will assess study quality independently, and their assessments will be compared for agreement, with any discrepancies resolved in discussion with a third reviewer.
## Synthesis Methods
Based on our knowledge and findings from an initial search, we expect substantial heterogeneity in the mHealth interventions across the targeted chronic conditions, targeted behavior (ie, diet, physical activity, and sedentary behavior), reported outcomes, and methods (ie, frameworks and technologies) to achieve personalization, real-time functions, and deliverable resources. Hence, meta-analysis is unlikely to be feasible or appropriate. Therefore, we will perform narrative syntheses following the Reporting Guidelines of Synthesis Without Meta-Analyses [40] guideline for each of the 3 design features of interest.
We will first present detailed descriptions of the included studies in both narrative and tabular formats. This description table will focus on reporting the year of publication, country, setting, study objectives, population, mHealth intervention, comparison group, and outcome measures. Next, the included mHealth interventions will be further evaluated for 3 key design features based on the targeted behavior (ie, diet, physical activity, or sedentary behavior): personalization, real-time functions, and deliverable resources. The study team will develop a separate table for each targeted behavior to map the personalization (ie, personalized intervention content and dose, delivery process, and feedback), real-time functions (ie, the inclusion of technologies to support real-time behavior monitoring and a dialogue system), and deliverable resources (ie, social support, access to facilities, diet recipes, and exercise videos). Finally, the effectiveness of the included mHealth interventions will be presented by showing their unique features, that is, features that they do not share with usual care or nonintervention. A narrative synthesis of the included mHealth interventions will be presented together with an evaluation of study quality (ie, the risk of bias assessment) to provide context for the study findings and support confidence in our evaluation of the state of the field.
Although we anticipate a low likelihood of quantitative synthesis, meta-analyses for outcomes that include a sufficient number of studies will be considered if deemed feasible. We will provide statistical descriptions for behavior change related to the mHealth intervention for specific targeted behaviors and design features, such as personalization and real-time functions. We will estimate the summary effect size and its $95\%$ CI through both fixed and random effects models. Between-study association will be estimated using the I² metric; values of $50\%$ are indicative of high heterogeneity, while values above $75\%$ suggest very high heterogeneity [41]. Whenever necessary, we will calculate the evidence of small-study effects (ie, whether small studies have inflated effect sizes compared to larger ones). To this end, we will use the regression asymmetry test developed by Egger and colleagues [42]. A P value of.10 with more conservative effects in large studies in random-effects meta-analyses is considered indicative of a small-study effect.
## Results
As of November 10, 2022, we have completed our database search and have begun searching by hand. After removing 607 duplicates, the initial search yielded 2961 studies; the review team will screen the titles, abstracts and full texts. We aim to complete the review by March 2023.
## Discussion
This systematic review will provide a comprehensive overview of the literature to better understand the design of mHealth tools and their unique features, such as support for personalization, real-time functions, and deliverable resources, in interventions targeting diet, physical activity, and sedentary behavior. The main contribution of our review will be an understanding of the current methods and technologies used in mHealth interventions. Any amendments or modifications made to the protocol will be reported in the final paper.
Lifestyles and environments have changed in modern society with industrialization, inducing dramatic shifts in the global disease burden, which is now dominated by chronic diseases [43]. As such, now more than ever, we must face the consequences of the massive societal burden of chronic diseases. Importantly, lifestyle behaviors are both a major cause of chronic diseases and the key to effective management of chronic diseases [44]. Therefore, it is critical to develop tools to support positive changes in lifestyle behaviors and to support individuals in adopting environmental resources for sustainable behavior change. To this end, mHealth tools offer promising avenues to deliver personalized interventions in real time, powered by technology and computing capacity. The coverage rate of mobile technology worldwide increased from $87\%$ to $95\%$ from 2011 to 2012 and is expected to rise to $96\%$ by 2026 [45]. However, best practices for designing mHealth tools to support positive behavior change are unclear.
We have conducted a preliminary search for existing systematic reviews and review protocols on mHealth-supported behavior change interventions. We identified several reviews that aimed to evaluate the efficacy of mHealth behavior change interventions in a range of populations [46-48], evaluate methodologies for assessing mHealth behavior change randomized trials [49], and assess the diversity of behavior change techniques and theories in mHealth interventions [35,48]. However, syntheses of features unique to mHealth intervention design, including personalization, real-time functions, and deliverable resources, are lacking in the literature.
Based on the synthesized data, the key outcomes of our review will be [1] identifying gaps in the existing literature and [2] informing future research to improve the design of mHealth interventions and incorporate their unique features to support sustainable behavior change. These findings will be summarized and reported in a peer-reviewed journal.
## Data Availability
The data sets generated during and/or analyzed during the current study will be made available from the corresponding author on reasonable request.
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|
---
title: 'Layperson-Facilitated Internet-Delivered Cognitive Behavioral Therapy for
Homebound Older Adults With Depression: Protocol for a Randomized Controlled Trial'
journal: JMIR Research Protocols
year: 2023
pmcid: PMC9996421
doi: 10.2196/44210
license: CC BY 4.0
---
# Layperson-Facilitated Internet-Delivered Cognitive Behavioral Therapy for Homebound Older Adults With Depression: Protocol for a Randomized Controlled Trial
## Abstract
### Background
Depression in older adults has serious biological, psychological, and social consequences. Homebound older adults experience a high burden of depression and significant barriers to accessing mental health treatments. Few interventions to address their specific needs have been developed. Existing treatment modalities can be challenging to scale up, are not tailored to unique population concerns, and require significant staffing support. Technology-assisted, layperson-facilitated psychotherapy has the potential to overcome these challenges.
### Objective
The aim of this study is to assess the efficacy of a layperson-facilitated internet-delivered cognitive behavioral therapy program tailored for homebound older adults. The novel intervention, Empower@Home, was developed based on user-centered design principles and partnerships between researchers, social service agencies, care recipients, and other stakeholders serving low-income homebound older adults.
### Methods
This 2-arm, 20-week pilot randomized controlled trial (RCT) with a waitlist control crossover design aims to enroll 70 community-dwelling older adults with elevated depressive symptoms. The treatment group will receive the 10-week intervention immediately, whereas the waitlist control group will cross over and receive the intervention after 10 weeks. This pilot is part of a multiphase project involving a single-group feasibility study (completed in December 2022). This project consists of a pilot RCT (described in this protocol) and an implementation feasibility study running in parallel with the pilot RCT. The primary clinical outcome of the pilot is the change in depressive symptoms after the intervention and at the 20-week postrandomization follow-up. Additional outcomes include acceptability, adherence, and changes in anxiety, social isolation, and quality of life.
### Results
Institutional review board approval was obtained for the proposed trial in April 2022. Recruitment for the pilot RCT began in January 2023 and is anticipated to end in September 2023. On completion of the pilot trial, we will examine the preliminary efficacy of the intervention on depression symptoms and other secondary clinical outcomes in an intention-to-treat analysis.
### Conclusions
Although web-based cognitive behavioral therapy programs are available, most programs have low adherence and very few are tailored for older adults. Our intervention addresses this gap. Older adults, particularly those with mobility difficulties and multiple chronic health conditions, could benefit from internet-based psychotherapy. This approach can serve a pressing need in society while being cost-effective, scalable, and convenient. This pilot RCT builds on a completed single-group feasibility study by determining the preliminary effects of the intervention compared with a control condition. The findings will provide a foundation for a future fully-powered randomized controlled efficacy trial. If our intervention is found to be effective, implications extend to other digital mental health interventions and populations with physical disabilities and access restrictions who face persistent disparities in mental health.
### Trial Registration
ClinicalTrials.gov NCT05593276; https://clinicaltrials.gov/ct2/show/NCT05593276
### International Registered Report Identifier (IRRID)
PRR1-$\frac{10.2196}{44210}$
## Background
Depression in older adults has serious consequences, including worsened physical health and chronic disease progression [1-3], increased functional limitations and disabilities [4,5], institutionalization [6], and premature mortality due to comorbid conditions and suicide [7,8]. Low-income and homebound older adults are especially at high risk for experiencing depression. In one US representative sample, estimates for rates of depression in homebound older adults were approximately $40\%$ [9]. Further complicating this burden of depression is a recent steep increase in the number of homebound older adults, likely in part driven by COVID-19 [10].
Although evidence-based pharmacological and nonpharmacological treatments exist, not all options are readily accessible to all populations of older adults [11]. Low-income and homebound older adults face significant access barriers to mental health treatments due to mobility, transportation, and financial difficulties. In addition, few providers have specialized training in working with older populations [12,13]. Given the lack of service providers, high rates of depression in specific subpopulations of older adults, and demographic trends toward a higher number of homebound older adults, interventions that are both accessible and scalable are urgently needed.
Digital mental health interventions have the potential to address the gaps in mental health treatment faced by low-income and homebound older adults. Affirming this, a recent national panel of expert stakeholders recommended that digital mental health interventions be broadly adopted and that insurers expand their coverage and reimbursement [14]. Cognitive behavioral therapy (CBT) is an evidence-based form of psychological treatment and has been adapted to multiple age groups [15-17] as well as medical and social contexts [18,19]. CBT is well suited for digital-based delivery, given its highly structured format and skill-based learning [20]. Hundreds of clinical trials have taken place in the last decade to test internet-delivered CBT (iCBT) and presented robust findings supporting the acceptability and effectiveness of this treatment modality [21-25]. iCBT is convenient and self-paced, less expensive than traditional psychotherapy, and scalable due to content delivery automation.
Research has consistently shown that tailoring treatment to specific populations, contexts, and settings is associated with increased uptake, acceptability, and sustainability [26,27]. In the case of older adults, procedural and content modifications to CBT have been discussed that incorporate differences in thinking styles in older adults and age-related adjustments [28]. However, when packaged for internet delivery, empirically supported iCBT programs often employ a one-size-fits-all approach, and very few have been designed to consider the needs of older adults [29-31]. *In* general, older adults are underrepresented in iCBT trials. A review found that only $3\%$ of iCBT trial participants were older adults [32]. Since then, older adults’ participation has increased. However, a recent systematic review found that older adults are still largely under-represented as participants in rigorously designed studies [25].
Recent studies involving homebound older adults have shown that modifications to both contents (eg, adding aging-related themes) and web interface (website or app) are needed to improve adherence and engagement with iCBT [33,34]. These studies suggest that older adults had difficulty applying lessons when presented with content that failed to address aging-related topics and were less tolerant to poor usability issues, possibly due to a combination of age-related changes (eg, cognitive, vision, motor control, speech, and hearing), physical disabilities, and their lack of familiarity with modern technology. Most well-established iCBT programs contain a large amount of text, are highly didactic, and are hosted on interfaces that are not age-friendly (eg, small text and buttons). Usability issues and technical challenges are frequently reported, which can lower adherence and ultimately reduce treatment effects. The lack of tailored solutions may be more problematic for homebound older adults, who tend to be older, less tech-savvy, have more cognitive and functional limitations, and are more likely to experience usability issues [35].
## Objective
The primary aim of this study is to evaluate the efficacy of a 9-session iCBT program in treating depression in low-income homebound older adults. Additional outcomes and potential covariates will be captured, including anxiety, loneliness, and health-related quality of life.
A lack of iCBT products suitable for homebound older adults reflects and contributes to the digital divide experienced by them. Barring treatment innovation, older adults will continue to be left behind and face persistent mental health disparities related to access. This study aims to fill this void by testing the preliminary effects of a novel iCBT program designed for homebound older adults.
## Intervention Model
Empower@*Home is* an iCBT program designed to treat the core symptoms of depression in older adults. It consists of 9 web-based sessions grounded in CBT principles and homework assignments (ie, weekly practices) made available over 10 weeks. Each lesson is presented as a series of short videos with voice-overs that include didactic text, images of diverse older adults, inspirational quotes, and interactive exercises. Sessions are written at an eighth-grade reading level, and all spoken content is captioned. The weekly home practice tasks are modest and typically require 20 minutes or less of commitment from the user. A workbook with summarized session content and home practices is also provided to participants.
The sessions are enhanced with an animated case story series featuring a 74-year-old homebound woman named Jackie. The animated story is embedded within each session, like an episode of a television show. This content serves to reinforce core skills and techniques taught. Prior research has found that including short multimedia entertainment is a useful approach for communicating learning objectives in psychoeducational materials [36].
The session scripts of Empower@Home were developed by a team of researchers, clinicians, older adults, and social service providers via a co-design process. The web platform that delivers Empower@*Home is* a custom learning management system built using state-of-the-art, agile development processes. All interface features are compatible with accessibility standards and current best practices for making an age-friendly user interface and experience design [37]. For example, the main user interface features large buttons, text, icons with text descriptions, high-contrast color schemes, and intuitive navigation.
Informed by the Efficiency Model of Support [38], support from trained laypersons called Empower Coaches supplements the core web-based sessions of Empower@Home. Human support aims at increasing a participant’s ability to use the technology to obtain the intended treatment outcome [38]. Previous studies have shown that supported iCBT is more effective and associated with better adherence than unsupported interventions [39-41]. However, support mechanisms are generally underspecified and understudied, and it is not entirely clear what type and intensity of support are needed for homebound older adults to engage in iCBT successfully.
Informed by our previous research and the Efficiency Model of Support, we provide weekly check-ins with a supportive guide (ie, Empower Coach). The weekly check-ins are Coach-initiated contacts designed to prevent and address potential failure points, including engagement, working knowledge of the iCBT platform, and implementation of iCBT. All weekly check-ins are telephone-based. The Empower Coaches are laypersons without specialized mental health training or independent clinical licensure. The support content includes simple feedback, encouragement, homework review, and assistance with self-help tools without additional traditional therapy. Coaching sessions are highly individualized to meet the particular needs of the participants. On the basis of participants’ preferences, they may use the weekly check-in sessions to go over the web-based program with their coaches, whereas others complete the web-based portion on their own before the phone-based check-in sessions. Coaching sessions can range from 5 minutes to an hour per week, depending on the participants’ needs and preferences.
Coach training involves reviewing all web-based sessions of Empower@Home (takes approximately 4-5 hours to complete), a self-directed asynchronous web-based training (approximately 1.5 hours), and a synchronous training workshop (approximately 3 hours). The total training burden is approximately 9-10 hours per coach.
## Preliminary Study
The procedures described in this protocol, including recruitment, assessments, coaching calls, and device shipments and returns, have been tested in a completed single-group uncontrolled feasibility study. Findings from the single-group study have not yet been published, but preliminary analysis shows that the intervention is feasible and acceptable, with high satisfaction ratings, and is associated with significant within-group reductions in depressive symptoms and several secondary clinical outcomes. Study procedures for the pilot randomized controlled trial (RCT) are refined based on lessons learned from the feasibility pilot.
## Study Setting and Aims
The study will be managed from the University of Michigan in collaboration with community agencies serving older adults. Participants will reside throughout Michigan in both rural and urban areas. All participants will be 60 years or older and are expected to be predominantly low income. Participants will complete the intervention in their own homes, with all assessments and technological support provided remotely by phone.
## Eligibility Criteria
To be eligible for the trial, participants must be at 60 years or older and have at least mild depression (score of ≥8 on the Patient Health Questionnaire-9 [PHQ-9]). Participants will be excluded if they [1] have probable cognitive impairment based on the Blessed Orientation-Memory-Concentration Test (score ≥10), [2] do not speak English, [3] have moderate to high risk of suicide based on the Columbia-Suicide Severity Rating Scale, [4] have a terminal illness or are in unstable physical health with a high risk of repeated hospitalizations within the next 3 months, [5] have a visual impairment based on self-reported inability to use a screen with correction, [6] have a current or recent history of substance use based on a score of ≥2 on the CAGE-AID Substance Abuse Screening Tool, [7] have a psychotic disorder based on self-report, or [8] are currently receiving psychotherapy. The study will not exclude participants based on racial or gender identification. Completing the intervention will require a tablet and internet accessibility. Participants are not required to have a computer or internet access; those who lack 1 or both resources will be provided a tablet and access to the internet at no cost during the active intervention period.
## Recruitment and Compensation
Participants will be recruited into the study on a rolling basis and will be drawn from multiple community agencies as well as other public places, including social media and local news outlets. The social service agencies that participated in this study’s intervention development and implementation feasibility phase were predominantly Area Agencies on Aging based throughout Michigan. These agencies serve older adults by offering services to enable their clients to maintain independence. These services may involve case management, caregiver services, and meals on wheels. These agencies will refer participants to the study based on their judgment of the appropriateness of the intervention for their clients.
After receiving referrals, study personnel will contact potential participants via phone to screen for eligibility. The initial screening is expected to take 20 minutes to complete. Participants will provide recorded verbal consent. Those who decline to participate will be referred back to their case managers or agency when possible. Advertisements placed via social media (eg, Facebook and Nextdoor) and through research registries will also allow participants to self-refer and be screened for eligibility.
Those who qualify for the trial will be paid up to US $100. Compensation is determined based on the number of assessments completed. Partial compensation is available (eg, participants who complete only the baseline assessment will receive US $30).
## Study Design
Eligible and consented participants will be assigned either to an active treatment or a waitlist condition group that will cross over to the intervention at 10 weeks. A computer-generated random sequence will be used for assignment into either condition at a ratio of 1:1. Given the content of the intervention and study design, blinding the participant and evaluator to the condition will not be feasible.
All participants will be assessed using the same schedule. All will be invited to complete the comprehensive baseline assessment, postintervention or control assessment at week 10, and a follow-up assessment at week 20. Participants on the waitlist control will receive the intervention after their post-test at week 10 (Figure 1).
**Figure 1:** *Study design.*
## Waitlist Condition
The attention control waitlist condition involves a comparable amount of phone contact to the intervention condition, with roughly 20 minutes of companionship calls per week. However, in the attention control, participants will not be exposed to CBT-related content. The purpose of companionship calls is to provide socialization and contact with supportive individuals without introducing structured therapeutic content.
## Discontinuation
Consented participants may still be deemed ineligible during study participation if they develop significant clinical worsening of depression, suicidal ideation, or other circumstances (eg, terminal illness or prolonged hospitalization) that preclude their ongoing involvement with the trial. Participants will be consulted regarding potential removal. Should a participant report suicidal ideation, a protocol will be in place that involves the participation of the study principal investigator and a clinical social worker. Should a participant’s circumstance interfere with their ongoing participation in the trial, the study team will discuss the risks and benefits of the participant’s ongoing involvement with the trial until a consensus is reached. If there is a clinically significant worsening of depressive symptoms, considerable effort will be made to expedite entry into the best available treatment outside the research protocol.
## Clinical Management and Storage
All data related to the study (screening, pretest, coaching sessions, posttest, and follow-up) will be entered directly by project staff into REDCap [42]. During the active intervention, participants will be asked to fill out the PHQ-9 every other week, administered as a part of the web-based therapy lesson. These assessment data are stored in a HIPAA (Health Insurance Portability and Accountability Act)–compliant database hosted by the University of Michigan.
## Measures
All measures are presented in Table 1. The primary clinical outcome in the study will be depressive symptoms as measured by the PHQ-9. The PHQ-9 is widely used in clinical research and has been used to evaluate older adults with medical and psychiatric illnesses in and outside of health care settings [43,44]. The PHQ-9 has been found to perform comparably to the Geriatric Depression Scale in identifying depression in older adults [45]. Scores on the PHQ-9 can range from 0 to 27 and indicate minimal, mild, moderate, moderately severe, or severe depression symptom severity. Sample items include “In the last week, have you had little interest or pleasure in doing things?” and “In the last two weeks, have you been feeling down, depressed, or hopeless?” Respondents indicate answers on a 4-point Likert scale ranging from “not at all” to “nearly every day.” In primary care patients, the measure has a sensitivity of $88\%$ and a specificity of $88\%$ [43]. The PHQ-9 will be administered at initial screening, baseline, after intervention, and 20-week follow-up. In addition, participants will complete a PHQ-9 self-assessment built into the web-based sessions every other week. To mimic the additional assessment patterns in the treatment group, participants in the control condition will also be administered the PHQ-9 every other week via phone during the 10-week waiting period.
**Table 1**
| Variable | Assessment or activity | Screening | Baseline | Postrandomization | Postrandomization.1 | Postrandomization.2 | Follow-up |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Variable | Assessment or activity | Screening | Baseline | Biweekly | Post | | Follow-up |
| Depression | PHQ-9a | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ |
| Anxiety | GAD-7b | | ✓ | | ✓ | ✓ | ✓ |
| Social isolation | PROMIS-SI 8ac | | ✓ | | ✓ | ✓ | ✓ |
| Health self-assessment | PROMIS-GHd | | ✓ | | ✓ | ✓ | ✓ |
| Well-being and quality of life | EQ-5D-5L | | ✓ | | ✓ | ✓ | ✓ |
| Skill acquisition | CBTSQe | | ✓ | | ✓ | ✓ | ✓ |
| Acceptability | Modified TEIf | | | | ✓ | | |
| Adherence | Session completion | | | | ✓ | | |
| Participant background | Sociodemographic, health status, and physical functioning | ✓ | ✓ | | | | |
Anxiety will be assessed with the General Anxiety Disorder-7, which has been validated in populations of older adults [46]. Sample items include “How often have you been bothered by feeling nervous, anxious, or on edge in the last two weeks?” and “How often have you been bothered by worrying too much about different things in the last two weeks?” Like the PHQ-9, respondents answer on a 4-point Likert scale ranging from “not at all” to “nearly every day.” In primary care patients, the measure has a sensitivity of $89\%$ and a specificity of $82\%$ [47].
Social isolation will be measured using the Patient-Reported Outcomes and Measures Information System–Social Isolation (PROMIS-SI 8a). This measure has been used extensively in older adults with chronic health conditions and depression [48-50]. This 8-item measure uses a 5-point Likert scale with responses ranging from “always” to “never.” Sample items include “I feel isolated from others” and “I feel that people avoid talking to me.” The PROMIS-SI has high internal consistency (Cronbach α=.91) [51].
The PROMIS–General Health (PROMIS-GH) will be used as a global measure of health and health-related quality of life. This 10-item measure uses a 5-point Likert scale ranging from “excellent” to “poor.” It contains items such as “*In* general, would you say your health is...?” and “*In* general, how would you rate your satisfaction with your social activities and relationships…?” The PROMIS-GH has been validated in older adults with chronic health conditions, demonstrating moderate internal reliability (ordinal α=.82-.88) [52].
Quality of life will be measured using the EQ-5D-5L. Respondents answer on a 5-point Likert scale ranging from “I have no problems with…” to “I am unable to…/I have extreme…” They rate their experience in 5 life areas (mobility, self-care, usual activities, pain, and anxiety or depression). It also includes an overall health ranking on a scale of 0-100. In a systematic review including 99 studies that included a diverse range of medical conditions, the EQ-5D-5L exhibited overall excellent psychometric properties [53].
To explore the role of CBT skill acquisition, participants will complete the Cognitive-Behavioral Therapy Skills Questionnaire. This validated 16-item questionnaire assesses a respondent’s ability to apply cognitive restructuring, behavioral activation, and other CBT-related skills [54]. Participants are asked how often they currently do sample activities, such as making plans over the weekend, motivating themselves to do things, and socializing even though they do not want to. Items are answered on a 5-point Likert scale ranging from “I don’t do this” to “I always do this.” Acceptability will be assessed by the number of sessions participants completed (ie, adherence) and the modified Treatment Evaluation Inventory. This 11-item inventory has been previously used to evaluate the acceptability of cognitive therapy for depression interventions among older adults and has good convergent validity and internal consistency (α=.92) [55]. The measure will be administered after the intervention. The measure is answered on a 5-point Likert scale ranging from “never” to “always.”
## Qualitative Interviews
After completing the intervention, participants will complete a semistructured qualitative interview lasting roughly 30 minutes. The interview probes several domains, including their experience with the program, likes and dislikes, and experience with specific program components (eg, storyline, home practice, materials, and ease of use). Additional questions will relate to participants’ relationship with their Empower Coach.
## Sample Size
A sample size of 70 ($$n = 35$$ per group) should provide at least $80\%$ power, allowing for a $20\%$ attrition rate for both arms. Specifically, we considered the case where the 2 arms are compared in terms of weekly changes in the mean PHQ-9 survey responses between the baseline, 5 interim in-app PHQ-9 assessments (roughly at weeks 1, 3, 5, 7, and 9), and the 10-week follow-up PHQ-9 survey. We assume that the changes in the mean response can be expressed in terms of a linear trend, and the treatment effect can be expressed in terms of the difference in slopes or rate of change (δ = –0.8 × 5 in the total PHQ-9 score over 10 weeks; 5 is the SD in the change scores in our feasibility study). Sample size estimates used the following inputs: [1] type I error rate (α)=.05; [2] the smallest meaningful difference to be detected (δ) over the 10 weeks; [3] power (γ)=.80; [4] we assumed that outcome within-subject variance (σ2=62; estimated from our feasibility study data) is constant over time; [5] the number of repeated measurements per person (n): 1 baseline, 5 in-app assessments, 1 final postbaseline for a total of 7 repeated assessments; and [6] regarding between-subject variation of the slopes, we assumed a variance of random slopes τ2=0.2 (estimated from our feasibility study data). The sample size is 52 for both arms with equal randomization probabilities; with an attrition rate of $20\%$ for both arms, the total required sample size is 65. We set the final target sample size to 70 to allow room for higher attrition and lower adherence.
## Analytic Plan
To reduce bias, analyses will involve an intention-to-treat design. Linear mixed models will be used to test within-group changes over time and to compare changes between the treatment and control groups in outcomes of interest. The LASSO (least absolute shrinkage and selection operator [56,57]) variable selection technique will be used to find the optional selection of covariates for the final model. Exploration of mechanisms of change variables involves a procedure described by Hayes [58] that uses bootstrapping to test indirect effects. Statistical analysis will be conducted using Stata 15 SE. Although this trial is not fully powered for mediation analysis, findings from these analyses will inform future research to identify mechanisms of change.
We will use a pragmatic approach involving inductive and deductive coding for qualitative data. Qualitative analysis of the interview data collected from the completed single-group feasibility study is underway, and a codebook will be created during the process. The codebook from the single group study will serve as a framework for us to code the qualitative data collected from the pilot RCT. The codebook will be iteratively refined by reviewing the data and themes several times. Qualitative analysis will be conducted using Dedoose, a web app for mixed methods research.
## Ethics Approval
This study was reviewed and approved by the Health Sciences and Behavioral Sciences institutional review board (IRB) at the University of Michigan. Approval was granted on April 19, 2022 (HUM00212950).
A waiver of documentation for written consent was approved by the IRB. Consent to participate in the study will be obtained orally from participants. Consent will be obtained at 2 points. At first contact, participants will provide consent to be screened. Second, after screening and before the baseline interview, study-eligible participants will be asked to provide oral consent to participate in the study. This will be recorded via a tape recorder after a verbal review of an informed consent document. To ensure the quality of consent, all participants will reconsent at the beginning of the first web-based session of the intervention. This will be accomplished by providing a brief written consent document on the first page of the web-based session. Participants must confirm their understanding of the document to proceed to the first web-based session.
The privacy of participants will be protected in several ways. All participants will complete web-based therapy sessions by themselves in their private homes. Direct identifiers are not stored in the web-based program. Mental health assessment data collected as part of the web-based sessions are stored in a HIPAA-compliant database. Other study data (eg, baseline and posttest assessments) are entered and managed on REDCap. Only the study team members directly authorized by the principal investigator will have access to study data with identifiers. Access is typically restricted to the principal investigator and project coordinators. As the study involves follow-up, direct identifiers will be retained for this purpose. After completion of the study and data cleaning, all direct identifiers will be removed from the database, rendering the final data set anonymous.
Those who qualify for the trial will be paid up to US $100 for participation. Compensation is determined based on the number of assessments completed. Partial compensation is available (eg, participants who complete only the baseline assessment will receive US $30). Participants will be provided US $30 for the baseline, US $50 for the posttest, and US $20 for the follow-up. Payment will be provided directly to the participant via mailed check or Visa gift card, whichever is preferred by the participant.
## Results
Funding for this multiphase project was awarded in November 2020. IRB approval for the single-group feasibility study and the proposed RCT was attained in April 2022. Enrollment for the single-group study began in May 2022, and all subjects completed the intervention by December 2022. Recruitment for the RCT began in January 2023, and the study completion is anticipated by December 2023. The trial is registered at ClinicalTrials.gov (NCT05593276).
## Principal Findings
To our knowledge, this is the first RCT of layperson-facilitated iCBT for low-income homebound older adults with depression. We hypothesize that the Empower@Home intervention will be feasible and acceptable, reduce depressive symptoms, and improve psychosocial functioning and health-related quality of life. Existing evidence-based mental health treatments are often inaccessible to homebound older adults, who have been largely excluded from digital mental health interventions. This trial addresses that gap by testing an intervention specifically designed to the needs of this population.
iCBT participants are exposed to the same components as conventional CBT, though they receive psychoeducational materials through a self-directed internet platform. A meta-analysis of iCBT trials with older adults found a large pooled within-group effect size, although most studies adopted weak designs [24]. In a recent iCBT pilot trial led by the last author, homebound older adults with low computer literacy found that iCBT was an acceptable way of treating depression and resulted in significant reductions in depressive symptoms at the posttest [33]. A recent RCT showed that iCBT also effectively prevented depression among older adults with multiple chronic conditions [29].
As a result of the COVID-19 pandemic, virtual care is gaining rapid traction, which provides unprecedented opportunities for integrating digital mental health interventions into community aging services. The COVID-19 pandemic has accelerated the adoption of technology and virtual care in community settings, which is likely to stay after the pandemic. Although notable age differences in technology use remain, the adoption of key technologies by older adults has grown markedly, and the age-related digital divide continues to narrow. For example, smartphone ownership increased from $46\%$ in 2018 to $61\%$ in 2021 among those 65 years and older [59]. Given the increased focus on digital inclusion, there is likely to be continued growth in access to broadband internet. The Affordable Connectivity Program (formerly Emergency Broadband Benefit), a long-term, US $14 billion program authorized by the 2021 Infrastructure Investment and Jobs Act, provides up to US $30 per month toward internet service and up to US $100 to purchase a computer or tablet device for eligible households. With providers and clients more willing to use technology, increased funding, regulatory changes, and improved technology infrastructure, digital mental health interventions are ripe for implementation in community aging services, where many homebound older adults receive services.
The pilot RCT will provide crucial data for a fully-powered effectiveness trial in the future. If confirmed effective, several probable funding mechanisms can facilitate long-term sustainability. These funding sources include Medicaid, federal grants, private pay, and commercial insurance. For example, community agencies can apply for federal grants authorized by the Older Americans Act that support community-based health-promotion programs. Many commercial insurance providers are already reimbursing for iCBT services [60,61]. Agencies could also charge a sliding scale fee to cover operating costs and raise revenues. In terms of training, most of the provider training materials are web-based, making it relatively easy to scale up.
## Strengths and Limitations
The strengths of this study include an iteratively developed intervention incorporating patient and provider feedback, randomized design, use of laypersons as interventionists, and recruitment of an underserved, hard-to-reach population with high unmet mental health needs. For limitations, this study uses a small sample size and a waitlist control design, which may lead to an overestimation of treatment effects [62]. Though this intervention is intended to address depression in older adults, it may also address related conditions not measured in this study, such as demoralization [63,64].
## Conclusions
The planned study addresses the shortage of iCBT products suitable for homebound older adults. Our treatment innovation, if found effective, will contribute to closing the digital divide and mental health disparities in homebound older adults. Study implications extend to other digital mental health interventions and populations with physical disabilities and access restrictions who face persistent disparities in mental health.
## Data Availability
The data sets generated or analyzed in this study will not be publicly available. Consent and ethical approval for this study does not include a provision for the sharing of data from this study.
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|
---
title: 'Changes in Mobile Health Apps Usage Before and After the COVID-19 Outbreak
in China: Semilongitudinal Survey'
journal: JMIR Public Health and Surveillance
year: 2023
pmcid: PMC9996426
doi: 10.2196/40552
license: CC BY 4.0
---
# Changes in Mobile Health Apps Usage Before and After the COVID-19 Outbreak in China: Semilongitudinal Survey
## Abstract
### Background
Mobile health (mHealth) apps are rapidly emerging technologies in China due to strictly controlled medical needs during the COVID-19 pandemic while continuing essential services for chronic diseases. However, there have been no large-scale, systematic efforts to evaluate relevant apps.
### Objective
We aim to provide a landscape of mHealth apps in China by describing and comparing digital health concerns before and after the COVID-19 outbreak, including mHealth app data flow and user experience, and analyze the impact of COVID-19 on mHealth apps.
### Methods
We conducted a semilongitudinal survey of 1593 mHealth apps to study the app data flow and clarify usage changes and influencing factors. We selected mHealth apps in app markets, web pages from the Baidu search engine, the 2018 top 100 hospitals with internet hospitals, and online shopping sites with apps that connect to smart devices. For user experience, we recruited residents from a community in southeastern China from October 2019 to November 2019 (before the outbreak) and from June 2020 to August 2020 (after the outbreak) comparing the attention of the population to apps. We also examined associations between app characteristics, functions, and outcomes at specific quantiles of distribution in download changes using quantile regression models.
### Results
Rehabilitation medical support was the top-ranked functionality, with a median 1.44 million downloads per app prepandemic and a median 2.74 million downloads per app postpandemic. Among the top 10 functions postpandemic, 4 were related to maternal and child health: pregnancy preparation (ranked second; fold change 4.13), women's health (ranked fifth; fold change 5.16), pregnancy (ranked sixth; fold change 5.78), and parenting (ranked tenth; fold change 4.03). Quantile regression models showed that rehabilitation (P75, P90), pregnancy preparation (P90), bodybuilding (P50, P90), and vaccination (P75) were positively associated with an increase in downloads after the outbreak. In the user experience survey, the attention given to health information (prepandemic: $\frac{249}{375}$, $66.4\%$; postpandemic: $\frac{146}{178}$, $82.0\%$; $$P \leq .006$$) steadily increased after the outbreak.
### Conclusions
mHealth apps are an effective health care approach gaining in popularity among the Chinese population following the COVID-19 outbreak. This research provides direction for subsequent mHealth app development and promotion in the postepidemic era, supporting medical model reformation in China as a reference, which may provide new avenues for designing and evaluating indirect public health interventions such as health education and health promotion.
## Background
In the wake of the COVID-19 outbreak, digital health technologies to assist medical service systems and people [1], such as telemedicine, have moved from a convenience to a demand [2-5]. Mobile health (mHealth) apps are a novel platform that uses mobile devices to acquire data across wellness and disease diagnosis, prevention, and management [6,7].
Before the COVID-19 outbreak, several studies investigated the characteristics of apps in China; these studies focused on only specific health domains, such as disease management, women's health, and sports, instead of elaborating on the overall mHealth app situation [8-11]. However, in the context of COVID-19, the development of mHealth apps has become a hot topic [12]. Only a few studies have investigated the mHealth app market status, focusing on the assessment of functional distribution but without refined classifications of mHealth apps and lacking information integrity [13]. Therefore, the classifications for mHealth apps have not been elaborated, and detailed descriptive research on mHealth apps is lacking.
After the outbreak, apps directly related to COVID-19 that were used to track high-risk groups and assist in diagnosis were the most studied [14-16]. One study provided an overview and classifications of mHealth apps currently available on the market to combat COVID-19, based on differences in basic functions and purpose [17]. However, in the face of the significant challenges posed by the pandemic, app types other than those related to COVID-19 were also considered valuable tools, such as easing the burden on hospitals, providing access to reliable information, tracking individuals’ symptoms and mental health, and discovering new predictors [18]. Previous studies have shown that mHealth apps can improve people's life needs (such as fertility), which promote their use [19]. However, the development trends for many types of mHealth apps have been complicated due to the pandemic, which requires attention.
At the same time, unlike other countries, China implemented normalized pandemic prevention and control and rarely used contact tracing apps in a personal form; thus, the apps developed directly as a result of the pandemic were not the focus of this research. There was a spike in the volume of phone calls asking medical questions after the outbreak with a great increase in online demand in China [17]. The unbalanced allocation of health resources between the east and west, due to the vast size of China, also led to the demand for telemedicine. During the COVID-19 pandemic, a multimodal telemedicine network in Sichuan Province in Western China was activated immediately, which was demonstrated to be feasible, acceptable, and effective [20]. Moreover, due to the unique national conditions of the 1-child policy and the aging population, changes in mHealth app fields before and after the outbreak are different from those in other countries [21,22]. Therefore, mHealth apps, as emerging tools, that focus on changes in various health functions before and after the outbreak are worth studying in China and could be important for prevention, diagnosis, treatment, and management decisions for future public health emergencies.
However, it is presently unclear what mHealth app characteristics, if any, have been influenced and appropriately deployed in the pre and postpandemic periods.
## Objectives
Here, we conducted a nationwide study of mHealth apps in China. The aim of this study was [1] to describe and compare digital health concerns before and after the COVID-19 outbreak, including mHealth app data flow and user experience, and [2] to analyze the impact of COVID-19 on mHealth apps.
## Data Acquisition
Before the COVID-19 outbreak, we conducted a comprehensive electronic search of 4 sources up to October 25, 2019: [1] apps on leaderboards of health-related categories in the 6 largest app markets in China, including the top 50 on the Huawei Android Market (Huawei Technologies Co Ltd; Shenzhen, China), top 50 on the OPPO Android Market (Oppo Electronics Co Ltd; Dongguan, Guangdong, China), top 100 on the Vivo Android Market (Vivo Co Ltd; Dongguan, Guangdong, China), top 50 on the Tencent Android Market (Tencent Holdings Limited; Shenzhen, China), top 100 on the 360 Android Market (Qihoo 360 Technology Co Ltd; Beijing, China), and top 100 on the Apple iTunes store for China (Apple; Cupertino, CA) [23]; [2] the mHealth apps on the first 20 web pages of the Baidu search engine, which is the largest search engine in China; [3] apps that can connect to smart devices from the 4 large online shopping sites (Tmall, JD, Pinduoduo, and Suning); and [4] apps affiliated with internet hospitals on the list of the top 100 Chinese hospitals in 2018. The exclusion criteria were [1] duplicated apps, [2] app descriptions irrelevant to health, [3] apps not available in the Chinese language, [4] apps that were not available for download through the official Android and Apple app stores or the Baidu search engine, and [5] apps that could not be opened or used due to technical problems. Apps with patient and clinician versions were evaluated as different items. Search terms, sample quantities, and data collection times for each source are provided in Table S1 in Multimedia Appendix 1.
We collected 4 types of data: [1] basic app characteristics from the description interface, including the size of apps, number of app downloads, and target users; [2] app developers’ information from the largest commercial inquiry platform, the Tianyancha website [24], including transaction amount, registered capital, number of staff, operating status, establishment date, and geographic location; [3] app permission listing data from the permission interface; and [4] app functions from app trials, except for apps only open to internal users. As the iOS App Store does not display the number of downloads, we replaced total downloads in iOS with the number of reviews and used statements of “downloads” in the following paragraphs. If an app existed on multiple platforms, the total number of app downloads was calculated as the sum of app downloads from all platforms. The app trial included at least two investigators who downloaded selected applications and independently identified application functionality according to a clear functional definition by using iPhones and Android phones. See Table S2 in Multimedia Appendix 1 for details on function definitions.
Following the outbreak, the same mHealth apps were investigated a second time in April 2021 as semilongitudinal samples with download data, and we determined whether COVID-19 content had been added. A download change was defined as the difference between post and prepandemic downloads.
## User Experience Survey
We recruited residents through a community health checkup program to explore the user experience with mHealth apps among a large community of more than 20,000 people with a balanced age distribution in southeastern China. We used an offline questionnaire to survey 400 participants from October 2019 to November 2019 before the COVID-19 outbreak and 200 participants from June 2020 to August 2020 after the outbreak. A total of 553 ($\frac{553}{600}$, $92.2\%$) participants completed the survey: 375 ($\frac{375}{400}$, $93.8\%$) before the outbreak and 178 ($\frac{178}{200}$, $89.0\%$) after the outbreak. A predesigned, structured questionnaire was provided to potential participants in the waiting areas of the medical examination center in this community. The questionnaire was designed to collect information on participants’ attention to health information, various aspects of mHealth technology usage, willingness to consume mHealth technology, and health status and demographic characteristics. Trained research assistants who were fluent in Chinese administered the questionnaire and provided verbal instructions about how to complete them.
## Ethical Considerations
Before taking the survey, informed consent was obtained from each participant. This study was approved by the Biomedical Research Ethics Committee of Fujian Medical University (2018 number 11). All procedures were performed using the relevant guidelines and regulations.
## Data Analysis
A descriptive analysis was conducted for mHealth app characteristics, developers, permission, functions, and user experiences in China. Data are presented using frequencies and percentages, bar charts, statistical maps, Venn diagrams, and heat maps. Continuous variables are presented as the mean and SD or the median and IQR, while categorical variables are presented as the frequency and percentage. Mann-Whitney U tests or chi-square tests were used to assess differences among variables.
We also compared the post and prepandemic app downloads of each category using paired t tests. To account for multiple comparisons, we calculated a Bonferroni-corrected P value criterion of$\frac{.05}{28.}$ Therefore, $P \leq 1.79$x10-3 was considered statistically significant.
Quantile regression (QR) models were used to explore the relationship between modeling covariates and quartiles of the outcome variable of interest [25]. Because of the wide range and non-normal distribution of download changes, we used QR models to examine associations between app characteristics, functions, and outcomes at specific quantiles of distribution in download changes. This analysis does not make assumptions about the residual distribution and is more robust to outliers in the outcome [26]. Unlike an ordinary linear regression model, which models the mean of only 1 dependent variable, QR examines the effect of covariates at different points of the conditional distribution of the response variable and gives more comprehensive results. QR models provided a more detailed view of associations of app characteristics, functions, and outcomes with download changes. We obtained estimates and plotted them at the following quantiles: 10th, 25th, 50th (median), 75th, and 90th. In the QR analysis, significance was assessed at the $10\%$ level.
All analyses were prespecified and performed using SPSS 25.0 (IBM Corp; Armonk, NY) and Stata version 13 (StataCorp; College Station, TX).
## App Characteristics
A total of 1593 mHealth apps were included in the analysis (see Figure 1). During the COVID-19 pandemic, various app downloads showed an overall upward trend (change in median: 61,561), and approximately $10\%$ ($\frac{182}{1593}$, $11.4\%$) of the apps had functions or content added for COVID-19 (see Table 1). Figures 2 and 3 show the target users and geographic distribution of the apps. Target users included healthy people ($\frac{921}{1593}$, $57.8\%$), patients ($\frac{513}{1593}$, $32.2\%$), and health care professionals ($\frac{393}{1593}$, $24.7\%$). The geographical distribution of mHealth app developers included in the study was concentrated in megacities (ie, Beijing, Shanghai, Guangzhou) and southeast China (coastal areas with a developed economy; see Figure 3). Additional permission requests were found in app markets (see Table S3 in Multimedia Appendix 1).
**Figure 1:** *Flow diagram of app selection.* TABLE_PLACEHOLDER:Table 1 **Figure 2:** *Sankey diagram of flow direction in apps. The width of the colored boxes and their connecting gray bands are directly proportional to the frequency of apps from every data source (left side) and flow quantities of these apps to the attributable user communities (right side). Hcps: health care professionals.* **Figure 3:** *(A) Geographical distribution and (B) distribution of the time of establishment of mobile health (mHealth) app developers in China. There is another developer in Canada.*
Of the 1593 mHealth apps, 1285 ($80.7\%$) apps had full functionality available to conduct the app trial, including apps for health management ($\frac{1248}{1593}$, $78.3\%$) and apps for medical support ($\frac{697}{1593}$, $43.8\%$). The frequency of each function available in Chinese mHealth apps is shown in Figure 4. Figure 5 shows the associations between the 5 app classifications, with the circles scaled to the number of apps in each app classification. The rating concepts for these 5 different classifications in app trials are available in Table S4 in Multimedia Appendix 1 [27].
**Figure 4:** *For each classification of mobile health (mHealth) apps in China: (A) frequency of app functions for 5 classifications; (B) ranking of the frequency of app functions is displayed on a color scale ranging from green (lowest charge rates) to orange, yellow, and red (highest charge rates).* **Figure 5:** *Venn diagrams illustrating the associations between app classifications: (A) user communities, (B) mobile health service function, (C) content or services provided by apps, (D) tertiary prevention, and (E) service time provided by apps.*
## Comparison of Download Changes in Apps With Different Functions Between Pre and Postpandemic
Overall upward trends in app downloads during the pandemic were driven by some key app functions (see Figures 6 and 7). Rehabilitation (prepandemic median 1,437,500; postpandemic median 2,741,890; $P \leq .001$) and pregnancy preparation (prepandemic median 480,520; postpandemic median 1,982,490; $P \leq .001$) were the most often used functions, with statistically significant differences between pre and postpandemic. The 9 most important drivers for increasing downloads were divided into 2 categories. Three functions related to children and maternal health were observed: women's health (fold change 5.16), pregnancy (fold change 5.78), and parenting (fold change 4.03). Increased downloads of these apps (women’s health, $P \leq .001$; pregnancy, $P \leq .001$; parenting, $P \leq .001$) were also observed between pre and postpandemic. The other 6 most popular functions regarding treatment needs and maintenance were treatment (fold change 5.31), plastic surgery (fold change 8.77), drug use (fold change 5.92), patient information management (fold change 5.06), nutrition (fold change 8.79), and Chinese medicine (fold change 10.04). Vaccination, which was most relevant to COVID-19, saw a large spike in download rates, with an increase of 6.26 times (absolute change in mean: 7,233,800) postpandemic compared with prepandemic.
The 6 functions that decreased in ranking the most in the postpandemic period were health education, genetic screening, medical service purchases, drug purchases, inquiries, and physiological testing via mobile phone; 4 additional functions that declined after the COVID-19 outbreak included medical examination, medical community, patient management, and disease management. Bodybuilding, which was closely related to outdoor activities, also declined in ranking, with an increase of only 2.71 times prepandemic rates, but its absolute change in mean was 28,247,800. All these functions with declining rankings were growing, albeit at lower speeds relative to high-ranking functions. For example, the increase in drug purchases was 2.05 times (absolute change in mean: 7,388,400) the prepandemic rate, and patient management downloads were 4.98 times (absolute change in mean: 3,039,900) the prepandemic rate.
**Figure 6:** *The number of occurrences of each function (ie, function frequency) in the 1593 mobile health (mHealth) apps with multiple functions in China.* **Figure 7:** *Leading functions of mobile health (mHealth) apps in the pre and postpandemic periods in China, connected by lines between the periods to show increased (solid line) or decreased (dashed line) ranking, while bold number indicate significant changes between the periods as determined using paired t tests with Bonferroni-corrected P values <1.79x10-3. Fold change in median of the number of app downloads=(Medpost-Medpre)/Medpre. Absolute change in mean of the number of app downloads=∑(npost-npre)/n.*
## Relationship Between App Characteristics and Download Changes
Based on the QR analysis, a positive effect for adding COVID-19 function and content on apps (P10: $$P \leq .001$$; P50: $$P \leq .01$$; P75: $$P \leq .009$$; P90: $$P \leq .01$$) was observed across download changes in most quantiles, with the largest effect at the 90th quantile (see Table 2, Figure 8, and Multimedia Appendix 2). Positive effects of the size of apps on download changes were also observed at high quantiles (P75: $$P \leq .03$$; P90: $$P \leq .002$$). Other characteristics of the QR are shown in Table 2.
## Relationship Between App Functions and Download Changes
We found that 4 of 28 functions, including rehabilitation (P75: $$P \leq .003$$; P90: $$P \leq .02$$), pregnancy preparation (P90: $$P \leq .09$$), bodybuilding (P50: $$P \leq .07$$; P90: $$P \leq .08$$), and vaccination (P75: $$P \leq .06$$), were positively associated with download changes, mainly in the higher quantiles (see Table 3, Figure 9, and Multimedia Appendix 3). Health education (P75: $$P \leq .09$$; P90: $$P \leq .09$$), drug use (P90: $$P \leq .08$$), cultivation of lifestyle (P25: $$P \leq .02$$; P50: $$P \leq .06$$; P75: $$P \leq .07$$), men’s health (P90: $$P \leq .07$$), and disease management (P75: $$P \leq .03$$) had negative correlations with download changes. Other functions in the QR are shown in Table S5 in Multimedia Appendix 1.
## User Experience
No significant difference was found in the sex ($$P \leq .41$$) or mean age ($$P \leq .52$$) of the participants. The attention given to health information (prepandemic: $\frac{249}{375}$, $66.4\%$; postpandemic: $\frac{146}{178}$, $82.0\%$; $$P \leq .006$$) and the percentage of people owning smartphones (prepandemic: $\frac{186}{375}$, $49.7\%$; postpandemic: $\frac{108}{178}$, $60.7\%$; $$P \leq .02$$) steadily increased after the outbreak (see Table 4). The vast majority of individuals (prepandemic: $\frac{129}{141}$, $92.1\%$; postpandemic: $\frac{89}{90}$, $98.9\%$) used social networks (eg, WeChat) to obtain health information online. Furthermore, the ratio of internet hospital use rose dramatically (prepandemic: $\frac{6}{375}$, $1.6\%$; post-pandemic: $\frac{23}{178}$, $12.9\%$; $P \leq .001$). Other characteristics of the user experience analysis are shown in Table 4.
**Table 4**
| Characteristics | Characteristics.1 | Characteristics.2 | Before the outbreak (n=375) | Before the outbreak (n=375).1 | After the outbreak (n=178) | After the outbreak (n=178).1 | P value |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Age (years), median (IQR) | Age (years), median (IQR) | Age (years), median (IQR) | 70 (66-74) | 70 (66-74) | 70 (66-75) | 70 (66-75) | .52 |
| Age (years), mean (SD) | Age (years), mean (SD) | Age (years), mean (SD) | 70.46 (6.329) | 70.46 (6.329) | 70.85 (7.680) | 70.85 (7.680) | —a |
| Sex, n (%) | Sex, n (%) | Sex, n (%) | Sex, n (%) | Sex, n (%) | Sex, n (%) | Sex, n (%) | .41 |
| | Male | 166 (44.3) | 166 (44.3) | 85 (48.0) | 85 (48.0) | | |
| | Female | 209 (55.7) | 209 (55.7) | 92 (52.0) | 92 (52.0) | | |
| Attention to health information, n (%) | Attention to health information, n (%) | Attention to health information, n (%) | Attention to health information, n (%) | Attention to health information, n (%) | Attention to health information, n (%) | Attention to health information, n (%) | .006 |
| | Never | 125 (33.3) | 125 (33.3) | 32 (18.0) | 32 (18.0) | | |
| | Sometimes | 131 (34.9) | 131 (34.9) | 75 (42.1) | 75 (42.1) | | |
| | Often | 73 (19.5) | 73 (19.5) | 57 (32.0) | 57 (32.0) | | |
| | Always | 45 (12.0) | 45 (12.0) | 14 ( 7.9) | 14 ( 7.9) | | |
| The ability to use electronic products (mobile phone, tablet, computer), n (%) | The ability to use electronic products (mobile phone, tablet, computer), n (%) | The ability to use electronic products (mobile phone, tablet, computer), n (%) | 186 (49.7) | 186 (49.7) | 108 (60.7) | 108 (60.7) | .02 |
| Getting health information offline in the past 6 months, n (%) | Getting health information offline in the past 6 months, n (%) | Getting health information offline in the past 6 months, n (%) | 108 (28.8) | 108 (28.8) | 104 (58.4) | 104 (58.4) | <.001 |
| Getting health information through the internet in the past 6 months, n (%) | Getting health information through the internet in the past 6 months, n (%) | Getting health information through the internet in the past 6 months, n (%) | 141 (37.6) | 141 (37.6) | 90 (50.6) | 90 (50.6) | .004 |
| Ways to obtain health information, n (%) | Ways to obtain health information, n (%) | Ways to obtain health information, n (%) | Ways to obtain health information, n (%) | Ways to obtain health information, n (%) | Ways to obtain health information, n (%) | Ways to obtain health information, n (%) | Ways to obtain health information, n (%) |
| | Social network (eg, WeChat, QQ) | 129 (92.1) | 129 (92.1) | 89 (98.8) | 89 (98.8) | .008 | .008 |
| | Portal web | 36 (25.9) | 36 (25.9) | 19 (22.6) | 19 (22.6) | .63 | .63 |
| | mHealth apps | 6 ( 4.3) | 6 ( 4.3) | 2 ( 2.5) | 2 ( 2.5) | .71 | .71 |
| | Search engine | 36 (25.5) | 36 (25.5) | 22 (26.5) | 22 (26.5) | .87 | .87 |
| | Internet hospitals | 6 ( 1.6) | 6 ( 1.6) | 23 (12.9) | 23 (12.9) | <.001 | <.001 |
| Consuming intention for internet health information per month (¥b), n (%) | Consuming intention for internet health information per month (¥b), n (%) | Consuming intention for internet health information per month (¥b), n (%) | Consuming intention for internet health information per month (¥b), n (%) | Consuming intention for internet health information per month (¥b), n (%) | Consuming intention for internet health information per month (¥b), n (%) | Consuming intention for internet health information per month (¥b), n (%) | .70 |
| | 0 | 316 (84.3) | 316 (84.3) | 150 (84.3) | 150 (84.3) | | |
| | 1-10 | 10 (2.7) | 10 (2.7) | 5 (2.8) | 5 (2.8) | | |
| | 11-50 | 19 (5.1) | 19 (5.1) | 9 (5.1) | 9 (5.1) | | |
| | 51-100 | 3 (0.8) | 3 (0.8) | 0 (0) | 0 (0) | | |
| | 101-200 | 2 (0.5) | 2 (0.5) | 1 (0.6) | 1 (0.6) | | |
| | 201-500 | 1 (0.3) | 1 (0.3) | 0 (0) | 0 (0) | | |
| | >500 | 0 (0) | 0 (0) | 0 (0) | 0 (0) | | |
| The actual cost for internet health information last month (¥b), n (%) | The actual cost for internet health information last month (¥b), n (%) | The actual cost for internet health information last month (¥b), n (%) | The actual cost for internet health information last month (¥b), n (%) | The actual cost for internet health information last month (¥b), n (%) | The actual cost for internet health information last month (¥b), n (%) | The actual cost for internet health information last month (¥b), n (%) | .45 |
| | 0 | 348 (92.8) | 348 (92.8) | 164 (92.1) | 164 (92.1) | | |
| | 1-10 | 0 (0) | 0 (0) | 2 (1.1) | 2 (1.1) | | |
| | 11-50 | 0 (0) | 0 (0) | 2 (1.1) | 2 (1.1) | | |
| | 51-100 | 1 (0.3) | 1 (0.3) | 0 (0) | 0 (0) | | |
| | 101-200 | 1 (0.3) | 1 (0.3) | 0 (0) | 0 (0) | | |
| | 201-500 | 1 (0.3) | 1 (0.3) | 0 (0) | 0 (0) | | |
| | >500 | 1 (0.3) | 1 (0.3) | 0 (0) | 0 (0) | | |
| Intention to avoid some unhealthy behaviors after obtaining health information from the internet, n (%) | Intention to avoid some unhealthy behaviors after obtaining health information from the internet, n (%) | Intention to avoid some unhealthy behaviors after obtaining health information from the internet, n (%) | Intention to avoid some unhealthy behaviors after obtaining health information from the internet, n (%) | Intention to avoid some unhealthy behaviors after obtaining health information from the internet, n (%) | Intention to avoid some unhealthy behaviors after obtaining health information from the internet, n (%) | Intention to avoid some unhealthy behaviors after obtaining health information from the internet, n (%) | .67 |
| | Never | 15 (4.0) | 15 (4.0) | 0 (0) | 0 (0) | | |
| | Sometimes | 12 (3.2) | 12 (3.2) | 16 (9.0) | 16 (9.0) | | |
| | Often | 65 (17.3) | 65 (17.3) | 47 (26.4) | 47 (26.4) | | |
| | Always | 25 (6.7) | 25 (6.7) | 9 (5.0) | 9 (5.0) | | |
## Principal Findings
Our study demonstrates that the usage and population utilization of mHealth applications increased after the COVID-19 outbreak. As a powerful tool for providing health care services, functions closely related to the pandemic, including rehabilitation, treatment, drug use, and vaccination, were positively associated with changes in app downloads. The high growth of app use related to maternal and child health, including pregnancy preparation and women’s health, shows the potentially increased desire for family among the Chinese population in the postpandemic era. Moreover, the user experience and high use of health management apps also reflect great attention to self-care. Overall, mHealth apps assist with health improvement against the background of normalized pandemic control and may improve fertility.
## COVID-19–Related Apps
The usage of COVID-19 pandemic-related apps, such as vaccination, increased in rank. Furthermore, adding pandemic-related functions positively correlated with increased downloads. The likely reason behind this rise was that apps inherently related to the pandemic can easily capture the attention of the public as a means of obtaining information. Some apps with larger user groups may also add COVID-19 modules to respond to normalized pandemic prevention and control policies [14].
## Medical Support Apps
Unprecedented large-scale quarantine measures and shortages of medical resources have made telemedicine care an important and real demand during the pandemic [28]. In our research, rehabilitation, including apps for long-term care or chronic disease management, was the most widely used function before and after the outbreak, overcoming the interruption of personal health care services caused by COVID-19. mHealth apps are used for many rehabilitation purposes [25]. One study showed that effective rehabilitation apps helped patients increase their health and happiness index during the pandemic period [29]. Furthermore, the use of mHealth apps can help improve adherence to treatment [30]. Use of apps with functions for rehabilitation, treatment, and drug use increased significantly after the outbreak, providing stable medical services to reduce the negative impact of home isolation. China’s emerging internet model provides the basis for remote pharmacy services [31], assisting patients who cannot always go to the pharmacy. At the same time, personalized medical plans are also an important part of precision medicine [32]. The city of Taizhou, China, had a successful experience using telemedicine to prevent and treat COVID-19 [33]. Consequently, mHealth apps are an effective medical tool in the context of COVID-19.
## Health Management Apps
Most apps were designed for health management by all people, mainly focusing on bodybuilding and nutrition. In our research, app flow and user experience surveys both showed that the rankings of most functions related to health management were rising, which reflects great attention to self-care postpandemic.
It is interesting that there was increased use of maternal and child health apps, including pregnancy preparation, women’s health, pregnancy, and parenting, after the outbreak, showing potentially increased desire for fertility among the Chinese population.
One study concluded that the pandemic is affecting people’s desire to become parents, which was consistent with our results [34]. Another previous study conducted a cross-sectional survey of 285 apps to analyze the current situation of maternity apps in Italy, finding that high-quality, targeted, and effective apps for pregnancy and postnatal health care had relevant implications in terms of maternal and newborn health prevention and promotion [35]. mHealth apps have the potential to be used extensively in improving maternal well-being [36]. The high growth of maternal and child health-related apps such as pregnancy preparation and women's health in our study coincides with this. Before the pandemic, the progression of population aging, decline of women in the childbearing period, and increase in high-risk pregnancies had led to the continuous decline in China’s fertility rate [21]. Women have suffered significant reproductive health disruption since the beginning of the COVID-19 pandemic [37,38], which may have aggravated the decline in fertility [39]. However, with long-term home isolation and 2-child and 3-child policies proposed by the government, people are giving more attention to family matters, including pregnancy preparation and raising children, which may slow the decline in fertility to a certain extent. Therefore, maternal and child health apps can effectively assist health management and fill the vacancy of in-person perinatal health care services [40], despite interrupted pregnancy checkups [39]. It has been suggested that a model of health management combined with continuous care using the WeChat platform can significantly improve patients’ postoperative medication compliance and quality of life by requiring them to complete their rehabilitation tasks in the WeChat group, which is worth applying and promoting [41]. In China, pregnant women are accustomed to using WeChat groups recommended by hospitals to discuss pregnancy health information and may promote apps in it, which may be a way to increase app usage.
For bodybuilding apps, this function had positive changes in downloads during the COVID-19 pandemic. One study found that the keyword “mHealth” was closely associated with “physical activity” and “ehealth” in the last 2 decades of research on digital health behavior change technologies [42]. Closed gyms and restricted outdoor activities during the pandemic reduced physical activity levels; however, the use of physical activity apps may counteract the decline in exercise [43].
Many kinds of apps attempted to provide health education, which was the most widely available function in our study. Although this function declined during the pandemic and had negative changes in downloads, the population’s attention to health information increased. The probable cause behind this phenomenon is that new media platforms in China, such as WeChat and Weibo, have been vigorously promoted as important means for pandemic-related health information dissemination, which may decrease interest in acquiring apps when the information is readily available on these platforms. The high levels of knowledge of the Chinese public about COVID-19 prevention mainly comes from WeChat [44]. This is consistent with the results of our study that people use WeChat most often to obtain health information.
The COVID-19 pandemic has caused health anxiety at the population level. Digital intervention by mHealth apps is suitable for alleviating such sociopsychological consequences [45]. However, it may be because of the vigorous development of the psychological counseling hotline project in China during the pandemic that mental health apps were not of great concern. Furthermore, a study that conducted a systematic assessment of self-guided cognitive behavioral therapy (CBT)–based apps concluded that only a few self-guided CBT-based apps offer comprehensive CBT programs or suicide risk management resources, which may also be one of the reasons for not getting much attention [46].
## Willingness to Use mHealth Apps
Consistent with the report of the rapid increase in older adult internet users during the COVID-19 pandemic in China, we found that people over 50 years old paid more attention to mHealth apps after the outbreak [47-50]. In conjunction with the increased interest of older adults in the mHealth space, the results of a previous study suggested that person-centered mHealth apps can be used to create mHealth solutions with positive outcomes for older adults [51]. Coupled with the higher probability of chronic diseases or other conditions, older individuals should be a primary and adaptable group for mHealth apps [52]. However, the digital divide makes optimizing mHealth apps among older individuals difficult [53]. The Chinese people strongly support public health measures proposed due to COVID-19, which makes it possible to develop the ability to use smart devices, reduce the digital divide, and provide QR codes for pass certificates [54]. It is worth noting that a larger software file size was positively associated with download changes, indicating that excessive software size has little effect on the willingness to use the software. Having the right function is what users are most concerned about, and the causal relationship needs to be further studied [55].
## Limitations
Our study should be considered in the context of important limitations. First, this study excluded apps that were used internally by medical staff because we could not log in as an internal account holder; this dilutes the results of mHealth apps designed for health care professionals. However, our research focused on apps for patients and healthy people rather than internal apps. Future work will be conducted with apps used internally in medical care. Second, the absence of newly emerging apps made it impossible to provide an overview of the mHealth market after the COVID-19 outbreak. Therefore, we compared the changes in downloads during the pre and postpandemic periods to explore the relationship between various types of apps and the pandemic. Third, as a semilongitudinal survey, this study measured exposure and outcome, and it was difficult to derive causal relationships from the analysis; thus, we only made assumptions based on the status quo.
## Strengths
The study has practical implications and applications. This study is the first to investigate the relationship between COVID-19 and population-level utilization of mHealth apps through a semilongitudinal study of app markets’ data and a field questionnaire, combined with the results of both the web-based survey and the population user experience survey. As *China is* one of the few countries to adopt more active public health prevention and control measures, this study, which involves a multilevel and broad research scope, can provide strong data support for future comparative studies between different countries and regions. In the user experience survey, we explored the changing attitudes of the population toward digital health technology, suggesting that there is a good development environment for mHealth apps in the postpandemic era. This study, with consistent definitions of variables and processes, allowed the investigators to consistently classify mHealth apps and ensure data integrity, underpinning its strength. Our research clarified the relationship between various types of apps and usage changes by conducting investigations in the pre and postpandemic periods. We believe our results provide a good reference for the subsequent development of future mHealth apps. In addition to the increasing number of COVID-19–related apps prompted by pandemic policies, app developers should be aware that maternal, child, and self-care management are app functions about which the population is concerned.
## Perspective
mHealth apps utilize information and telecommunications technology to transfer medical information for diagnosis, therapy, and education and played a significant role following the COVID-19 outbreak. The pandemic made people aware of the value of mHealth in promoting universal health coverage, which promotes stronger management of self-care. Against the backdrop of an increased desire to raise a family among the Chinese population in the postpandemic era, maternal and child health apps, as a health education tool, promote a healthy lifestyle for women’s self-management in the antenatal and postpartum periods. Further research is needed to understand the users’ requirements for these apps, which will influence their adoption. The explicit design of apps is another potential factor that can facilitate or hinder user engagement and requires further investigation.
## Conclusion
mHealth apps are an effective approach to providing health care in the context of COVID-19. This study clarifies the increasing usage of different apps during the pre and postpandemic periods, showing greater attention to self-care and the Chinese population’s increasing desire to raise a family. Moreover, our research provides direction for subsequent mHealth app development and promotion in the postepidemic era, supporting medical model reformation in China as a reference. This may provide new avenues for designing and evaluating indirect public health interventions such as health education and health promotion. Further research is needed to investigate the functions in each kind of app, which will contribute to the personalized development and specific improvement measures of mHealth apps as a health promotion strategy.
## Data Availability
Scientists wishing to use mobile health app study data for noncommercial purposes can obtain the data set by contacting the corresponding author.
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|
---
title: Injectable FHE+BP composites hydrogel with enhanced regenerative capacity of
tendon-bone interface for anterior cruciate ligament reconstruction
authors:
- Eunshinae Cho
- Yi Qiao
- Changan Chen
- Junjie Xu
- Jiangyu Cai
- Yamin Li
- Jinzhong Zhao
journal: Frontiers in Bioengineering and Biotechnology
year: 2023
pmcid: PMC9996450
doi: 10.3389/fbioe.2023.1117090
license: CC BY 4.0
---
# Injectable FHE+BP composites hydrogel with enhanced regenerative capacity of tendon-bone interface for anterior cruciate ligament reconstruction
## Abstract
Features of black phosphorous (BP) nano sheets such as enhancing mineralization and reducing cytotoxicity in bone regeneration field have been reported. Thermo-responsive FHE hydrogel (mainly composed of oxidized hyaluronic acid (OHA), poly-ε-L-lysine (ε-EPL) and F127) also showed a desired outcome in skin regeneration due to its stability and antibacterial benefits. This study investigated the application of BP-FHE hydrogel in anterior cruciate ligament reconstruction (ACLR) both in in vitro and in vivo, and addressed its effects on tendon and bone healing. This BP-FHE hydrogel is expected to bring the benefits of both components (thermo-sensitivity, induced osteogenesis and easy delivery) to optimize the clinical application of ACLR and enhance the recovery. Our in vitro results confirmed the potential role of BP-FHE via significantly increased rBMSC attachment, proliferation and osteogenic differentiation with ARS and PCR analysis. Moreover, In vivo results indicated that BP-FHE hydrogels can successfully optimize the recovery of ACLR through enhancing osteogenesis and improving the integration of tendon and bone interface. Further results of Biomechanical testing and Micro-CT analysis [bone tunnel area (mm2) and bone volume/total volume (%)] demonstrated that BP can indeed accelerate bone ingrowth. Additionally, histological staining (H&E, Masson and Safranin O/fast green) and immunohistochemical analysis (COL I, COL III and BMP-2) strongly supported the ability of BP to promote tendon-bone healing after ACLR in murine animal models.
## 1 Introduction
Anterior cruciate ligament (ACL) is an essential ligament responsible for the maintenance and stability of the knee joint (Duthon et al., 2006; Zantop et al., 2006). However, this ligament is associated with limited healing ability due to the lack of proper vascularization. Thus, ACL reconstruction (ACLR) is the recommended surgical approach in the case of injury (Yao et al., 2021).
Generally, the human bone structure is unique and contains a variety of molecules and cells such as type I collagen, calcium, phosphate, osteoblasts, osteocytes and osteoclasts (Rho et al., 1998; Mohammadi et al., 2018). Such components play an important role in the formation and regeneration of bones, guaranteeing proper functioning and regulation (Qing et al., 2020). Therefore, research focuses on investigating the bone structure and optimal strategies to enhance its recovery (Lopes et al., 2018). Points of interest include the value of certain materials in stimulating recovery and the optimal delivery methods to maximize effects and shorten the time of recovery (Williams, 2008; Mondschein et al., 2017; Wang et al., 2022).
Considering the importance of phosphorus in the human body (making up to $1\%$ of total body mass) (Yang et al., 2018a; Ouyang et al., 2020), especially in human bones and teeth (Cui et al., 2016; González Díaz et al., 2018), many studies focused on this element and its role in bone recovery and generation. Of the different types of phosphorous, black phosphorous (BP) has been presented as a promising element with great potential. Synthesized from white phosphorous, BP proved to have better stability and compatibility in bioengineering applications and is suggested as an important osteogenic inducer with great potential in the treatment of bone injury due to its features of single element structure and easy degradability (Doganov et al., 2015; Huang et al., 2019; Cheng et al., 2020; Pandey et al., 2020).
The further invention of BP nanosheets brought additional features such as optimized electron mobility, charge carrier capability, optical properties, enhanced mineralization and reduced cytotoxicity which led to great enhancements in the field of bone regeneration (Dadsetan et al., 2012; Li et al., 2014; Erande et al., 2016; Zhu et al., 2016; Yang et al., 2018a; Yang et al., 2018b; Wang et al., 2018; Qing et al., 2020; Sun et al., 2020).
The main mechanism of BP nanosheets is related to its in vivo degradation that enhances mineralization and leads to better adhesion and differentiation of bone cells. Thus, it translates into accelerated proliferation and tissue regeneration (Goretti Penido and Alon, 2012; Rashdan et al., 2016; Qing et al., 2020). That led to the idea of BP nanosheets incorporation in novel delivery systems such as bio-degradable hydrogels to optimize osteogenesis regardless to the presence of inductive elements (Qing et al., 2020). Such incorporation can also present an efficient sustained in vivo release of BP to increase efficacy and limit general side-effects (Cheng et al., 2020).
The benefits of hydrogel application (such as the easy injectability, biocompatibility, degradability and limited toxicity) (Rowley et al., 1999; Hennink and van Nostrum, 2002; Hoffman, 2002; Zhang, 2003; Hou et al., 2019) favored it as a delivery vessel in relevant researches. Relevant investigations include the use of hydrogel in ACLR animal models (Chen et al., 2008) and the combination of gelatin methacryloyl (GelMa) with BP nanosheets (Huang et al., 2019; Miao et al., 2019).
Among the different types of hydrogels, those responsive to external stimulation (light, temperature, pH, etc.) grabbed the attention (Klouda and Mikos, 2008). Of which, thermo-responsive hydrogels have been strongly present in therapeutic applications such as drug and cell delivery (Stile et al., 1999; Guan et al., 2008; Vermonden et al., 2008; Fundueanu et al., 2009; Misra et al., 2009), tissue engineering (Tang et al., 2010) and myocardial injections (Fujimoto et al., 2009) due to their minimal invasiveness and toxicity, convenient preparation, and long-term effectiveness (Lin et al., 2014).
FHE hydrogel (F127/OHA-EPL) is a thermo-responsive hydrogel (mainly composed of oxidized hyaluronic acid (OHA), poly-ε-L-lysine (ε-EPL) and F127) which showed a desired outcome in skin regeneration due to its stability and antibacterial benefits. In addition to thermal sensitivity, FHE hydrogel provides advanced biocompatibility and cellular adhesion (Wang et al., 2019).
In light of the great potential of BP and FHE hydrogel application, and since neither has been explored thus far in ACLR research, this study investigated the application of BP-FHE hydrogel after ACLR to address their effects on tendon and bone healing. This combination is expected to bring the benefits of both components (thermo-sensitivity, induced osteogenesis and easy delivery) to optimize the clinical application of ACLR and enhance recovery.
## 2.1.1 Materials
BP nanoplates were obtained from HWRK Chem (Beijing, China). Sodium hyaluronate (HA, Mw = 1.5×106) was purchased from Shanghai Yuanye Biotechnology Co. (Shanghai, China). Sodium periodate (NaIO4), F127, and ε-polylysine (EPL) were gained from Aladdin Reagent Co. (Shanghai, China). All materials and solvents were used as received without any further purification unless otherwise noted.
## 2.1.2 Synthesis of oxidized hyaluronic acid (OHA)
Hyaluronic acid (HA) was oxidized by sodium periodate (NaIO4) to obtain oxidized hyaluronic acid (OHA). The solution of $1\%$ (w/v) was prepared by dissolving 2 g sodium hyaluronate in 200 mL deionized water at room temperature for 24 h. Then, 1.08 g sodium periodate was weighed and completely dissolved in 10 mL deionized water, and slowly added into sodium hyaluronate solution, the whole process was operated away from light. The mixed solution was stirred at room temperature away from light for 2 h, and then 2 mL glycol was added to stop the reaction for 1 h. The final product solution was purified by dialysis in deionized water for 72 h. Finally, the purified solution was pre-frozen in a −80°C refrigerator, and the dried OHA was purified by freeze-drying machine under vacuum drying conditions.
In order for oxidative degree to accurately present dialdehyde content, we adopted the definition of the oxidized uronic acid unit to total hyaluronic acid unit mole ratio. Iodometric titration and hydroxylamine hydrochloride were used (according to previous method) to measure OHA’s oxidative degree (Balakrishnan and Jayakrishnan, 2005; Yuan et al., 2017). Oxidative degree’s maximum was $75.8\%$ when the mole ratio (NalO4/hyaluronic acid repeat unit) was 1.5.
## 2.1.3 Preparation of injectable FHE + BP composites hydrogel
OHA was freeze-dried and dissolved into an 80 mg/mL solution using distilled water as solvent. ε-EPL was dissolved into a solution by using the same method with a concentration of 50 mg/mL and 100 mg/mL. Then F127 was dissolved into a 400 mg/mL solution under 4°C according to the volume ratio of F127: ε-EPL: OHA as 3:1:1. The F127 solution and the ε-EPL solution were sequentially mixed at 4°C, and the OHA solution was added after mixing evenly, the solution was then put in a thermostatic shaker for gelation, hydrogels were named as FHE.
BP nanoplates combined with FHE hydrogel were prepared similarly to the above procedure of FHE. After mixing F127 and ε-EPL solutions at 4°C, BP was dispersed in the mixed solution with a relative mass fraction [WBP (WBP + WFHE)] of $5\%$. After mixing, the solution was continuously stirred using a stirrer for 2 days at 37°C until completely dissolved to obtain FHE + BP composites hydrogel.
## 2.1.4 Characterization and testing of hydrogel
Attenuated total reflectance-Fourier transform infrared spectrometer (ATR-FITR, Thermo Nicolet, United States) was employed to characterize the chemical structure of synthetic gels in the range of 400–4,000 cm-1 under a resolution of 4 cm-1. Synthetic gels were characterized by 1H spectrum nuclear magnetic resonance (1H-NMR, AVANCE-400MHz, Bruker, Switzerland) with DMSO-d6 as solvent. The morphology and surface structure of gels were carried out using a scanning electron microscope (SEM, Phenom XL, Netherlands) operating with sputter gold plating for 35 s at 5 mA at an accelerating voltage of 10 kV. ImageJ (National Institutes of Health, United States) was adopted to determine the pore diameter of gels.
## 2.1.5 Swelling ratio and water retention ratio testing of hydrogel
Phosphate buffer solution (PBS) was dropped into the test tube with hydrogels for several times until the volume no longer changed and then the mass was weighed. The hydrogel was lyophilized and weighed, and its water absorption ratio was computed through Eq. 1: Swelling ratio=Wt−W0/W0×$100\%$ [1] When the hydrogel was at swelling equilibrium, the weight was marked as Wt, and when it was lyophilized, the weight was marked as W0.
The prepared hydrogel was weighed, then placed in an environment of 37°C with a relative humidity of $70\%$. After 12 h or 24 h of air-dry operation, the weight of hydrogel was recorded, respectively. Water retention ratio was defined as Eq. 2: Water retention ratio=Wt−W/W0−W×$100\%$ [2] At the beginning, the initial weight of the hydrogel was recorded as W0; Wt was the weight of the hydrogel after 12 h or 24 h of air-dry operation; The weight of hydrogel after lyophilized was recorded as W.
## 2.2.1 Rat BMSCs (rBMSCs) isolation and culture
First, the bone marrow–derived mesenchymal stem cells (BMSCs) were isolated from Sprague Dawley (SD) rats based on a previously described protocol (Li et al., 2013). Considering mesenchymal stem cells ability of differentiation, their application in regeneration medicine has been increasingly growing. Many studies counted on MSCs (from different origins) to successfully investigate and promote tendon-bone healing (Chen et al., 2021). Obtained cells were seeded on culture plates and cultured in a complete medium containing α-MEM, $10\%$ FBS, and $1\%$ penicillin/streptomycin (all from Gibco, United States). Plates were incubated with $5\%$ CO2 at 37°C and culturing medium was changed once every 2 days. Propagation into new plates was carried at $80\%$ confluence and further experiments were only carried after three propagations. Approval by Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Ethics Committee (No. DWSY 2021-0127) was obtained for all carried animal experiments.
## 2.2.2 Proliferation and attachment of rBMSCs
Cytotoxicity of BP nanosheets and FHE hydrogel on BMSCs was investigated by cell counting kit-8 (CCK-8, Beyotime, China). First, BP solution (0.125 mg/mL) was added to hydrogel solution (1:10) then vacuum dried. Then, 1 mg of vacuum dried powder was added to 1 mL of medium. After that, a leaching solution was prepared by adding 10 μL of the previous solution to 1 mL of medium. An additional leaching solution was prepared using vacuum dried hydrogel powder (10 μL/mL). Leaching solutions were stored at 4°C after being sterilized and filtered.
A 96-well plate was used to seed BMSCs (1 × 103 cells per well). When the cells were attached, the different leaching solutions were used to replace the original medium and then refreshed every other day. For the control group, the medium was used alone (no leaching solution was added). Incubated for 1, 3 and 5 days, cells were then washed (PBS/2 times) and cultured in medium and CCK-8 solution (10:1 (v/v)) (2h, 37°C, $5\%$ CO2). A microplate reader (Thermo Scientific, United States) was used to determine the optical density (OD) (450 nm absorbance).
As for cell viability analysis, rBMSC seeding in 24-well plate was carried and Propidium iodide (PI) (1 μl/mL) and AM (1 μl/mL) (Live/Dead viability/Cytotoxicity Assay Kit/Beyotime, China) were mixed in PBS and used to incubate (30 min) and stain live/dead cells. After the incubation, Zeiss 880 fluorescence microscope (Zeiss, Germany) was used to capture fluorescent images.
## 2.2.3 Alizarin red S (ARS) staining
First, cells were washed and fixed in PBS buffer for 4 h (4°C, $2.5\%$ glutaraldehyde) before being washed again. Different solutions were added based on the group and refreshed regularly with Osteogenesis induction medium (OIM) ($10\%$ FBS +0.1 μM dexamethasone +50 μg/mL ascorbic acid +10 mM sodium β-glycerophosphate +$1\%$ penicillin/streptomycin + DMEM). After 14 days, ARS (Sigma-Aldrich, Germany) staining for 30 min in room temperature (20 mg/mL ARS, pH 4.2) was used to evaluate bone-like inorganic calcium deposits. Cells were then washed until all ARS dye was removed from washed liquid and quantitative analysis was carried. Using $10\%$ (w/v) cetylpyridinium chloride (Sigma Aldrich, United States), optical density at 562 mm was measured and ImageJ software was used to complete the analysis.
## 2.2.4 Real-time quantitative polymerase chain reaction (RT-qPCR)
mRNA expression of COL I, RUNX-2, and OCN (osteogenic-relevant genes) was detected through RT-qPCR. First, 6-well plate was used to seed rBMSCs for 14 days. OIM was added and refreshed every other day. Then, Trizol-up (EZBioscience) was used to extract total RNA and 4X Reverse Transcription Master Mix (EZBioscience) to convert it to complementary DNA. Applied Biosystems 7500 Real-Time PCR system (2 X SYBR Green qPCR Master Mix (EZBioscience)) was used to perform RT-PCR following the protocol of manufacturer. Expression levels’ normalization was based on β-actin expression and 2−ΔΔCT was used to calculate expression values. Table 1 describes the primers for RT-qPCR.
**TABLE 1**
| Gene | Primer |
| --- | --- |
| COL1 | 5′- CTGGGTGGGAGAGACTGTT-3′ (forward) |
| COL1 | 5′- CGGTGACACACAAAGACAAG-3′ (reverse) |
| RUNX-2 | 5′- ATCATTCAGTGACACCACCAG-3′ (forward) |
| RUNX-2 | 5′-GTAGGGGCTAAAGGCAAAAG-3′ (reverse) |
| OCN | 5′-CCTCTCTCTGCTCACTCTGCT-3′ (forward) |
| OCN | 5′-CTTACTGCCCTCCTGCTTG-3′ (reverse) |
| β-actin | 5′-CCTCTATGCCAACACAGT-3′ (forward) |
| β-actin | 5′-AGCCACCAATCCACACAG-3′ (reverse) |
## 2.3 In vivo experiments
This study included 72 SD rats (males, 12-13 weeks, 280–320 g) and divided them into control group ($$n = 24$$), FHE group ($$n = 24$$) and FHE + BP group ($$n = 24$$) (Oka et al., 2013). Unilateral ACLR was carried out in all rats by two investigators and each group was further divided into 4-week and 8-week subgroups. Euthanasia was carried out after 4 or 8 weeks (based on the group) via a CO2 overdose and biomechanical testing, micro-CT and histological analysis were carried out to assess collected samples of the femur-graft-tibia complex.
## 2.3.1 Surgical procedure
Intraoperative $3\%$ pentobarbital injection (1.0 mL/kg) was first used for anesthesia via intraperitoneal injection and the skin of lower limbs was shaved and sterilized. Then, a harvest of ipsilateral flexor digitorum longus tendons was completed (from the lateral aspect of ankle joints) and muscles on harvested grafts were removed. The knee was exposed through medial parapatellar arthrotomy and a lateral dislocation of the patella exposed native ACL. A careful excision of the native ACL was completed and confirmed after the tibia was translated anteriorly. After the knee was flexed (90°), tibial and femoral tunnels (diameter: 1.5 mm, length:7 mm) were created by 1.5 mm diameter Kirschner wire starting from the original ACL footprint to the tibia’s medial side (tibial tunnel) or the femoral condyle’s anterolateral side (femoral tunnel) (Lui et al., 2014). A 4-0 Ethibond (Ethicon) was used to attach one side of the graft and drag it into the tunnel. Previously stored at 4° FHE (or FHE + BP) were injected into tunnels before grafts’ immediate placement (dragging). The knee joint was flexed to 30° and 4N graft pretention was applied before the suturing of grafts to the surrounding periosteum at both tibial and femoral ends was completed. Layered wound closure was then carried and the anterior stability of the knee was validated by Lachman test. Intramuscular anti-infectious injections (penicillin, 50 KU/kg) were given to animals before returning to cages and being allowed free movement.
## 2.3.2 Macroscopic observation
At two time points (4 and 8 weeks), an assessment of infection in the wound site was carried. Afterwards, observed knee joints were fully exposed and an assessment of graft was completed by two independent investigators. Scoring criteria included the stiffness and integration of the graft with surrounding tissues in addition to the appearance and color of articular surface. Details of the scoring system are provided in Supplementary Table S1 (Cheng et al., 2016).
## 2.3.3 Micro-CT scanning
At 4 or 8 weeks after the operation, animals were sacrificed to collect samples. Femur-tibia complex samples ($$n = 6$$/group) were thawed and all structures and soft tissues were removed, maintaining only the reconstructed ACL graft. Samples were then frozen (−80°C) until further use. After being brought to room temperature, samples were scanned perpendicularly to the long axis of bone tunnel (spatial resolution: 18 μm, Skyscan 1176 micro-CT imaging system (Bruker, Kontich, Belgium)). Bone ingrowth was analyzed through focusing on cylinder-shaped region of interest (ROI) (diameter: 2 mm, height: 3 mm) and mean cross-sectional areas (mm2) of bone tunnels and tunnels’ bone volume/total volume (BV/TV) ratios were calculated to complete the quantitative analysis (Sun et al., 2019; Xu et al., 2022a).
## 2.3.4 Biomechanical testing
After micro-CT scanning, Femur-tibia complex samples were used to complete biomechanical testing. Material testing machine (Model 2712-004; Instron Corp) was used to test the healing interface. The tested joint was mounted and both bone tunnels were aligned with the tensile load direction (Lui et al., 2014; Xu et al., 2022a). After that, preconditioning (5 cycles (maximum displacement of 0.5 mm) was completed. The failure mode and ultimate failure load was investigated by applying loads from 0-N to 0.5-N with 3 mm/min displacement rate (until failure). Failure was represented by the presence of ruptured graft or pulling out of the tunnel (Cai et al., 2021a). Samples were moisturized (saline solution) during testing and stiffness was determined through load-displacement curve.
## 2.3.5 Histological analysis and immunohistochemical staining
Samples were fixed ($10\%$ formalin, 36 h) then decalcified ($10\%$ EDTA, 6 weeks) before a dehydration and paraffin embedding were carried. Then, samples were sliced (5μm, SM2500; Leica, Nussloch, Germany) parallelly to tunnels’ longitudinal axis and fixed on glass slides (40°F oven). Standard Hematoxylin and eosin (H&E) staining was completed to evaluate graft-bone interface.
Patterns of intra-articular collagen alignment were visualized by Masson’s trichrome staining and Safranin O/fast green staining was also carried to observe fibro-cartilage formation patterns and glycosaminoglycans (GAGs) content (Chen et al., 2021).
All staining procedures were carried based on manufacturer’s instructions before an inverted light microscopy (Leica DM4000 B, Germany) was used for observation and Leica DFC420C camera (Leica Microsystems GmbH) to capture images.
Obtained results were analyzed and quantified by two observers. Three parameters (fibrocartilage formation, new bone formation and graft bonding to adjacent tissues) were considered in the final scoring (0-3 points/item, 0-9 points for total score) with higher scores representing enhanced results. Details of the scoring system are provided in Supplementary Table S2 (Cheng et al., 2016).
Immunohistochemical staining (IHC) for COL I, COL III and BMP-2 was carried. First, samples’ dewaxing and rehydration were carried before antigen-retrieval. Then, $0.3\%$ hydrogen perioxide (20 min) and $2\%$ bovine serum albumin (1 h) were used for blocking and primary anti-body incubation was carried over-night (4°C). Secondary antibody was used for incubation for 1 h at 37°C. Samples were then washed. Finally, observation of obtained images was completed under a light microscopy (Leica DM4000 B, Germany).
## 2.4 Statistical analysis
GraphPad Prism 9 (California), Origin 8.0 statistical software (Origin Lab Inc., United States), ANOVA and Tukey’s test were applied to statistically analyze data. All data are expressed as mean ± standard deviation (Mean ± SD). p-values < 0.05 (*) were deemed to be statistically significant.
## 3.1 Physicochemical properties of hydrogels
As mentioned earlier, NaIO4 was used to oxidize HA into OHA which contains side chain aldehyde group. Figure 1 shows the optical pictures of the different hydrogels. The reaction mechanism is shown in Figure 2A. Since the degree of oxidation of HA to OHA needs to be controlled artificially, the mole ratio of NaIO4 and HA was set as 1:1, and the oxidation time was 2 h.
**FIGURE 1:** *Optical picture of hydrogel (A) FHE hydrogel (left), FHE + BP hydrogel (right) (B) Gelation FHE hydrogel (left), FHE + BP hydrogel (right) (C) Injectable FHE hydrogel.* **FIGURE 2:** *Physicochemical properties of hydrogels (A) The synthetic reaction mechanism route of OHA (B) 1H-NMR spectrum of HA and synthetic OHA (C) FTIR spectrum of HA, OHA, and OHA/BP (D) Schiff base reaction mechanism route between OHA and EPL (E, F) The surface and cross section SEM images of FHE hydrogel and FHE + BP hydrogel, respectively (G) The pore diameter of FHE hydrogel and FHE + BP hydrogel (H) The swelling ratio of FHE hydrogel and FHE + BP hydrogel (I) The water retention ratio of FHE hydrogel and FHE + BP hydrogel in an environment of 37°C with a relative humidity of 70% for 12 h and 24 h, respectively.*
After the oxidation, 1H-NMR spectrum test was applied to analyze the variation of HA absorption peak before and after modification (Figure 2B). Compared to the absorption peak on the HA 1H-NMR spectrum, the aldehyde group revealed a characteristic peak at the chemical shift δ = 5.0–5.1 ppm of OHA, indicating a successful synthesis of OHA.
After the successful synthesis, BP nanoplates were homogenized with OHA. The infrared spectrum test results revealed that compared to HA, OHA showed a new absorption peak at 1,729 cm-1, corresponding to the stretching vibration of the double bond of the aldehyde group -C=O (Figure 2C). The FTIR spectrum of OHA/BP also presented the existence of BP in OHA/BP composites hydrogels.
The synthesis mechanism of FHE hydrogel is shown in Figure 2D. As for the analysis of SEM images of FHE and FHE + BP hydrogels, our results showed that the pore size of the hydrogel with BP nanoflakes and the FHE hydrogel are not very different (Figure 2E–G), indicating that BP is evenly dispersed in the FHE hydrogel since the pore size of the hydrogel measured by the ImageJ software before and after the adulteration was basically unchanged. In addition, As shown in Figures 2H, I, the water absorption of the hydrogel increased significantly after the addition of BP.
## 3.2.1 Proliferation and attachment of rBMSCs
The rBMSCs were successfully cultured and propagated (X3) before carrying further experiments. We started our in vitro experiments by investigating the biocompatibility and cytotoxicity of BP and FHE hydrogel using CCK-8. Three groups were included in this investigation: control group, FHE and FHE + BP groups (10 μL/mL) and OD values on days 1, 3 and 5 were observed (Figure 3A). Our results showed no significant differences among groups at day 1; however, OD values differed significantly at day 3 and 5. As expected, in comparison with day 1, all groups had significantly higher OD values at day 3 (mean differences: CON: 0.2474, $p \leq 0.0001$; FHE: 0.4488, $p \leq 0.0001$; FHE + BP: 0.4087, $p \leq 0.0001$) and day 5 (mean differences: CON: 1.245, $p \leq 0.0001$; FHE: 1.39, $p \leq 0.0001$; FHE + BP: 1.352, $p \leq 0.0001$). On day 3, both FHE and FHE + BP groups showed significantly higher OD values than the control group (mean differences: FHE: 0.1968, $$p \leq 0.0001$$; FHE + BP: 0.1804, $$p \leq 0.0005$$). Similar results were observed at day 5 (mean differences: FHE: 0.1407, $$p \leq 0.0156$$; FHE + BP: 0.1261, $$p \leq 0.0452$$). No significant difference was observed between FHE and FHE + BP group at either day 3 or day 5. Our live/dead staining results (Figure 3B) further supported the findings, which showed a stronger proliferation of cells (green staining) at day 5 in FHE + BP group when compared to FHE and control groups.
**FIGURE 3:** *Evaluation of biocompatibility and cytotoxicity of BP and FHE (A) CCK-8 assay and observed OD values of control, FHE and FHE + BP groups at day 1, 3 and 5 (B) Fluorescent images of live/dead cellular staining at day 1, 3 and 5 in all three groups. *p < 0.05 compare to control group.*
## 3.2.2 Osteogenic differentiation
To confirm osteogenesis differentiation, ARS staining was first used to evaluate bone-like inorganic calcium deposits. *The* general observation revealed a stronger ARS staining in FHE + BP group (represented by the red color) (Figure 4A). As shown in Figures 4A, B significant effect of BP on formation at day 14 was observed. The analysis of optical density at 562 mm showed that OD value of FHE + BP group was significantly higher than those of the other two groups with a mean difference of 0.056 with FHE group ($$p \leq 0.0002$$) and 0.2788 with control group ($p \leq 0.0001$).
**FIGURE 4:** *Analysis of osteogenic differentiation (A) Images of Alizarin Red S staining of all three groups after 14 days of cell culture (B) Absorbance and optical density at 562 mm in the three groups after ARS staining (C) mRNA expression levels of COL-1, OCN and RUNX2 in the three groups after 14 days of cell culture. *p < 0.05 compare to control group, **p < 0.01 compare to control group, ****p < 0.0001 compare to control group, ###p < 0.001 compare to FHE group.*
The osteogenic effects of BP were further proved by RT-qPCR. Three osteogenic relevant genes were included (COL1, OCN and RUNX-2). The results showed a significant increase of expression in FHE + BP group of all three genes at day 14 compared to FHE and control groups (Figure 4C). For COL1, no significant difference was found between the control and FHE groups ($$p \leq 0.9165$$), while BP was significantly higher than both control group (mean difference: 0.954, $$p \leq 0.0068$$) and FHE group (mean difference: 0.8414, $$p \leq 0.0173$$). Similar results were observed for OCN (CON vs FHE: 0.1764, $$p \leq 0.2233$$; CON vs FHE + BP: −0.2601, $$p \leq 0.048$$; FHE vs FHE + BP: −0.4364, $$p \leq 0.0008$$) and RUNX-2 (CON vs FHE: 0.1618, $$p \leq 0.4549$$; CON vs FHE + BP: −0.3858, $$p \leq 0.0206$$; FHE vs FHE + BP: ‒0.5477, $$p \leq 0.0011$$).
## 3.3.1 Macroscopic observation
We started our in vivo investigation by completing a macroscopic observation of grafts at 4 and 8-week timepoints. No signs of infection were observed at any time point and all grafts appeared fully intact. As for detailed scores, although no significant differences were recorded among groups, higher scores were observed at 4 and 8 weeks in FHE + BP (compared to control and FHE groups) due to enhanced stiffness, integration, appearance and color of graft (4 weeks: CON = 2.167, FHE = 2.333, FHE + BP = 2.667; 8 weeks: CON = 5.167, FHE = 5.833, FHE + BP = 6.333) (Figure 5A).
**FIGURE 5:** *Macroscopic observation and biomechanical testing (A) Macroscopic scores of all three groups after 4 and 8 weeks (B, C) Representative images of the procedure of biomechanical testing (D) Comparison of failure load (N) in all three groups after 4 and 8 weeks (E) Comparison of stiffness (N/mm) in all three groups after 4 and 8 weeks **p < 0.01 compare to control group, ***p = 0.001 compare to control group, ****p < 0.0001 compare to control group, #p < 0.05 compare to FHE group, ##p < 0.005 compare to FHE group, ####p < 0.0001 compare to FHE group.*
## 3.3.2 Biomechanical testing
Biomechanical testing of failure load and stiffness after 4 and 8 weeks was carried in all three groups (Figures 5B, C). At week 4, a significantly higher failure load was recorded in FHE + BP group (15.02 N) in comparison with the control group (5.07 N, $p \leq 0.0001$) and FHE group (8.723 N, $$p \leq 0.0045$$). Similar results were observed at week 8 (FHE + BP = 28.06 N; CON = 11.54, $p \leq 0.0001$; FHE = 23.25, $$p \leq 0.0474$$) (Figure 5D). Results of stiffness (N/mm) also favored FHE + BP group after 4 and 8 weeks (4 weeks: FHE + BP = 5.863; CON = 2.722, $$p \leq 0.0001$$; FHE = 2.404, $p \leq 0.0001$) (8 weeks: FHE + BP = 7.555; CON = 5.136, $$p \leq 0.0038$$; FHE = 5.494, $$p \leq 0.0179$$) (Figure 5E).
## 3.3.3 Micro-CT scanning
Micro-CT scanning was completed to evaluate the formation of bone in the tunnels and two parameters (average bone tunnel area (mm2) and BV/TV (%)) were calculated at 4 and 8-week time-points (Figure 6A). Our results showed that after 4 weeks, the average bone tunnel area was 1.482 mm2 for FHE + BP group, compared to 2.14 mm2 for the control group ($p \leq 0.0001$) and 2.023 mm2 for FHE group ($$p \leq 0.001$$). After 8 weeks, the groups’ average bone tunnel areas were 0.8887 mm2 for FHE + BP group, 1.989 mm2 for control group ($p \leq 0.0001$) and 1.754 mm2 for FHE group ($p \leq 0.0001$) (Figure 6B). Analysis of BV/TV (%) revealed a similar influence of BP. At 4 weeks, FHE + BP group’s percentage was $12.9\%$ higher than the control group (FHE + BP = $42.77\%$, CON = $29.87\%$, $$p \leq 0.0002$$) and $8.986\%$ higher than FHE group (FHE = $33.78\%$, $$p \leq 0.0129$$). Larger differences were recorded after 8 weeks with $46.15\%$ for FHE + BP group, $30.83\%$ for control group ($p \leq 0.0001$) and $32.93\%$ for FHE group ($$p \leq 0.0001$$) being recorded (Figure 6C).
**FIGURE 6:** *Micro-CT scanning (A) Representative images of cross-sectional areas of bone tunnels in all three groups after 4 and 8 weeks (B) Comparison of average bone tunnel areas (mm2) of the three groups at 4 and 8 weeks (C) Comparison of BV/TV (%) of the three groups at 4 and 8 weeks ***p = 0.002 compare to control group, ****p < 0.0001 compare to control group, #p = 0.0129 compare to FHE group, ##p = 0.001 compare to FHE group, ###p = 0.001 compare to FHE group, ####p < 0.0001 compare to FHE group.*
## 3.3.4 Histological analysis and immunohistochemical staining
Additional histological analysis was further carried out to evaluate graft-bone interface (H&E staining (Figure 7A), Masson’s trichrome staining (Figure 7B) and Safranin O/fast green staining (Figure 7C). All three staining methods revealed better integration between the graft and bone tissues, judging by the cell morphology and distribution. HE staining showed an enhanced formation of graft within bone tissues in FHE + BP group while a new growth of bone tissues into fibrous tissues of graft was observed in both Masson’s trichrome staining (represented by purple color) and Safranin O/fast green staining (represented by red color).
**FIGURE 7:** *Results of histological analysis (A) Hematoxylin and eosin (H&E) staining (B) Masson’s trichrome staining and (C) Safranin O/fast green staining of the three groups at 4 and 8 weeks. IF: interface.*
Our in vivo investigation was concluded by IHC analysis of COL I (Figure 8A), BMP-2 (Figure 8B) and COL III (Figure 8C). Our results showed an increase in the expression of all targets in FHE + BP group after 8 weeks compared to CON and FHE groups. The enhanced integration and graft formation into bone tissues in FHE + BP group were represented by the higher positive staining (darker shades of brown) within and around the bone tissues, especially in COL I and COL III results. Both graft growth within bone tissues and bone growth within graft tissues can be observed.
**FIGURE 8:** *Results of immunohistochemical (IHC) analysis; IHC staining of (A) COL-I (B) BMP-2 and (C) COL-III in the three groups at 4 and 8 weeks. IF: interface.*
Overall, our analysis also revealed no significant difference at week 4 between groups’ average scores (CON: 2.4, FHE: 2.4, FHE + BP: 3.2). However, results at week 8 showed a significant improvement in the FHE + BP group (7.7 compared to FHE (5.5 $p \leq 0.0001$) and CON (3.4, $p \leq 0.0001$) groups (Figure 9).
**FIGURE 9:** *Histological score ****P < 0.0001 compare to control group, ####
P < 0.0001 compare to FHE group.*
## 4 Discussion
Hyaluronic acid (HA) is a biodegradable biomaterial with good biocompatibility and a hydrophilicity that plays an important role in cell adsorption, growth and differentiation. This biomaterial can be used as a temporary skeleton supporter and stimulator of new bone tissue growth. After a certain period of mechanical support, HA is gradually degraded and replaced by new bone tissue. However, the rapid biodegradation rate of HA does not match the tissue growth cycle (Khunmanee et al., 2017; Cai et al., 2023); thus, certain chemical modification is necessary.
The tendon bone interface repair in ligament section is a challenge in bone tissue engineering, since a loose interface between soft and hard tissues may result in inflammation. Therefore, the repair of the tendon bone interface requires injectable gels with good mechanical adaptation and regulation of the inflammatory microenvironment. Considering its unique hydromechanical properties, viscosity, water retention ability, physical properties, good biocompatibility, high viscoelasticity, permeability and plasticity, HA is a strong and common candidate in the field of tendon bone interface repair.
Considering the properties and advantages of HA, we designed and prepared a HA-based hydrogel to match the regenerative environment and effectively repair the tendon bone interface through Schiff base reaction combined with homogenized hybrid technology. First, HA was oxidized by NaIO4 into OHA. Since hypo-oxidation leads to poor gelatinization performance of prepared hydrogels while over-oxidation produces excessive toxic aldehyde group, the degree of oxidation had to be controlled artificially. Thus, NaIO4/HA ration and time of oxidation were strictly controlled. The successful synthesis of OHA was further proven by the analysis of HA absorption peak before and after modification through 1H-NMR spectrum test.
The various benefits and favorable characteristics of BP in tissues’ bioengineering made it a target of many relevant researches. In addition to clarifying BP’s relevant mechanisms and specific aspects, efforts have focused on identifying the best delivery vessel such as hydrogels (Huang et al., 2019; Miao et al., 2019; Xu et al., 2022b). However, no research has investigated the potential benefits of BP in ACLR recovery. Therefore, we aimed in this study to explore and illustrate the benefits of BP-FHE hydrogels in ACLR in both in vitro and in vivo settings.
First, BP nanoplates were homogenized with OHA and the presence of BP in OHA/BP composites hydrogels was confirmed through infrared spectrum test and FTIR spectrum. OHA molecule contains dialdehyde group, which can react with the primary amino group on the molecular chain of ε-polylysine (EPL) to form a reversible imine bond. The pore size analysis of the hydrogel with BP nanoflakes and the FHE hydrogel revealed no significant difference, indicating that BP is evenly dispersed in the FHE hydrogel. In short, FHE + BP Hydrogel has larger pore size, which is more conducive to cell growth and nutrient exchange. When applied in the tendon bone interface, it is more conducive to wound healing, and plays an effective role in filling and preventing potential loosening.
In addition, our findings showed an increase in water absorption of the hydrogel after adding BP, which may be due to the presence of BP which can form more mesh structures to store more water, thus improving the water absorption and swelling capacity of the hydrogel. Due to the inevitable loss of water (caused by the extension of time), the water locking capacity of the hydrogel with BP only increased slightly. It is speculated that the reason is that the groups on the molecules form hydrogen bond with the water molecules, increasing the intermolecular force and making the water molecules harder to volatilize, but since mixed BP made only $5\%$, no obvious difference in the water holding capacity was observed. It is very important to have good water absorption capacity for wound repair.
Our in vitro investigation used rBMSCs (Li et al., 2013). Cells were only used in further experiments after three propagations to ensure their quality and stability. First, the potential cytotoxicity of BP was investigated by CCK8 to analyze the biocompatibility and toxic effects of FHE + BP at different time points (1, 3 and 5 days) and compare it to similar features in FHE and control groups. Our results showed that compared to control group, both FHE and FHE + BP groups had significantly higher OD values at both day 3 and 5, while no significant differences were recorded at day 1. Thus, it is safe to assume that neither FHE nor BP result in any increased cytotoxicity and can be safely used in further experiments. The limited cytotoxicity of BP was further indicated in the live/dead staining analysis which showed stronger cell proliferation in FHE + BP group in comparison with the other two groups. The limited cytotoxicity not only introduces BP as a potential key element in bone healing and regeneration but also allows a comparison with other types of phosphorous which are usually associated with high toxicity that prevents any beneficial application in bioengineering (Gui et al., 2018).
Afterwards, our investigation focused on exploring the effect of BP on osteogenic differentiation through the ability to stimulate bone-like inorganic calcium deposition (ARS staining) and mRNA expression of COL1, OCN and RUNX-2 (RT-qPCR). The choice of these genes was based on their relevance to osteogenic activities (Miao et al., 2019; Cai et al., 2021b). Results of ARS staining analysis showed that FHE + BP group had significantly higher OD value when compared to FHE and control group, which indicates that BP can indeed stimulate and increase osteogenic differentiation and thus enhances bone-like inorganic calcium deposition. In addition, FHE + BP group was associated with higher levels of mRNA expression of all three included genes. Such results confirmed the potential role of BP in vitro settings on both RNA and cellular levels and indicated the need for further investigation in ACLR animal models.
Similar results have been reported by previous researches. For example, Li et al. ( Li et al., 2021) observed a significant increase in mRNA expression of osteogenesis relevant genes after using BP based nanoparticles. Such influence has been associated by Bestami et al. ( Bastami et al., 2017) with the ability of BP hydrogel to provide additional P5+ and therefore promote certain pathways like BMP-RUNX2. BP hydrogels’ influence on osteogenesis has also been attributed by Huang et al. ( Huang et al., 2019) to their ability to enhance the release of phosphorus ions and capturing of calcium ions.
Since previous BP-relevant literature mainly focused on exploring the influence of BP on bone growth, most animal models were established through inflicting bone defects (Cheng et al., 2020). However, since our study focused on bone-tendon healing, our animal model was established through ACLR application. Starting by a macroscopic observation, we found no signs of infection in any of the animals in both groups which further confirmed the high standards and preventive measures carried through the procedure. Although no statistical significance was observed, macroscopic scores of FHE + BP group were higher than the other groups at both 4 weeks and 8 weeks. In addition, a larger difference was observed at 8 weeks, indicating BP’s extended positive effect. Such a positive influence of BP was further observed in our biomechanical testing which focused on failure load and stiffness of joints and revealed a significant difference in both parameters that favored FHE + BP group at both 4 and 8 weeks. Therefore, BP influence not only enhances the healing process but also strengthen the function recovery after reconstruction. Such findings are extremely important since they indicate the positive influence of BP application on biomechanical performance in weight-bearing sites, considering that the majority of researches focusing on bone-defect recovery usually choose bones in non-weight-bearing sites to simplify the process of recovery and minimize the complications (Miao et al., 2019).
To further evaluate the ability of BP to stimulate bone formation, micro-CT scanning of the tunnel was carried to analyze average bone tunnel area (mm2) and BV/TV (%). Our results showed that both parameters started to improve at week 4 but peaked at week 8. Such results not only indicate the BP effects but also illustrate the usefulness of FHE in guaranteeing a sustained release and effects for longer periods of times.
Further confirmation was provided by results of histological staining which showed an enhanced integration, bone and fibrocartilage formation in FHE + BP group. The different staining methods allowed a clear observation of cell morphology and distribution. Through HE staining, a new formation of graft cells can be observed in bone tissues, suggesting an enhanced integration in FHE + BP group, while the larger distribution of purple in Masson’s trichrome staining and red in Safranin O/fast green staining within graft tissues represent the formation of bone cells into graft. Such results clearly show that the positive influence of FHE + BP hydrogel is not limited to stimulating osteogenesis but also enhancing the tendon-bone healing.
Finally, IHC results showed increased expression of COL I, COL III and BMP-2. Again, higher positive staining (especially of COL I and COL III) in FHE + BP group represented by darker brown shades and clearer distribution within and around bone tissues indicates the graft growth and integration into the bone. The importance of COL I, COL III and BMP-2 has been investigated by other studies. Li et al. ( Li et al., 2020) who investigated the application of tissue-engineering decellularized allografts for ACLR observed an increase in COL I and COL III expression followed by a decrease in COL III at month 3. As for BMP-2, an increase in expression was observed by Huang et al. ( Huang et al., 2019) who investigated BP hydrogel scaffolds and their influence on bone generation rabbit animal models with bone defects.
Certain limitations partially affected the results of this study and can be addressed in future investigations. Our results can benefit from further investigations with larger sample-sizes and different animal models that can better mimic the microenvironment of human patients. Further analyses can also include wider range of osteogenesis-related markers to better represent all aspects of healing process and osteogenesis, such as ALP staining and protein electrophoresis data of phase osteogenesis. Non-etheless, our study is the first to show the beneficial role of BP in ACLR recovery and the usefulness of thermosensitive FHE hydrogel in guaranteeing long-term effects.
To conclude, BP-FHE hydrogels can successfully optimize the recovery of ACLR through enhancing osteogenesis and improving the integration of graft-bone interface. Such application provides histological and biomechanical benefits in addition to stimulating osteogenic differentiation on cellular levels.
## 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.
## Ethics statement
The animal study was reviewed and approved by Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Ethics Committee.
## Author contributions
EC contributed to the design, experiments, writing and completion of this study. YQ contributed to the design and completion of this study. CC contributed to the design and toxicity experiment completion in this study JX contributed to the analysis of biomechanical testing and micro CT scanning results. JC contributed to the in vivo experiments. YL contributed to the completion and supervision of this study. JZ contributed to the design completion, supervision and corespondance of this study.
## 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/fbioe.2023.1117090/full#supplementary-material
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|
---
title: Effect of Sacubitril/Valsartan vs Valsartan on Left Atrial Volume in Patients
With Pre–Heart Failure With Preserved Ejection Fraction
authors:
- Mark Ledwidge
- Jonathan D. Dodd
- Fiona Ryan
- Claire Sweeney
- Katherine McDonald
- Rebecca Fox
- Elizabeth Shorten
- Shuaiwei Zhou
- Chris Watson
- Joseph Gallagher
- Niall McVeigh
- David J. Murphy
- Kenneth McDonald
journal: JAMA Cardiology
year: 2023
pmcid: PMC9996460
doi: 10.1001/jamacardio.2023.0065
license: CC BY 4.0
---
# Effect of Sacubitril/Valsartan vs Valsartan on Left Atrial Volume in Patients With Pre–Heart Failure With Preserved Ejection Fraction
## Key Points
### Question
Can neprilysin inhibition improve markers of cardiovascular structure and function in patients with pre–heart failure with preserved ejection fraction?
### Findings
In this randomized clinical trial of 250 asymptomatic patients, sacubitril/valsartan vs valsartan was associated with a reduction in blood pressure, pulse pressure, and N-terminal pro-b-type natriuretic peptide; an increase in maximal left atrial volume index measured by cardiac magnetic resonance imaging despite lower filling pressures; less decline in kidney function; and fewer serious adverse cardiovascular events.
### Meaning
These findings could reflect improved cardiac and vascular compliance or adverse cardiac remodeling, and more work is required to understand the long-term implications.
## Abstract
This randomized clinical trial evaluates the efficacy of sacubitril/valsartan vs valsartan in the treatment of left ventricular diastolic function in patients with pre–heart failure with preserved ejection fraction.
### Importance
Pre–heart failure with preserved ejection fraction (pre-HFpEF) is common and has no specific therapy aside from cardiovascular risk factor management.
### Objective
To investigate the hypothesis that sacubitril/valsartan vs valsartan would reduce left atrial volume index using volumetric cardiac magnetic resonance imaging in patients with pre-HFpEF.
### Design, Setting, and Participants
The Personalized Prospective Comparison of ARNI [angiotensin receptor/neprilysin inhibitor] With ARB [angiotensin-receptor blocker] in Patients With Natriuretic Peptide Elevation (PARABLE) trial was a prospective, double-blind, double-dummy, randomized clinical trial carried out over 18 months between April 2015 and June 2021. The study was conducted at a single outpatient cardiology center in Dublin, Ireland. Of 1460 patients in the STOP-HF program or outpatient cardiology clinics, 461 met initial criteria and were approached for inclusion. Of these, 323 were screened and 250 asymptomatic patients 40 years and older with hypertension or diabetes, elevated B-type natriuretic peptide (BNP) greater than 20 pg/mL or N-terminal pro-b-type natriuretic peptide greater than 100 pg/mL, left atrial volume index greater than 28 mL/m2, and preserved ejection fraction greater than $50\%$ were included.
### Interventions
Patients were randomized to angiotensin receptor neprilysin inhibitor sacubitril/valsartan titrated to 200 mg twice daily or matching angiotensin receptor blocker valsartan titrated to 160 mg twice daily.
### Main Outcomes and Measures
Maximal left atrial volume index and left ventricular end diastolic volume index, ambulatory pulse pressure, N-terminal pro-BNP, and adverse cardiovascular events.
### Results
Among the 250 participants in this study, the median (IQR) age was 72.0 (68.0-77.0) years; 154 participants ($61.6\%$) were men and 96 ($38.4\%$) were women. Most ($$n = 245$$ [$98.0\%$]) had hypertension and 60 ($24.0\%$) had type 2 diabetes. Maximal left atrial volume index was increased in patients assigned to receive sacubitril/valsartan (6.9 mL/m2; $95\%$ CI, 0.0 to 13.7) vs valsartan (0.7 mL/m2; $95\%$ CI, −6.3 to 7.7; $P \leq .001$) despite reduced markers of filling pressure in both groups. Changes in pulse pressure and N-terminal pro-BNP were lower in the sacubitril/valsartan group (−4.2 mm Hg; $95\%$ CI, −7.2 to −1.21 and −$17.7\%$; $95\%$ CI, −36.9 to 7.4, respectively; $P \leq .001$) than the valsartan group (−1.2 mm Hg; $95\%$ CI, −4.1 to 1.7 and $9.4\%$; $95\%$ CI, −15.6 to 4.9, respectively; $P \leq .001$). Major adverse cardiovascular events occurred in 6 patients ($4.9\%$) assigned to sacubitril/valsartan and 17 ($13.3\%$) assigned to receive valsartan (adjusted hazard ratio, 0.38; $95\%$ CI, 0.17 to 0.89; adjusted $$P \leq .04$$).
### Conclusions and Relevance
In this trial of patients with pre-HFpEF, sacubitril/valsartan treatment was associated with a greater increase in left atrial volume index and improved markers of cardiovascular risk compared to valsartan. More work is needed to understand the observed increased cardiac volumes and long-term effects of sacubitril/valsartan in patients with pre-HFpEF.
### Trial Registration
ClinicalTrials.gov Identifier: NCT04687111
## Introduction
The 2022 American Heart Association/American College of Cardiology (AHA/ACC) guidelines for management of heart failure highlight the importance of community identification of pre–heart failure (pre-HF; also termed stage B HF) because of its association with increased heart failure and cardiovascular risk.1 This approach, supported by natriuretic peptide screening, was shown in the St Vincent’s Screening to Prevent Heart Failure (STOP-HF) randomized clinical trial2 to reduce major adverse cardiovascular events and progression of pre-HF, most of which was associated with pre-HF with preserved ejection fraction (pre-HFpEF). However, the STOP-HF trial2 lacked a specific pharmacological therapy, and the intervention was directed at a general population with cardiovascular risk factors. Studies of other pharmacological therapies in patients with cardiovascular and metabolic risk factors in whom pre-HF was present have shown reduced incident heart failure and cardiovascular events.3,4,5,6,7,8,9 In patients with pre-HF with reduced left ventricular ejection fraction, angiotensin-converting enzyme inhibitor therapy has been associated with a reduction in incident heart failure.10 However, the high prevalence of pre-HFpEF in community populations, an estimated $30\%$ to $63\%$ of older adults with hypertension or diabetes,11,12,13 underlines a need for more studies targeting this cohort.
The pre-HFpEF diagnosis requires the absence of symptoms of heart failure, which is highly subjective. Nonetheless, the 2022 AHA/ACC guidelines say it can be diagnosed in asymptomatic patients with preserved ejection fraction by the presence of at least 1 of the following: structural heart disease, including elevated left atrial size; increased filling pressures; cardiovascular risk factors with increased natriuretic peptides; or persistently elevated cardiac troponins, in the absence of competing diagnoses.1 Aging, long-standing hypertension, diabetes, obesity, and chronic inflammatory comorbidities predispose individuals to abnormalities in cardiac chamber and vascular compliance leading to tissue stiffness, increased filling pressures, hypertrophy, reduced stroke volume, and reactive myocardial fibrosis.14,15,16,17,18,19,20,21,22 The release of natriuretic peptides, predominantly in the myocardium, is an endogenous protective response, helping to preserve compliance and function by enhancing cardiac and vascular smooth muscle cyclic guanosine monophosphate.23,24,25 Sacubitril/valsartan is a first-in-class angiotensin neprilysin receptor inhibitor that reduces degradation of a range of vasoactive substances including natriuretic peptide. Along with other therapies,26,27,28 it has been investigated in symptomatic HFpEF.29,30 Yet, to our knowledge, no study to date has evaluated sacubitril/valsartan in a prespecified cohort with pre-HFpEF.
## Hypothesis, Trial Design, and Oversight
The Personalized Prospective Comparison of ARNI [angiotensin receptor/neprilysin inhibitor] with ARB [angiotensin-receptor blocker] in Patients With Natriuretic Peptide Elevation (PARABLE) randomized, double-blind, double-dummy, active comparator trial was conducted from April 2015 to June 2021 at a single outpatient cardiology center in Dublin, Ireland. The trial was designed to investigate the hypothesis that sacubitril/valsartan vs valsartan would reduce left atrial volume index over 18 months using volumetric cardiac magnetic resonance imaging in patients with pre-HFpEF. Ethical approval was obtained by the St Vincent’s University Hospital Ethics Committee and competent authority approval was obtained from the Health Protection Regulatory Authority in Ireland (EudraCT: 2015-002928-53; ClinicalTrials.gov identifier: NCT04687111). The protocol is available in Supplement 1. Further details on trial oversight, supply of investigational medicinal products, and contractual arrangements with the funder are provided in eMethods 1 in Supplement 2.
## Trial Patients
Patients gave written informed consent to participate in the study before any study-related assessments were performed. Patients 40 years and older with systemic hypertension (medicated for more than 1 month) and/or type 2 diabetes were included and had the following at screening or within 6 months prior to screening: elevated blood B-type natriuretic peptide (BNP; 20 pg/mL to 280 pg/mL) or N-terminal pro b-type natriuretic peptide (NT-proBNP; 100 pg/ml to 1000 pg/mL); and enlarged transthoracic left atrial volume index (LAVI; >28 mL/m2 obtained using echocardiography).22 As detailed in the protocol, we reduced the entry BNP and LAVI thresholds early in the study from 35 pg/mL and 34 mL/m2 respectively to include a low-risk population, based on the observation that almost 3 in 10 major adverse cardiovascular events in the original STOP-HF trial2 occurred in patients with baseline BNP in the range of 20 to 49 pg/mL. Detailed inclusion and exclusion criteria are provided in eTable 1 in Supplement 2. Patients were ineligible if they had a history of or any features of symptomatic heart failure, left ventricular systolic dysfunction (ejection fraction <$50\%$), or serious valvular disease or kidney dysfunction.
## Trial Procedures and Interventions
The study design is presented in eFigure 1 in Supplement 2. The trial consisted of a screening period, a washout period of 36 hours (if participants were previously taking an angiotensin-converting enzyme inhibitor), and a randomized, double-blind, double-dummy, treatment period including dose titration in 2 arms. The intervention arm received sacubitril/valsartan starting at 49 mg/51 mg twice daily, titrated after 2 weeks to 97 mg/103 mg twice daily in addition to usual medical care and a valsartan dummy. The active control arm received valsartan, 80 mg twice daily, titrated after 2 weeks to 160 mg twice daily in addition to usual medical care and a sacubitril/valsartan dummy.
Lower starting doses were used for patients with low systolic blood pressure (SBP; ≥100 mm and <110 mm Hg) or on low or no dose of angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker at the baseline visit. Full details of the medication manufacture, randomization, blinding, allocation concealment, and titration protocol are in eMethods 2 and eTable 2 in Supplement 2.
## Study Assessments
Full details of the study assessments and schedule are included in eMethods 3 and eTable 3 in Supplement 2. Routine assessment involved clinical examination, biochemistry, and evaluation of adverse events and was supplemented with natriuretic peptide levels, electrocardiography monitoring, 24-hour ambulatory blood pressure monitoring and Doppler echocardiography at baseline, 9 months, and the final study visit (18 months). Cardiac magnetic resonance imaging (MRI) was performed at 2 centers (St. Vincent’s University Healthcare Group and Blackrock Clinic) at baseline and at 18 months using 1.5 Tesla scanners (Aera; Siemens Healthcare; and Signa HD; GE Healthcare, respectively). The imaging protocol included a balanced steady-state free precession cine stack of the entire left atrium and left ventricle using a 6- or 8-mm slice thickness and 2-mm slice gap. All scans were reported by 2 independent fellowship-trained cardiothoracic attending radiologists blinded to the randomization arm and all clinical details (intracorrelation and intercorrelation coefficients of 0.95 and 0.96, respectively). Further details regarding the cardiac MRI protocol and analysis are presented in eMethods 3 and eTable 3 in Supplement 2.
## Trial Outcomes
The primary end point was the adjusted change in maximal LAVI measured by volumetric cardiac MRI, indexed to body surface area using the DuBois formula. Secondary outcomes were changes in NTproBNP, pulse pressure (PP), cardiac MRI measurements of structure (left ventricular end diastolic volume index, minimal LAVI, and left ventricular mass index) and function (left atrial and left ventricular stroke volume index, left atrial emptying fraction, and left ventricular ejection fraction), indirect assessment of left ventricular filling pressure using Doppler echocardiography, average ratio of early transmitral flow velocity to early diastolic mitral annular tissue velocity (E/e′), and time to first major adverse cardiovascular event over the course of the study. Major adverse cardiovascular event was defined as cardiovascular death or a serious adverse cardiovascular event due to arrythmia (including atrial fibrillation/flutter), transient ischemic attack, stroke, valvular heart disease, myocardial infarction, deep vein thrombosis or pulmonary embolus, or heart failure requiring hospital admission. Serious adverse events related to the cardiac or vascular system were defined using European Medicines Agency guidelines as events that cause death, are life threatening, require inpatient hospitalization or prolongation of existing hospitalization, or result in serious disability/incapacity.31
## Sample Size and Statistical Methods
Details on sample size calculations and statistical methods are presented in eMethods 4 in Supplement 2. Based on previous work in patients with HFpEF using Doppler echocardiography29 and the use of a more precise imaging technique (volumetric cardiac MRI) for the primary end point, the expected effect size ($\frac{2.0}{5.0}$ = 0.40) required 96 patients in each group, using a 2-tailed α value of $5\%$ and β of $20\%$. The study also had at least $80\%$ power with a 2-tailed α of $5\%$ to detect a 6 g/m2–difference in left ventricular mass index change by cardiac MRI and a 2-unit difference in E/e′ or e′ using tissue Doppler measurements. Primary and secondary outcome measures were analyzed with adjustment for the following variables, unless otherwise specified: age, sex, diabetes, hypertension, obesity, and vascular disease and for baseline measures of the outcome of interest. Categorical end points (eg, major adverse cardiovascular event) were analyzed through hazard ratios by deploying Cox proportional hazards modeling. COVID-19–related procedures are outlined in eMethods 3 in Supplement 2. Before the completion of the trial, data lock, and unblinding of the final data set, we prespecified a per-protocol analysis of the primary end point and analysis of the major adverse cardiovascular event end point prior to the first COVID-19 pandemic lockdown in Ireland (March 14, 2020).
## Results
From April 5, 2015, to December 12, 2018, among 1460 patients with available BNP and Doppler echocardiography attending the St Vincent’s University Healthcare Group STOP-HF program2 or outpatient cardiology clinics, 461 ($31.6\%$) had BNP greater than 20 pg/mL, ejection fraction greater than $50\%$, and LAVI greater than 28 mL/m2. Of these, 323 were willing to participate in screening for eligibility and inclusion in the PARABLE trial. After exclusions, 250 were randomly assigned to double-blind, double-dummy treatment with sacubitril/valsartan ($$n = 122$$) or valsartan ($$n = 128$$). In total, 245 of these patients ($98\%$) had at least 1 feature of pre-HFpEF according to the 2022 AHA/ACC guidelines. The CONSORT diagram, including a breakdown of these data, is presented in Figure 1.
**Figure 1.:** *CONSORT DiagramAHA/ACC indicates American Heart Association/American College of Cardiology; BNP, B-type natriuretic peptide; e′, mitral annular early diastolic velocity; E/e′, the ratio between early mitral inflow velocity and e′; eGFR, estimated glomerular filtration rate; LAVI, left atrial volume index; LVH, left ventricular hypertrophy; MRI, magnetic resonance imaging; STOP-HF, St Vincent’s Screening to Prevent Heart Failure.*
Baseline characteristics by treatment group are given in Table 1. The median (IQR) population age was 72.0 (68.0-77.0) years; 154 participants ($61.6\%$) were men and 96 ($38.4\%$) were women. A total of 207 patients ($82.8\%$; 102 in sacubitril/valsartan and 105 in valsartan) were overweight, defined as body mass index greater than 25 kg/m2. Population blood pressure control, dyslipidemia management, glucose control, and kidney function was generally good. Almost all participants ($$n = 245$$ [$98.0\%$]) had hypertension and 60 ($24.0\%$) had type 2 diabetes. Median (IQR) blood levels of BNP and echocardiographic LAVI at baseline were 57 [33-100] pg/mL and 33.2 (30.7-38.0) mL/m2, respectively. Tricuspid regurgitation velocity was measurable in less than half of the population. Similar numbers of patients with sacubitril/valsartan ($$n = 33$$) and valsartan ($$n = 28$$) had abnormal (ie, >35 mm Hg) pulmonary artery systolic pressure and 34 patients total ($13.6\%$) had high-risk H2FPEF scores (≥6) (Table 1). More detailed information on titration and cardiometabolic medications at baseline and follow-up are shown in eTables 4 and 5 in Supplement 2, respectively. Forty-six patients ($17.6\%$ of the study population) discontinued study medication prematurely (Figure 1). The trial terminated when the last patient completed the follow-up period (June 11, 2021). Paired MRI studies were available for analysis of the primary end point in 200 patients. Of the 241 patients monitored to completion, only 1 patient in the valsartan arm had a confirmed diagnosis of COVID-19.
**Table 1.**
| Characteristic | No. (%) | No. (%).1 |
| --- | --- | --- |
| Characteristic | Sacubitril/valsartan (n = 122) | Valsartan (n = 128) |
| Age, median (IQR), y | 72.0 (68.0-78.0) | 72.0 (67.8-76.0) |
| Female | 43 (35.2) | 53 (41.4) |
| Male | 79 (64.8) | 75 (58.6) |
| Body weight, kg | 83.2 (75.2-93.8) | 82.0 (71.1-91.5) |
| BMI, median (IQR) | 29.0 (26.3-31.4) | 28.8 (26.0-32.1) |
| Heart rate, median (IQR), bpma | 61.0 (56.0-70.0) | 64.0 (57.0-70.0) |
| Blood pressure, mm Hg | | |
| Systolic | 135 (125-147) | 137 (123-149) |
| Diastolic | 74.0 (68.0-82.0) | 75.0 (68.0-83.0) |
| Total cholesterol, median (IQR), mg/dLb | 160.5 (135.3-194.5) | 170.1 (147.0-197.2) |
| LDL cholesterol, median (IQR), mg/dLc | 85.1 (61.9-100.5) | 85.1 (68.8-112.1) |
| Triglycerides, median (IQR), mg/dLd | 124.0 (79.7-168.3) | 119.6 (79.7-168.3) |
| HbA1c, median (IQR), %e | 5.7 (5.4-6.1) | 5.6 (5.4-6.1) |
| Hb, median (IQR), g/dLf | 13.4 (12.5-14.4) | 13.5 (12.5-14.4) |
| eGFR, median (IQR), mL/ming | 78.0 (64.0-90.0) | 77.0 (65.8-90.0) |
| BNP, median (IQR), pg/mLh | 59.0 (33.5-109) | 52.4 (32.7-91.2) |
| NTproBNP, median (IQR), pg/mLi | 136 (88.2-278) | 138 (84.0-247) |
| Hypertension | 120 (98.4) | 125 (97.7) |
| Dyslipidemia | 109 (89.3) | 113 (88.3) |
| Diabetes | 35 (28.7) | 25 (19.5) |
| Angina | 10 (8.20) | 11 (8.59) |
| Myocardial infarction | 18 (14.8) | 20 (15.6) |
| Coronary artery disease | 49 (40.2) | 57 (44.5) |
| Paroxysmal atrial fibrillation | 15 (12.3) | 10 (7.81) |
| Stroke/transient ischemic attack | 8 (6.56) | 15 (11.7) |
| Peripheral vascular disease | 2 (1.64) | 4 (3.12) |
| Chronic kidney disease | 23 (18.9) | 24 (18.8) |
| H2FPEF score, median (IQR)j | 3.50 (3.00-5.00) | 3.00 (2.00-5.00) |
| High-risk H2FPEF score (≥6)j | 21 (17.2) | 13 (10.2) |
| Pretrial ACE inhibitor | 60 (49.2) | 63 (49.2) |
| Pretrial ARB | 52 (42.6) | 49 (38.3) |
| α-Blocker | 21 (17.2) | 25 (19.5) |
| β-Blocker | 70 (57.4) | 69 (53.9) |
| Calcium channel blocker | 49 (40.2) | 61 (47.7) |
| Statin | 98 (80.3) | 102 (79.7) |
| Thiazide diuretic | 33 (27.0) | 41 (32.0) |
| Aspirin | 74 (60.7) | 86 (67.2) |
| Nonaspirin antiplatelet | 8 (6.56) | 11 (8.59) |
| DOAC | 11 (9.02) | 8 (6.25) |
| Warfarin | 3 (2.46) | 0 (0.00) |
| Oral antidiabetic | 28 (23.0) | 23 (18.0) |
| Insulin | 6 (4.92) | 5 (3.91) |
## Effect of Treatment on Cardiac Structure
Detailed Doppler echocardiography and cardiac MRI imaging data are presented in Table 2. Cardiac MRI-estimated change in maximal LAVI was greater in patients assigned to receive sacubitril/valsartan (6.9 mL/m2; $95\%$ CI, 0.0 to 13.7) vs valsartan (0.7 mL/m2; $95\%$ CI, −6.3 to 7.7; $P \leq .001$) (Figure 2A). Similarly, sacubitril/valsartan treatment was associated with greater increase in left ventricular end diastolic volume index (7.1 mL/m2; $95\%$ CI, −1.7 to 15.9) vs the valsartan group (1.4 mL/m2; $95\%$ CI, −7.2 to 10.0; $$P \leq .02$$) (Figure 2B).
In post hoc analyses, we found that sacubitril/valsartan was associated with a reduction in left atrial and left ventricular diastolic stiffness index compared to valsartan. PP, arterial stiffness, and systemic vascular resistance were also reduced, while arterial compliance was increased in the sacubitril/valsartan vs valsartan treatment groups (Table 2). Change in maximal LAVI also remained significant when adjusted for changes in PP or SBP. However, there was a significant interaction between the changes in maximal LAVI and PP (odds ratio, 0.98; $95\%$ CI, 0.97-0.99; $$P \leq .002$$) as well as SBP (odds ratio, 0.97; $95\%$ CI, 0.96-0.99; $$P \leq .002$$). The LVEDV standardized to a filling pressure of 30 mm Hg (EDV30) was increased by a median (IQR) 13.8 mL (0.36 to 30.0) in the sacubitril/valsartan group vs 3.85 (−12.76 to 12.3); 30.0 in those treated with valsartan ($P \leq .001$). Left ventricular mass index was reduced in both groups without between-group differences. An analysis of the primary end point including only those patients who completed the study cardiac MRI per-protocol in the same institution prior to onset of COVID-19 restrictions showed the same result as the primary analysis (eFigure 2 in Supplement 2). LAVI measured by echocardiography did not demonstrate any significant within- or between-group changes at 18 months (Table 2).
## Effect of Treatment on Cardiac Function
Sacubitril/valsartan was associated with increased change in left atrial stroke volume index and left ventricular stroke volume index compared with valsartan. There were no between-group differences in the change in left ventricular ejection fraction or left atrial emptying fraction. Doppler echocardiography–measured E/e′ was significantly reduced in both treatment groups with no between-group difference (Table 2).
## Effect of Treatment on 24-Hour Ambulatory Blood Pressure, Pulse Pressure, and Estimated Glomerular Filtration Rate
Ambulatory blood pressure data over 24 hours showed significant reductions in SBP, diastolic blood pressure, and PP in patients treated with sacubitril/valsartan vs those treated with valsartan (Table 2). Using repeated measures analysis, the adjusted estimated marginal mean change in PP over the study was −4.2 mm Hg ($95\%$ CI, −7.2 to −1.21) in patients assigned to sacubitril/valsartan vs −1.2 mm Hg ($95\%$ CI, −4.1 to 1.7) in those assigned to valsartan ($P \leq .001$). More detail on biochemistry measures at baseline and follow-up are presented in eTables 6 and 7 in Supplement 2. There were no changes in body weight (Table 1) or hemoglobin (eTable 7 in Supplement 2) in either group. The reduction in mean (SD) estimated glomerular filtration rate in patients assigned to valsartan (−4.9 [11.4] mL/min/1.72 m2) was significantly greater than in those assigned to sacubitril/valsartan (−0.9 [10.6] mL/min/1.72 m2; $$P \leq .005$$).
## Effect of Treatment on Natriuretic Peptide
There was a significant reduction in NTproBNP over time observed in the sacubitril/valsartan vs valsartan group, which was sustained throughout the study (Table 2). Using repeated measures analysis, N-terminal pro-BNP decreased by −$17.7\%$ ($95\%$ CI, −36.9 to 7.4) in patients treated with sacubitril/valsartan vs an increase of $9.4\%$ ($95\%$ CI, −15.6 to 4.9) in patients treated with valsartan ($P \leq .001$) over the study period. BNP levels were maintained at baseline levels throughout the study.
In post hoc analyses, small but significant differences between the 2 groups at baseline were noted in the mean (SD) body surface area among those who attended for cardiac MRI (sacubitril/valsartan 1.96 [0.22] m2 vs valsartan 1.90 [0.21] m2; $$P \leq .04$$), median (IQR) Doppler echocardiographic E/e′ (sacubitril/valsartan 11.1 [9.10-to 13.0] vs valsartan 9.87 [8.05 to 12.3], $$P \leq .01$$) and mean (SD) cardiac MRI measured LAVI max (sacubitril/valsartan 47.4 [9.59] vs valsartan 52.4 [11.3]; $$P \leq .01$$).
## Adverse Cardiovascular Events
Overall, both treatments appeared to be well tolerated (all serious adverse event data are presented in eTable 8 in Supplement 2). There were 55 serious adverse events reported in patients assigned to sacubitril/valsartan vs 69 in those assigned to valsartan treatment (time-to-event analysis in eFigure 3 in Supplement 2). Four people developed symptomatic HFpEF (2 in each treatment group). The most common category of serious adverse cardiovascular event was atrial fibrillation or flutter, which occurred in 5 people treated with sacubitril/valsartan and 10 people treated with valsartan, 4 and 6 of whom, respectively, did not have any history of atrial fibrillation or flutter at baseline. There were 4 deaths that occurred during the 18-month study period, 3 in the valsartan group (2 cardiovascular deaths and 1 cancer death) and 1 (cancer death) in the sacubitril/valsartan group. Major adverse cardiovascular events (including cardiovascular deaths) occurred in 6 patients ($4.9\%$) in the sacubitril/valsartan group and in 17 ($13.3\%$) in the valsartan group. The time to first major adverse cardiovascular event is presented in Figure 3 and showed reduced risk in those treated with sacubitril/valsartan (adjusted hazard ratio, 0.38; $95\%$ CI, 0.17 to 0.89; adjusted $$P \leq .04$$).
**Figure 3.:** *Time to Cardiovascular Death and First Major Adverse Cardiovascular Event (MACE)Sacubitril/valsartan vs valsartan was associated with an unadjusted hazard ratio (HR) for cardiovascular death and major adverse cardiovascular events of 0.35 (95% CI, 0.17 to 0.74; P = .006). Error bars indicate 95% CIs.*
## Discussion
Pre-HFpEF is common, has no specific therapy aside from cardiovascular risk factor management, and is growing due to aging populations, increased prevalence of diabetes, persistent hypertension, and obesity. The 2022 AHA/ACC guidelines for heart failure1 and the 2021 Universal Definition and Classification of Heart Failure33 recommend increased focus on presymptomatic HF, citing the STOP-HF trial,2 despite concerns raised about large numbers of patients with the condition.34 However, the STOP-HF trial lacked a specific therapy. The PARABLE trial builds on this and other work to date in pre-HFpEF35 by targeting a population in which neprilysin inhibition and downstream preservation of natriuretic peptide may be of benefit, despite some evidence that circulating neprilysin levels are not elevated in patients who are presymptomatic.36 The effect of neprilysin inhibition on the primary end point in PARABLE was unexpected. Increases in maximal LAVI and left ventricular end diastolic volume index are ordinarily associated with a poorer outlook. While treatment with sacubitril/valsartan vs valsartan in a post–myocardial infarction population did not alter cardiac volumes,37 sacubitril/valsartan in symptomatic HFpEF was associated with a small decrease in LAVI measured by echocardiography over 9 months.29 However, cardiac chamber volume increases with sacubitril/valsartan in PARABLE were seen using more precise volumetric cardiac MRI. This occurred in the setting of reduced NT-proBNP, SBP, and PP as well as preservation of kidney function relative to valsartan. Could the evidence of reduced time to major adverse cardiovascular event, albeit in small numbers, suggest this LA enlargement is beneficial?
The aging myocardium of sedentary adults is associated with cardiac and vascular stiffness, reduced cardiac chamber size and stroke volumes.20,38 An increase in LVEDV due to afterload reduction, reduced cardiac stiffness and improved vascular compliance in older adults with stiff hearts and normal ejection fraction may be a marker of healthier or more successful aging.39 Exercise training augments cyclic guanosine monophosphate signaling40 and can reverse myocardial stiffness and increase LVEDV in patients with pre-HFpEF.41,42 LAVI is known to increase in endurance athletes.43 In PARABLE, the observed reduction in filling pressures, vascular stiffness, and cardiac chamber stiffness could explain a change in the pressure-volume relationship, with increased EDV30 in those treated with sacubitril/valsartan vs valsartan therapy. Interactions were seen between cardiac volume changes and beneficial PP changes. Similarly, natriuretic peptide augmentation of cyclic guanosine monophosphate and reversal of cardiomyocyte stiffness, mediated by titin hypophosphorylation, has been shown to increase cardiac chamber volumes in animal models.18 Conversely, increased cardiac chamber volumes in PARABLE could reflect adverse effects of neprilysin inhibition, which is known to preserve proremodeling vasoconstrictor peptides.44 Atrial natriuretic peptide, augmented by sacubitril/valsartan, can promote natriuretic peptide receptor-A–mediated adipogenesis, potentially increasing epicardial adipose tissue,45 in turn associated with worsening left atrial reservoir function in patients with HFpEF but not in those with HFrEF.45,46 More work is needed to understand the unexpected results in PARABLE and to balance them with the consistent improvement with sacubitril/valsartan vs valsartan in important surrogate markers of cardiovascular risk, such as SBP, PP, estimated glomerular filtration rate, and NTproBNP.
## Limitations
There are a number of important limitations to the present study. PARABLE is a phase II, single-center randomized clinical trial in a selected population of predominantly European ancestry. The follow-up time was limited to 18 months and overlapped with the COVID-19 pandemic. Nine patients were lost to follow-up, 46 withdrew from the treatment phase, not all of whom were willing to return for the follow-up cardiac MRI, and a further 49 had delayed cardiac MRI examination. There were differences in the baseline characteristics of the population, including in the primary end point. While the primary analysis adjusted for these baseline differences and there were no baseline differences in left ventricular end diastolic volume index, we cannot exclude a contribution of regression to the mean in the imaging results. Confirmation of asymptomatic HFpEF at baseline was carried out by an experienced cardiology fellow and heart failure nurse specialist. However, 34 patients ($13.6\%$) had high H2FpEF scores, and we did not carry out formal functional assessments, such as the Kansas City Cardiology Questionnaire or 6-minute walk test, nor did we carry out cardiac MRI assessments of patients at an interim 9-month period. Accordingly, we cannot comment on the time course of the observations reported. PARABLE is not designed or powered to provide definitive evidence on the cardiovascular outcomes over longer follow-up. Moreover, the generalizability of the PARABLE study maybe limited by the exclusion criteria adopted and the homogenous ancestry of the participating population.
## Conclusions
The results of the PARABLE trial showed that sacubitril/valsartan can increase left atrial volume index in the setting of reduced markers of filling pressure in hypertension or diabetes in patients with pre-HFpEF. This may reflect improved vascular compliance and reduced cardiac chamber stiffness mediated pharmacologically by natriuretic peptide modulating therapy. However, more work is required to exclude adverse effects of increased chamber volumes and to understand the long-term benefits and risks of sacubitril/valsartan in treating pre-HFpEF.
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|
---
title: Combined adipose-derived mesenchymal stem cell and antibiotic therapy can effectively
treat periprosthetic joint infection in rats
authors:
- Yuki Yamamuro
- Tamon Kabata
- Takayuki Nojima
- Katsuhiro Hayashi
- Masaharu Tokoro
- Yoshitomo Kajino
- Daisuke Inoue
- Takaaki Ohmori
- Junya Yoshitani
- Takuro Ueno
- Ken Ueoka
- Atsushi Taninaka
- Tomoyuki Kataoka
- Yoshitomo Saiki
- Yu Yanagi
- Hiroyuki Tsuchiya
journal: Scientific Reports
year: 2023
pmcid: PMC9996572
doi: 10.1038/s41598-023-30087-z
license: CC BY 4.0
---
# Combined adipose-derived mesenchymal stem cell and antibiotic therapy can effectively treat periprosthetic joint infection in rats
## Abstract
Periprosthetic joint infection (PJI) is characterized by biofilm infection, which is difficult to alleviate while preserving implant integrity. Furthermore, long-term antibiotic therapy may increase the prevalence of drug-resistant bacterial strains, necessitating a non-antibacterial approach. Adipose-derived stem cells (ADSCs) exert antibacterial effects; however, their efficacy in PJI remains unclear. This study investigates the efficacy of combined intravenous ADSCs and antibiotic therapy in comparison to antibiotic monotherapy in a methicillin-sensitive *Staphylococcus aureus* (MSSA)-infected PJI rat model. The rats were randomly assigned and equally divided into 3 groups: no-treatment group, antibiotic group, ADSCs with antibiotic group. The ADSCs with antibiotic group exhibited the fastest recovery from weight loss, with lower bacterial counts ($$p \leq 0.013$$ vs. no-treatment group; $$p \leq 0.024$$ vs. antibiotic group) and less bone density loss around the implants ($$p \leq 0.015$$ vs. no-treatment group; $$p \leq 0.025$$ vs. antibiotic group). The modified Rissing score was used to evaluate localized infection on postoperative day 14 and was the lowest in the ADSCs with antibiotic group; however, no significant difference was observed between the antibiotic group and ADSCs with antibiotic group ($p \leq 0.001$ vs. no-treatment group; $$p \leq 0.359$$ vs. antibiotic group). Histological analysis revealed a clear, thin, and continuous bony envelope, a homogeneous bone marrow, and a defined, normal interface in the ADSCs with antibiotic group. Moreover, the expression of cathelicidin expression was significantly higher ($$p \leq 0.002$$ vs. no-treatment group; $$p \leq 0.049$$ vs. antibiotic group), whereas that of tumor necrosis factor (TNF)-α and interleukin(IL)-6 was lower in the ADSCs with antibiotic group than in the no-treatment group (TNF-α, $$p \leq 0.010$$ vs. no-treatment group; IL-6, $$p \leq 0.010$$ vs. no-treatment group). Thus, the combined intravenous ADSCs and antibiotic therapy induced a stronger antibacterial effect than antibiotic monotherapy in a MSSA-infected PJI rat model. This strong antibacterial effect may be related to the increased cathelicidin expression and decreased inflammatory cytokine expression at the site of infection.
## Introduction
Periprosthetic joint infection (PJI), an implant-related infection, is one of the most serious complications that can occur following total joint arthroplasty. PJI is characterized by biofilm infections, wherein biofilm-forming bacteria escape the host’s immune response and become resistant to antibiotics1. Moreover, prolonging antibiotic therapy to quell infection while preserving the integrity of the implant is extremely difficult, and revision surgeries are often required2. Furthermore, with an increase in the number of arthroplasty procedures being performed, an increase in associated infections will also inevitably occur, posing a significant threat to society3. In clinical practice, removing implants might be impossible as this process can negatively impact quality of life, and the development of bacterial resistance during long-term antibiotic therapy is also a concern4. Therefore, a non- antibiotics strategy is needed to prevent biofilm infection while preserving implant integrity.
Previous studies have focused on strategies that activate and modulate the immune system5,6 or induce the release of antimicrobial peptides from mesenchymal stem cells (MSCs)7,8. MSCs have been studied in various fields and have exhibited therapeutic efficacy against coronavirus disease 2019 infection, sepsis, and pneumonia9. Additionally, MSCs, including adipose-derived stem cells (ADSCs), may be effective for treating biofilm infections and infection-related sepsis when administered intravenously in combination with antibiotics10,11. However, this therapy has not yet been evaluated in detail for PJI. Furthermore, the antibacterial efficacy of the combined intravenous ADSCs and antibiotic therapy, as well as their effect on host physiology, including bone and soft tissue structure, remain unknown. Therefore, in the current study, we examined whether combined ADSCs and antibiotic therapy is superior to antibiotic monotherapy in a methicillin-sensitive *Staphylococcus aureus* (MSSA)-infected PJI rat model.
## Infection model and systemic response
Fidelity of the ADSCs was confirmed using flow cytometry. Flow cytometric analysis showed that the subgroup of CD90+ ADSCs had the highest population. Moreover, the cells were spindle-shaped, a typical morphological feature of ADSCs (Fig. 1). All rats survived and were active during the observation period. The systemic response to infection was indirectly quantified by measuring changes in body weight. Rats in all three groups lost weight initially after surgery, reaching the lowest point on postoperative day (POD) 7. However, the weight change was significantly smaller in the ADSCs with antibiotic group than in the other groups on POD7 (Welch ANOVA followed by Tukey HSD post-test, $$p \leq 0.003$$ vs. no-treatment group; $$p \leq 0.015$$ vs. antibiotic group) and POD14 (Welch ANOVA followed by Tukey HSD post-test, $p \leq 0.001$ vs. no-treatment group; $$p \leq 0.009$$ vs. antibiotic group). Notably, the weight in the ADSCs with antibiotic group recovered to baseline on POD 14 (Fig. 2).Figure 1Flow cytometric analysis. ( a‒e) Flow cytometric analysis after cell culturing. ( f) Percentage of ADSCs in each sub-population. ( g) Spindle-shaped cells were observed in the culture plate, a typical appearance of mesenchymal stem cells. ADSC adipose-derived mesenchymal stem cell. Figure 2Evaluation of weight change. Weight percent change from pre-operation day 1 baseline values. Weight change was significantly smaller in the ADSCs with antibiotic group than in the other groups on POD7 and POD14. All statistical analyses were performed using Welch ANOVA followed by Tukey HSD post-test ($$n = 6$$ rats per group). The error bars are defined as standard error of the mean. * $p \leq 0.05.$ ADSC adipose-derived mesenchymal stem cell.
## Local response
The modified Rissing score was used to evaluate localized infection on POD14. This score was the lowest in the ADSCs with antibiotic group; however, no significant difference was observed between the antibiotic group and ADSCs with antibiotic group (Welch ANOVA followed by Tukey HSD post-test, $p \leq 0.001$ vs. no-treatment group; $$p \leq 0.359$$ vs. antibiotic group; Fig. 3). Assessment of the intra-rater reliability of the modified Rissing score revealed an intra-class coefficient of 0.882 ($95\%$ confidence interval, 0.740–0.952).Figure 3Evaluation of the modified Rising scale. All statistical analyses were performed using Welch ANOVA followed by Tukey HSD post-test ($$n = 6$$ rats per group). Data are reported as the median ± interquartile range. * $p \leq 0.05.$ ADSC adipose-derived mesenchymal stem cell.
## Bone quality evaluation by μ-computed tomography (µCT)
Ex vivo μCT analysis on POD14 showed that the mean periprosthetic bone mineral density (BMD) was 506.6 ± 12.7 mg/cm3 ($95\%$ confidence interval $95\%$ CI 493.3–520.0) in the no-treatment group, 516.2 ± 23.1 mg/cm3 ($95\%$ CI 491.9–540.5) in the antibiotic group, and 589.2 ± 46.8 mg/cm3 ($95\%$ CI 540.0–638.3) in the ADSCs with antibiotic group. The ADSCs with antibiotic group exhibited the highest BMD (Welch ANOVA followed by Tukey HSD post-test, $$p \leq 0.015$$ vs. no-treatment group; $$p \leq 0.025$$ vs. antibiotic group; Fig. 4a).Figure 4Evaluation of μCT. ( a) Quantification of the femoral bone mineral density of each femur in the three groups 14 days after insertion of the K-wire. ( b) Three‐dimensional μCT axial images of the distal femoral region 14 days after insertion of the K-wire. All statistical analyses were performed using Welch ANOVA followed by Tukey HSD post-test ($$n = 6$$ rats per group). The error bars are defined as standard error of the mean. * $p \leq 0.05.$ ADSC adipose-derived mesenchymal stem cell.
The three-dimensional (3D) rendering and qualitative evaluation of the μCT images revealed a clear difference in bone quality among the three groups. The 3D μCT axial slice images showed osteolysis and an increased bone size associated with periosteal reaction, which was clearly observed in the distal region of the infected femurs in the no-treatment group and antibiotic group (Fig. 4b).
## Culture-based quantification of bacteria on the implant
Ex vivo colony-forming units (CFUs) from sonicated Kirschner wires (K-wires) were enumerated on POD14. The implants from the no-treatment group had a mean CFU count of 150.7 ± 72.1 ($95\%$ CI 75.0–226.3) × 104 CFU/mL; those from the antibiotic group had a mean of 55.0 ± 72.1 ($95\%$ CI 30.3–79.7) × 104 CFU/mL; and from the ADSCs with antibiotic group had a mean of 17.0 ± 15.3 ($95\%$ CI 0.9–33.1) × 104 CFU/mL. Although bacteria were detected in the ADSCs with antibiotic group, the bacterial burden was significantly lower than that in the no-treatment or antibiotic groups (Welch ANOVA followed by Tukey HSD post-test, $$p \leq 0.013$$ vs. no-treatment group; $$p \leq 0.024$$ vs. antibiotic group; Fig. 5a and b).Figure 5CFU assay. ( a) Antibacterial activity determined using the spread plate method for implants. ( b) Photograph of a tryptic soy broth agar plate of each group. All statistical analyses were performed using Welch ANOVA followed by Tukey HSD post-test ($$n = 6$$ rats per group). Data are reported as the median ± interquartile range. * $p \leq 0.05.$ CFU colony-forming unit, ADSC adipose-derived mesenchymal stem cell.
## Histological analysis
To determine the microscopic location of the inflammatory infiltrate and bacterial inoculum around the implant, histological sections of the distal femur were evaluated from the three groups at the end of the experiment on POD14. The hematoxylin and eosin-stained sections collected from the transverse plane across the implant revealed a clear, thin, and continuous bony envelope and homogeneous bone marrow and a defined normal interface between these structures in the ADSCs with antibiotic group. The antibiotic group showed a clear, thick, and continuous bony envelope; however, the bone marrow became less cellular, and the interface was not well-defined. In contrast, abscess formation and gradual disruption of bone integrity with severe discontinuity were observed in the no-treatment group (Fig. 6).Figure 6Histologic analysis. Representative photomicrographs of histologic sections (1 of the 6 rats per group, with similar results). Low and high magnification of H&E-stained joint specimens. The H&E-stained histology sections taken from the transverse plane across the implant revealed a clear, thin, and continuous bony envelope and homogeneous bone marrow as well as a defined normal interface between these structures in the ADSCs with antibiotic group. ( A), (B), and (C) represent high-magnification images of the boxed regions. ※abscess formation, *bone marrow. H&E hematoxylin and eosin, ADSC adipose-derived mesenchymal stem cell.
At the established endpoint (POD14), the femurs isolated from the rats were fixed in $10\%$ neutralized formalin solution and dehydrated using an ethanol gradient ($70\%$, $80\%$, $90\%$, and $100\%$). The fixed specimens were decalcified in $10\%$ formic sodium citrate solution, embedded in paraffin, and sectioned in the coronal plane. The sections were stained with hematoxylin and eosin, and the slides were observed using an optical microscope (Biorevo BZ-9000; Keyence Corp., Osaka, Japan).
## Gene expression levels of rat cathelicidin-related antimicrobial peptide (rCRAMP), tumor necrosis factor(TNF)-α, interleukin (IL-6), IL-1β, and glyceraldehyde 3-phosphate dehydrogenase (GAPDH)
The control GAPDH gene was stably expressed at the site of infection in all groups. Reverse transcription Polymerase Chain Reaction (RT-PCR) revealed a significant increase in the expression of the rCRAMP gene in the ADSCs with antibiotic group (Welch ANOVA followed by Tukey HSD post-test, $$p \leq 0.002$$ vs. no-treatment group; $$p \leq 0.049$$ vs. antibiotic group; Fig. 7a). The TNF-α and IL-6 expression in the ADSCs with antibiotic group was lower than that in the no-treatment group but not significantly lower than that in the antibiotic group (Welch ANOVA followed by Tukey HSD post-test, TNF-α, $$p \leq 0.010$$ vs. no-treatment group; IL-6, $$p \leq 0.010$$ vs. no-treatment group; Fig. 7b, c). The IL-1β gene expression did not significantly differ among the three groups (Fig. 7d).Figure 7Real-time reverse transcription–polymerase chain reaction (RT-PCR). ( a-d) Gene expression of rCRAMP,TNF-α, IL-6, and IL-1β in the three groups. All statistical analyses were performed using Welch ANOVA followed by Tukey HSD post-test ($$n = 6$$ rats per group). The error bars are defined as standard error of the mean. * $p \leq 0.05.$ mRNA messenger ribonucleic acid, rCRAMP rat cathelicidin-related antimicrobial peptide, TNF tumor necrosis factor, IL interleukin, ADSC adipose-derived mesenchymal stem cell.
## DiI labeling studies
On POD14, DiI-positive (red) cells were distributed through the trabecular bone around the K-wire (Fig. 8). The observed fluorescence was specific to ADSCs, and was not an experimental artefact or autofluorescence as confirmed by the absence of staining in antibiotic-only-treated samples (Fig. 8).Figure 8Representative images of DiI labeling at the site of infection. Frozen sections were prepared from rats in the antibiotic group not injected with labelled cells (top), and 14 days after transplantation of ADSCs labeled with DiI (bottom) (1 of the 3 rats per group, with similar results). For identification of tissues following DiI labeling, the gray-scale scale (16 bit) of the DiI-labeled section was used. On POD14, DiI-positive (red) cells were distributed through the trabecular bone around the K-wire. ( A), (B), (C), and (D) represent high-magnification images of the boxed regions. ADSC adipose-derived mesenchymal stem cell.
Tissue sections were evaluated to determine the location of ADSCs following injection. To confirm the location of the injected ADSCs, they were labeled with the fluorescent dye DiI (Vybrant DiI Cell Labeling Solution; Life Technologies, Carlsbad, CA, USA) before injection. DiI binds to cellular thiols and has long-term stability, enabling the tracing of DiI-labeled transplanted cells in the host tissue. The concentration of ADSCs was adjusted to 5.0 × 105 cells/mL; DiI (5 μL/mL) was dissolved in the cell culture media and incubated for 15 min at 37 °C in a $5\%$ CO2 incubator for ADSCs labeling. The filtrate was centrifuged at 180×g for 5 min at 25 °C and the supernatant was removed to separate the DiI from the filtrate. The ADSCs were centrifuged twice with Dulbecco’s modified *Eagle medium* under the same conditions and the supernatant was removed. We used separate rats for this experiment ($$n = 3$$ rats per antibiotic group and DiI-labeled ADSCs with antibiotic group). On day 14 post-injection, a frozen section was prepared using Kawamoto’s film method in the sagittal plane43. For identification of tissues following DiI labeling, the gray-scale scale (16 bit) of the DiI-labeled section was used.
## Discussion
The combined intravenous ADSCs and antibiotic therapy exhibited good antibacterial effects in the MSSA-infected PJI rat model. Moreover, the combined therapy was superior to antibiotic monotherapy in reducing weight loss, bacterial counts in implant biofilms, and abscess formation. The combined therapy also effectively minimized peri-implant osteolysis and reduced BMD, significantly increasing cathelicidin expression at the site of infection. These results emphasize that ADSCs do not interfere with, but rather enhance, the effects of antibiotic agents and that the underlying mechanism may include increased cathelicidin expression at the site of infection. MSCs, including ADSCs, exert antimicrobial activity through multiple complementary mechanisms of action: indirectly through immunomodulators and directly through the release of antimicrobial peptides7,12–15. We selected ADSCs as they are abundant in the subcutaneous adipose tissue and can be readily harvested in clinical settings using syringes or minimally invasive liposuction.
PJI has systemic consequences with negative adverse effects on patient mortality and quality of life16,17. The systemic response to infection can be determined by measuring weight change because slow, subtle weight loss represents the earliest reliable sign of a worsening systemic condition18. A recent study has shown that intravenous ADSCs act synergistically with antibiotics to reduce organ damage within the urinary system and mortality in a rat model of sepsis in which enteric bacteria were intraperitoneally injected, suggesting that intravenous ADSCs therapy is effective against systemic infections11. Similarly, in present study, the combined intravenous ADSCs and antibiotic therapy prevented the exacerbation of systemic conditions caused by PJI. Although no significant difference was observed between the ADSCs with antibiotic group and antibiotic group, the combined intravenous ADSCs and antibiotic therapy suppressed the local infection score and expression of inflammatory cytokines (TNF-α and IL-6) at the site of infection, suggesting an association between ADSCs and an improved antibacterial effect.
PJI causes periarticular osteolysis and periosteal osteogenesis, which may adversely affect clinical outcomes. Established infections worsen bone quality over time, resulting in a decreased BMD and cortical thickening19,20. In clinical practice, the reduction of bone stock around PJI is a major problem21. Our results suggest that the combined intravenous ADSCs and antibiotic therapy inhibits bone stock reduction around infected implants and facilitates two-stage reconstruction, which is necessary in clinical practice. A previous study has shown that ADSCs regulate B cells, promote osteoblast formation, and inhibit osteoclasts, thereby restoring the regenerative capacity of bone defects after infection22. However, even in the intravenous ADSCs with antibiotic group, the femoral BMD was lower than that in normal rats.
Johnson et al. reported a significant antimicrobial effect of intravenous ADSCs in an in vivo subcutaneous mesh mouse model, showing a reduction in the number of bacteria in the peri-implant biofilm due to the interaction with antibiotics10. In our MSSA-infected PJI rat model, the combined intravenous ADSCs and antibiotic therapy significantly reduced the number of bacteria in the biofilm and enhanced the antibacterial effect compared with the results of antibiotic monotherapy. Furthermore, pathological evaluation revealed that the extent of the abscess was reduced.
Notably, we found that the cathelicidin expression at the site of infection was significantly increased in the ADSCs with antibiotic group. ADSCs and other MSCs secrete various antimicrobial peptides, including cathelicidin8,15. Bacteria are less likely to develop resistance to antimicrobial peptides than to antibiotics; therefore, antimicrobial peptides have attracted considerable attention in recent years23. Cathelicidins comprise a major antimicrobial peptide family in mammals that exerts its killing effect by disrupting bacterial membrane integrity and inhibiting biofilms24–26. Thus, cathelicidin at the site of infection may have enhanced the antibacterial effects. Additionally, systemically administered MSCs tend to remain in the lungs; however, they can be distributed to multiple organs and tissues away from the administration site27. Particularly, compared to locally administered ADSCs, intravenously administered ADSCs effectively suppress deep-seated bacterial infections by migrating from the lungs to the site of infection over several days, resulting in lower bacterial counts in the wounds10. ADSCs then accumulate around implants, where direct administration is difficult, leading to an increase in cathelicidin expression at the site of infection. Therefore, our study findings suggest that the increase in cathelicidin expression at the site of infection is related to the accumulation of ADSCs. Intravenous ADSCs may, therefore, prove effective as a treatment against PJI.
Several limitations were noted in the current study. The study was conducted exclusively in rats; therefore, the results may differ in large mammals, including humans. The PJI rat model was created by inserting K-wires retrogradely, which may not reproduce the actual loading environment of the artificially infected joint. Moreover, our results may differ from those of other studies owing to differences in the expression markers of ADSCs. At present, no specific marker or combination of markers has been identified that specifically defines MSCs. Phenotypically, ex vivo-expanded MSCs express several nonspecific markers, including CD90, CD105, CD73, CD166, CD44, and CD2928,29. MSCs are devoid of hematopoietic and endothelial markers, such as CD11b, CD14, CD31, CD34 and CD4528. Our ADSCs were consistent with the characteristics of the MSCs. In addition, in our study, ADSCs localization to the site of infection was confirmed by in vivo cell tracking; furthermore, the combined therapy increased the expression of cathelicidin and decreased that of inflammatory cytokines at the site of infection. However, further investigation is needed to elucidate the relationship and mechanism between the antibacterial activity of the combined therapy and the expression of inflammatory cytokines and cathelicidin. Furthermore, the data were collected only after 14 days, whereas results obtained at 3 and 7 days would provide additional information regarding the evolution of the antibacterial effect of ADSCs combined with antibiotic treatment. Therefore, additional studies are required encompassing larger total numbers of animals to permit evaluation at multiple time points. Finally, only the effects of ciprofloxacin were examined; different results may occur using other antibiotics. However, quinolones are excellent antimicrobial agents in terms of their bioavailability, antibacterial activity, and tolerability30. Moreover, ciprofloxacin has been extensively tested for the long-term treatment of implant-related staphylococcal infections and can be applied in clinical practice31.
In conclusion, the combined intravenous ADSCs and antibiotic therapy induces a stronger antibacterial effect than antibiotic monotherapy in a MSSA-infected PJI rat model, with earlier recovery from weight loss, reduced peri-implant bacterial counts, and reduced peri-implant BMD. This strong antibacterial effect may be related to the increased expression of cathelicidin and decreased expression of inflammatory cytokines at the site of infection. Our results suggest that the combined intravenous ADSCs and antibiotic therapy may be used to treat patients with PJI who show an inadequate response to conventional antibiotic monotherapy.
## Bacteria and biofilm formation
MSSA strain ATCC29213 (American Type Culture Collection, Manassas, VA, USA) was used as it tends to form biofilms32,33. MSSA was streaked onto plates containing tryptic soy broth and Bacto agar (BD Biosciences, Franklin Lakes, NJ, USA) and grown overnight in 5 mL of tryptic soy broth at 37 °C in a shaking incubator. MSSA cells in the incubation medium were grown to the early exponential growth phase (0.2–0.3 optical density at 600 nm), corresponding to 5.0 × 107 CFU/mL.
## Isolation of ADSCs
Adipose tissue (~1.5 g) was obtained from Wistar rats (female; 12 weeks old; Japan SLC Corp., Shizuoka, Japan). ADSCs were prepared by modifying previously reported methods34. Further details can be found in the Supplementary file. Cellular characteristics (i.e. expression of stem cell surface markers) were determined using flow cytometric analysis after labeling ADSCs with appropriate antibodies of cultivation.
## Rat PJI model, surgical procedures, and animal grouping
Wistar rats (female; 12 weeks old; Japan SLC Corp.) were housed under specific pathogen-free conditions with a 12-h light/dark cycle and ad libitum access to a certified diet (CRF-1; Oriental Yeast Corp., Tokyo, Japan) and water (chlorine concentration; 10 ppm). The drinking, feeding behavior, and body weight of the rats were monitored regularly. The animals were acclimatized for 7 days before undergoing the implant operation.
Rats were anesthetized with midazolam (2.5 mg/kg; Astellas Pharma, Tokyo, Japan), medetomidine (0.5 mg/kg; Zenoaq, Fukushima, Japan), and butorphanol tartrate (2.5 mg/kg; Meiji Seika Pharma, Tokyo, Japan). To establish infection, A medical-grade K-wire (1.2 mm diameter; Synthes Inc., West Chester, PA, USA) was incubated in an overnight culture with MSSA strain ATCC29213 and then air-dried for 20min prior to insertion. This MSSA strain exposure coats the screw with 5×107 CFU. The K-wire was surgically placed into the distal femur as previously described34–36. Briefly, the skin overlying the leg was shaved and cleaned with iodine solution. A medial parapatellar approach was used, and the patella was dislocated laterally to access the knee joint. The femoral medullary canal was reamed with an 18-gauge needle and the K-wire was placed in a retrograde fashion with 1 mm of the wire protruding into the joint space. The quadriceps-patellar complex was reduced to the anatomic position, and the wound was closed with nylon 4-0 sutures. Rats were randomly assigned and equally divided into three groups: no-treatment, antibiotic (ciprofloxacin [3.0 mg/kg per day intravenously]), and ADSCs [5.0 × 105 cells intravenously 30 min, 6 h, and 18 h after the surgical procedure]) with antibiotic (ciprofloxacin [3.0 mg/kg per day intravenously] groups. The ADSC dose, based on a previous report37, is considered to not induce adverse effects, including a high mortality rate. Additionally, a previous report showed that a ciprofloxacin dose of 3.0 mg/kg per day caused no adverse effects or unstable conditions in rats11. MSSA induced infection in $100\%$ of the untreated rats with no significant differences in the initial body weights between the different groups.
After evaluating the general overall condition and soft tissue swelling, the rats were euthanized on POD 14 using thiopental sodium (100 mg/kg body weight). Tissues from the knee joint space, femur, and implant were harvested in a sterile manner for ex vivo analyses.
## Weight monitoring
Weight change ($$n = 6$$ rats per group) was calculated as a percentage change based on the preoperative weight to quantitatively measure the systemic response to infection. Preoperative baseline measurements were performed on the day before surgery. The weight of the rats was also evaluated on PODs 1, 3, 7, and 14.
## Local tissue scoring
Soft tissue and bone damage ($$n = 6$$ rats per group) on POD14 was scored by three examiners (D.I, A.T. and T.K.) blinded to the rats according to a modified Rissing scoring38,39. Further details can be found in Supplementary file.
## µCT
μCT imaging ($$n = 6$$ rats per group) was performed on POD14 to determine the degree of infection within the femoral region of interest. Considering that image artifacts from the K-wires may cause artifacts in the reconstructed μCT images, isolated femurs from rats with the wire removed were subjected to μCT scanning (LaTheta LCT-200; Hitachi Aloka Medical, Tokyo, Japan), operating at 50 kV and 0.5 mA (radiation exposure remained below 40 mSv). BMD was calculated automatically using LaTheta software (version 3.51). Reconstructed μCT images were initially visualized in three dimensions (3D) to evaluate changes in bone morphology resulting from implant infection. A threshold-limited 3D rendering was created to visualize bone damage.
## Quantitative evaluation with the spread plate method
Implants were harvested ($$n = 6$$ rats per group) from each group. Based on a previous report, the bacterial burden on the implants was determined using a CFU assay40,41. To quantify living bacteria adherent to the implant within the biofilm, the removed implants were placed individually into 1.5-mL microtubes containing PBS (1 mL at 4 °C), vortexed for 15 s and sonicated for 5 min at 40 Hz in a water bath (Bransonic 5210; Branson Ultrasonics, Brookfield, CT, USA), followed by an additional 1 min of vortexing. The spread plate method was used to quantitatively evaluate the biofilm; the solution containing each bacterium from the biofilm was serially diluted 10-fold with PBS, followed by culturing on an agar plate at 37 °C for 24 h. MSSA was cultured on tryptic soy broth agar plates. The bacterial CFUs obtained from the implant were determined by counting the CFUs after culturing on plates overnight.
## Real-time RT-PCR
At the established endpoint (POD14), total RNA was extracted from the knee tissue of the rats ($$n = 6$$ rats per group). The mRNA expression of rCRAMP, TNF-α, IL-6 and IL-1b was evaluated by quantitative PCR. All values were normalized to the level of the GAPDH gene, and relative gene expression levels were calculated using the 2−ΔΔCt method42. Further details can be found in the Supplementary file (Supplementary Table S1).
## Statistical analysis
All continuous variables were assessed for normality using the Shapiro–Wilk test. Normally distributed data were expressed as the mean ± standard error. Data were analyzed using SPSS software (version 25.0; SPSS, Inc., Armonk, NY, USA). Multiple groups were compared using the Welch ANOVA followed by Tukey HSD or Games-Howell post-hoc test. For all analyses, results were considered statistically significant at $p \leq 0.05.$
## Ethical review committee statement
The investigational protocol was approved by the Kanazawa University Advanced Science Research Centre (Approval Number: AP-194052), and all animals were treated in accordance with Kanazawa University Animal Experimentation Regulations. The study was carried out in compliance with the ARRIVE guidelines.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-30087-z.
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|
---
title: Psychometric performance of the Chichewa versions of the EQ-5D-Y-3L and EQ-5D-Y-5L
among healthy and sick children and adolescents in Malawi
authors:
- Lucky G. Ngwira
- Hendramoorthy Maheswaran
- Janine Verstraete
- Stavros Petrou
- Louis Niessen
- Sarah C. Smith
journal: Journal of Patient-Reported Outcomes
year: 2023
pmcid: PMC9996597
doi: 10.1186/s41687-023-00560-4
license: CC BY 4.0
---
# Psychometric performance of the Chichewa versions of the EQ-5D-Y-3L and EQ-5D-Y-5L among healthy and sick children and adolescents in Malawi
## Abstract
### Objectives
The EuroQol Group has developed an extended version of the EQ-5D-Y-3L with five response levels for each of its five dimensions (EQ-5D-Y-5L). The psychometric performance has been reported in several studies for the EQ-5D-Y-3L but not for the EQ-5D-Y-5L. This study aimed to psychometrically evaluate the EQ-5D-Y-3L and EQ-5D-Y-5L Chichewa (Malawi) versions.
### Methods
The EQ-5D-Y-3L, EQ-5D-Y-5L and PedsQL™ 4.0 Chichewa versions were administered to children and adolescents aged 8–17 years in Blantyre, Malawi. Both of the EQ-5D-Y versions were evaluated for missing data, floor/ceiling effects, and validity (convergent, discriminant, known-group and empirical).
### Results
A total of 289 participants (95 healthy, and 194 chronic and acute) self-completed the questionnaires. There was little problem with missing data (< $5\%$) except in children aged 8–12 years particularly for the EQ-5D-Y-5L. Ceiling effects was generally reduced in moving from the EQ-5D-Y-3L to the EQ-5D-Y-5L. For both EQ-5D-Y-3L and EQ-5D-Y-5L, convergent validity tested with PedsQL™ 4.0 was found to be satisfactory (correlation ≥ 0.4) at scale level but mixed at dimension /sub-scale level. There was evidence of discriminant validity ($p \leq 0.05$) with respect to gender and age, but not for school grade ($p \leq 0.05$). For empirical validity, the EQ-5D-Y-5L was 31–$91\%$ less efficient than the EQ-5D-Y-3L at detecting differences in health status using external measures.
### Conclusions
Both versions of the EQ-5D-Y-3L and EQ-5D-Y-5L had issues with missing data in younger children. Convergent validity, discriminant validity with respect to gender and age, and known-group validity of either measures were also met for use among children and adolescents in this population, although with some limitations (discriminant validity by grade and empirical validity). The EQ-5D-Y-3L seems particularly suited for use in younger children (8–12 years) and the EQ-5D-Y-5L in adolescents (13–17 years). However, further psychometric testing is required for test re-test reliability and responsiveness that could not be carried out in this study due to COVID-19 restrictions.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s41687-023-00560-4.
## Introduction
The adult EQ-5D-3L, is one of the most widely used preference-based health-related quality of life (HRQoL) measures for health economic evaluations [1]. Despite this prominence, the EQ-5D-3L has been criticized for its simplicity and insensitivity to small changes in health status, leading to the development of the five response level, the EQ-5D-5L [2]. Evidence suggests that the EQ-5D-5L performs better, is less affected by ceiling effects and improves known-group validity compared to the EQ-5D-3L [3–5].
The youth friendly three response level, EQ-5D-Y-3L, and the experimental five response level EQ-5D-Y-5L have emerged from the adult EQ-5D versions [6, 7]. The EQ-5D-Y-5L was developed on the same premise as the adult EQ-5D-5L version to increase sensitivity and reduce ceiling effects [2]. Psychometric performance of the EQ-5D-Y-3L has been reported in studies involving children with different health conditions [8–10]. To a large extent, it has demonstrated good reliability, with acceptable levels of convergent, discriminant and known-group validity [11–13], but has reported problems with missing values [14]. The performance of the newly developed EQ-5D-Y-5L has only been reported in a small number of studies [15–22]. The EQ-5D-Y-5L has demonstrated feasibility and minimal ceiling effects in these studies, but it has not performed differently on other psychometric properties compared to the EQ-5D-Y-3L [15, 17, 23].
Neither the EQ-5D-Y-3L nor the EQ-5D-Y-5L have been psychometrically evaluated in Malawi where economic evaluation in health programs is becoming increasingly important [24]. This study set out to psychometrically evaluate the Chichewa (Malawi’s national language) versions of the EQ-5D-Y-3L and EQ-5D-Y-5L among children and adolescents.
## Participants, recruitment and procedure
The study recruited participants from a convenience sample of healthy and sick children (8–12 years) and adolescents (13–17 years) in urban Blantyre, Malawi. Children and adolescents attending schools and seeking any health care services through out-patient department at the Queen Elizabeth Central Hospital made up healthy and sick participants, respectively. Written assent and consent was obtained from children and their parents/guardians. For sick participants, the invitation came at the end of clinical care. For healthy participants, invitations were made through the school via a teacher. Participants took the study information leaflets and consent forms home for receipt of consent by their respective parents/guardians and these were brought back to the school the following day. For both sets of participants, once consent was obtained, the questionnaires were distributed by the research team at the end of clinical care or interviews were arranged on a school day. Once the participants completed the questionnaires (in clinic or classroom settings, respectively), the forms were handed over and collected by the study staff. Only children who were literate (as evident from the written consenting process) and therefore able to self-complete the questionnaires were included, but the critically ill were excluded from recruitment. As previous research had revealed a tendency for respondents to avoid the middle responses when completing the adult EQ-5D-5L questionnaire if the EQ-5D-3L is administered first [3], the EQ-5D-Y-5L was administered before the EQ-5D-Y-3L. This was followed by the self-report Pediatric Quality of Life (PedsQL)™ 4.0 Generic Core Scales for children (8–12 years) or teens (13–17 years). Ethical approval for this study was granted by Ethics Committees at the Malawi College of Medicine (now KUHeS) (P$\frac{.10}{18}$/2509) and Liverpool School of Tropical Medicine [19-045]. A sample size of 200 participants was calculated to provide $80\%$ power, at the two-sided significance level of 0.05, to address the minimum psychometric criteria for convergent and discriminant validity.
## The EQ-5D-Y-3L
The EQ-5D-Y-3L consists of five dimensions: ‘mobility’, ‘looking after myself’, ‘doing usual activities’, ‘having pain or discomfort’, and ‘feeling worried, sad or unhappy’. Responses in each dimension are separated into three ordinal levels: [1] no problems, [2] some problems /a bit, and [3] a lot of problems/very. Self-rated health status was also assessed with the measure’s visual analogue scale (EQ VAS), a vertical scale with scores ranging between 0 (representing worst imaginable health) and 100 (representing best imaginable health). The EQ-5D-Y has a same day recall period [6].
## The EQ-5D-Y-5L
The EQ-5D-Y-5L consists of the same five dimensions as the EQ-5D-Y-3L but with five responses each: [1] no problems/not, [2] a little bit of a problem, [3] some problems /quite, [4] a lot of problems/really, and [5] extreme problems/extremely/cannot.
The cross-cultural adaptation of both the EQ-5D-Y-3L and EQ-5D-Y-5L into Chichewa has been reported elsewhere [25]. Briefly, this included forward and backward translation, and cognitive debriefing among children and adolescents aged 8–15 years. Sociodemographic and medical data were also recorded for each participant on a separate page.
The EQ-5D-Y-3L and EQ-5D-Y-5L were scored using the sum scores by summing the responses. The sum score is a crude measure with some limitations, but for psychometric evaluation it gives a better indication of the dimension performance [26]. A health state (represented by responses) ‘11111’ (denoting a one for each of the five dimensions) had a level sum score of 5. The sum scores ranged between 5 and 15 (EQ-5D-Y-3L) or 25 (EQ-5D-Y-5L) (lower = better). Secondly, utility scores indexed at 0 and 1 (higher = better) for the EQ-5D-Y-3L and EQ-5D-Y-5L were calculated using value sets for adults as no EQ-5D-Y-5L value sets were available at the time of conducting this study. Few countries have adult value sets for both the EQ-5D-3L and EQ-5D-5L, and none of these are in Africa [27]. Thus, the utility scores were calculated using the adult value sets (for the United States of America (US)) developed by Shaw et al. [ 28] and Pickard et al. [ 29], respectively. The 2005 US EQ-5D-3L ($$n = 4048$$) value set (range -0.109, 1) used the Measurement and Valuation of Health (MVH) protocol which uses a different approach for states worse than dead, whereas the 2019 US EQ-5D-5L ($$n = 1134$$) value set (range -0.573, 1) used a composite time trade-off (cTTO) in estimating utilities.
## Self-rated general health
A self-rated general health rating was included through the question: How would you rate your health today? Excellent, very good, good, fair, or poor? Although limited, a single question health rating is an efficient measure of health status that can provide a useful comparison [17, 30].
## The Pediatric Quality of Life ™ version 4.0 Generic Core Scales
The Chichewa versions of the Pediatric Quality of Life™ version 4.0 Generic Core Scales (GCS) child self-report (8–12 years) or the PedsQL™ 4.0 GCS teen self-report (13–18 years) were administered, dependent on the age of the respondent. The translation processes and approvals for these measures were provided by the Mapi Trust [31]. Both the PedsQL™ 4.0 GCS versions (herein referred to as PedsQL™ 4.0 GCS for brevity) have 23 items across four subscales: [1] Physical Functioning (8 items), [2] Emotional Functioning (5 items), [3] Social Functioning (5 items), and [4] School Functioning (5 items). The only difference between the child and teen versions is the use of the terms ‘kids’ or ‘teens’ for some items. Responses for each item are on a 5-point scale coded: [0] never a problem, [1] almost never a problem, [2] sometimes a problem, [3] often a problem, or [4] almost always a problem. Responses are reverse scored and linearly transformed on to a 0–100 scale (0 = 100, 1 = 75, 2 = 50, 3 = 25, 4 = 0). The PedsQL™ 4.0 GCS total scale score is obtained by scoring across all 23 items (higher = better). The Physical Functioning subscale score is obtained by summing the scores for the eight Physical Functioning items, whereas the last three subscales (15 items) are combined to form the Psychosocial Health scale score. The subscale scores are obtained through the summation of scores divided by the number of items answered to give a score ranging from 0–100, thereby accounting for missing responses if present [32, 33].
The cross-cultural adaptation of both the PedsQL™ 4.0 GCS child self-report and PedsQL™ 4.0 GCS teen self-report into *Chichewa is* being prepared for publication elsewhere. Briefly, this process similarly included forward and backward translation, as well as cognitive debriefing among children and adolescents aged 8–15 years.
## Psychometric analyses
Data analysis were performed using IBM SPSS 26.0.0 for Mac (IBM Corp. Armonk, New York, USA) [34]. The sample was divided into two groups: children and adolescents to reflect the age ranges for the self-report PedsQL™ 4.0 GCS child (8–12 years) and teen (13–18 years) scales. Psychometric analyses were evaluated using these age groups, as well as combined age groups, and by health conditions (acute and chronic).
## General performance and feasibility
The analysis of the EQ-5D-Y-3L and EQ-5D-Y-5L followed that of Janssen et al. [ 32] for comparison of the EQ-5D-3L and EQ-5D-5L. Frequency of dimensions responses was summarised across age groups and health condition. Feasibility was examined by comparing the number of missing responses for the two EQ-5D-Y versions across age groups and health condition. Missing responses ≥ $5\%$ per dimension was considered problematic since higher values may imply that the item is either not understood or does not make sense [35].
The ceiling and floor effects of the EQ-5D-Y-3L and EQ-5D-Y-5L were defined as the proportion of children/adolescents scoring “no problem” [11111] or the “most severe problems” ($\frac{33333}{55555}$) across all five dimensions, respectively. A reduction (absolute or relative) in ceiling or floor effect would suggest enhanced classification efficiency. The absolute reduction was calculated as the difference in proportion scoring 11111 or $\frac{33333}{55555}$ from the EQ-5D-Y-3L to the EQ-5D-Y-5L. The relative reduction was calculated as ([ceiling/floorEQ-5D-Y-3L- ceiling/floorEQ_5D-Y-5L)]/ceiling/floorEQ-5D-Y-3L. It was hypothesized that the ceiling effect would be reduced both by age group and health condition when moving from the EQ-5D-Y-3L to the EQ-5D-Y-5L.
## Redistribution properties of the EQ-5D-Y-3L to the EQ-5D-Y-5L
Paired dimension responses on the EQ-5D-Y-3L and EQ-5D-Y-5L were assessed for inconsistency across age groups and health condition using previously established criteria [16, 34]. A response pair was considered inconsistent if the EQ-5D-Y-5L response was more than two levels away from that of the EQ-5D-Y-3L. For example, a respondent choosing level 2 (some problems) in the EQ-5D-Y-3L but answering 5 (extreme problems) in the EQ-5D-Y-5L was considered inconsistent. The Chichewa versions are semantically equivalent to the English EQ-5D-Y versions such that level 3 on the EQ-5D-Y-3L (mavuto aakulu) matches level 4 on the EQ-5D-Y-5L (mavuto aakulu).
Inconsistent responses were similar across dimensions and age groups (Additional file 2: Table S2) except for “looking after myself”, which had significantly higher inconsistency for 8–12 year olds ($14\%$) compared to 13–18 year olds ($3\%$). Across age groups and dimensions, the greatest inconsistency was in the “having pain or discomfort” dimension, $15\%$ in children and $8\%$ among adolescents. Similarly, for all respondents, the highest inconsistency ($10\%$) was in the “having pain or discomfort” dimension. Across age groups and dimensions, this inconsistency happened mainly by moving from some problems on the EQ-5D-Y-3L to no problems on the EQ-5D-Y-5L.
## Discriminatory power
Discriminator power was evaluated using the Shannon Index (H′) and the Shannon Evenness Index (J′) informativity (absolute and relative) [3, 36]. The Shannon index has shown evidence of assessing spread of information within dimensions. The Shannon indices are defined as:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$H^{\prime} = \mathop \sum \limits_{$i = 1$}^{L} p_{i} \log_{2} p_{i} \,\,{\text{and}}\,\,J^{\prime} = \frac{H^{\prime}}{{H^{\prime}_{\max } }}$$\end{document}H′=∑$i = 1$Lpilog2piandJ′=H′Hmax′where H′ is the absolute amount of informativity, L is the number of dimensions levels and pi is the proportion of observations in the ith level where the EQ-5D-Y-3L has three levels and the EQ-5D-Y-5L has five levels. A higher H’ index reflects that the descriptive system has captured more information; the maximum H′index is 1.58 and 2.32 on the EQ-5D-Y-3L and EQ-5D-Y-5L, respectively [3]. It was anticipated that the H′index would increase for the EQ-5D-Y-5L compared to the EQ-5D-Y-3L. The Shannon Evenness index (J’) reflects the spread of the responses across levels regardless of the number of levels included in the descriptive system [3]. It was hypothesized that the J’index would remain the same or marginally decrease (as its not dependent on response levels) for the EQ-5D-Y-5L compared to the EQ-5D-Y-3L.
Informativity of dimensions did not improve across all dimensions on the EQ-5D-Y-5L compared to the EQ-5D-Y-3L (Table 4). In contrast to what was hypothesized, the EQ-5D-Y-3L had a higher H’ index in all dimensions compared to the EQ-5D-Y-5L. It was anticipated that the J’ index (spread of responses) would remain the same or marginally decrease on the EQ-5D-Y-5L compared to the EQ-5D-Y-3L. The small difference (0.021–0.073) in the J’ index shows that the spread of responses on the EQ-5D-Y-5L and EQ-5D-Y-3L was distributed evenly. The EQ-5D-Y-3L had a higher J’ in all dimensions except “feeling worried, sad or unhappy” in comparison to the EQ-5D-Y-5L.Table 4Shannon Index (H′) and Shannon Evenness Index (J′) for the EQ-5D-Y-3L and EQ-5D-Y-5L dimensionsEQ-5D-Y-5LEQ-5D-Y-3LDifference in H′Difference in J′Shannon Index H′Shannon Eveness Index J′Shannon Index H′Shannon Eveness Index J′Mobility− 0.6850.173− 0.5470.236− 0.138− 0.063Looking after myself− 0.6160.184− 0.4760.257− 0.140− 0.073Usual activities− 0.7480.171− 0.5940.235− 0.154− 0.064Pain or discomfort− 0.9810.140− 0.7470.199− 0.234− 0.059Worried, sad or unhappy− 0.8830.102− 0.6760.081− 0.2070.021Average difference− 0.175− 0.048
## Convergent validity
Convergent validity is the extent to which similar dimensions of two or more instruments are related. It is expected that similar dimensions will have a moderate to strong correlation. It was therefore hypothesized that the EQ-5D-Y-3L and EQ-5D-Y-5L sum and utility scores would be correlated (Pearson) with PedsQL™ 4.0 GCS total scale scores. It was further hypothesized that for both of the EQ-5D-Y versions, the dimensions of “mobility”, “doing usual activities”, and “feeling worried, sad or unhappy” would be correlated with PedsQL™ 4.0 GCS physical, school, and emotional functioning scores, respectively. It was hypothesized that the PedsQL™ 4.0 GCS correlation would be negative with the EQ-5D-Y levels sum score (better = lower score) but positive for the EQ-5D-Y utility score (better = higher score). A correlation ≥ 0.4 is considered moderate to strong [37].
Results of tests of convergent validity are summarised in Additional file 3: Table S3. Correlations were consistently in the right direction and met the criterion (≥ 0.4) for the EQ-5D-Y-3L and the teen PedsQL™ 4.0 GCS summary scores, and the EQ-5D-Y-5L with the child PedsQL™ 4.0 GCS summary scores. Most of the sub-scales also met the criterion of 0.4, except a few that did not (e.g., school/usual activities for all respondents (8–17-years), physical/mobility for child version and emotional/worried, sad or unhappy for the teen version).
## Discriminant validity
Discriminant validity is the extent to which unrelated dimensions between scales should not be similar. Further, it was anticipated that age, school grade and gender would not be factors in self-completion of the EQ-5D-Y-3L and EQ-5D-Y-5L. A Pearson correlation < 0.2 indicates lack of correlation. It was anticipated that there would be a lack of correlation between EQ-5D-Y-3L, EQ-5D-Y-5L sum and utility scores with age. It was also hypothesised that the correlation direction for sum score and age would be negative, and positive between age and utility scores. This is because a lower value is better for sum scores and vice versa for utility scores. No association at the $5\%$ significance level was hypothesized between both the EQ-5D-Y-3L and EQ-5D-Y-5L sum and utility scores, with gender (t-test) and grade (one-way ANOVA). School grade was dichotomised based on general distribution and in line with the former scaling for primary school education in Malawi: grades 1–5 made group 1, grades 6–8 made group 2, and secondary/high school made group 3.
There was no significant difference ($p \leq 0.05$) between gender and EQ-5D-Y-3L nor EQ-5D-Y-5L sum scores or utility scores with exception of the direction of the relationship (Table 5).Table 5EQ-5D-Y-3L and EQ-5D-Y-5L discriminant validity by gender, school grade and ageCharacteristicMeasureScore*Age 8–12 yearsAge 13–17 yearsAge 8–17 yearsdfMean difftp valuedfMean difftp valuedfMean difftp valueGenderEQ-5D-Y-3LLSS76− 0.27− 0.6270.532170− 0.36− 1.7990.074248− 0.33− 1.6460.101US760.020.6780.5001700.031.8710.0632480.021.7070.089EQ-5D-Y-5LLSS860.390.5580.578174− 0.05− 0.1430.8862620.090.2520.802US86− 0.02− 0.3000.7651740.000.0170.986262− 0.00− 0.1810.856School grade#EQ-5D-Y-3LLSS10.3890.1140.73725.1223.0740.049223.11010.335< 0.001US10.0000.0130.91120.0303.8480.02320.12311.450< 0.001EQ-5D-Y-5LLSS11.5390.1460.703228.5854.7410.010256.4357.491< 0.001US10.0000.0040.94920.1495.3420.00620.3378.601< 0.001Age 8–12 yearsAge 13–17 yearsAge 8–17 yearsAgeEQ-5D-Y-3LLSS0.1− 0.2− 0.3US− 0.10.20.3EQ-5D-Y-5LLSS0.1− 0.2− 0.2US− 0.10.20.2*LSS, level sum score; US, utility score#Grade group1 = grade 1–5; group2 = grade 6–8; group3 = grade 9–12; (age 8–12 years: grp1 = 53, grade grp 2 = 40; age 13–17 years: grp1 = 18, grade grp 2 = 57, grade grp3 = 111; age 8–17 years: grp1 = 71, grade grp 2 = 97, grade grp3 = 111). Bold indicates significance $p \leq 0.05$ There was a low Pearson correlation (0.1–0.2) and thus no association between age and both the sum and utility scores for the EQ-5D-Y-3L, and EQ-5D-Y-5L. The direction of correlation was as hypothesized in adolescents but not for children. However, this correlation between age and both the sum and utility scores improved (0.2–0.3) and was in the hypothesized direction in all respondents.
There was no evidence of difference between either EQ-5D-Y version’s sum (and utility) scores and school grade categories in children ($p \leq 0.05$), but this was statistically significant among adolescents ($p \leq 0.05$), and for all respondents ($p \leq 0.001$).
## Known-group validity
Known-group validity is the extent to which scores differ for two or more groups that are known to be different in some other aspects e.g., health status. It was hypothesised that for the two EQ-5D-Y versions, sum and utility scores would be worse for the sick compared to the healthy children. A t-test evaluated the relationship and the effect size was interpreted according to Cohen’s criterion: < 0.2 poor, 0.3–0.49 small, 0.5–0.8 moderate, and > 0.8 large [35, 38].
In children, although this might have skewed by a small number of healthy participants in this group ($$n = 12$$), the effect size was low (0.23) for the EQ-5D-Y-5L compared to high (− 1.15) for the EQ-5D-Y-3L. In adolescents, effect sizes were generally higher (> 0.5) suggesting reasonably good known-group validity (Additional file 4: Table S4). A similar effect size was observed for the utility scores between the healthy and sick groups although, as expected, the direction of the effect size was opposite to the sum scores.
## Utility score performance (empirical validity)
The EQ-5D-Y-3L and EQ-5D-Y-5L are preference-based instruments used not only for measuring HRQoL but also in economic evaluation. As such, the EQ-5D measures the preference (value or utility) placed on specific health states [39]. It is important to evaluate how and to what extent the utilities generated by these instruments reflect revealed preferences, stated preferences or hypothesised preferences. In the absence of revealed preference and stated preference data, it was hypothesised that utility scores for both EQ-5D-Y versions would detect differences in external indicators of health status with the EQ-5D-Y-5L being more efficient at detecting differences (reflecting greater empirical validity) than the EQ-5D-Y-3L. It was further hypothesized that people would ‘prefer’ lower mild health problems.
The relative ability to assess external indicators of health status was investigated by comparing the utility scores with self-reported general health and the PedsQL™ 4.0 GCS total scale scores using the relative efficiency (RE) statistic. RE was defined as ‘the ratio of the square of the t-statistic of the comparator instrument over the square of the t-statistic of the reference instrument’ [40]. The EQ-5D-Y-5L acted as the comparator instrument and the EQ-5D-Y-3L as the referent since the latter has been widely used and psychometrically validated [7]. RE = 1.0 indicates that the EQ-5D-Y-5L has the same efficiency as the EQ-5D-Y-3L at detecting differences in health status; > 1.0 indicates that the EQ-5D-Y-5L is more efficient than the EQ-5D-Y-3L; and the converse is true [40].
Self-reported general health status was dichotomised using a frequency distribution [40] into two categories: (i) excellent or very good versus good or fair or poor, and (ii) excellent versus very good or good or fair or poor. The mean for the total scale scores provided a cut-off for the PedsQL™ 4.0 GCS such that less than mean, and mean and above formed two categories. The cut-off points used to create these dichotomous variables were necessarily arbitrary and may lead to different conclusions depending on which cut-offs are chosen. Therefore, in a series of sensitivity analyses, we dichotomised the self-reported general health status and PedsQL™ 4.0 GCS variables in alternative ways and replicated the analyses.
All empirical validity analyses were based on participants who completed both the EQ-5D-Y-5L and EQ-5D-Y-3L, thus any respondents with missing responses for either measure were excluded from this analysis. However, for the PedsQL™ 4.0 GCS, a volume of missing values of < $50\%$ are taken into account as per the scoring algorithm [32]. There is a possibility that utility scores below 0 (which could lead to under predicting poorest heath states) would be different for the EQ-5D-Y-5L and EQ-5D-Y-3L since the utility scores are based on two different valuation models [29]. To overcome this,
## Participant characteristics
A total of 289 participants completed the EQ-5D-Y, EQ-5D-Y-5L, and PedsQL™ 4.0 GCS, aged 8–17 years (mean 13.6, median 14) as presented in Table 1. There were slightly more participants that were: females ($56\%$), in primary school ($60\%$) or ill ($67\%$). The majority of the participants were adolescents ($66\%$), and as expected all these were in high school. Table 1Participant characteristicsCharacteristicSub-categoryN (%)Children(8–12 years)n (%)Adolescents (13–17 years)n (%)ParticipantsAll28998 ($34\%$)191 ($66\%$)Gender*Male121 ($44\%$)39 ($32\%$)82 ($68\%$)Female153 ($56\%$)51 ($33\%$)102 ($67\%$)Health conditionhealthy95 ($33\%$)12 ($13\%$)83 ($87\%$)acute155 ($54\%$)85 ($55\%$)70 ($45\%$)chronic39 ($13\%$)1 ($3\%$)38 ($97\%$)School grade#1–571 ($25\%$)53 ($75\%$)18 ($25\%$)6–897 ($35\%$)40 ($41\%$)57 ($59\%$)9–12111 ($40\%$)0111 ($100\%$)*Missing data: 15 (age group 1 = 8, age group 2 = 7)#Missing data: 10 (5 in each age groups)Age group 1 completed EQ-5D-Y, EQ-5D-Y-5L and PedsQL 4.0 child self-reportAge group 2 completed EQ-5D-Y, EQ-5D-Y-5L and PedsQL 4.0 teen self-report
## General instrument performance and feasibility
The EQ-5D-Y-3L had missing responses in all dimensions among children compared to none among adolescents (Table 2). For the EQ-5D-Y-5L, missing responses were observed in three dimensions among both children and adolescents. Across all respondents (aged 8–17 years), there were fewer dimensions with missing responses for the EQ-5D-Y-3L (two) compared to the EQ-5D-Y-5L (four).Table 2Proportion of reported problems in the EQ-5D-Y-3L and the EQ-5D-Y-5LDimensionEQ-5D-Y-5LEQ-5D-Y-3L8–12 yearsn (%)13–17 yearsn (%)8–17 yearsn (%)8–12 yearsn (%)13–17 yearsn (%)8–17 yearsn (%)MobilityNo67 ($68\%$)154 ($81\%$)221 ($76\%$)63 ($64\%$)164 ($86\%$)227 ($79\%$)A little bit19 ($19\%$)21 ($11\%$)40 ($14\%$)Some5 ($5\%$)6 ($3\%$)11 ($4\%$)27 ($28\%$)21 ($11\%$)48($17\%$)A lot1($1\%$)2 ($1\%$)3 ($1\%$)2 ($2\%$)2 ($1\%$)4 ($1\%$)Cannot2 ($2\%$)0 ($0\%$)2 ($1\%$)Missing4 ($4\%$)8 ($4\%$)12 ($4\%$)6 ($6\%$)4 ($2\%$)10 ($3\%$)Looking after myselfNo68 ($68\%$)164 ($86\%$)232 ($80\%$)68 ($69\%$)168 ($88\%$)236 ($82\%$)A little bit18 ($18\%$)9 ($5\%$)27 ($9\%$)Some4 ($4\%$)7 ($4\%$)11 ($4\%$)18 ($18\%$)16 ($8\%$)34 ($12\%$)A lot2 ($2\%$)0 ($0\%$)2 ($1\%$)3 ($3\%$)1 ($1\%$)4 ($1\%$)Cannot2 ($2\%$)2 ($1\%$)4 ($1\%$)Missing4 ($4\%$)9 ($5\%$)13 ($5\%$)9 ($9\%$)6 ($3\%$)15 ($5\%$)Usual activitiesNo67 ($68\%$)150 ($79\%$)217 ($75\%$)64 ($65\%$)156 ($82\%$)220 ($76\%$)A little bit16 ($16\%$)16 ($8\%$)32 ($11\%$)Some6 ($6\%$)14 ($7\%$)20 ($7\%$)22 ($22\%$)29 ($15\%$)51 ($18\%$)A lot3 ($3\%$)2 ($1\%$)5 ($2\%$)4 ($4\%$)2 ($1\%$)6 ($2\%$)Cannot1 ($1\%$)1 ($1\%$)2 ($1\%$)Missing5 ($5\%$)8 ($4\%$)13 ($5\%$)8 ($8\%$)4 ($2\%$)12 ($4\%$)Pain or discomfortNo55 ($56\%$)123 ($64\%$)178 ($62\%$)50 ($51\%$)137 ($72\%$)187 ($65\%$)A little bit18 ($18\%$)36 ($19\%$)54 ($19\%$)Some15 ($15\%$)19 ($10\%$)34 ($12\%$)36 ($37\%$)46 ($24\%$)82 ($28\%$)A lot3 ($3\%$)2 ($1\%$)5 ($2\%$)5 ($5\%$)4 ($2\%$)9 ($3\%$)Extreme2 ($2\%$)1 ($1\%$)1 ($0\%$)Missing5 ($5\%$)10 ($5\%$)15 ($5\%$)7 ($7\%$)4 ($2\%$)11 ($4\%$)Worried, sad or unhappyNo62 ($63\%$)137 ($72\%$)199 ($69\%$)60 ($61\%$)142 ($74\%$)202 ($70\%$)A little bit13 ($13\%$)30 ($16\%$)43 ($15\%$)Some/quite10 ($10\%$)12 ($6\%$)22 ($7\%$)24 ($25\%$)41 ($22\%$)65 ($23\%$)Really3 ($3\%$)0 ($0\%$)3 ($1\%$)6 ($6\%$)1 ($1\%$)7 ($2\%$)Extremely5 ($5\%$)3 ($2\%$)8 ($3\%$)Missing5 ($5\%$)9 ($5\%$)14 ($5\%$)8 ($8\%$)7 ($4\%$)15 ($5\%$)EQ-VASMean (SD)82.5 (20.4)89.2 (16.3)87.0 (18.0)82.7 (19.7)89.2 (15.5)87.0 (17.2)Missing5 ($5\%$)11 ($6\%$)23 ($8\%$)15 ($15\%$)13 ($7\%$)22 ($8\%$)ScoresLSS Mean (SD)7.7 (3.6)6.5 (2.5)6.9 (3.0)6.8 (2.0)5.9 (1.3)6.2 (1.6)US Mean (SD)0.81 (0.28)0.91 (0.17)0.87 (0.22)0.87 (0.14)0.94 (0.10)0.92 (0.11)Bold indicates proportion of missing responses ≥ $5\%$ For the analysis based on health condition (Additional file 1: Table S1), both the EQ-5D-Y-3L and the EQ-5D-Y-5L had missing responses in all five dimensions among the acute (highest proportion) and chronically ill, but not in the healthy population.
The dimensions “looking after myself” and “having pain or discomfort” had the highest and lowest proportion of responses for both the EQ-5D-Y-3L and EQ-5D-Y-5L, respectively. This was similarly the case when the data were stratified by age and health condition. The dimensions of “mobility” ($86\%$), “looking after myself” ($88\%$), and “doing usual activities” ($82\%$) had consistently higher proportions of “no problems” among adolescents, compared to children for the EQ-5D-Y-3L. Similarly, this was evident for the EQ-5D-Y-5L “mobility” ($81\%$) and “looking after myself” ($86\%$) dimensions.
The ceiling effect [11111] for all dimensions was generally reduced ($9\%$) from the EQ-5D-Y-3L to EQ-5D-Y-5L for all participants (8–17 years) and among adolescents (Table 3). The greatest reduction in ceiling effect was in the ‘having pain or discomfort’ dimension for all participants ($5\%$) and adolescents ($11\%$). Among children, however, ceiling effects increased overall ($48\%$) and for “having pain or discomfort” ($10\%$). Overall, the floor effect ($\frac{33333}{55555}$) was mostly low except in the “having pain or discomfort” dimension (50–$100\%$). Table 3Ceiling effect for the EQ-5D-Y-3L and EQ-5D-Y-5L across age groups and health conditionEQ-5D-Y-3L compared to EQ-5D-Y-5LChildren ($$n = 98$$)Adolescents ($$n = 191$$)All ($$n = 298$$)Y-3LY-5LARRRY-3LY-5LARRRY-3LY-5LARRRn%n%n%n%n%n%Ceiling effect [11111]25273640− 13− 48104559650591284413246− 2− 5Mobility (walking about)6768676800164861548156227792217634Looking after myself6869686811168881648622235822328022Doing usual activities64656768− 3− 5156821507934230762177511Having pain or discomfort50515556− 5− 101377212364811187651786235Feeling worried, sad, or unhappy60616263− 2− 3142741377223202701996911Floor effect [11111]11001100000000100000Mobility (walking about)22220021001100412100Looking after myself3322133112100414100Doing usual activities44113752111006221150Having pain or discomfort5522360421115093103100Feeling worried, sad, or unhappy66551177495− 1− 2515514500EQ-5D-Y-3L compared to EQ-5D-Y-5LAcute ($$n = 155$$)Chronic ($$n = 39$$)General population ($$n = 95$$)Y-3LY-5LARRRY-3LY-5LARRRY-3LY-5LARRRn%n%n%n%n%n%Ceiling effect [11111]41264428− 2− 829743385− 11− 155861586100Mobility (walking about)10367986346359033855689949095− 1− 1Looking after myself10970108700035903487339297909522Doing usual activities104671026611348732825682868387− 1− 1Having pain or discomfort754870453635903385567781757922Feeling worried, sad, or unhappy92599360− 1− 232823180227882757934Floor effect [11111]11001100000000000000Mobility (walking about)322115013003100000000Looking after myself434300000000000000Doing usual activities6421375000000000000Having pain or discomfort853236013003100000000Feeling worried, sad, or unhappy7575000000000011− 10AR, absolute reduction; RR, relative reduction There was an increase in ceiling effect among the acute and chronically ill, but not among healthy participants. At a dimension level, the reduction was largest ($6\%$) for “having pain or discomfort” in the acute and chronically ill. Additionally, there was a $6\%$ ceiling effect reduction for “mobility” and “doing usual activities” among the chronically ill. Among the healthy participants, the largest ceiling effect reduction was in “feeling worried, sad or unhappy”. As with age, the floor effect, reporting most severe problems across all dimensions ($\frac{33333}{55555}$) ranged between 1 and $3\%$ among the acutely and chronically ill. There was no floor effect reduction in any dimension for healthy participants.
## Empirical validity
Table 6 presents the relative efficiency statistics for the EQ-5D-Y-3L and EQ-5D-Y-5L over the dichotomous self-reported general health status and PedsQL™ 4.0 GCS measures, respectively. When the EQ-5D-Y-3L was referenced at 1.0, the EQ-5D-Y-5L was between 31 and $91\%$ and between 5 and $44\%$ less efficient than the EQ-5D-Y-3L at detecting differences in self-reported general health and the PedsQL™ 4.0 total scale score, respectively. Table 6Efficiency of the EQ-5D to detect differences in self-reported health statusMeasureAgeCategorisation of self-reported health statusUtility score#t-test*Relative efficiencymean(SD)t-statisticp valueEQ-5D-Y-3LAge 8–12 years ($$n = 81$$)Excellent or v. good0.8380.2222.0750.0411.000Good or fair0.7460.175EQ-5D-Y 5LExcellent or v. good0.8120.3280.5100.6120.060Good or fair0.7800.243EQ-5D-Y-3LExcellent0.8720.2002.1970.0331.000v. good, good, fair or poor0.7660.205EQ-5D-Y 5LExcellent0.8320.3210.6600.5130.090v. good, good, fair or poor0.7830.284EQ-5D-Y-3LAge 13–17 years ($$n = 172$$)Excellent or v. good0.9030.1370.1480.8831.000Good or fair0.8990.150EQ-5D-Y 5LExcellent or v. good0.9110.1750.1230.9020.693Good or fair0.9070.160EQ-5D-Y-3LExcellent0.9330.1162.2050.0291.000v. good, good, fair or poor0.8870.150EQ-5D-Y 5LExcellent0.9240.2100.7040.4830.102v. good, good, fair or poor0.9020.147EQ-5D-Y-3LCombined ages 7–17 years ($$n = 253$$)Excellent or v. good0.8830.1691.7330.0851.000Good or fair0.8440.175EQ-5D-Y 5LExcellent or v. good0.8810.2350.7020.4840.164Good or fair0.8620.202EQ-5D-Y-3LExcellent0.9130.1503.0270.0031.000v. good, good, fair or poor0.8480.178EQ-5D-Y 5LExcellent0.8950.2530.9450.3460.098v. good, good, fair or poor0.8640.208MeasureAgePedsQL 4.0 scale scoreMeanSDt-statisticp valueRelative efficiencyEQ-5D-Y-3LAge 8–12 years ($$n = 81$$)≥ 72.790.8400.1592.2980.0251.000< 72.790.7270.249EQ-5D-Y 5L≥ 72.790.8650.2232.2370.0300.948< 72.790.7050.363EQ-5D-Y-3LAge 13–17 years ($$n = 172$$)≥ 78.680.9460.0873.8370.0001.000< 78.680.8640.167EQ-5D-Y 5L≥ 78.680.9470.1512.8630.0050.557< 78.680.8720.177EQ-5D-Y-3LCombined ages 7–17 years ($$n = 253$$)≥ 76.810.9180.1144.7160.0001.000< 76.810.8120.210EQ-5D-Y 5L≥ 76.810.9290.1704.1020.0000.756< 76.810.8080.265#US utilities*Assuming equal variance Restricting the analyses to participants with utility scores between 0 and 1 had the same outcome with the exception of the sensitivity analysis that dichotomised self-reported general health status as excellent versus very good, good or fair, which found that the EQ-5D-Y-5L was $736\%$ more efficient than the EQ-5D-Y-3L at detecting differences in self-reported general health status. ( Additional file 5: Table S5).
## Discussion
In this urban Malawian setting, both the EQ-5D-Y Chichewa versions demonstrated mixed evidence of instrument performance and feasibility, and validity. Both Chichewa versions demonstrated that they can be used with some limitations in missing responses, convergent and discriminant validity in this setting. The EQ-5D-Y-3L seems particularly suited for use in younger children (8–12 years) and the EQ-5D-Y-5L in adolescents (13–17 years). Other psychometric properties like test–retest reliability and responsiveness also need to be evaluated in this context.
Generally, the use of childhood preference-based HRQoL measures in sub-Saharan African settings is limited, as previously reported [41], and so the ability to generalize these findings in an African context is limited. Missing responses were relatively high in this study compared to other general population studies [9, 20]. The particularly high level of missing values among children (8–12 years) may point to sub-optimal reading skills in this age group in Malawi. This may indicate difficulty in providing good quality self-reported HRQoL assessment [24, 42] suggesting that younger children may benefit from an interviewer assisted approach [43].
The proportion reporting ‘no problems’ was similar between the EQ-5D-Y-3L and the EQ-5D-Y-5L, with the highest proportion for “looking after myself” and lowest in “having pain or discomfort” for both versions. This is consistent with findings from other studies with general population samples [9, 20, 42]. The proportion of ‘no problems’ was similarly spread across health conditions indicating that participants in this study may have had ‘milder’ health conditions. Like the adult EQ-5D-5L [44–46], the EQ-5D-Y-5L edged the EQ-5D-Y-3L in reducing ceiling effects, which may point to its improved sensitivity. However, the reduction but not elimination of the ceiling effect may indicate that this problem could be due to a true phenomenon as opposed to EQ-5D-Y-3L deficiency [18]. Further, the lack of ceiling effect reduction among the healthy group [18] is expected as this group should be experiencing fewer problems and may indicate that it is not necessary to include them in between-instruments ceiling effect comparisons in future studies.
The greatest proportion of inconsistencies was in the “having pain or discomfort” and “feeling worried, sad or unhappy” dimensions across age groups. As observed elsewhere [3, 20], these dimensions pertain to psychosocial concepts as opposed to physical aspect conveyed by the “mobility”, “looking after myself”, and “doing usual activities”. However, this variability originated from high ceiling effects, which may explain that among healthy participants (where reporting of no problems is expected) both versions work consistently well.
The discriminative power of the EQ-5D-Y-3L was marginally higher than that of the EQ-5D-Y-5L. This may imply that the informativity of dimensions does not improve on the EQ-5D-Y-5L in this setting. This has been observed in a previous study of idiopathic scoliosis [15], but is different from the general population [20] and those with other health conditions [47]. Considering that the application of Shannon indices is relatively new in HRQoL measurements, this might require further investigation.
The evidence for convergent validity shows that pre-specified criteria were met at scale but not at dimension level. This might imply that the EQ-5D-Y-3L and EQ-5D-Y-5L are best suited to assess physical functioning as opposed to other aspects of HRQoL. While the adult EQ-5D-5L has been found to be highly correlated with other health measures compared to the EQ-5D-3L [48–50], this was not the case with the two youth versions. These correlations were low to moderate, which is similar with other findings [12, 18, 46].
The discriminant ability of the EQ-5D-Y-3L and EQ-5D-Y-5L as regards gender and age is consistent with the adult EQ-5D-3L and EQ-5D-5L versions [45, 51]. The criterion was met for age groups but not across all respondents. Also, there were mixed relationships between sum and utility scores with age, which could not be established in this study but needs further research. While age has been associated with different scores for the EQ-5D-3L and EQ-5D-5L [45], this study did not find such differences between the EQ-5D-Y-3L and EQ-5D-Y-5L. Also, discriminant validity between both the EQ-5D-Y versions and school grade was met in children, but not among adolescents and across all respondents. This may indicate that years of education contributes to better completion and comprehension of questionnaires. Both the EQ-5D-Y-3L and EQ-5D-Y-5L showed evidence of known-group validity, which has been observed elsewhere [9, 17, 19, 21]. While the EQ-5D-Y-3L had the largest effect size in children, this was the case for the EQ-5D-Y-5L among adolescents. This study shows that the EQ-5D-Y-5L may be best suited for adolescents due to their ability to better distinguish responses, which is consistent with adult findings [52].
Tests of empirical validity demonstrated that the EQ-5D-Y-3L was generally more efficient than the EQ-5D-Y-5L at detecting hypothesised differences in external health status. This was surprising as the adult EQ-5D-5L has demonstrated greater relative efficiency compared to the EQ-5D-3L [53–55]. Our results may partly be due to the fact that the US EQ-5D-3L value set has additional interaction terms that may add more disutility to the weights compared to the US EQ-5D-5L value set. Also, the adult EQ-5D-5L has been found to overestimate health problems, leading to underestimation of utilities [4], which may have been the case with the sample in this study. Full understanding of why the EQ-5D-Y-3L outperformed the EQ-5D-Y-5L could benefit from future research.
Finally, it should be noted that there were no major differences in the psychometric tests focussed on utility values and the sum scores. The only difference was in the direction of the correlation. While the higher values were associated with better health outcomes for the utilities and vice versa for lower values, the opposite was true for the sum scores.
Limitations of this study include COVID-19 restrictions that led to collection of data in one wave and therefore test–retest reliability and responsiveness could not be evaluated. Secondly, preference-based value sets are not available for the EQ-5D-Y-5L and these have only recently been developed in three countries (at the time of doing this research) for the EQ-5D-Y-3L [56–58]. The use of adult values for childhood health states has been extensively discussed elsewhere [59]. The development of country-specific preference-based values for the EQ-5D-Y-5L is clearly an area that will benefit from further research although this may still be a limitation for the empirical validity i.e., whether EQ-5D reflect patient preferences in comparison to stated or revealed preferences.
## Conclusion
The two EQ-5D-Y versions established convergent and known-group validity among children and adolescents. Both versions had issues with missing values in younger children and discriminant validity by school grade as well as utilization of response options suggesting that the instruments can be used with caveats in this setting. These issues are likely not to be specific to Malawi as shown by evidence from elsewhere. Although the EQ-5D-Y-3L could be used across the age groups studied, it seems particularly suited (due to less nuanced responses) for use in younger children (8–12 years) whilst the EQ-5D-Y-5L seems particularly suited for use in adolescents (13–17 years) in Malawian contexts. Further psychometric testing for test re-test reliability and responsiveness is required, which could not be carried out in this study.
## Supplementary Information
Additional file 1: Table S1. Proportion of reported problems in the EQ-5D-Y-3L and the EQ-5D-Y-5L by health conditionAdditional file 2: Table S2. Redistribution of the EQ-5D-Y-3L and EQ-5D-Y-5L dimension scoresAdditional file 3: Table S3. Convergent validity of the EQ-5D-Y and EQ-5D-Y-5L with PedsQL™ 4.0 self-report sub-scale. Additional file 4: Table S4. EQ-5D-Y-3L and EQ-5D-Y-5L sum score known group validityAdditional file 5: Table S5. Efficiency of the EQ-5D to detect differences in self-reported health status (utility set to between 0 and 1 only for both EQ-5D-Y and EQ-5D-Y-5L)
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---
title: Circadian clock molecule REV-ERBα regulates lung fibrotic progression through
collagen stabilization
authors:
- Qixin Wang
- Isaac Kirubakaran Sundar
- Joseph H. Lucas
- Jun-Gyu Park
- Aitor Nogales
- Luis Martinez-Sobrido
- Irfan Rahman
journal: Nature Communications
year: 2023
pmcid: PMC9996598
doi: 10.1038/s41467-023-36896-0
license: CC BY 4.0
---
# Circadian clock molecule REV-ERBα regulates lung fibrotic progression through collagen stabilization
## Abstract
Molecular clock REV-ERBα is central to regulating lung injuries, and decreased REV-ERBα abundance mediates sensitivity to pro-fibrotic insults and exacerbates fibrotic progression. In this study, we determine the role of REV-ERBα in fibrogenesis induced by bleomycin and Influenza A virus (IAV). Bleomycin exposure decreases the abundance of REV-ERBα, and mice dosed with bleomycin at night display exacerbated lung fibrogenesis. Rev-erbα agonist (SR9009) treatment prevents bleomycin induced collagen overexpression in mice. Rev-erbα global heterozygous (Rev-erbα Het) mice infected with IAV showed augmented levels of collagens and lysyl oxidases compared with WT-infected mice. Furthermore, Rev-erbα agonist (GSK4112) prevents collagen and lysyl oxidase overexpression induced by TGFβ in human lung fibroblasts, whereas the Rev-erbα antagonist exacerbates it. Overall, these results indicate that loss of REV-ERBα exacerbates the fibrotic responses by promoting collagen and lysyl oxidase expression, whereas Rev-erbα agonist prevents it. This study provides the potential of Rev-erbα agonists in the treatment of pulmonary fibrosis.
The molecular clock REV-ERBα regulates lung injury during fibrosis, but the role of REV-ERBα in fibrogenesis remains unknown. Here, the authors show that REV-ERBα interacts with the lysyl oxidase-collagen axis during fibrogenesis and demonstrate the therapeutic potential of Rev-erbα agonist against lung fibrosis.
## Introduction
Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease characterized by progressive lung scar tissue formation that is typically accompanied by impaired lung function and difficulty breathing1. The onset of pulmonary fibrosis is usually initiated by the dysregulation of tissue repair mechanisms which can be induced by various causes, such as air pollution (asbestos), antineoplastic drugs, and respiratory viral infections such as influenza A virus (IAV) and even coronavirus (SARS-CoV-2) infection2,3. In previous decades, rigorous basic studies have improved our understanding of pro-fibrotic pathogenesis and developed many candidates for anti-fibrotic therapy. However, there are no effective therapeutics for IPF, and the detailed molecular mechanism of fibrogenesis is still poorly understood4–6.
Currently, nintedanib and pirfenidone are the only Food and Drug Administration (FDA)-approved drugs for the treatment of pulmonary fibrosis, which only serve to slow the progression of pulmonary fibrosis7. Investigating new promising molecular pathways involved in fibrogenic responses is urgently needed, and Rev-erbα has become a promising candidate8,9. REV-ERBα is a transcriptional repressor that regulates mRNA transcriptions involved in circadian rhythms, metabolism, and inflammatory responses10–13. Oscillations in circadian rhythm are controlled by the competition of two nuclear receptors, REV-ERBα, and retinoic acid-like orphan receptor alpha (RORα)14. REV-ERBα inhibits the transcription and translation of circadian locomotor output cycles kaput (CLOCK)/brain and muscle ARNT-like 1 (BMAL1, also known as ARNTL), which will form a heterodimer and bind to E-box and promote the transcription/translation of either core clock molecules or downstream targets15. For regulating BMAL1 and CLOCK expression, RORα competes with REV-ERBα to bind with ROR response elements (ROREs) to activate the transcription of BMAL1 and CLOCK15 forming an auto-feedback system with REV-ERBα and providing stability and precision to molecular clock regulation. Interestingly, the downstream gene targets of E-box include various fibrotic markers such as α-smooth muscle actin (αSMA) and vimentin (VIM)16. Moreover, the removal of REV-ERBα has been associated with increased risks of lung inflammation and premature senescence, which has been confirmed by our and others’ previous studies17–19.
Circadian clock molecules are identified as essential mediators of pulmonary injuries with various causes, such as cigarette smoke (CS) and IAV20–23. Previous studies have described the importance of circadian molecules in key cell subtypes, including club cells, alveolar macrophages, and fibroblasts, in the lung microenvironment in response to injury and inflammatory mediators8,19,24,25. Previous findings showed that CS exposure and IAV infection-induced lung injuries are associated with disruption of the circadian clock and impaired lung function, survival rate, and daily ambulatory activity26,27. Various studies to date demonstrate the fundamental interactions of core clock molecules, such as REV-ERBα or BMAL1, with lung inflammatory responses and the development of chronic obstructive pulmonary disease (COPD) by CS exposure23. Currently, only one study has shown that REV-ERBα deficiency in lung fibroblasts exaggerates bleomycin-induced lung fibrogenesis8. However, the mechanism and role of REV-ERBα in lung fibrogenesis via collagen synthesis and its regulation during IAV infection are not known. Stabilization of collagen fibers is regulated by lysyl oxidase, a copper-dependent amino oxidase, via crosslinking the extracellular matrix proteins (collagen and elastin), thereby preventing collagen degradation28.
Our previous study has identified the potential of REV-ERBα in regulating epithelial-mesenchymal transition (EMT) and fibroblast differentiation induced by CS and TGFβ27. We, therefore, hypothesize that REV-ERBα is important in regulating fibrotic progression in the lungs, by targeting collagen synthesis and its stabilization pathways.
Here we show, the abundance of REV-ERBα is decreased during fibrogenesis, and loss of REV-ERBα augments the fibrotic responses caused by IAV infection. Furthermore, enhanced REV-ERBα activity/abundance will reduce abnormal collagen accumulation by inhibiting the expression of lysyl oxidases during myofibroblast differentiation.
## Dysregulated protein abundance of REV-ERBα, COL1A1 and LOX were observed in IPF patients compared with healthy controls
It is well known that excessive extracellular matrix (ECM) protein production occurs during fibrosis and is deposited within the lesion areas. Human lung sections with verified pathology were purchased from Origene Inc. All the healthy controls were within the normal limits with $100\%$ normal area and at least $80\%$ alveoli area, while the IPF samples were composed of at least $40\%$ lesion area (Supplementary Table 1). As shown in Fig. 1a, we observed high expression of type 1 collagen (COL1A1) over the injured tissue area and elevated lysyl oxidase (LOX) protein in IPF patients compared with healthy controls. Both COL1A1 and LOX were highly expressed among ECM in the lesion tissues in IPF samples, whereas limited COL1A1 and Lox were expressed in healthy controls. Consistent with previous data, we observed the diminished protein abundance and distribution of REV-ERBα in the fibrotic lesions from IPF samples, whereas REV-ERBα was highly expressed in the nuclei of healthy controls with limited protein abundance observed in the cytoplasmic area. Similar results were observed in the healthy and lesion area in IPF samples as well (Fig. 1b). A decreased trend of REV-ERBα was found in the lesion area compared to the healthy sections, and upregulation of COL1A1 and LOX was found in the lesion area compared to the healthy area. Compared to the control groups, the protein abundance of REV-ERBα was decreased in the healthy area (Control: $21.294\%$ vs. healthy area from IPF: $11.296\%$) from IPF samples, and slightly increased protein levels of COL1A1 (Control: $37.074\%$ vs. healthy area from IPF: $52.604\%$) and LOX (Control: $30.439\%$ vs. healthy area from IPF: $40.836\%$) in the healthy areas from IPF samples compared to control group were observed (Fig. 1). A previous study has identified that REV-ERBα is fundamental in IPF progression8, and we determined how Rev-erbα affects the development of pulmonary fibrosis. Fig. 1Decreased REV-ERBα protein abundance and increased protein levels of COL1A1 and LOX in IPF lungs compared to healthy control. Healthy control and IPF formalin fixed-paraffin embedded (FFPE) lung samples were purchased from Origene Inc. Healthy controls contained $100\%$ normal lung architecture with $85\%$ alveoli surface area. IPF patient samples contained at least $50\%$ lesion surface area. The protein abundance of REV-ERBα, COL1A1, and LOX were visualized and determined by IHC. a The comparisons of protein distribution and abundance were performed between healthy control and IPF patient ($$n = 10$$ per group), b or between the healthy area and lesion area from the same IPF patient. The images were taken, and the positive stained area was calculated by ImageJ ($$n = 5$$ per group). Data were shown as mean ± SEM, unpaired t-test was used for a and b. Bar size: 50 µm. (* $p \leq 0.05$, ***$p \leq 0.001$; scale bar: 50 μm).
## Circadian clock genes, including REV-ERBα were dysregulated in bleomycin-induced fibrosis
To understand the expression of Rev-erbα and related circadian genes in the in vivo model of fibrosis, we treated C57BL/6J wild-type (WT) mice with bleomycin (1.5 units/kg) to induce fibrosis and determine the gene expression of circadian and fibrotic-related genes. According to previous studies, most of the fibrotic markers were dysregulated significantly at day 14, and there was no significant difference at day 14, 21, and 28 post-injury29–31. Another report also described that variable outcomes appeared after day 21 post-injury, and even recovered to baseline32. Hence, we have selected day 14 post-injury as our end-time point for bleomycin-induced lung injury. After 14 days of bleomycin-induced lung injury, we found decreased gene expression of REV-ERBα (Gene symbol: NR1D1), REV-ERBβ (Gene symbol: NR1D2), RORα (Gene symbol: RORA), CLOCK, CRY$\frac{1}{2}$, PER$\frac{1}{2}$/3 and DBP (Fig. 2a, b and Supplementary Fig. 1). There was no change in gene expression of BMAL1 (Gene symbol: ARNTL), or NFIL3 transcript level (Supplementary Fig. 1). As expected, gene expression of fibrotic markers, such as COL1A1, COL1A2, COL3A1, COL5A2, TGFB1, TGFB2, VIM1, FN1, and MMP2 was increased at 14 days post bleomycin injury (Fig. 2a, b and Supplementary Fig. 1). Decreased expression of gene levels of OCLN, TJP1, TJP3, and CDH1 were also observed, while SMAD2 and TJP2 showed no significant changes in the bleomycin group compared with PBS control (Fig. 2a, b and Supplementary Fig. 1). Similarly, we also observed the decreased protein expression of REV-ERBα in the bleomycin-treated group, as well as increased protein levels of LOX (total and activated) and COL1A1 (Fig. 2c).Fig. 2Altered circadian and profibrotic mRNA and protein expressions were observed in bleomycin induced fibrotic responses. Lungs from C57BL/6J WT mice (Combined male and female ($$n = 2$$–3 each) for analysis) dosed with bleomycin at day 14 were snap-frozen and used for RNA isolation. RNA isolated from lung homogenates were used to identify the circadian and profibrotic related gene expressions by our customized nanostring panel through nCounter SPRINT Profiler. The transcripts levels of RNA targets (Normalized Count) were normalized and visualized by nSolver software. a *Dysregulated* genes are shown as a heatmap with circadian genes on top and profibrotic genes on the bottom. b *Selected* gene expressions were shown as a bar graph ($$n = 6$$ mice per group). c Proteins isolated from lung homogenates were used to detect the abundance of REV-ERBα, LOX, Activated LOX, and COL1A1. Represented blots are shown here, and protein expression fold change was calculated based on the normalization of β-ACTIN ($$n = 4$$–6 mice per group). Data were shown as mean ± SEM, unpaired two-side t-test was used for b and c. (*$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$ vs. PBS).
## Exacerbated fibrotic progression and lung injury induced by bleomycin dosed at night
Since REV-ERBα expression occurs in circadian oscillation, the expression of REV-ERBα starts to increase from 6 a.m. (ZT0) and starts to decrease at 6 p.m. (ZT12)19. Thus, we dosed the mice at the beginning of the day (lights on) and night (lights off) cycles to determine whether the oscillation of REV-ERBα expression affects the fibrotic progression induced by bleomycin injury (Fig. 3). Interestingly, we found that mice treated with bleomycin at 7 p.m. exhibited exacerbated body weight loss compared to those dosed at 7 a.m. from days 11–14 post-injury (Fig. 3a). In addition, mice dosed at 7 a.m. had a $100\%$ survival rate, whereas only $75\%$ of the mice dosed at 7 p.m. survived (Fig. 3a). We also noticed that mice dosed with bleomycin at both 7 a.m. and 7 p.m. showed dramatic lung injury, and mice dosed at 7 p.m. showed more injury area in lung sections compared to mice dosed at 7 a.m. (Fig. 3a and Supplementary Fig. 2a).Fig. 3The health status of mice, circadian genes and fibrotic genes and protein expressions were affected by bleomycin injury in different time points (7 a.m. vs. 7 p.m.).C57BL/6J WT female mice were used for testing. a The body weights and survival rate were monitored until day 14 post-injury. ( $$n = 3$$–5 mice per group, *$p \leq 0.05$, **$p \leq 0.01$ vs. bleomycin 7 a.m. group). Lungs were harvested, and H&E staining was performed to identify the injured area percentage. b RNA was isolated from lungs homogenates, and gene expression analysis was conducted using customized nanostring panel through nCounter SPRINT Profiler, and transcripts levels were normalized and visualized by nSolver software. The dysregulated genes were shown as a heatmap with circadian and profibrotic genes. *Selected* gene expressions were shown as bar graph ($$n = 3$$ mice per group). c Proteins isolated from lung homogenates were tested via western blot (REV-ERBα, LOX, Activated LOX, LOXL2, and COL1A1) represented blots were showing and change fold was normalized to β-ACTIN ($$n = 4$$–5 mice per group). d Lung sections were used for IHC, and the abundance and localization of COL1A1 and LOX were detected ($$n = 3$$–4 mice per group). Data were shown as mean ± SEM, two-way ANOVA followed Tukey’s multiple comparisons test was performed in a (Body weight change (%)) and one-way ANOVA followed Šídák’s multiple comparisons test was used in a (injured area (%); and b–d). Bar size: 1000 µm in a, and 25 µm in d. (*$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$ between groups; ##$p \leq 0.01$ vs. Bleo 7 a.m. group; &&&$p \leq 0.001$ vs. PBS 7 a.m. group).
We also investigated the gene expression from mouse lungs dosed with bleomycin at different times of day (Fig. 3b). Interestingly, the gene expression of REV-ERBα/β (NR1D1/NR1D2) was decreased after bleomycin injury during the night (dusk), whereas no significant difference was observed when dosed during the day (dawn) (Fig. 3b). The other circadian genes inhibited by REV-ERBα/β, such as BMAL1 (ARNTL), and CLOCK, were differentially decreased during the day and had no change during the night (Supplementary Fig. 2b, c). The REV-ERBα/β competitor RORA showed decreased expression levels after bleomycin injury in either daytime or nighttime. We also observed the decreased gene expression levels of PER$\frac{1}{2}$ and CRY$\frac{1}{2}$ either bleomycin was dosed during the daytime or nighttime (Supplementary Fig. 2b, c). *The* gene expression of fibrotic markers, such as COL1A1, COL5A2, FN1, and SERPINE1 was significantly upregulated when dosed at night (Fig. 3b and Supplementary Fig. 2b, c). Bleomycin injury increased VIM regardless of the time of dosing (Fig. 3b). *The* gene expression of COL1A2 and COL3A1 was upregulated after bleomycin dosing, and there was no time of a day difference during nighttime or daytime (Supplementary Fig. 2b, c). *The* gene expression of tight junction proteins responsible for cell-cell interaction: TJP1 and TJP3 showed significant downregulation when bleomycin was dosed at both day and nighttime, and TJP3 showed further decreased during nighttime dosing (Supplementary Fig. 2b, c).
To further identify the expression level of target genes, we have detected the protein expression by western blot and IHC (Fig. 3c, d). Similarly, higher protein expression of REV-ERBα was observed in PBS 7 p.m. group compared to the PBS group at 7 a.m., and bleomycin injury significantly downregulated the protein abundance of REV-ERBα, whereas no changes in 7 a.m. groups (PBS vs. Bleo) (Fig. 3c). We also observed an increasing trend of protein level of total LOX after dosing bleomycin at either 7 a.m. or 7 p.m., while activated LOX only showed an increasing trend when bleomycin was dosed at 7 p.m. (Fig. 3c). The significantly increased protein abundances of LOXL2 and COL1A1 were observed in mice dosed with bleomycin at 7 p.m. while non-significant increase when dosed at 7 a.m. (Fig. 3c). Except for the western blot, we also detected the protein abundance and localization of LOX and COL1A1 via IHC (Fig. 3d). Bleomycin dosed at 7 p.m. showed higher protein levels of COL1A1 and LOX, especially in the injured area compared to mice dosed with bleomycin at 7 a.m. (Fig. 3d).
## Rev-erbα agonist attenuated the collagen overexpression during bleomycin-induced fibrogenesis
Decreased abundance of REV-ERBα has been noticed after bleomycin injury, we treated mice with Rev-erbα agonist (SR9009, 100 mg/kg, intraperitoneally (i.p.)) for 14 days to determine it’s protective potential against fibrotic progression (Fig. 4 and Table 1). During 14 days post bleomycin injury, there was a significant reduction in body weight starting from day 1, and there is no significant difference between bleomycin and bleomycin + SR9009 groups (Fig. 4a). Surprisingly, only a $60\%$ survival rate was observed in mice that received bleomycin + SR9009 while there was no death in the bleomycin-treated group (Fig. 4a). From the H&E stained sections, we identified that bleomycin-induced significant lung injury, and SR9009 treatment helped alleviate the injury but without significant difference (Fig. 4b). Since we were interested in how REV-ERBα is involved in pro-fibrotic progression, we measured the gene and protein expressions of fibrotic markers in the lungs (Fig. 4c–e and Supplementary Fig. 3). Although the ACTA2 gene level was not significantly increased after bleomycin injury, the bleomycin + SR9009 group showed a significantly reduced expression of the ACTA2 gene (Fig. 4c). We have noticed the significant upregulation of collagens (COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, COL5A1, and COL5A3), and SR9009 treatment helped to reduce the levels of COL1A1, COL1A2, and COL5A1 without significant difference, and the gene level of COL4A1 was significantly downregulated after SR9009 treatment (Fig. 4c). Gene expression of lysyl oxidases (LOX, LOXL1, LOXL2, and LOXL4) were significantly increased after bleomycin injury, but SR9009 treatment did not help reduce the gene abundances (Fig. 4c and Supplementary Fig. 3). Other ECM proteins, such as ELN and FN1, were upregulated after bleomycin injury and SR9009 treatment helped to lower the transcript level but without a significant difference (Fig. 4c and Supplementary Fig. 3). As potential regulators of ECM remodeling and dysregulated repair, there were upregulated gene levels of TGFB1, TGFBR1, and TGFBR2 after bleomycin injury, but no difference was observed between bleomycin and bleomycin+SR9009 treatment groups (Fig. 4c and Supplementary Fig. 3). Based on the gene expression results, we have performed a pathway analysis. Most significantly, ECM degradation, collagen biosynthesis and modification, and ECM synthesis were activated after bleomycin injury and slightly inhibited by SR9009 treatment (Table 1). Hence, we focused on how protein levels of collagen were affected. Fig. 4Rev-erbα agonist (SR9009) treatment helped to reduce the collagen overexpression occurred in bleomycin induced lung fibrosis. C57BL/6J WT mice (equal number of male and female mice) were dosed with bleomycin for 14 days, and SR9009 was given via i.p. injection at a dose of (100 mg/kg) daily. a The body weights and the survival rate was monitored until day 14 post-injury ($$n = 8$$–12 mice per group). b Lungs were harvested, and H&E staining was performed to identify the injured area percentage ($$n = 8$$ mice per group). c RNA was isolated, and gene expression analysis was conducted using nCounter Fibrosis panel via nCounter SPRINT Profiler, and transcripts levels were normalized and visualized by nSolver. The dysregulated genes focused on collagen dynamics and ECM remodeling were shown as a heatmap and selected gene expressions were shown as bar graphs ($$n = 8$$ mice per group). d Proteins isolated from lung homogenates were detected via western blot (COL1A1, COL4A1, LOXL2, and Activated LOX), represented blots were shown and fold change was normalized to β-ACTIN ($$n = 8$$ mice per group). e Lung sections were stained by COL1A1 and COL4A1 via IHC, and the abundance and localization were determined by ImageJ ($$n = 8$$ mice per group). Data were shown as mean ± SEM, multiple unpaired t-test was used for a, and one-way ANOVA followed Šídák’s multiple comparisons test was used in b–d. Bar size: 1000 µm in b and e, ×4 magnification, and 50 µm in e, ×20 magnification. (* $p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$ vs. PBS group; #$p \leq 0.05$, ###$p \leq 0.001$ vs. Bleo group).Table 1Dysregulated pathways after Bleomycin injury with or without SR9009 in C57 miceTerm IDDirected enrichment scoreBleo vs. PBSBleo + SR9009 vs. PBSECM degradation10.06129.6061Collagen biosynthesis and modification9.58669.1477ECM synthesis8.57457.81PDGF signaling7.01176.5104SASP6.1975.7895Myofibroblast regulation4.21473.9987TGF-beta3.62613.132M1 activation3.44943.0848De novo lipogenesis2.50273.223M2 activation1.80040.8803mTOR1.6895−0.3849PPAR signaling−1.66520.3668 Since we have observed the decreased transcript levels of multiple collagens in bleomycin + SR9009 group compared to bleomycin group, we tested the protein abundances of COL1A1, COL4A1, LOX, and LOXL2 as well (Fig. 4d, e). Similarly, we have observed increased protein levels of COL1A1 and COL4A1, and non-significant decreased trends of COL1A1 and COL4A1 after SR9009 injection (Fig. 4d, e). Interestingly, we have noticed a decreased trend of activated LOX, and a significantly decreased level of LOXL2 in the bleomycin + SR9009 group compared to the bleomycin group (Fig. 4d). From IHC staining, we observed overexpression protein levels of COL1A1 and COL4A1 in the injured sections from both the bleomycin group and the bleomycin + SR9009 group. However, the positive distribution of COL1A1 and COL4A1 were inhibited by SR9009 injection, and abundances of both collagens were slightly decreased in the bleomycin + SR9009 group compared to bleomycin group (Fig. 4e). There was no significant sex-dependent difference between bleomycin group and bleomycin + SR9009 group, hence we have combined male and female mice for further analysis.
## Rev-erbα deficiency exaggerated IAV-induced lung injury
To directly understand the role of Rev-erbα in pulmonary fibrogenesis and lung injury, we infected WT and Rev-erbα Het mice with IAV (103 PFU) for 15 days to induce lung injury and fibrotic responses (Fig. 5). At 6–10 days post infection (p.i.), we found that IAV-induced weight loss was exacerbated in Rev-erbα Het mice compared with WT (Fig. 5a). We also monitored locomotor activity after IAV infection, and observed reduced ambulatory counts during the nighttime at 5 days to 9 days p.i. The locomotor activity showed no change during the daytime, and there was no significant difference between WT and Rev-erbα Het mice (Supplementary Fig. 4). After 15 days p.i., we collected the serum to detect IAV-specific antibody (IgG2a and IgA) levels. Both WT and Rev-erbα Het mice infected with IAV showed detectable levels of IgG2a and IgA in serum, and Rev-erbα Het mice showed higher levels of IgG2a and IgA compared to WT mice (Fig. 5a), most likely because of the highest level of infection. We also looked at the viral replication on 2 days and 4 days p.i. There was a significant increase in the viral titer at 4 days p.i., compared to 2 days p.i., while there was no significant difference in viral harboring and replication in the lungs between WT and Rev-erbα Het mice (Supplementary Fig. 5).Fig. 5IAV induced lung injury and profibrotic responses exaggerated in Rev-erbα Het mice compared to WT mice. WT and Rev-erbα Het mice were infected (103 PFU/mouse) with IAV or PBS control for 15 days. a Body weights were monitored during infection, and virus-specific antibodies in serum were detected by ELISA ($$n = 5$$–19 mice per group, *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.01$ vs. IAV infected WT mice). b During sacrifice, lung mechanics (resistance, compliance, and elastance) were measured. ( $$n = 3$$–4 mice per group). c H&E stained lung sections were used to analyze the injured area induced by IAV infection. Regions within the black squares were shown with ×20 magnification ($$n = 4$$–6 mice per group). Data were shown as mean ± SEM, two-way ANOVA followed Tukey’s multiple comparisons test was performed in a (bodyweight change (%)), multiple unpaired t-test was used for a (virus-specific antibodies titer), one-way ANOVA followed Šídák’s multiple comparisons test was used in b, c, unpaired two-side t-test was used in b (Resistance IAV-WT vs. IAV Rev-erbα Het; Elastance PBS-WT vs. IAV-WT; Compliance PSB-WT vs. IAV-WT and IAV-WT vs. IAV Rev-erbα Het). Bar size: 1000 µm in c (×4 magnification), and 50 µm in c (×20 magnification) (*$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$ between groups; #$p \leq 0.05$, ##$p \leq 0.01$ vs. IAV infected WT mice).
Furthermore, we determined the lung mechanical properties (airway resistance, elastance, and compliance). We have observed increased resistance and elastance, as well as decreased compliance after IAV infection compared with PBS control, both in WT and Rev-erbα Het mice. Intriguingly, the Rev-erbα Het mice exhibited increased resistance, elastance, and decreased compliance compared with WT mice in response to IAV infection (Fig. 5b). The H&E-stained lung sections showed IAV infection-induced dramatic lung injury with scaring progression in the alveoli of both WT and Rev-erbα Het mice. More importantly, larger injury areas were observed in Rev-erbα Het mice infected with IAV compared with IAV infected WT mice (Fig. 5c).
## Rev-erbα deficiency aggravated dysregulated gene expression during IAV-induced fibrogenesis
After sacrificing the mice at 15 days p.i., we collected the lung tissues for RNA expression analysis (Figs. 6 and 7). At the gene transcript level, the most significantly dysregulated genes due to Rev-erbα knockdown were downregulated. There are a significant number of genes that were dysregulated when infected with IAV in both WT and Rev-erbα Het mice. Intriguingly, IAV infection in Rev-erbα Het mice led to an upregulation of significantly dysregulated gene transcripts compared to WT mice infected with IAV, which suggests that the altered gene expressions were not due to genotype differences (most dysregulated genes are decreased), but that Rev-erbα affects the specific gene expression during lung injury induced by IAV (Fig. 6a). Following the cutoff filters ($10\%$ fold change with p value < 0.05) used for volcano plots, we also analyzed the gene cluster via Venn diagrams analysis. Compared to WT mice treated with PBS, a total of 67 genes were dysregulated because of the genotype difference (vs. Rev-erbα PBS group) (Fig. 6b). In WT mice infected with IAV, 486 genes were significantly altered, and 430 genes were similarly altered in both WT and Rev-erbα Het mice. Intriguingly, 71 genes started to show a significant difference in Rev-erbα Het mice infected with IAV with no change in WT mice, and 56 genes showed a significant difference in WT mice with no change in Rev-erbα Het mice infected with IAV (Fig. 6b). By comparing IAV vs. PBS in the same genotype (WT IAV vs. WT PBS, and Rev-erbα Het IAV vs. Rev-erbα Het PBS), a total of 414 genes were commonly dysregulated when infected with IAV (Fig. 6c). Specifically, 121 genes were statistically dysregulated in Rev-erbα Het mice infected with IAV, and 72 genes showed a significant difference only in WT groups (PBS vs. IAV). The detailed gene lists corresponding to each comparison are listed in Supplementary Data 1. Based on the dysregulated gene lists, we identified pathways modified by IAV and Rev-erbα, which included collagen dynamics, EMT, TGFβ, myofibroblast regulation, as well as M1/M2 macrophage activation. These pathways were upregulated after IAV infection and were further exacerbated when Rev-erbα was diminished (Table 2). Additionally, collagen biosynthesis and modification, ECM degradation, and ECM synthesis were among the most upregulated pathways in Rev-erbα Het IAV-infected mice compared to IAV-infected WT mice (Table 2). Hence, we focused our study on the alteration of specific genes/proteins related to collagen biosynthesis, modification, and degradation. Fig. 6IAV infection induced dysregulation of profibrotic gene expression exacerbated in Rev-erbα Het mice. WT and Rev-erbα Het mice (equal number of male and female mice) were dosed with IAV (103 PFU) for 15 days, and lungs were homogenized for RNA isolation. Gene expression analysis was conducted using nCounter Fibrosis *Panel via* nCounter SPRINT Profiler. RNA expressions were normalized and analyzed via nSolver software and ROSALIND service. a The dysregulated gene expressions between groups were shown as volcano plots, the cut off filter is at least $10\%$ change (up or downregulation), and $p \leq 0.05.$ b, c *Overlapping* gene expression changes among groups were shown by Venn diagrams with the same cutoff line used for volcano plots. d The overview of gene expression focused on collagen dynamics were shown as a heatmap, and the selected gene transcript levels (collagens and lysyl oxidases) were shown as a bar graph separately. Data are shown as mean ± SEM, one-way ANOVA followed Šídák’s multiple comparisons test was used in d, unpaired two-side t-test was used in d (COL1A1 PBS-WT vs. IAV-WT and COL3A1 PBS-WT vs. IAV-WT). ( $$n = 6$$ mice per group; *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$ between groups; ##$p \leq 0.01$ compared with IAV infected WT group).Fig. 7IAV infection induced dysregulation of profibrotic progression exacerbated in Rev-erbα Het mice. WT and Rev-erbα Het mice (equal number of male and female) infected (103 PFU/mouse) with IAV for 15 days, and lungs were separated for RNA/protein isolation, or fixed with $10\%$ formalin for FFPE sections. a The protein abundance of COL1A2, VIM and activated LOX were measured by western blot. Representative blot images were shown. Different targets were run on the same membrane: COL1A2, VIM and activated LOX were probed in the same membrane and β-ACTIN was used as an endogenous control ($$n = 5$$–6 mice per group). b The localizations of COL1A1 and LOX were determined by immunohistochemical staining, and red arrows were used to indicate the regions of interest. The positive staining area was calculated via ImageJ ($$n = 4$$–6 mice per group). c RNA isolated from lung homogenates was used to measure the gene expression (COL1A1, FN1, TJP1 and TGFB1) via qRT-PCR, and GAPDH was used as an endogenous gene for normalization ($$n = 5$$–6 mice per group). Data are shown as mean ± SEM, one-way ANOVA followed Šídák’s multiple comparisons test was used in a–c. Bar size: 50 µm in b. ($$n = 4$$–6; *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$ between groups; #$p \leq 0.05$, ##$p \leq 0.01$ compared with IAV infected WT group).Table 2Dysregulated pathways after IAV infection in both WT and Rev-erbα Het miceTerm IDDirected enrichment scoreRev-erbα Het PBS vs. WT PBSWT IAV vs. WT PBSRev-erbα Het IAV vs. Rev-erbα Het PBSRev-erbα Het IAV vs. WT IAVCollagen biosynthesis and modification−0.731.50864.81612.6449De novo lipogenesis−1.13651.6092.43690.811ECM degradation−0.72211.26254.17532.1627ECM synthesis−0.56741.96234.13072.2267EMT−1.18942.02733.74851.2953Focal adhesion kinase−1.07131.06233.34231.4581Inflammasome−1.30591.58152.93380.4385M1 activation−1.13241.58273.11530.7314M2 activation−0.2606−1.07651.76170.6458Myofibroblast regulation−1.28262.28234.02811.3925TGFβ−1.24660.99783.15021.3947Wnt−1.65231.2623.21240.2816
## Absence of Rev-erbα exacerbated the activated collagen stabilization and modification during IAV-induced fibrogenesis
To further determine the role of Rev-erbα on collagen dynamics, we measured gene expression related to collagen modification, ECM markers, matrix metalloproteinases (MMPs), and TGFβ pathways (Fig. 6d and Supplementary Fig. 6). We noticed that collagens were significantly upregulated after IAV infection in both WT and Rev-erbα Het mice at the gene expression level. In particular, COL1A1, COL1A2, COL3A1, and COL5A1, were significantly increased in IAV-infected Rev-erbα Het mice compared with the WT IAV group (Fig. 6d and Supplementary Fig. 6a). Interestingly, we observed decreased COL14A1 and COL16A1 in IAV-infected mice, but there was no difference between the WT IAV group and Rev-erbα Het IAV group (Fig. 6d). We also noticed that lysyl oxidases (LOX, LOXL1, and LOXL2) were upregulated only in IAV-infected Rev-erbα Het compared to PBS-treated Rev-erbα Het mice, and there were no significant difference between IAV infected and PBS-treated WT mice (Fig. 6d). In addition, we noticed the upregulation of other ECM-related genes, such as FN1, ELN, VIM, ITGA4, ITGA9, and HSPG2, and genes responsible for focal adhesion such as LAMA3 and OCLN were downregulated after IAV infection in either genotype, and there is no significant difference between IAV infected WT and Rev-erbα Het mice (Fig. 6d and Supplementary Fig. 6a). One of the key genetic pathways activated during fibrotic progression is the TGFβ pathway, and we found increased activation of TGFβ signaling following IAV infection, which showed increased TGFB1, TGFB1I1, TGFBR1 TGFBR2, SMAD2, SMAD3, and SMAD4. However, there was no significant difference between the IAV WT and the IAV Rev-erbα Het groups (Supplementary Fig. 6b). Since we observed increased collagen abundance, we also determined the expression of related collagenases, MMPs (Supplementary Fig. 6c). *Increased* gene expression of MMP2, MMP12, MMP14, and MMP12 was observed only in Rev-erbα Het mice infected with IAV compared with PBS in the same genotype. *The* gene transcript levels of MMP2 and MMP14 showed a significant increase in Rev-erbα Het mice infected with IAV compared to WT mice infected with IAV (Supplementary Fig. 6c). Other MMPs, such as MMP9, MMP8, and MMP3, showed decreased gene transcript levels when IAV infection occurred, and there is no difference between the two genotypes. The inhibitor of MMPs: TIMPs, showed increased TIMP2 in Rev-erbα Het IAV group compared to Rev-erbα Het PBS group (Supplementary Fig. 6c).
## Lack of Rev-erbα augments collagen overexpression during IAV-induced fibrogenesis
Since we observed that type 1 collagen and lysyl oxidases were upregulated in gene transcript levels, we also tested the protein abundance and localization (Fig. 7). Overall, from lung homogenates, we found an increasing trend of type 1 collagen (COL1A2) without statistical significance. Meanwhile, we noticed a significant increase in LOX and VIM in Rev-erbα Het mice infected with IAV compared to either the Rev-erbα Het PBS group or the WT mice infected with IAV (Fig. 7a). Further, we looked at the protein abundance and localization of COL1A1 and LOX. We performed IHC staining of COL1A1 and LOX (Fig. 7b). The distribution of COL1A1 in PBS treated group, either WT or Rev-erbα Het mice, was around the small airways or bronchial. When IAV infection occurred, COL1A1 was augmented in the injured area, mainly around the alveoli. Furthermore, no COL1A1 was observed in alveoli in the PBS-treated group (Fig. 7b). However, for the protein expression of LOX, relatively lower level of LOX were observed in IAV-infected WT mice, while the abundance of LOX were increased in Rev-erbα Het mice infected with IAV, primarily localized to the injured area (Fig. 7b). Since lysyl oxidase is responsible for collagen stabilization via crosslinking the collagen fibers, the co-localization of LOX and collagen in areas of injury was observed as expected (Fig. 7b, red arrow). We also applied the qRT-PCR to detect the gene expression fold change, and we noticed a similar trend compared with NanoString analysis (Fig. 7c). *The* gene transcript level of COL1A1, FN1, and TGFB1 showed an increasing trend after IAV infection, and Rev-erbα knockdown exacerbated the upregulation. *The* gene expression of TJP1 was decreased in the WT group with IAV infection (Fig. 7c).
## Rev-erbα agonist attenuated the TGFβ-induced abnormal collagen stabilization and fibrotic responses in lung fibroblasts
To determine the role of Rev-erbα in the abnormal collagen modification via lysyl oxidase, we treated primary adult human lung fibroblasts (HLF) and human fetal lung fibroblasts (HFL1) with TGFβ (2 ng/ml) with or without Rev-erbα agonist (GSK4112, 20 μM) and antagonist (SR8278, 20 μM) for 2 days (Fig. 8 and Supplementary Figs. 7 and 8).Fig. 8Rev-erbα agonist inhibits TGFβ induced fibroblast differentiation and antagonists exacerbate it. Human primary lung fibroblast were treated with TGFβ (2 ng/ml) with or without Rev-erbα agonist (GSK4112, 20 µM) or antagonist (SR8278, 20 µM) for 2 days. a Protein was isolated for western blot analysis (αSMA, COL1A1, LOX, and Fibronectin (FN)). Represented blots are shown with densitometry analysis ($$n = 3$$–4 cells per group). b Immunofluorescence staining showed the distribution and protein abundance of COL1A1 and αSMA, DAPI was used for nuclear staining (×20). Relative fluorescence intensity was calculated in ImageJ, as fluorescence intensity per cell ($$n = 4$$ cells per group). c RNA was isolated for gene expression measurement via qPCR (ACTA2, COL1A1, COL4A1, FN1, LOX, LOXL1, LOXL2, and NR1D1). GAPDH was used as an endogenous control for RNA and protein fold change normalization ($$n = 4$$ cells per group). Data are shown as mean ± SEM, one-way ANOVA followed Šídák’s multiple comparisons test was used in a–c, unpaired two-side t-test was used in a (COL1A1 Ctrl vs. TGFβ, LOX TGFβ vs. TGFβ + GSK4112, FN TGFβ vs. TGFβ + SR8278). d Schematic demonstrating how both Rev-erbα agonist and antagonist regulates ECM deposition in lung fibroblast induced by TGFβ, and the schematic is created with Biorender.com. Bar size: 50 µm in b. (*$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$ vs. Ctrl group; #$p \leq 0.05$, ###$p \leq 0.001$ vs. TGFβ group).
We previously found that Rev-erbα agonist, GSK4112, inhibited the myofibroblast differentiation induced by TGFβ27. Here we noticed that TGFβ induced myofibroblast differentiation was inhibited by GSK4112, while exacerbated by SR8278 (Fig. 8). Significantly, GSK4112 inhibited the TGFβ induced overexpression of αSMA and COL1A1 in protein levels (Fig. 8a, b), and TGFβ upregulated gene levels of ACTA2, COL1A1, FN1, and LOX were inhibited by GSK4112 treatment (Fig. 8c). In addition, treatment of SR8278 exacerbated the TGFβ upregulated COL1A1 and FN in protein levels (Fig. 8a), and augmented the TGFβ increased transcript levels of COL1A1, COL4A1, FN1, LOX, and LOXL2 (Fig. 8c). Interestingly, we found that both GSK4112 and SR8278 increased the gene level of NR1D1 (Fig. 8c). Based on our results, we concluded that Rev-erbα agonist helped to attenuate the TGFβ-induced fibroblast differentiation and collagen overexpression, while Rev-erbα antagonist exacerbated it (Fig. 8d).
We also tested our hypothesis in HFL1 (Human fetal lung fibroblast) (Supplementary Figs. 7 and 8). GSK4112 treatment showed a significantly increased gene transcript level of NR1D1 while SR8278 showed no change in HFL1 (Supplementary Fig. 7). In addition, GSK4112 inhibited TGFβ-induced ACTA2 and slightly decreased gene expression of COL1A1 and FN1 without significant difference in TGFβ + GSK4112 group compared to TGFβ treatment alone (Supplementary Fig. 7). Intriguingly, we also found that GSK4112 alleviated the upregulated gene expression of lysyl oxidases (LOX, LOXL1, and LOXL2) (Supplementary Fig. 7). We also measured the protein abundance of COL1A1 and LOX. Similarly, GSK4112 suppressed the upregulated protein level of LOX induced by TGFβ. In contrast to the gene expression results, TGFβ-induced COL1A1 protein was significantly inhibited by GSK4112 treatment, and the protein fibers overexpressed by TGFβ were also significantly repressed by GSK4112 (Supplementary Fig. 8). Treatment with Rev-erbα antagonist (SR8278) showed no significant effects on TGFβ-induced fibroblast differentiation or collagen stabilization in HLF1 (Supplementary Fig. 8).
## Rev-erbα agonist and antagonist exacerbated the TGFβ-induced epithelial-mesenchymal transition (EMT) in lung epithelium
We have also treated primary human small airway epithelial cells (SAEC) and human bronchial epithelial cell line (BEAS-2B) with TGFβ (2 ng/ml) with or without Rev-erbα agonist (GSK4112, 20 μM) and antagonist (SR8278, 20 μM) for 2 days (Supplementary Figs. 9 and 10). SAEC treated with TGFβ showed activated EMT tendency via increased VIM, LOXL2, and COL1A1, as well as decreased CDH1, TJP1, and OCLN. The treatment of GSK4112 and SR8278 showed exacerbated gene dysregulation of both epithelial and mesenchymal markers (Supplementary Fig. 9a). Protein levels of COL1A1 and VIM were upregulated by TGFβ and the upregulation was prevented by GSK4112 (Supplementary Fig. 9b). Similar to HLF, both agonist and antagonist treatment showed increased gene expression of NR1D1.
In BEAS-2B, increased gene levels of COL1A1 and FN1 were noticed after TGFβ treatment, and GSK4112 attenuated the upregulation, whereas SR8278 exacerbated the gene levels of FN1 with significance and COL1A1 without significance (Supplementary Fig. 10). A significantly increased LOX gene level was observed after GSK4112 or SR8278 treatment compared to the TGFβ group. Either GSK4112 or SR8278 inhibited increased LOXL1 by TGFβ treatment. TGFβ inhibited the gene expressions of LOXL2 and ACTA2, and SR8278 treatment eliminated the downregulation, but there was no effect with GSK4112 treatment (Supplementary Fig. 10).
## Discussion
Pulmonary fibrosis is a lethal chronic lung disease without effective therapeutic options, and the pathogenesis of fibrogenesis remains unclear6,33,34. Recent studies have demonstrated the novel role of the circadian molecular clock in the pathobiology of chronic lung diseases and highlighted the potential for circadian clock-based therapeutics23,35. Targeting specific circadian clock genes has been implicated with anti-fibrotic potential in vitro in cells or in vivo in mouse models of lung injury8,36–38. In previous studies, Rev-erbα deficiency exacerbated the EMT induced by CS and fibrogenesis induced by bleomycin, and Rev-erbα agonist inhibited the fibroblast differentiation induced by TGFβ8,27. In this study, we have characterized the Rev-erbα abundance in human IPF patients histologically, as well as in a bleomycin mouse model, and we found the decreased REV-ERBα protein abundance, especially in IPF lesion areas and bleomycin-induced fibrogenesis. Based on our results, the lower protein abundance of REV-ERBα in the healthy portion of IPF patients could promote the progression of fibrogenesis toward a lesion phenotype. Since the abundance of REV-ERBα is in circadian oscillation, mice dosed with bleomycin when REV-ERBα expression naturally starts to decrease (dark phase/nighttime) exhibited higher mortality and exacerbated fibrotic progression compared with those dosed in the daytime. We have administrated the Rev-erbα agonist (SR9009) to mice treated with bleomycin, and we noticed that SR9009 injection helped ease the collagen overexpression during bleomycin-induced fibrogenesis. We also analyzed whether diminished REV-ERBα exacerbated the fibrotic progression induced by IAV infection. Our results show that Rev-erbα regulated collagen stabilization via lysyl oxidase, and its agonist prevented TGFβ induced overexpression of collagen.
Circadian clock molecules RORα (nuclear receptor), REV-ERBα, BMAL1, and CLOCK, have been implicated in the crosstalk between inflammation and lung tissue injuries19,22,39,40. The critical circadian molecules: BMAL1 and CLOCK form a heterodimer that binds to E-Box and subsequently promotes the expression of Rev-erbα. Rev-erbα binds to RORE to prevent the expression of BMAL1 and CLOCK, while RORα activates the expression of BMAL1 and CLOCK. Both RORE and E-box are associated with EMT41,42, which is initiated at the early stage of fibrosis. Hence, the regulators of RORE and E-box (RORα, REV-ERBα, BMAL1, and CLOCK) are equally critical in fibrogenesis. In our results, we noticed that the gene level of BMAL1 (ARNTL) in the bleomycin model depended on the time of dosing. Decreased BMAL1 was observed when dosed during the day, while an increasing trend of BMAL1 transcript level was observed when treatment occurred during the nighttime. Similar time-dependent changes also occurred with CLOCK expression. Upregulated BMAL1 was identified in fibrotic mouse lungs induced by TGFβ transfection, and BMAL1 silencing helped to inhibit the fibrotic progression induced by TGFβ in the lung epithelium36. In the same report, TGFβ transfected into mouse lungs decreased the gene levels of REV-ERBα. REV-ERBα was also shown to be inhibited during fibrotic progression36. These published results agree with our data that show decreased REV-ERBα and increased BMAL1 expression in bleomycin-induced fibrosis. Bleomycin-induced downregulation of REV-ERBα and increased BMAL1 during the night might be one of the reasons for fibrotic progression and exacerbation. Interestingly, the gene expression of CLOCK showed very similar results to BMAL1 gene alterations. It is known that CLOCK disruption exacerbates fibrotic progression, which partially agrees with our data on night dosing that CLOCK level showed lower expression during the night37. In the same study, bleomycin dosing at night showed more collagen deposition in the injured area, which supports our results37. Another study demonstrated that infection with IAV that occurred during the night showed worse body weight loss, higher mortality, and more severe tissue injury22. Our data also indicate the possibility that targeting BMAL1 or CLOCK as a potential candidate might need to consider the dosing time, and inhibition or activation of BMAL1 or CLOCK might be time-dependent.
Our results showed decreased REV-ERBα after bleomycin injury during the nighttime. The expression of REV-ERBα starts to decrease naturally during the night (dusk) and dosing with bleomycin-induced significantly decreased REV-ERBα levels could dampen the basal expression of REV-ERBα which might result in worse fibrotic phenotypes and health status. Since Rev-erbα is a key component of circadian molecular clock that shows a rhythmic expression21, i.e., the level of REV-ERBα starting to decrease from 6 p.m. could result in less protection against bleomycin-induced lung inflammation in mice dosed at ZT13, while increasing oscillation of REV-ERBα during the day could attenuate the inflammatory response caused by bleomycin dosed at ZT1. As mentioned before, dosing with bleomycin at night exacerbated the collagen deposition in the lungs, which agrees with our gene and protein expression results37. Surprisingly, we have observed the augmented protein expression of LOX in the lung injured area when dosed at 7 p.m. compared to the 7 a.m. group, and LOX is responsible for crosslinking collagen fibers to prevent the degradation of collagens43,44. To measure the REV-ERBα expressions in IPF patients, we stained for REV-ERBα in pulmonary fibrotic lesion areas. We observed a decreased abundance of REV-ERBα especially in the lesion area, while REV-ERBα was fully expressed in the healthy samples. Currently, limited studies directly report the expression levels of REV-ERBα (NR1D1) in IPF patients or bleomycin-induced fibrosis45,46. The single-cell RNA sequencing comparison between IPF and healthy patients identified the significant downregulation of NR1D1 in ATII cells in IPF patients compared to healthy controls45. Our results showed significantly decreased REV-ERBα protein abundance, especially within the injured areas, which partially agrees with the previous study. Another study reported that the REV-ERBα mRNA level was decreased in bleomycin-induced lung fibrosis in young mice, as well as in the naturally aged mice lungs46. Our results show decreased REV-ERBα after bleomycin injury. Moreover, our study shows that bleomycin-induced downregulation of REV-ERBα occurs only during the nighttime. The level of REV-ERBα was unchanged when dosing in the daytime. Decreased REV-ERBα has been reported as a cause of exacerbation of fibrotic progress in either mouse or human lung fibroblasts8. Our results and previous publications suggest that REV-ERBα is inhibited during fibrogenesis and that decreased REV-ERBα either by transgenic methods or natural circadian oscillation exacerbates the fibrotic progression and worsens the lung injury.
We administered Rev-erbα agonist (SR9009) into mice dosed with bleomycin, and tested the protective effect of Rev-erbα agonist against fibrosis. We did not observe any difference in body weight decline between bleomycin alone and bleomycin with SR9009, however, we observed a lower survival rate in mice when received SR9009 post bleomycin injury. It has been proven that Rev-erbα agonist could increase body weight loss and fat mass loss12. Moreover, SR9009 has been shown to decrease cell viability and dysregulate cellular metabolism47. There are clinical reports describing that body weight loss and lower body mass index could worsen IPF progression and even lower the survival probability48,49. SR9009 accelerated body weight loss could be one of the reasons for the higher death rate in the bleomycin + SR9009 group compared to the bleomycin group. However, other side-effects of SR9009 might be contributing to the cause of death as well. More detailed studies should be conducted to understand the molecular mechanism of the off-target effects of SR9009 during fibrogenesis. Besides the side effects of SR9009, injection of the agonist helped inhibit the collagen contents at the gene and protein levels, which agrees with our results from the cell model. Our and other results showed that SR9009 had specificity in regulating the overexpression of collagen and helped to prevent fibrogenesis while the side effects need further investigation for the pre-clinical trial.
Previously, we have shown that Rev-erbα was associated with fibrotic responses during IAV infection in Rev-erbα Het mice, which led to fibrogenesis. After 15 days p.i., we noticed that Rev-erbα Het mice infected with IAV showed worse health status. The exaggerated upregulation of lung elastance was observed in Rev-erbα Het mice, demonstrating that Rev-erbα deficiency exacerbated the fibrotic progression functionally. To support our hypothesis, we have measured multiple fibrotic markers, such as type $\frac{1}{3}$/5 collagens and lysyl oxidases (LOX, LOXL1, and LOXL2), which were only significantly upregulated by IAV in Rev-erbα Het mice. A previous study proved that Rev-erbα knockdown could exacerbate the fibrotic response by increasing αSMA protein expression8. Collagens are equally important in pulmonary fibrosis as αSMA, both of which are overexpressed during fibrogenesis and induce irreversible scarring. Our results elaborate on the previous reports and demonstrate that Rev-erbα is essential in regulating αSMA and correlated with collagen expression. As we mentioned before, Rev-erbα starts to decrease naturally during the nighttime, and it has been identified that IAV infection during the night is associated with worse health outcomes in mice, as well as higher mortality and more severe lung injury compared to daytime infection22. Another study reported that dosing bleomycin during the night increased collagen deposition compared to dosing during the day37, which also concurred with our findings here. Our data support previously published results and provide a possible explanation for why IAV infection at nighttime induces worse lung injury and higher mortality than during the day, when Rev-erbα starts to decrease. Our conclusion raises a possibility that working during the night shift could be more vulnerable to environmental hazards, which could contribute to developing fibrosis.
To understand the correlated signaling pathways involved with Rev-erbα in IAV infection-induced pulmonary fibrotic responses, we analyzed the directed enrichment scores to determine the related pathway. We noticed an exacerbated upregulation of multiple biological processes, such as collagen biosynthesis and modification, ECM degradation and synthesis, M2 macrophage activation, myofibroblast regulation, TGFβ pathway, and EMT. The most abnormal activation was in collagen synthesis and modification pathways, and we found the exaggerated upregulation of lysyl oxidases in Rev-erbα Het mice compared with WT mice infected with IAV. *Both* gene and protein expressions of lysyl oxidases were upregulated in Rev-erbα Het mice infected with IAV, but not in WT mice infected with IAV. Lysyl oxidases are known to stabilize the collagen fibers via crosslinking to prevent collagen degradation and improve tissue scarring43,44. Other than collagen, lysyl oxidases are also responsible for crosslinking elastin, which was further upregulated in Rev-erbα Het mice infected with IAV. Besides the collagen stabilization and synthesis, we also determined the expression of related collagenases (i.e., MMPs). We found the exacerbated increased MMP2, MMP12, and MMP14 in Rev-erbα Het mice infected with IAV. The substrates of MMP2, MMP12, and MMP14 include gelatin, type 1 and 4 collagen, and elastin;50 Upregulated MMPs could be a self-regulating method for digesting the overexpressed ECM. Other MMPs, such as MMP9 and MMP8, which are responsible for digesting gelatin, collagen, and elastin, showed downregulation after IAV infection. The balance of MMPs as ECM regulators during fibrosis progress needs more detailed studies to understand how MMPs are involved in collagen dynamics, particularly during episodes of fibrogenesis.
Our previous study showed the therapeutic potential of Rev-erbα agonist in preventing EMT induced by cigarette smoke (CS) and fibroblast differentiation induced by TGFβ27. In this study, we found that Rev-erbα agonist treatment can prevent the abnormal collagen modification induced by TGFβ, and inhibits the overexpression of collagen. A previous study showed that Rev-erbα agonist could attenuate the fibrotic responses in vivo, ex vivo, and in vitro by measuring traditional fibrotic markers: ACTA2 and COL1A18. Our results further support the role of Rev-erbα in the fibrotic response. Rev-erbα agonist prevents the overexpression of collagen 1 and 4, lysyl oxidase, fibronectin, and αSMA, whereas Rev-erbα antagonist augments it. We found that Rev-erbα agonist treatment suppressed mRNA and protein expression of collagen caused by TGFβ with significance. Our results further described that Rev-erbα involvement in fibrotic progression might be through lysyl oxidase, which is known for stabilizing collagen content. It has been shown that SR8278 could promote myogenesis in myoblasts, but it has a very poor half-life: 0.17 h51,52. Similarly, we noticed that the Rev-erbα antagonist (SR8278) exacerbates the myofibroblast differentiation by augmenting the expression of collagen, lysyl oxidase, and fibronectin. Surprisingly, either Rev-erbα agonist or antagonist exacerbated the EMT induced by TGFβ in SAEC, while the cell-type specific role of Rev-erbα in vitro in lung cells needs further investigation. From our results, we showed a decreased Rev-erbα abundance during lung fibrogenesis, loss of Rev-erbα could exacerbate the fibrotic process induced by IAV infection via collagen-lysyl oxidase interaction, and pharmacological activation of Rev-erbα prevented the overexpression of collagens. Our and others studies demonstrate that mice dosed with bleomycin at night show worse fibrotic progress than during the day, which might be the result of decreasing Rev-erbα levels37. Based on our and others findings, circadian clock is critically involved in disease development, and night shift workers could face a higher chance of fibrotic disease development22, targeting the clock molecule Rev-erbα might be one potential therapeutic strategy to overcome the risk.
Current FDA-approved anti-fibrosis drugs: Nintedanib and Pirfenidone, are not targeting the lysyl oxidase mediating collagen stabilization53,54. Our findings suggest that Rev-erbα agonists possess great potential in protecting fibrogenesis by disrupting collagen fibers. Currently, there is a drug, PXS-5505: Pan-Lysyl Oxidase Inhibitor, that is in phase 1 clinical trials for myelofibrosis55. Rev-erbα agonists also preserve the possibility of treating pulmonary fibrosis, while the first generation of agonist: GSK4112 has poor pharmaceutical properties, and new Rev-erbα ligands are needed12,51. Based on the chemical structure of GSK4112, there are new agonists currently designed and available, such as SR9009, SR9011, GSK2945, SR12418, GSK2667, and GSK507212. We have proven that SR9009 daily injection can prevent the EMT in lungs induced by 10 days of CS exposure27. Moreover, SR9009 attenuated liver fibrosis in mice with inhibited collagen expression56. Other agonists, such as SR12418, GSK5072, and GSK2667, have been identified that can inhibit inflammatory responses in THP1 cells57,58. Although there are disadvantages of Rev-erbα agonists in in vivo models, such as short half-life and off-target effects47,51, numerous reports including this study demonstrate the fundamental role of Rev-erbα in lung injury, and Rev-erbα agonists can prevent lung inflammation and injury induced by CS or IAV infection. A detailed study of the anti-fibrotic properties of different Rev-erbα agonists is needed to identify a proper agonist with suitable pharmaceutical characteristics for in vivo study, and even clinical trials.
Overall, Rev-erbα abundance was decreased in fibrotic progression, and naturally reduced Rev-erbα exacerbated the fibrogenesis. Rev-erbα deficiency exaggerated the fibrotic responses and lung injury induced by IAV infection, and Rev-erbα was involved in the activation of collagen stabilization via lysyl oxidase during the fibrotic progression caused by IAV. Treatment with Rev-erbα agonist can prevent the induction of collagen-lysyl oxidase interactions and stabilization. Our results support the fundamental role of Rev-erbα in fibrogenesis development. Rev-erbα agonists offer promising potential in preventing collagen overexpression and may help break down collagen fibers by inhibiting lysyl oxidase overexpression. Investigating other circadian clock molecules in fibrogenic progression might help us understand the molecular mechanism as well as discover novel therapeutic targets for treating pulmonary fibrosis.
## Ethical approval
The experiments performed in this study were approved by the Animal Research Committee of the University of Rochester, University of Rochester Institutional Biosafety Committee, and the ethical standards from United States Animal Welfare Act and NIH.
## Human lung tissue slides declaration
Human lung samples (formalin fixed-paraffin embedded (FFPE) blocks, both Normal and IPF patients) were purchased from Origene (OriGene Technologies Inc). The detailed patient information and Sample/Label IDs are listed in Supplementary Table 1. Lung sections were prepared from the FFPE blocks with 5 μm thickness using a microtome. The sections are used for immunohistological chemistry (IHC) staining.
## Animals and treatments
Rev-erbα global heterozygous (Rev-erbα Het) mice (male and female mice, 2–4 months old) were purchased from Jackson laboratory (Strain #:018447), and adult C57BL/6 wild-type (WT, male and female mice, 2–4 months old) were bred in the vivarium at the University of Rochester Medical Center. Before treatment, mice were transferred to the inhalation core facility and allowed 1 week acclimatization period. The mice were housed on a $\frac{12}{12}$ h light–dark cycle with ad libitum access to water and food. WT C57BL/6 mice were used for bleomycin dosing. Mice received 1.5 units/kg for 14 days, and bleomycin (Cat#1076308, Sigma) was delivered by oropharyngeal inhalation, after anesthetizing with isoflurane. During 14 days of dosing, Rev-erbα agonist (SR9009, Cat#554276, Sigma) was injected intraperitoneally (i.p.) between 11 a.m.–12 p.m. every day, at the dosage of 100 mg/kg body weight. SR9009 was prepared in $15\%$ Kolliphor EL (Cat#: C5135, Sigma) as described previously27. The mice were sacrificed 14 days post-dosing, and the lungs were snap-frozen for further analysis. For IAV dosing, mice were anesthetized with isoflurane, and a total amount of 103 plaque-forming units (PFU)/mouse of influenza A/Puerto Rico $\frac{8}{1934}$ H1N1 virus (PR8) was given to mice intranasally59. A total of 3 female mice were housed individually ad libitum food and water were supplied in a special cage with a running wheel connected to an automatic counter. Mice were accommodated in the wheel running cage for 1 week, and counters were adjusted during this week. The locomotor activity was monitored from day 0 to day 14, and the mice were sacrificed on day 15 post-infection (p.i.). During 15 days of infection, body weights were monitored daily. A separate group of mice was placed in cages with the running wheel assembled, and the running wheel was connected to an automatic counter. Each cage has one mouse with access to regular water and food. The locomotor activity was recorded during 14 days of infection. During sacrifice, mice were anesthetized with pentobarbital (100 mg/kg) via i.p. injection. Lung function parameters (resistance, compliance, and elastance) were measured during the sacrifice via the Flexivent FX1 Legacy system (Scireq) following the manufacturer’s instructions. Each measurement was performed 3 times per animal. Mice lungs were also inflated with $1\%$ low melting agarose and fixed with $10\%$ formalin overnight for histological staining. Bleomycin and IAV dosing was regularly conducted between 11 a.m.–1 p.m. and sacrificed during a similar time. The time of day bleomycin dosing (7 a.m. and 7 p.m.) was performed in C57BL/6 female mice, and mice were sacrificed at the same time of day as their respective dosing. During 14 days, body weight was monitored. Another group of mice was dosed with an equal volume of PBS as the control group.
## Viral titer in lungs and IgG2a and IgA in serum measurement
Mice were sacrificed at 2 and 4 days p.i., lungs were collected and snap frozen for preparing the lung homogenates for viral titer measurement according to our previous publication60. Mice were sacrificed at 15 days p.i., and whole blood was collected through the posterior vena cava vein. Serum was separated from whole blood by centrifugation (12,000 × g, 10 min at room temperature). The IAV-specific IgG2a and IgA antibodies in serum were determined using ELISA via serial dilution as described in our previous publication60.
## Cell culture and treatment
Primary human lung fibroblast (Cat# CC-2512) and small airway epithelial cells (SAEC) (Cat# CC-2547) were purchased from Lonza. Lung fibroblasts were cultured in FGM-2 Fibroblast Growth Medium (Cat# CC-3132), and SAEC were cultured in SABM Small Airway Epithelial Cell Growth Basal Medium (Cat# CC-3119). Cells were seeded into 6 well plates for the treatment of 2 ng/ml TGF-β with or without 20 μM GSK4112 (Cat#: 3663; TOCRIS) and SR8278 (Cat#: S9576 Sigma) for 2 days. Human Fetal Lung fibroblast (HFL-1, Cat#: CCL-153) and human bronchial epithelial (BEAS-2B, Cat#: CRL-9609) cells were purchased from the American Type Culture Collection (ATCC) and stored in liquid nitrogen. The cells were thawed and cultured in DMEM/F12K medium (Cat#:113-20033; Thermo Fisher Scientific) with $1\%$ Penicillin-Streptomycin-Glutamine (Cat#: 103-78016; Thermo Fisher Scientific), and $10\%$ FBS (Cat#: 10082147; Thermo Fisher Scientific) for HFL-1 and $1\%$ Penicillin-Streptomycin-Glutamine, $5\%$ FBS for BEAS-2B. Cells were maintained under $5\%$ CO2 and $95\%$ humidity. Before treatment, HFL-1 cells were starved in serum-free DMEM/F12K medium for 12 h, and BEAS-2B cells were serum-deprived in DMEM/F12K medium with $1\%$ FBS. Then, the cells were treated with 2 ng/ml TGF-β with or without 20 μM GSK4112 (Cat#: 3663; TOCRIS) and SR8278 (Cat#: S9576 Sigma) for 2 days. After treatment, the cells were either lysed for protein/RNA quantification or fixed with $4\%$ paraformaldehyde for immunofluorescence staining.
## RNA isolation and qRT-PCR
Frozen lungs or cells were homogenized and lysed by QIAzol reagent (Cat#:79306, Qiagen), and mixed with chloroform for 10 s. The mixtures were centrifuged at 12,000 × g for 30 min, at 4 °C. Then, the aqueous phase was transferred into a new tube. Equal volumes of isopropanol were added to the samples and mixed universally, then incubated at −20 °C for 2 h. The mixtures were centrifuged at 15,000 × g for 15 min at 4 °C, and the supernatants were removed. A total 1 ml $75\%$ EtOH was added to wash the RNA pellet and then spun down at 15,000 × g, for 30 min at 4 °C. The EtOH was removed and the RNA precipitates were resuspended in 50 μl of RNase-free water. The concentrations and qualities of all the samples were quantified by Nano-drop spectrophotometer (ND-1000, NanoDrop Technologies). Equal amounts of RNA samples were used for reverse transcription via RT2 First Strand Kit (Cat# 330401, Qiagen) and real-time PCR quantification based on SYBR green expression master-mix (Cat# 330509, Qiagen). The primers used in this study were purchased from BioRad: COL1A1 (Mouse, qMmuCED0044222), FN1 (Mouse, qMmuCEP0054113), TJP1 (Mouse, qMmuCID0005277), TGFB1 (Mouse, qMmuCED0044726), NR1D1 (Mouse, qMmuCID0014284), ARNTL (Mouse, qMmuCED0049609), CLOCK (Mouse, qMmuCED0046959), GAPDH (Mouse, qMmuCEP0039581), COL1A1 (Human, qHsaCEP0050510), ACTA2 (Human, qHsaCIP0028813), FN1 (Human, qHsaCEP0050873), LOX (Human, qHsaCED0043469), LOXL1 (Human, qHsaCED0044245), LOXL2 (Human, qHsaCED0044522), and GAPDH (Human, qHsaCEP0041396). A qRT-PCR thermal cycle is 10 min at 95 °C, 40 cycles of 95 °C, 15 s, and 60 °C, 1 min, the fluorescence intensity was checked at the end of 60 °C incubation. A melting curve was performed for a quality check of cDNA amplification. The BioRad CFX96 qPCR machine was used, and the change fold was calculated based on 2−ΔΔCt methods with GAPDH as the endogenous control.
## NanoString measurement
RNA samples isolated from lungs were used for NanoString measurement with a total of 100 ng RNA for each group. Our customized codeset (circadian genes and fibrotic markers) was used for bleomycin treatment groups, and nCounter Fibrosis Panel was used in Bleomycin + SR9009-treated group as well as IAV infected groups. All the RNA samples were mixed with the master mix and incubated at 65 °C for 16 h for RNA hybridization. All the samples were loaded into a NanoString running cartridge, and profiling reading was performed by nCounter SPRINT Profiler (NanoString Technologies, Inc.). All the gene expressions were normalized by nSolver 4.0 software, and normalized counts were used for data representation. The RLF files generated by the profiler were uploaded to ROSALIND (https://www.rosalind.bio/) for advanced analysis to generate volcano plots and pathway direct enrichment scoring. The significantly dysregulated genes were filtered and uploaded to an online tool (http://bioinformatics.psb.ugent.be/webtools/Venn/) to generate the Venn diagram and overlapped dysregulated gene list.
## Protein isolation and Western blot
Snap frozen lung lobes or cells were lysed in RIPA buffer with a protease inhibitor cocktail, and the protein concentrations were measured by Pierce BCA Assay Kit (Cat#: 23227, Thermo Fisher Scientific). A total 20 µg protein for each sample was used for analysis. The protein samples were separated by $10\%$ sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE), and then transferred to a nitrocellulose membrane (Cat# 1620112, BioRad). The membranes were then blocked with EveryBlot Blocking Buffer (Cat#: 12010020, BioRad) for 20 min, and incubated with primary antibody diluted in blocking buffer overnight at 4 °C. Primary antibodies used here included anti-REV-ERBα (1:1000, 13418, Cell Signaling), anti-COL4A1 (1:1000, ab227616, Abcam), anti-LOXL2 (1:1000, ab197779, Abcam), anti-E-Cadherin (1:1000, 3195, Cell Signaling), anti-Fibronectin (1:1000, ab, Abcam), anti-vimentin (1:1000, ab92547, Abcam); anti-COL1A2 (1:1000, NBP2-92790, Novus Biologicals), anti-COL1A1 (1:1000, NBP1-30054, Novus Biologicals), anti-activated LOX (1:1000, NB100-2527, Novus Biologicals) for Fig. 7 only, and anti-LOX (1:1000, ab174316, abcam). Then, the primary antibody was removed, and the membranes were washed with Tris-buffered saline containing $0.1\%$ Tween 20 (TBS-T) 3 times, 10 min each. Then, membranes were incubated with secondary antibody (goat-anti-rabbit, 1:5000, #1706515, BioRad) for 1 h at room temperature. The membranes were then washed with TBS-T 4 times, 15 min each. The membranes were developed with Pierce ECL Western Blotting Substrate (Cat#: 32106, Thermo Scientific), and the signals were detected by Bio-Rad ChemiDoc MP imaging system Densitometry was calculated using ImageLab software (BioRad), and fold changes were calculated based on PBS groups, with normalization to β-actin (1:2500, ab20272, Abcam) for mice and GAPDH (1:1000, ab9482, Abcam) for human samples.
## H&E staining
Lung sections (5 µm) were prepared through the microtome, then deparaffinized and rehydrated with xylene, and 100, 95, and $70\%$ EtOH. Then, the sections were stained with hematoxylin for 1 min, rinsed with water for 5 min, and blued with $0.1\%$ ammonia-water for 10 s. The slides were washed with running water for 10 min. Then, the slides were incubated with $95\%$ EtOH for 1 min, then stained with Eosin for 1 min, and quickly washed with $95\%$ EtOH. Then, the slides were sequentially dehydrated with $95\%$, $100\%$ EtOH, and xylene. Then, all the slides were mounted with Permount, ×4 and ×20 pictures were taken with a light microscope (Nikon ECLIPSE Ci), and the total injured area was measured via ImageJ.
## Immunohistological chemistry (IHC) staining
Lung sections (5 µm) were deparaffinized and rehydrated via xylene, 100, 95, and $70\%$ EtOH, and washed with water for 5 min. Slides were incubated with antigen retrieval solution (Cat#: S1699, Dako, Denmark) at 95 °C for 30 min. Then, the slides were cooled to room temperature and washed with TBS + $0.25\%$ triton-100 (wash buffer) 2 times, 5 min each. Sections were then blocked with $10\%$ normal goat serum and incubated with anti-COL1A1 (1:100, NBP1-30054, Novus Biologicals), anti-Lox (1:100, NB100-2527, Novus Biologicals), anti-Col4A1 (1:200, ab227616, Abcam), and anti-Rev-erbα (1:100, NBP1-84931, Novus Biologicals) at 4 °C overnight. Slides were washed with wash buffer 10 min, 2 times, then incubated with $0.3\%$ hydrogen peroxide for 15 min. Slides were washed with TBS 10 min, 2 times, and washed with wash buffer 5 min, 3 times. Slides were incubated with secondary antibody (1:1000, ab7090, Abcam) at room temperature for 1 h. Then, washed with wash buffer 10 min, 2 times, and developed with DAB Quanto Chromogen and Substrate (Cat#: TA-125-QHDX, Thermo Fisher Scientific) for 10 min. Excess DAB substrate was washed away with water, and counter stained with hematoxylin. Then, the sections were dehydrated and mounted for light microscopy (×20 and ×40 with Nikon ECLIPSE Ci and ×4 with BioTek Cytation 5). All the antibodies were prepared in $10\%$ normal goat serum. ImageJ was used to calculate the positively stained area percentage via color deconvolution.
## Immunofluorescence (IF) staining
Cells were seeded in chamber slides, treated with TGFβ and Rev-erbα agonist/antagonist for 2 days, and then fixed with $4\%$ paraformaldehyde for 15 min. The slides were washed with TBS for 10 min, 2 times, stored at 4 °C, and then blocked with $10\%$ normal goat serum. Cells were incubated anti-COL1A1 (1:100, NBP1-30054, Novus Biologicals) and anti-αSMA (1:200, A2547-2ML, Sigma Life) Sciences at 4 °C overnight, and washed with TBS 3 times, 10 min each. The chamber slides were then incubated with goat anti-rabbit IgG (H + L) secondary antibody Alexa Fluor 488 (1:1000, Catalog # A-11008, Thermo Fisher) and goat anti-Mouse IgG (H + L) Cross-Adsorbed Secondary Antibody-Alexa Fluor 488 (1:1000, Catalog # A-11001, Thermo Fisher) for 1 h at room temperature. Then, cells were washed with TBS 3 times, 15 min each, and the slides were mounted by Diamond Antifade Mountant with DAPI (Cat#: S36964, Fisher Scientific). Slides were imaged by fluorescence microscopy, and ImageJ was used to quantify the fluorescence intensity with the following equation: integrated Density (IntDen) − (Area of cells * Mean fluorescence of background). The intensity was normalized to cell number, and cell number was counted based on DAPI staining via cell counter in ImageJ.
## Statistical analysis
The significant difference was calculated by one-way ANOVA or Student’s t test via GraphPad Prism software (V.9.0), and $p \leq 0.05$ was considered a significant difference. All the data were presented as mean ± SEM.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary Information Description of Additional Supplementary Files Supplementary Data 1 Reporting Summary The online version contains supplementary material available at 10.1038/s41467-023-36896-0.
## Source data
Source Data
## Peer review information
Nature Communications thanks Martin Kolb and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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|
---
title: 'The effects of prebiotics on gastrointestinal side effects of metformin in
youth: A pilot randomized control trial in youth-onset type 2 diabetes'
authors:
- Sydney A. Dixon
- Sidharth Mishra
- Katrina B. Dietsche
- Shalini Jain
- Lilian Mabundo
- Michael Stagliano
- Andrea Krenek
- Amber Courville
- Shanna Yang
- Sara A. Turner
- Abby G. Meyers
- Doris E. Estrada
- Hariom Yadav
- Stephanie T. Chung
journal: Frontiers in Endocrinology
year: 2023
pmcid: PMC9996666
doi: 10.3389/fendo.2023.1125187
license: CC BY 4.0
---
# The effects of prebiotics on gastrointestinal side effects of metformin in youth: A pilot randomized control trial in youth-onset type 2 diabetes
## Abstract
### Disclosure summary
Dr. Yadav is Chief Scientific Officer and Co-Founder of Postbiotics Inc and has no conflict of interest with this work. All other authors have no conflicts of interest to disclose.
### Background
Metformin is the only approved first-line oral glucose lowering agent for youth with type 2 diabetes mellitus (Y-T2DM) but often causes gastrointestinal (GI) side effects, which may contribute to reduced treatment adherence and efficacy. Prebiotic intake may reduce metformin’s side effects by shifting microbiota composition and activity.
### Objective
The aims of this study were to determine the feasibility and tolerability of a prebiotic supplement to improve metformin-induced GI symptoms and explore the changes in glycemia and shifts in the microbiota diversity.
### Methods
In a two-phase pilot clinical trial, we compared, stool frequency and stool form every 1-2 days, and composite lower GI symptoms (weekly) at initiation of daily metformin combined with either a daily prebiotic or a placebo shake in a 1-week randomized double-blind crossover design (Phase 1), followed by a 1-month open-labeled extension (Phase 2). Plasma glycemic markers and stool samples were collected before and after each phase.
### Results
Six Y-T2DM (17.2 ± 1.7y (mean ± SD), $67\%$ male, BMI (42 ± 9 kg/m2), HbA1c (6.4 ± $0.6\%$)) completed the intervention. Stool frequency, stool composition, and GI symptom scores did not differ by group or study phase. There were no serious or severe adverse events reported, and no differences in metabolic or glycemic markers. After one week Phase 1metformin/placebo Proteobacteria, Enterobacteriaceae, and Enterobacteriales were identified as candidate biomarkers of metformin effects. Principle coordinate analyses of beta diversity suggested that the metformin/prebiotic intervention was associated with distinct shifts in the microbiome signatures at one week and one month.
### Conclusion
Administration of a prebiotic fiber supplement during short-term metformin therapy was well tolerated in Y-T2DM and associated with modest shifts in microbial composition. This study provides a proof-of-concept for feasibility exploring prebiotic-metformin-microbiome interactions as a basis for adjunctive metformin therapy.
### Clinical trial registration
https://clinicaltrials.gov/, identifier NCT04209075.
## Background
Metformin is the most widely prescribed anti-diabetic agent in the world and a first line treatment for type 2 diabetes (T2DM) in both adults and children [1]. However, metformin non-responsiveness is an important clinical challenge, occurring in 20-$50\%$ of youth and adults. Reduced treatment adherence may be multifactorial and is a well-recognized and potentially modifiable risk factor of non-responsiveness [2, 3]. We and others have shown that medication-related gastrointestinal (GI) side effects (bloating, diarrhea, cramping, nausea, and vomiting) are a common barrier to metformin adherence and maximal dose escalation [4, 5]. Side effects are observed in >$80\%$ of individuals newly initiated on metformin and ~10-$30\%$ of patients on long-term therapy with estimates of 1 in 4 youth taking metformin experiencing at least one GI side effect (5–7). Challenges are magnified in youth-onset T2DM (Y-T2DM), as metformin is the only oral medication that is approved for use in the 10-17 year age group and age-related factors, including pubertal-related differences in medication responsiveness and microbial signatures, may play a role in the elevated risk [8]. Metformin-induced shifts in gut microbiota have been implicated in the occurrence of side effects [9], yet, there is a paucity of studies examining the mechanisms of metformin inducing GI side effects and ways to mitigate this burden in youth.
While the precise mechanism of metformin-induced GI side effects remains elusive, emerging data strongly suggest that certain dietary fibers or fiber supplements, including prebiotics which affect the microbiome, may benefit patients with diabetes [10, 11]. Prebiotics are specific types of non-digestible carbohydrates that selectively stimulate the growth and activity of healthy host colonic microbiota, yielding potential benefits [12]. These types of fiber may improve gut inflammation [13, 14] and metabolic profiles in patients with and without diabetes (15–17). However, prebiotic supplements—when used in isolation and at high doses—have variable effects and may worsen GI symptoms and increase flatulence, due to an increase in methane and hydrogen sulfate producing bacteria [18, 19]. Prebiotic supplements combined with polyphenols —naturally occurring compounds metabolized by the short chain fatty acid (SCFA)-producing bacteria (e.g. acetogens)—decrease flatulence and side effects by promoting growth of acetogens and moderating overgrowth of methanogens and sulfate reducers [20]. In a small study in adults with T2DM, a prebiotic agent (a complex of inulin, beta-glucan, and polyphenols from blueberry pomace) improved metformin tolerability and fasting glycemia [21]. The prebiotic cocktail also improved glucose profiles in healthy adults with overweight and obesity but the study did not explore changes in gut microbiome composition [22]. Further, the gut-based mechanisms by which prebiotics influence metformin-induced GI side effects remain to be elucidated. Importantly, it remains to be established whether using this supplement is feasible in Y-T2DM. Age and socio-demographic differences in Y-T2DM, compared to adults with T2DM, include differences in dietary fiber intake, distinct microbiome signatures, and variations in taste and texture preferences (23–26). We propose that together the prebiotic-polyphenol would promote and support SCFA-producing bacteria and limit the overgrowth of metformin-induced *Escherichia spp* that have been associated with virulence factors and hydrogen sulfide gas production [9], which contributes in gut disturbances including bloating.
This pilot study examined the use of a prebiotic with polyphenols as an adjunct to improving metformin tolerability and facilitating short-term dose escalation and explored the underlying gut-based mechanisms of metformin and fiber in Y-T2DM. Our primary objective was to compare GI symptoms at initiation of daily metformin therapy when used with a daily metformin/prebiotic agent versus a metformin/placebo agent. We hypothesized that the metformin/prebiotic agent would be associated with higher tolerability scores (a composite score of lower GI-related side effects and stool consistency) compared to the placebo. Additional exploratory aims included evaluating changes in glucose and insulin concentrations and changes in gut microbiota diversity and bacterial phylogenetic abundances after the daily metformin/prebiotic agent use in contrast to the placebo.
## Methods and materials
The Metformin Influences Gut Hormones in Youth (MIGHTY) studies were designed to evaluate the pathophysiology of Y-T2DM and metformin mechanisms of action. Y-T2DM subjects were recruited from and evaluated consecutively at the Metabolic Clinical Research Unit at the National Institutes of Health Clinical Center (NIH CC), Bethesda, MD, USA (ClinicalTrials.gov registration no. NCT04209075). The protocol was approved by the Institutional Review Board of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Parents provided written informed consent and youth gave assent prior to enrollment. Nine [9] participants were screened and 6 enrolled between February 2020 and May 2021 (Supplemental Figure S1). Enrollment was prematurely halted in July 2021 because of the COVID-19 pandemic interruptions in prebiotic supply.
## Study design and participants
This was a pilot randomized double-blind crossover trial in Y-T2DM (Supplementary Figure S1). Youth aged 10–25 years, diagnosed with Y-T2DM by the American Diabetes *Association criteria* [27], Tanner stage IV or V, with Hemoglobin A1c (HbA1c) ≤$8\%$ were recruited to participate in this MIGHTY-Fiber Study. Exclusion criteria included: positive diabetes related autoantibodies (GAD-65 and IA-2 autoantibodies); consumption of ≥ 2 or more servings of ≥6 oz of yogurt per day; chronic GI disease; gastric bypass surgery; cancer diagnosis or auto-immune disease; chronic insulin therapy within 3 months of the study; or use of antibiotics, immunosuppressants, hormonal contraceptives, lipid-lowering agents, proton-pump inhibitors, supraphysiologic systemic steroids, cholesterol medications, prebiotics, or probiotics in the previous month at time of screening.
## Study timeline and protocol
The study timeline and protocol are presented in Supplementary Figure S2. After screening, if the participants were on metformin, they discontinued their metformin therapy for a 7 day washout period., Participants wore the Dexcom G6® continuous glucose monitor (CGM) for glycemic management and monitoring. This study was conducted in two phases: Phase 1 (a randomized double-blind crossover trial) and Phase 2 (an open-label extension). Weekly GI symptom questionnaires were completed to assess metformin tolerability.
Phase 1 consisted of two interventions including 1) metformin/prebiotic supplementation BiomeBliss®, and 2) metformin/placebo, with 5 study visits over 5 weeks. Participants were randomized to either intervention during Period 1 or Period 2. Following Period 1, participants underwent a two week washout based on literature documenting microbiome changes occurring within 24 hours [28] and to exceed >5 half-lives off metformin elimination (6-8 hours, and in the erythrocytes is 23 hours [29].
Phase 2 was an open label 4-week extension of metformin/prebiotic supplementation during which all participants were asked to continue taking metformin (850mg) with the prebiotic shake twice daily. After one month (visit 6), participants were evaluated with a protocol that was identical to visits 3 and 5.
During Periods 1 and 2, participants were provided with prepared pack-out meals with controlled macronutrient content and dietary fiber. The energy provided was based on estimated energy needs using the Mifflin St Jeor equation and a standard activity factor of 1.3 [30] with the goal of weight maintenance during the study. Menus were individualized to the participant’s food preferences and aimed to meet a macronutrient distribution of $15\%$ protein, $35\%$ fat, and $50\%$ carbohydrate. Fiber content was not controlled across participants but was consistent within participants for Period 1 and Period 2 based on food record. Menu items avoided dietary sources of probiotics (yogurt) and non-nutritive sweeteners. Pack-out meals were not provided during Phase 2.
## Study medication, randomization, and blinding
Participants were randomized to the prebiotic or a placebo shake to be administered with metformin (850mg tablets) prior to visit 2 (Supplemental Figure S2). Three study agents were used: metformin standard release 850mg oral tablet, BiomeBliss® powder, and placebo powder. Metformin 850 mg tablets were used within the approved dosing regimens as follows: at the start of each period (visit $\frac{2}{4}$), participants took metformin 850mg once daily x 3 days, and the dose increased to 850mg twice daily for the remainder of the study period. The placebo or prebiotic supplement was dispensed as packets, for which participants received 1 packet once daily x 3 days and 1 packet twice daily for the remainder of the study period. All study medications were taken together. The macronutrient composition of the prebiotic supplement and placebo composition are illustrated in Supplemental Table S1. Study randomization was performed by an independent statistician with 1:1 allocation ratio. Blinding of the investigators and participants was maintained throughout the study. Medication adherence was determined by co-author (LM) who adjudicated pill and sachet counts at each visit.
## GI symptom questionnaires
We conducted ecological momentary assessments of GI symptoms, stool frequency, and King’s Stool Chart [21, 31] via mobile text messaging (Supplemental Figure S2). Stool consistency (not applicable, very hard, hard, formed, loose, watery), urgency to evacuate (no need to evacuate within 3 hours after dosing, need to evacuate within 3 hours, need to evacuate within 2 hours, need to evacuate within 1 hour), daily bowel movements (at least 1 movement every 3-4 days, at least 1 movement every 2 days, at least 1 movement per day, at least 2 movements per day), bloating sensation (not applicable, mild, moderate, severe), flatulence (less than normal, normal, moderately increased, greatly increased), and evacuation completeness (not applicable, incomplete, constipated) were assessed (Supplemental Figure S3) [21].
## Stool collection and microbiome analysis
Stool was collected up to 24-hours prior to the visit or during visit 2, 3, 4, 5, or 6. Stool collected at home was stored in a sterile plastic vial at 4°C and processed immediately after receiving at NIH CC. Samples were flash frozen with liquid nitrogen and transferred to -80°C and stored until further analysis for microbiome sequencing and run in one batch. Fecal DNA was isolated for microbial compositional analysis using PowerSoil® DNA Isolation Kit. Our well standardized 16S rRNA sequencing and bioinformatics pipelines were used to analyze gut microbiome signatures (32–34). In brief, universal primer pairs 515 F (barcoded) and 806 R, the bacterial V4 hypervariable region were used to amplify bacterial 16S rDNA [35]. Amplified and uniquely barcoded amplicons were purified using AMPure® magnetic purification beads (Agencourt, Beckman Coulter, CA, USA) and quantified in a Qubit-3 fluorimeter (InVitrogen, Carlsbad, CA, USA). The normalized amplicon library of concentration equal to 8pM was subjected for sequencing using Illumina MiSeq sequencer (using Miseq reagent kit v3) [35]. Each sample bacterial sequences were de-multiplexed, quality filtered, clustered, and analysis were done by using base-space, R-based analytical tools, and quantitative insights into microbial ecology (QIIME) [32, 34].
## Metabolites
Glucose and insulin were measured in plasma on the Cobas 6000 instrument (Roche Diagnostics, USA) using an enzymatic hexokinase assay or electrochemiluminescence, respectively. HbA1c was determined using the HPLC D10 instrument (Bio-Rad, USA). High sensitivity C-reactive protein (hsCRP) was measured in plasma with the immunoturbidometric method assay (Abbott Architect, USA). Fructosamine was measured in serum via colorimetric rate reaction (Roche Diagnostics, USA). Cholesterol, triglyceride and HDL cholesterol concentrations were measured by enzymatic assays (Abbott Architect, USA). LDL was calculated with the following equation: LDL = (1.06*Chol) – (1.03*HDLC) – (0.117*Trig) – (0.00047*(TRIG*(Chol-HDLC))) + (0.000062*(Trig*Trig)) – 9.44
## Continuous blood glucose monitoring
The Dexcom G6® CGM was used for the duration of the study. Outcomes collected for each Period/Phase included percent time in range (70-180 mg/dL), percent wear time, mean glucose, glucose standard of deviation, and glucose coefficient of variation.
## Activity and sleep monitoring
Average daily steps, waking and sleep time were quantified using a small, non-invasive, portable watch accelerometer (GT3X+ by Actigraph Inc., Pensacola FL) worn on the participant’s wrist.
## Quality of life questionnaire
Participants completed PedsQL quality of life questionnaires at Visit 3, 5, and 6 [36].
## Assessment of dietary intake
Three-day food records (2 weekdays and 1 weekend day) were completed prior to visit 2 and 6 and reviewed with the metabolic nutrition team (SY, ST) and coded into Nutrition Data Systems for Research (NDSR, Minneapolis, MN: University of Minnesota, version 2019) to estimate daily caloric and macronutrient intake. At visit 3 and 5, participants returned a daily checklist that was reviewed with the metabolic nutrition team to confirm what was consumed during the controlled ad libitum periods and that participants refrained from consuming other foods or beverages containing prebiotics or probiotics (e.g., yogurt, kefir, kombucha).
## Metabolites and mixed meal test
Fasting plasma samples and a mixed meal tolerance test was conducted after a 10-12 hour fast. A liquid meal ($50\%$ carbohydrates, $33\%$ fat, and $17\%$ protein) was administered to provide ~$30\%$ of the estimated daily calorie requirements for weight maintenance determined by the Mifflin St. Jeor equation with an activity factor of 1.3 [30]. Blood samples were obtained at 0, 10, 20, 30, 40, 50, 60, 90, 120, 150, and 180 minutes to measure glucose and insulin concentrations.
## Body composition
A dual X-ray absorptiometry (DXA) scan was performed once during visit 3 to measure fat mass and lean body mass.
## Statistical analysis
The primary outcome was the composite tolerability score based on four [4] GI symptom profile categories (stool consistency, urgency to evacuate, bloating sensation, and flatulence) over 1 week. The composite score of tolerability was constructed using the principal component analysis (PCA) based on the 4 GI side effect profile categories. PCA was used to account for the expected high inter-patient variability. Comparison of mean tolerability scores over 1 week was analyzed by linear mixed models, adjusting for baseline score. Pre-specified covariates were treatment period and sequence effects from the crossover design. Demographic and exploratory metabolic variables were analyzed with repeated measures analysis of variance accounting for sequence and period effects. Statistical analyses were performed using SAS (PCA analysis) and STATA (version 17.1; Stata Corp, College Station, TX).
For the microbial analysis, the Kruskal–Wallis and Wilcoxon signed-rank test, implemented among classes set to 0.01, were used to determine alpha-diversity indices. Differences in beta-diversity were determined using permutational multivariate analysis of variance (PERMANOVA), a permutation-based multivariate analysis of variance to a matrix of pairwise distance to partition the inter-group and intra-group distance. An unpaired two-tailed Student’s t-test was used to compare alpha-diversity indices and bacterial abundance between the two groups. The LEfSe (Linear discriminatory analysis [LDA] Effect Size) was used to identify unique bacterial taxa that drive differences between different study groups [37] and logarithmic LDA score cut-off was set to 3, and the strategy for multiclass analysis was set to “all-against-all”. All the statistically analyzed bar graphs are presented in the form of mean ± SEM. QIIME and R packages were used for statistical analyses.
## No change in side effects or glycemic markers with metformin/prebiotic
Youth participants ($$n = 6$$) were $67\%$ male and aged 17.2 ± 1.7 years with a mean baseline BMI of 42 ± 9 kg/m2, HbA1c 6.4 ± $0.6\%$, and were within 5 years of diagnosis of diabetes (Table 1). All participants were prescribed metformin therapy before trial initiation. Two of the six participants reported a history of non-adherence to metformin therapy because of diarrhea and bloating. Average total energy intake (placebo: 2353 ± 319 vs prebiotic: 2385 ± 775 kcal, $$P \leq 0.936$$) and percent intake from carbohydrates and fiber (data not shown) did not differ in Phase 1. Dietary total intake and macronutrient composition were also similar between Phase 1 and 2 (Phase 2: 2262 ± 710 kcal). Metformin and prebiotic adherence throughout the study were 92 ± $16\%$ and 90 ± $23\%$, respectively.
**Table 1**
| Metabolic Characteristics | Baseline | Phase 1Placebo | Phase 1 Prebiotic | Phase 2Prebiotic | P-value |
| --- | --- | --- | --- | --- | --- |
| Age, years | 17.2 ± 1.7 | | | | |
| Male, n (%) | 4 (67) | | | | |
| Duration of diabetes (years) | 2.3 ± 1.6 | | | | |
| Hemoglobin A1C (%) | 6.4 ± 0.6 | | | | |
| Lean body mass, kg | 62.5 ± 8.7 | | | | |
| Fat body mass, kg | 52.0 ± 12.2 | | | | |
| Weight, kg | 125.0 ± 19.2 | 123.7 ± 19.8 | 124 ± 20.2 | 124.0 ± 21.2 | 0.18 |
| BMI, kg/m2 | 41.3 ± 9.0 | 40.8 ± 8.6 | 41.2 ± 8.9 | 41.2 ± 9.4 | 0.24 |
| Systolic Blood Pressure, mmHg | 125 ± 10 | 126 ± 13 | 126 ± 12 | 124 ± 18 | 0.85 |
| Diastolic Blood Pressure, mmHg | 70 ± 8 | 70 ± 5 | 74 ± 7 | 70 ± 8 | 0.15 |
| Fructosamine mmol/L | | 232 ± 32 | 235 ± 32 | 229 ± 45 | 0.45 |
| hsCRP, mg/L | | 7.2 ± 9.1 | 7.6 ± 8.1 | 11.5 ± 16.2 | 0.37 |
| ESR, mm/hour | | 18 ± 20 | 15 ± 14 | 15 ± 11 | 0.42 |
| Fasting LDL Cholesterol, mg/dL | | 74 ± 28 | 69 ± 34 | 72 ± 30 | 0.16 |
| Fasting HDL Cholesterol, mg/dL | | 38 ± 5 | 37 ± 5 | 34 ± 7 | 0.91 |
| Fasting Triglycerides, mg/dL | | 48 (41, 137) | 59 (51, 125) | 54 (50, 88) | 0.52 |
| Mixed Meal Tolerance Test | Mixed Meal Tolerance Test | Mixed Meal Tolerance Test | Mixed Meal Tolerance Test | Mixed Meal Tolerance Test | Mixed Meal Tolerance Test |
| Fasting glucose (mg/dL) | | 110 ± 24 | 102 ± 15 | 109 ± 19 | 0.44 |
| Glucose AUC (mg/dL●min) | | 27251 ± 6956 | 24274 ± 4229 | 27113 ± 6705 | 0.38 |
| Fasting insulin (uU/mL) | | 36.2(17.5, 64.5) | 38.1(14.8, 43.8) | 31.5(10.9, 66) | 0.40 |
| Insulin AUC ( uU/mL●min) | | 40658 ± 25385 | 33215± 14007 | 37789 ±16367 | 0.07 |
| Continuous Glucose monitor | Continuous Glucose monitor | Continuous Glucose monitor | Continuous Glucose monitor | Continuous Glucose monitor | Continuous Glucose monitor |
| CGM Active (%) | 90 ± 12 | 90 ± 12 | 88 ± 16 | 85 ± 14 | 0.81 |
| Average Glucose (mg/dL) | 153 ± 25 | 135 ± 23 | 122 ± 26 | 136 ± 31 | 0.07 |
| Time in range (%) | 76 ± 20 | 92 ± 13 | 93 ± 11 | 85 ± 18 | 0.97 |
| Glucose SD (mg/dL) | 34 ± 14 | 24 ± 11 | 22 ± 13 | 28 ± 14 | 0.15 |
| Glucose CV (mg/dL) | 22 ± 7 | 17 ± 5 | 17 ±7 | 20 ± 6 | 0.87 |
| Daily Activity and Sleep (n=5) | Daily Activity and Sleep (n=5) | Daily Activity and Sleep (n=5) | Daily Activity and Sleep (n=5) | Daily Activity and Sleep (n=5) | Daily Activity and Sleep (n=5) |
| Waking (min/day) | | 938(915, 1025) | 990(917, 1009) | 915(910-945) | 0.44 |
| Sleep minutes (min/day) | | 354(322, 372) | 394(375, 396) | 332(290-346) | 0.15 |
| Steps (daily) | | 8158(6667, 8794) | 8878(8621, 9581) | 9082(7779, 10270) | 0.34 |
At baseline, youth had bowel movements every 1-2 days and soft-form stool. There were no differences in stool frequency, consistency, or composite GI symptom scores between one week Phase 1 metformin/prebiotic, Phase 1 metformin/placebo, or one month Phase 2 metformin/prebiotic (Figure 1). For the primary outcome of the composite score, there was no carryover effect, no difference between Phase 1 metformin/placebo and Phase 1 metformin/prebiotic ($$P \leq 0.3243$$), and no difference between Phase 1 metformin/placebo and Phase 2 metformin/prebiotic ($$P \leq 0.8257$$). Using the common terminology criteria for adverse events, CTCAE version 4 [38], grade 1 nausea was reported by one participant and grade 1 diarrhea by another participant (Supplemental Table S2). No adverse events were observed in Phase 2 and there were no moderate or severe adverse events (Grade 2 or higher) during the entire study (Supplemental Table S2). Quality of life did not differ by group or phase (data not shown).
**Figure 1:** *Comparison of stool frequency, consistency, and composite gastrointestinal symptoms. There were no differences in (A) stool frequency, (B) stool consistency, (C) composite gastrointestinal (GI) symptoms score between Phase 1 metformin/placebo green) vs. Phase 1 metformin/prebiotic (orange) vs. Phase 2 metformin/prebiotic (red). Comparisons between groups were made by linear mixed models, adjusting for baseline.*
Glycemic and metabolic variables during the mixed meal tolerance test and continuous glucose monitoring are shown in Table 1. There were no changes in overall glycemia (fructosamine or glucose AUC), markers of inflammation (hsCRP/ESR), or lipid panel markers in either the placebo/metformin or Phase 1 or Phase 2 prebiotic groups. There were no significant differences in CGM glycemic measures across period or phase, however there was a trend for lower mean average glucose with Phase 1 metformin/prebiotic supplementation (Table 1).
## Global changes in gut microbiome during phase 1 and 2
To evaluate metformin and prebiotic mediated effects on the gut microbiome, we explored changes in beta diversity throughout the course of the study. Principal coordinate analyses revealed that during Phase 1 and Phase 2 there were overall trends for shifts in beta diversity (Figures 2A, B). These results suggest that there are distinct shifts in the microbiome signature when metformin is administered in combination with placebo or in combination with prebiotic supplementation in Y-T2DM participants.
**Figure 2:** *Principal component analysis of microbiome composition in Phase 1 and Phase 2. Trend for shifts in microbiome beta-diversity (A) between Pre_Placebo (pre metformin/placebo), Post_Placebo (post metformin/placebo) vs Phase2_Fiber (Phase 2 metformin/prebiotic) and (B) between Pre_Fiber (pre Phase 1 metformin/prebiotic), Post_Fiber (post Phase 1 metformin/prebiotic) vs Phase2_Fiber (post Phase 2 metformin/prebiotic).*
## One week phase 1 metformin/placebo associated with shifts in gut microbiome
To understand more specific metformin-induced changes in the microbiome, we compared pre-metformin/placebo to one week of Phase 1 metformin/placebo (Figure 3). One week of metformin/placebo induced marginal shifts in the principal coordinate analysis of beta-diversity (Figure 3A) and no significant change in alpha-diversity indices (Shannon index) or the number of operational taxonomic units (OTUs) (Figure 3B). Compared to pre-metformin/placebo, Phase 1 metformin/placebo was associated with trends for changes in phyla, genus, and species (Figures 3C–E). The abundance of Firmicutes decreased and Bacteroidetes and Verrucomicrobia increased (Figure 3C), Bacteroides increased and Roseburia decreased (Figure 3D), and *Akkermansia muciniphila* increased while *Roseburia faecis* and *Bifidobacterium adolescentis* decreased (Figure 3E) after Phase 1 metformin/placebo. LefSe analysis revealed unique changes in abundance of Proteobacteria, Enterobacteriaceae, and Enterobacteriales, as biomarkers of metformin/placebo effects (Figures 3F, G).
**Figure 3:** *Microbiome signatures in pre metformin/place compared to Phase 1 Metformin/Placebo. (A, B) Principal component analysis of beta-diversity and alpha-diversity measure, Shannon Index, and number of OTUs did not differ between Pre_Placebo (pre metformin/placebo) to Post_Placebo (Phase 1 metformin/placebo). (C–E)There were modest changes in abundance of major phyla, genus, and species differs between Pre_Placebo and Post_Placebo. (F, G) LEfSe (Linear discriminant analysis Effect Size) analysis showed unique biomarkers in Pre_Placebo vs Post_Placebo. Data are mean and standard error of mean. Microbiome beta-diversity was assessed using the Bray-Curtis dissimilarity index and visualized with principal component analysis. The alpha-diversity indices and bacterial proportions were compared using the Kruskal-Wallis test followed by the Mann-Whitney multiple pairwise comparison test.*
## One week phase 1 metformin/prebiotic associated with shifts in gut microbiome
To explore short term effects of prebiotic and metformin on the gut microbiota, we compared pre-metformin/prebiotic to one week of Phase 1 metformin/prebiotic. Phase 1 metformin/prebiotic altered microbiome beta-diversity; however, alpha-diversity and number of OTUs remained unchanged (Figures 4A, B). There were also trends for changes in phyla, genus, and species (Figures 4C–E). Marginal changes were detected in the relative abundance with an an increase in Bacteroidetes and a decrease in Firmicutes (Figure 4C), increased Blautia and decreased Faecalibacterium and Lachnospira, (Figure 4D), increased Blautia spp and Bifidobacterium spp, and decreased Faecalibacterium prausnitzi and *Clostridium clostridioforme* (Figure 4E) after Phase 1 metformin/prebiotic. LefSe analyses revealed that only Lachnospira abundance decreased specific for Phase 1 metformin/prebiotic (Figures 4F, G).
**Figure 4:** *Microbiome signatures in pre metformin/prebiotic and Phase 1 metformin/prebiotic. (A) Principal component analysis of beta-diversity shows that microbiota composition differed between Pre_Fiber (pre Phase 1 metformin/prebiotic) and Post_Fiber (phase 1 metformin/prebiotic). (B) The alpha-diversity measure, Shannon Index and number of OTUs did not differ between groups. (C–E) The abundance of major phyla, genus, and species show marginal differences between Pre_Fiber and Post_Fiber. (F, G) LEfSe (Linear discriminant analysis Effect Size) analysis showed unique biomarkers in Pre_Fiber vs Post_Fiber. Data are mean and standard error of mean. Microbiome beta-diversity was assessed using the Bray-Curtis dissimilarity index and visualized with principal component analysis. The alpha-diversity indices and bacterial proportions were compared using the Kruskal-Wallis test followed by the Mann-Whitney multiple pairwise comparison test.*
## One month phase 2 prebiotic/metformin showed distinct gut microbiota shifts
To explore the changes with longer interventions of prebiotic and metformin use in a real-world setting, we compared one month Phase 2 metformin/prebiotic to one week Phase 1 metformin/placebo (Figure 5) and one week Phase 1 metformin/prebiotic (Figure 6). Compared to metformin/placebo, the Phase 2 metformin/prebiotic intervention was not associated with significant differences in beta or alpha-diversity (Figures 5A, B). There were trends of a modest increase in Firmicutes, a decreased Bacteroides (Figure 5C), no change in genus (Figure 5D), and an increased abundance of Bifidobacterium spp and Blautia spp (Figure 5E).
**Figure 5:** *Microbiome signatures in Phase 2 prebiotic/metformin vs metformin/placebo. (A, B) Principal component analysis of beta-diversity and the alpha-diversity measure, Shannon Index and number of OTUs showed that microbiota composition was not different between Phase2_Fiber (Phase 2 prebiotic/metformin) and Post_Placebo (metformin/placebo). (C–E) The abundance of major phyla, genera, and species were similar in both groups. Data are mean and standard error of mean. Microbiome beta-diversity was assessed using the Bray-Curtis dissimilarity index and visualized with principal component analysis. The alpha-diversity indices and bacterial proportions were compared using the Kruskal-Wallis test followed by the Mann-Whitney multiple pairwise comparison test.* **Figure 6:** *Microbiome signatures in Phase 2 prebiotic/metformin compared to Phase 1 prebiotic/metformin. (A) Principal component analysis of beta-diversity showed that microbiota composition differed between Phase2_Fibre (Phase 2 prebiotic/metformin) and Post_Fiber (Phase 1 prebiotic/metformin). (B) The α-diversity measure, Shannon Index and number of OTUs showed no differences between groups. (C–E) There were modest changes in abundance of major phyla, genus, and species. (F, G) LEfSe (Linear discriminant analysis Effect Size) analysis in Phase2_Fiber vs Post_Fiber showed unique biomarkers. Data are mean and standard error of mean. Microbiome beta-diversity was assessed using the Bray-Curtis dissimilarity index and visualized with principal component analysis. The α-diversity indices and bacterial proportions were compared using the Kruskal-Wallis test followed by the Mann-Whitney multiple pairwise comparison test.*
Compared to Phase 1 metformin/prebiotic, the Phase 2 metformin/prebiotic was associated with changes in microbiome beta-diversity but not alpha-diversity (Figures 6A, B). There were incremental increases in Actinobacteria (Figure 6C), with trends for increased abundance of Bifidobacterium, decreased abundance of Roseburia, Bacteroides, (Figure 6D), increased Bifidobacterium adolescentis, and decreased *Roseburia faecis* spp after Phase 2 metformin/prebiotic (Figure 6E). Further, LefSe analyses revealed Sutterella, Burkholderiales and Alcaligenaceae were uniquely enriched and Lachnobacterium were uniquely suppressed after Phase 2 metformin/prebiotic (Figures 6F, G).
## Discussion
Metformin intolerance is an important clinical barrier to care and a potentially modifiable target for adjunctive treatment in Y-T2DM. This novel pilot study evaluated the tolerability and feasibility of using a prebiotic supplement and microbiome modulator at the time of metformin treatment initiation and dose escalation. The prebiotic supplement was feasible and well tolerated in youth and was not associated with increased GI symptoms or adverse reactions. Participants tolerated the rapid metformin dose escalation without adverse events. Our findings are consistent with studies in adults supporting prebiotics as adjunctive therapy in adults on metformin pharmacotherapy [21] [22]. This study provided the proof-of-concept needed to further explore prebiotic dietary supplements as adjunctive management with metformin in a vulnerable population of youth with T2DM who have high rates of metformin failure associated with GI symptoms [5].
Prebiotics and dietary adjuncts to traditional medicines in Y-T2DM are attractive candidates for addressing the complex multi-faceted care considerations in youth, with studies already suggesting beneficial glycemic effects when combining prebiotics and polyphenols with metformin in adults with T2DM [39]. Dietary prebiotic fiber is safe, relatively inexpensive, and may promote optimal cardiometabolic health, but is often under-consumed by adolescents in the United States [40]. Prebiotic supplementation in this trial was designed to provide ~$40\%$ of recommended daily fiber intake value. Prebiotics exert their beneficial effects as they promote the growth and/or activity of SCFA-producing bacteria that may improve gut health [12, 19]. In addition to the direct fiber-related metabolic effects to reduce cholesterol absorption and increase colonic transit time, prebiotics may also improve intestinal permeability and gut inflammation – two factors closely linked to obesity and T2DM [13, 14]. However, excessive prebiotic use may increase carbon dioxide and hydrogen sulfide gas production [13]. To balance the prebiotic effect, we employed a supplement of prebiotics and polyphenols –to preferentially promote acetate-producing bacteria growth and SCFA production and minimize methane- and hydrogen sulfate-producing bacteria [16, 17].
Notably, this study filled an important knowledge gap by demonstrating, for the first time in Y-T2DM, distinct metformin-induced shifts in gut microbiota signatures after one week of monotherapy or in combination with prebiotics. These findings extend previous studies in adults indicating metformin-induced shifts in microbiota occurred within 24 hours of drug initiation [21, 41]. We demonstrated that metformin/placebo was associated with increases in SCFA-producing bacteria (Akkermansia muciniphila), findings that are consistent with the growing evidence supporting gut-based modulation as important mechanisms of metformin action (9, 41–44). We further identified that an increased abundance of Proteobacteria, Enterobacteriaceae, and Enterobacteriales were candidate biomarkers of metformin effects in Y-T2DM. With more research, these bacteria could be implicated as early biomarkers of metformin response.
The short term combination of metformin/prebiotic supplementation also resulted in potentially beneficial microbial shifts towards greater enrichment in some SCFA producing bacteria such as Bifidobacterium adolescentis, Blautia, and Acintobacter, but a decrease in others (Firmucutes and Roseburia spp). Notably, the addition of prebiotics to metformin therapy prevented increased abundance of Enterobacteriaceae, the family associated with *Escherichia spp* which are linked with metformin-associated GI side effects. These findings, while suggestive of a beneficial shift in microbiota with use of prebiotics and metformin, were limited and not directly associated with improvements in side effects or glycemia. Overall, the unique enrichments illustrated by the cladograms of metformin monotherapy and metformin/prebiotic supplement may be useful biomarkers of treatment response in future microbiome studies in youth on metformin therapy.
These foundational findings support the design of larger studies to evaluate whether the shifts in microbiome could be associated with metabolic improvement in Y-T2DM. Incremental trends for improved glucose and triglyceride homeostasis were observed during Phase 1 of our trial and abolished during the Phase 2 open-label period. A strength of this study, therefore, was to demonstrate that detailed metabolic phenotyping combining ecological momentary assessments of glycemia (CGM) and standardized mixed meal tests, were useful for identifying targets for metabolic phenotyping. The rigorous double-blind crossover design and controlled feeding periods during Phase 1 reduced the chances of carry-over effects in metformin, dietary intake, and prebiotics. The run-in and washout periods exceeded the five half-lives needed to eliminate metformin from the plasma and red blood cell compartments and minimized the carry-over effects of metformin and prebiotic changes in the microbiota. The cross-over study design also accounted for interindividual heterogeneity in microbiome signatures and host-environmental effects, increasing the ability to identify small microbiota shifts.
Generalizability of this pilot study was restricted by the small sample size with limited recruitment and product availability secondary to COVID-19 pandemic 2020-2022. Participants also had few GI symptoms at baseline and additional studies will be needed to determine the effectiveness in youth with increased frequency and/or severity of GI symptoms. Other limitations include multiple exploratory metabolic and microbiome analyses with a lack of correlation with stool metabolites such as short-chain fatty acids. Additionally, metformin treatment is associated with reduced lipopolysaccharide (LPS) and improvements in metabolic endotoxemia [45], but these were not measured in this study and could be important for assessing response in future analyses. Lastly, this pilot feasibility study was not designed to determine whether specific shifts in microbiome signatures would be associated with an improved metabolic profile. Rather, these data provide estimates of effect sizes for larger clinical trials using prebiotic-based supplements in youth.
## Conclusions
Metformin-induced side effects are an important clinical problem in Y-T2DM. This innovative study found that adjunctive prebiotic treatment was well tolerated and facilitated timely dose escalation without inducing GI side effects. Metformin alone and the prebiotic-metformin combination resulted in unique shifts in the beta-diversity of the microbiome that were detectable under controlled feeding conditions and in the free-living environment. Prebiotics should be considered in larger trials to evaluate their effectiveness in mitigating GI side effects and improving metabolisms and quality of life in a broader population of youth.
## Data availability statement
The data presented in the study are deposited in the BioProject repository, accession number PRJNA912677.
## Ethics statement
The studies involving human participants were reviewed and approved by Institutional Review Board of the National Institute of Diabetes & Digestive & Kidney Diseases. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.
## Author contributions
SC conceptualized and designed the study, recruited, and collected the data, conducted the analysis, and wrote the manuscript. AM, HY conceptualized and designed the study, revised, and edited the manuscript. SJ, AK, SM, AC, DE, LM, MS, SD, KD, SY, and ST made substantial contributions to data collection and analysis, revising, and editing the manuscript. SC is the guarantor of this work and, as such, had full access to all data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors contributed to the article and approved the submitted version.
## Conflict of interest
HY is Chief Scientific Officer and Co-Founder of Postbiotics Inc and has no conflict of interest with this work.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be constructed 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.2023.1125187/full#supplementary-material
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